Automation Control Method for Canal Lining Construction of Water Conservancy Project

Abstract

The present invention relates to the technical field of data processing for supervision, and particularly relates to an automatic control method for the lining construction of a water conservancy project channel. The method includes the steps of: during the lining construction of the water conservancy project channel, collecting multi-dimensional data and elevation data of each monitoring point in the channel in real time; based on the elevation data, determining whether there is a problem with the quality of the channel lining at the current moment; in the case where there is a problem with the quality of the channel lining at the current moment, determining the target cause information of the channel lining quality problem based on the multi-dimensional data; based on the target cause information, executing the corresponding target control instruction; through the automatic control method for the lining construction of the water conservancy project channel of the present invention, it is possible to accurately and efficiently find out the cause of the problem and perform targeted adjustment and control, which not only helps the construction personnel quickly locate and solve the problem, but more importantly, effectively guarantees the efficiency of the automatic lining construction of the water conservancy project channel.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing for supervision, and particularly to an automatic control method for the lining construction of a water conservancy project channel. Background Art

[0002] The automatic control method for the lining construction of a water conservancy project channel refers to the use of modern information technology, automation technology, and mechanical equipment to improve the efficiency, quality, and safety of the lining construction of the channel. Currently, a lining trolley is used for the construction of a trapezoidal channel, and its construction efficiency is extremely high, greatly reducing the manual input and construction cost.

[0003] During the lining construction of a water conservancy project channel, there are various reasons for problems in the lining quality of the channel, including inadequate preparatory work before construction, such as defective feeding quality; equipment failures during construction, and external factor influences during construction, etc. Each reason leading to problems in the lining quality of the channel requires different solutions. When encountering problems in the lining quality of the channel, generally a comprehensive manual inspection is directly required, and this method seriously affects the construction efficiency. Summary of the Invention

[0004] In order to solve the technical problem that the direct manual inspection seriously affects the construction efficiency of the lining of a water conservancy project channel when encountering problems in the lining quality of the channel, the purpose of the present invention is to provide an automatic control method for the lining construction of a water conservancy project channel, and the specific technical solution adopted is as follows:

[0005] The present invention provides an automatic control method for the lining construction of a water conservancy project channel, and the method includes:

[0006] During the lining construction of a water conservancy project channel, multi-dimensional data and elevation data of each monitoring point in the channel are collected in real time;

[0007] Based on the elevation data, it is determined whether there are problems in the lining quality of the channel at the current moment;

[0008] In the case where there are problems in the lining quality of the channel at the current moment, based on the multi-dimensional data, the target cause information of the problems in the lining quality of the channel is determined;

[0009] Based on the target cause information, the corresponding target control instruction is executed;

[0010] Wherein, the multi-dimensional data includes vibration data, humidity data, and wind speed data; the elevation data represents the distance value from the monitoring point in the channel to the uppermost surface of the channel; the target cause information includes construction equipment failures, external factor influences, and feeding quality; the target control instructions include a deceleration instruction, a stop instruction, and an alarm instruction.

[0011] Further, the elevation data includes: a target elevation data sequence composed of elevation data of multiple uniformly distributed monitoring points along the channel width direction at the current moment;

[0012] The steps of determining whether there is a problem with the channel lining quality at the current moment based on the elevation data include:

[0013] Compare the target elevation data sequence with the standard elevation data sequence for each corresponding data to determine the problem data in the target elevation data sequence;

[0014] Based on the problem data, determine the degree of the channel lining quality problem at the current moment;

[0015] According to the degree of the channel lining quality problem, determine whether there is a problem with the channel lining quality at the current moment.

[0016] Further, the steps of determining the degree of the channel lining quality problem at the current moment based on the problem data include:

[0017] Determine the degree of the problem data, the first quantity, and the second quantity of the total data in the target elevation data sequence;

[0018] Determine the sequence segments formed by adjacent problem data sequences in the target elevation data sequence, and determine the maximum length value in the sequence segments;

[0019] Use the degree of the problem, the first quantity, the second quantity, and the maximum length value to calculate the degree of the channel lining quality problem at the current moment.

[0020] Further, the vibration data includes the traveling vibration signal when the construction equipment moves forward;

[0021] The steps of determining the target cause information of the channel lining quality problem based on the multi-dimensional data include:

[0022] Determine the vibration data sequence of each vibrator on the construction equipment;

[0023] Based on the vibration data sequence, determine the traveling vibration signal;

[0024] Based on the traveling vibration signal, the humidity data, and the wind speed data, calculate the degree of influence of external factors;

[0025] According to the degree of influence of external factors, determine whether the target cause information of the channel lining quality problem is the influence of external factors.

[0026] Further, the steps of determining the traveling vibration signal based on the vibration data sequence include:

[0027] Decompose the vibration data sequence into multiple intrinsic mode function components through empirical mode decomposition;

[0028] Divide the intrinsic mode function components of two adjacent vibrators into component groups to obtain multiple non - overlapping component groups;

[0029] Based on the intrinsic mode function components of the two vibrators in the component group, calculate the dynamic time warping sets of all component groups;

[0030] Arbitrarily select any data in each dynamic time warping set and obtain multiple updated sets in a full - permutation manner;

[0031] Calculate the sum values in each updated set to determine the target set with the minimum sum value;

[0032] Based on the intrinsic mode function components corresponding to the data in the target set, obtain the traveling vibration signals corresponding to each vibrator.

[0033] Further, calculating the dynamic time warping sets of all component groups based on the intrinsic mode function components of the two vibrators in the component group includes:

[0034] Arbitrarily select any intrinsic mode function component of each of the two vibrators in the component group and calculate the dynamic time warping distance between the two intrinsic mode function components;

[0035] Calculate the dynamic time warping distances between any two non - repeating intrinsic mode function components from different vibrators in the same group without repetition to obtain the dynamic time warping sets of all component groups.

[0036] Further, the steps of calculating the influence degree of external factors based on the traveling vibration signals, the humidity data, and the wind speed data include:

[0037] Determine the peaks and valleys of the traveling vibration signals, the humidity data, and the wind speed data respectively;

[0038] Based on their respective peaks and valleys, determine the relative maximum vibration of the traveling vibration signal, the degree of humidity drop of the humidity data, and the degree of wind speed increase of the wind speed data respectively;

[0039] Use the relative maximum vibration, the degree of humidity drop, and the degree of wind speed increase to calculate the maximum mutation degree;

[0040] Determine the time nodes corresponding to the relative maximum vibration, the humidity drop, and the wind speed increase respectively, and determine the time differences from each time node to the current moment;

[0041] Use the maximum mutation degree, the time nodes, and the time differences to calculate the influence degree of external factors.

[0042] Further, the step of determining the target cause information of the channel lining quality problem based on the multi-dimensional data includes:

[0043] Remove the traveling vibration signals in all the intrinsic mode function components corresponding to the vibration data sequence to obtain the remaining intrinsic mode function components;

[0044] Reconstruct the remaining intrinsic mode function components into an updated vibration data sequence through empirical mode decomposition;

[0045] Based on the wave peaks and wave valleys in the updated vibration data sequence, determine the corresponding fitting fluctuation curve;

[0046] Calculate the failure probability at the current moment by using the degree of influence of external factors, the fitting fluctuation curve, and the number of vibrators;

[0047] According to the failure probability, determine whether the target cause information of the channel lining quality problem is a construction equipment failure.

[0048] Further, the step of determining the corresponding fitting fluctuation curve based on the wave peaks and wave valleys in the updated vibration data sequence includes:

[0049] Divide the updated vibration data sequence into multiple vibration fluctuations based on its wave peaks and wave valleys to obtain the amplitude and vibration period of each vibration fluctuation;

[0050] Calculate the instability value of the vibration fluctuation by using the amplitude and vibration period of the vibration fluctuation and the amplitude and vibration period of adjacent vibration fluctuations;

[0051] Use the least squares method to perform fluctuation curve fitting on the instability values of all vibration fluctuations to obtain the fitting fluctuation curve.

[0052] Further, the step of executing the corresponding target control instruction based on the target cause information includes:

[0053] In the case where the degree of influence of external factors is greater than the external influence threshold, execute the corresponding deceleration instruction; or

[0054] In the case where the degree of influence of external factors is less than or equal to the external influence threshold and the failure probability is greater than the failure probability threshold, execute the shutdown instruction and the equipment failure alarm instruction; or

[0055] In the case where the degree of influence of external factors is less than or equal to the external influence threshold and the failure probability is less than or equal to the failure probability threshold, execute the shutdown instruction and the feeding problem alarm instruction.

[0056] The present invention has the following beneficial effects:

[0057] By steps: during the construction of the lining of a water conservancy project channel, multi-dimensional data and the elevation data of each monitoring point in the channel are collected in real time; based on the elevation data, it is determined whether there is a problem with the quality of the channel lining at the current moment; in the case that there is a problem with the quality of the channel lining at the current moment, the target cause information of the channel lining quality problem is determined based on the multi-dimensional data;

[0058] Based on the target cause information, the corresponding target control instruction is executed. Based on the analysis of the elevation data of each monitoring point, the present invention can accurately determine whether there is a problem with the quality of the channel lining. Also, based on the analysis of multi-dimensional data including the external environment and construction equipment, etc., thus in the case that there is a problem with the quality of the channel lining, the cause of the problem can be accurately and efficiently found, and targeted adjustment and control can be carried out, which not only helps the construction personnel quickly locate and solve the problem, but more importantly, effectively guarantees the efficiency of the automation of the lining construction of the water conservancy project channel. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0060] Figure 1 It is a flowchart of the steps of an automatic control method for the lining construction of a water conservancy project channel provided by an embodiment of the present invention;

[0061] Figure 2 It is a refined flowchart of step S3 in an automatic control method for the lining construction of a water conservancy project channel provided by an embodiment of the present invention;

[0062] Figure 3 It is a refined flowchart of step S31 in an automatic control method for the lining construction of a water conservancy project channel provided by an embodiment of the present invention;

[0063] Figure 4 It is a refined flowchart of step S32 in an automatic control method for the lining construction of a water conservancy project channel provided by an embodiment of the present invention;

[0064] Figure 5 It is another refined flowchart of step S3 in an automatic control method for the lining construction of a water conservancy project channel provided by an embodiment of the present invention;

[0065] Figure 6 It is a refined flowchart of step S4 in an automatic control method for the lining construction of a water conservancy project channel provided by an embodiment of the present invention;

[0066] Figure 7 It is a schematic structural diagram of the hardware operating environment of the automatic control equipment for the canal lining construction of the water conservancy project involved in the embodiment solution of the present invention;

[0067] Figure 8 It is a schematic framework diagram of the automatic control device for the canal lining construction of the water conservancy project involved in the embodiment solution of the present invention;

[0068] Figure 9 It is a schematic diagram of elevation data measurement in an automatic control method for canal lining construction of a water conservancy project provided by an embodiment of the present invention. Detailed implementation manners

[0069] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features and effects of an automatic control method for canal lining construction of a water conservancy project proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0070] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0071] The following specifically describes the specific solution of an automatic control method for canal lining construction of a water conservancy project provided by the present invention with reference to the accompanying drawings.

[0072] Embodiment 1:

[0073] For an automatic control method for canal lining construction of a water conservancy project provided by the present invention, please refer to Figure 1 , which shows the flowchart of the steps of an automatic control method for canal lining construction of a water conservancy project provided by an embodiment of the present invention.

[0074] Among them, the multi-dimensional data in the following embodiments includes vibration data, humidity data, and wind speed data; the elevation data represents the distance value from the monitoring point in the canal to the uppermost surface of the canal; the target cause information includes construction equipment failure, external factor influence, and feeding quality; the target control instructions include deceleration instructions, stop instructions, and alarm instructions.

[0075] The method includes:

[0076] Step S1, during the construction of the canal lining of the water conservancy project, multi-dimensional data and the elevation data of each monitoring point in the canal are collected in real time;

[0077] In this embodiment, during the construction process of the lining of the water conservancy project channel, it mainly refers to the process of concrete pouring, vibrating, troweling, etc. by construction equipment (usually a lining trolley) in the channel base. The real-time here means that corresponding sensors can be used to collect the above-mentioned multi-dimensional data at a certain acquisition frequency. For example, the acquisition frequency can be once per second. The sensors generally include a vibration sensor to collect vibration data, a humidity sensor to collect humidity data, and a wind speed sensor to collect wind speed data. More specifically, the vibration data here can include the vibration data of each vibrator itself, the traveling vibration data of the vibrator, and the vibration superposition data (the signal superposition of the vibration and the traveling vibration). The humidity data and the wind speed data can refer to the temperature and wind speed of the environment around the construction equipment. The data of each dimension can be standardized to unify the dimension.

[0078] Among them, the vibrators in the lining trolley can be evenly distributed along the width direction of the channel, that is, evenly arranged perpendicular to the direction of the channel (the lining trolley in this embodiment can be a small device, so there can be only one row of multiple vibrators, and there can also be multiple rows for large devices). This arrangement helps to ensure that the concrete is evenly vibrated and compacted on the cross-section of the channel.

[0079] During the operation of the lining trolley, a lidar can also be used to collect the elevation data of each monitoring point evenly distributed on a line along the width direction of the concrete surface after troweling.

[0080] Among them, as the lining trolley advances, the elevation data of each monitoring point is collected at preset intervals (such as 1 cm) along the direction of the channel. Each collection corresponds to a moment. Each collection is to collect the elevation data of each monitoring point at equal intervals on a line along the width direction of the channel. The elevation data is the distance from each monitoring point to the top of the channel. Please refer to Figure 9 for understanding.

[0081] Step S2: Based on the elevation data, determine whether there is a problem with the quality of the channel lining at the current moment;

[0082] Compare the collected elevation data with the standard elevation data corresponding to the situation without quality problems in the channel lining during normal construction, which can reflect the presence or absence of quality problems in the channel lining and the degree of quality problems, so as to determine whether there is a problem with the quality of the channel lining at the current moment. The standard elevation data here can be obtained through the following methods:

[0083] In the design drawing of the channel, according to the design standards of the channel (that is, the length, width, height, inclination angles of the two inclined planes, etc. of the channel), the standard elevation data of each monitoring point can be obtained.

[0084] In a preferred embodiment, the elevation data includes: a target elevation data sequence composed of elevation data of a plurality of uniformly distributed monitoring points along the width direction of the channel at the current moment;

[0085] Step S2 includes:

[0086] Comparing the target elevation data sequence with the standard elevation data sequence for each corresponding data to determine the problematic data in the target elevation data sequence;

[0087] Based on the problematic data, determining the degree of channel lining quality problems at the current moment;

[0088] According to the degree of channel lining quality problems, determining whether there are channel lining quality problems at the current moment.

[0089] Regarding how to compare the elevation data collected up to the current moment with the standard elevation data, in this embodiment, first, according to the elevation data of each monitoring point uniformly distributed on a line along the width direction of the channel collected each time, a target elevation data sequence corresponding to each moment can be obtained, that is, a sequence composed of the elevation data of all monitoring points from one side of the channel along the width direction to the other side. The target elevation data sequence is compared with the standard elevation data sequence for each corresponding data. For example, the first data in the target elevation data sequence is compared with the first data in the standard elevation data sequence, and their second data are compared separately, and so on.

[0090] After the comparison, if the difference between any two participating data exceeds a certain limit (which can be set as needed), the corresponding data in the target elevation data sequence can be considered as problematic data. Then, all the problematic data are found. Combining the deviation, quantity, and distribution of these problematic data, the degree of channel lining quality problems at the current moment can be determined comprehensively, effectively, and accurately.

[0091] Generally speaking, when the difference between the elevation data of the monitoring points and the standard elevation data is larger, and the number of problematic monitoring points and problematic data is more and more continuous, it indicates that the channel lining quality problem is larger at this time, that is, the degree of channel lining quality problems is larger or more serious. When the problem degree exceeds a certain limit value (which can be set as needed), it can be considered that there are channel lining quality problems, otherwise not. Thus, the channel lining quality at each moment is determined.

[0092] In a preferred embodiment, the step of determining the degree of channel lining quality problems at the current moment based on the problematic data specifically includes:

[0093] Determining the problem degree of the problematic data, the first quantity, and the second quantity of the total data in the target elevation data sequence;

[0094] Determine the sequence segments formed by adjacent problem data sequences in the target elevation data sequence, and determine the maximum length value in the sequence segments;

[0095] Using the problem degree, the first quantity, the second quantity, and the maximum length value, calculate the degree of the canal lining quality problem at the current moment.

[0096] In this embodiment, the above step process can be represented as the following data processing process:

[0097] For the target elevation data sequence corresponding to the j-th moment, calculate the normalized value of the absolute value of the difference between each elevation data and its corresponding standard elevation data, that is , where and are the i-th elevation data and its corresponding standard elevation data respectively, is a linear normalization function, normalized to (0, 1), as the problem degree of each elevation data.

[0098] It can be set that the threshold is 0.6, and the elevation data with a problem degree greater than 0.6 is recorded as problem data.

[0099] At this time, the process of obtaining the degree of the canal lining quality problem at the j-th moment is as follows:

[0100] For the j-th moment, obtain the ratio of the number of problem data (i.e., the first quantity) in the target elevation data sequence to the total number of all data in the target elevation data sequence (i.e., the second quantity) as the first ratio; obtain the ratio of the maximum value of the lengths of the sequence segments formed by adjacent problem data in the target elevation data sequence (i.e., the maximum length value, reflecting the continuity of the problem data) to the total number of all data in the target elevation data sequence (i.e., the second quantity) as the second ratio; obtain the sum value of the problem degrees of all elevation data in the target elevation data sequence; perform normalization processing on the product of the sum value, the first ratio, and the second ratio to obtain a normalized value, and use this normalized value as the degree of the canal lining quality problem at the j-th moment. In this embodiment, use a linear normalization function to normalize the product to obtain the degree of the canal lining quality problem.

[0101] When is greater than 0.6, it is determined that there is a canal lining quality problem at the j-th moment.

[0102] During the forward operation of the lining trolley, if there is a canal lining quality problem at the current moment, then it is necessary to analyze the data at the historical moments before the current moment to determine the cause of the problem.

[0103] Step S3, in the case that a channel lining quality problem occurs at the current moment, determine the target cause information of the channel lining quality problem based on the multi-dimensional data;

[0104] In the case of determining that a channel lining quality problem occurs at the current moment, the target cause information of the channel lining quality problem can be further determined by comprehensively considering the multi-dimensional data mentioned above. For example, vibration data, humidity data, and wind speed data can be combined to determine whether the cause of the channel lining quality problem is the influence of external factors. If external factors can be excluded, further analysis can be carried out to determine whether there is a malfunction in the construction equipment, especially whether there is a malfunction in the vibrator.

[0105] For the convenience of understanding this step, it should be further noted that:

[0106] Before the lining trolley operates, two tracks need to be laid first, and the trolley is hoisted and placed on the tracks that have been leveled in advance, and then it moves forward for construction on the tracks.

[0107] Regarding the influence of external factors, during the operation of the lining trolley, the channel lining quality problems caused by external factors are mainly the vibration when the lining trolley moves forward (dust, sand, debris, etc. floating into the tracks during the construction process cause the lining trolley to vibrate when moving forward, resulting in poor leveling effect), and sudden changes in environmental conditions.

[0108] Since the construction location of the channel lining in water conservancy projects is often near rivers and lakes, the humidity and wind speed in this environment change greatly and quickly, while the temperature change is relatively small. This is because water bodies have a large heat capacity and thus change temperature slowly, while the land temperature changes quickly. This temperature difference will cause air flow, forming the so-called "lake breeze" phenomenon, and the evaporation and condensation of water bodies will affect the humidity change. A sudden decrease in humidity and a sudden increase in wind speed will cause the water on the concrete surface to evaporate quickly, which may lead to surface drying and the formation of microcracks.

[0109] Therefore, before obtaining the current moment when the channel lining quality problem occurs, it is necessary to collect the vibration data sequence of each vibrator of the lining trolley, as well as the humidity data sequence and wind speed data sequence to comprehensively, effectively, and accurately determine whether the quality problem occurs due to the influence of external factors.

[0110] In addition, the main reasons affecting the channel lining quality also include the malfunction of the vibrator in construction equipment failures, which results in poor discharge effect of air bubbles inside the concrete, thereby making the compactness of the concrete poor and causing problems in the channel lining quality, as well as the feeding problem of the lining trolley (that is, the concrete quality problem in the preparation work, which causes problems in the channel lining quality).

[0111] After excluding the influence of external factors, the vibration data sequence can be further combined to determine whether the cause of the problem with the quality of the channel lining is a vibrator failure, and further determine that the cause of the problem with the quality of the channel lining may be related to the feeding quality.

[0112] In a preferred embodiment, the vibration data includes a traveling vibration signal when the construction equipment advances;

[0113] Please refer to Figure 2 , the step S3 includes:

[0114] Step S30, determining the vibration data sequence of each vibrator on the construction equipment;

[0115] Step S31, based on the vibration data sequence, determining the traveling vibration signal;

[0116] Each vibration data sequence is affected by the vibration when the lining trolley advances, that is, the traveling vibration signal when the lining trolley advances will be superimposed on the total vibration signal of each vibrator. That is, the vibration signal (vibration data sequence) collected from the vibrator during the construction process includes both the vibration signal of the vibrator and the traveling vibration signal when the lining trolley advances superimposed on the vibrator. In order to determine whether the quality problem of the channel lining is related to external factors such as dust, sand grains, and sundries floating into the track that will cause the lining trolley to vibrate during advancement, it is necessary to separate the two vibration signals from the vibration data sequence, determine the traveling vibration signal among them, and then analyze based on the traveling vibration signal.

[0117] In a preferred embodiment, please refer to Figure 3 ; the step S31 specifically includes:

[0118] Step S310, decomposing the vibration data sequence into multiple intrinsic mode function components by empirical mode decomposition;

[0119] Step S311, dividing the intrinsic mode function components of two adjacent vibrators into component groups to obtain multiple non-repeating component groups;

[0120] Step S312, calculating the dynamic time warping sets of all component groups based on the intrinsic mode function components of the two vibrators in each component group;

[0121] Step S313, arbitrarily taking any data in each dynamic time warping set and obtaining multiple updated sets in a full permutation manner;

[0122] Step S314, calculating the sum values in each updated set to determine the target set with the minimum sum value;

[0123] Step S315: Based on the intrinsic mode function components corresponding to the respective data in the target set, obtain the traveling vibration signal corresponding to each vibrator.

[0124] Among them, the step S312 specifically includes:

[0125] Arbitrarily select any two intrinsic mode function components of two vibrators in the component group, and calculate the dynamic time warping distance between the two intrinsic mode function components;

[0126] Without repetition, calculate the dynamic time warping distance between any two intrinsic mode function components of different vibrators from the same group to obtain the dynamic time warping set of all component groups.

[0127] In this embodiment, Empirical Mode Decomposition (EMD) (a well-known technique) is used to decompose each vibration data sequence into several Intrinsic Mode Function (IMF) components. One of the IMF components should be the traveling vibration signal when the lining trolley moves forward.

[0128] Taking two adjacent vibrators as a group, several non-repeating groups can be obtained.

[0129] For example, for vibrators A, B, C, D, component groups (A, B) and (C, D) are obtained.

[0130] In any group (C, D), take any one IMF component in C and any one IMF component in D, and calculate the DTW (Dynamic Time Warping) distance between the two IMF components (DTW is a well-known technique, and the smaller the DTW distance, the more similar the two IMF components). Without repetition, calculate the DTW distance between any two IMF components in this way to obtain the DTW set composed of all DTW distances in group (C, D).

[0131] It should be noted that, by way of example, the non-repetitive calculation here means:

[0132] Let C: {1, 2}; D: {5, 6}.

[0133] The DTW set is then: {15, 16, 25, 26}.

[0134] That is, there is no repeated form such as 15, 51.

[0135] This is only for exemplary understanding here and does not represent the actual DTW distance.

[0136] Similarly, obtain the DTW sets of all component groups.

[0137] In all groups, take any data from the DTW sets of each group to form an updated set. Thus, by using the method of permutation, several different updated sets are obtained.

[0138] For example, for the DTW sets {1, 2} and {3, 4}, the updated sets {1, 3}, {1, 4}, {2, 3}, and {2, 4} can be formed.

[0139] Calculate the sum of all data in each updated set as the vibration signal similarity of each updated set.

[0140] Take the updated set with the minimum vibration signal similarity as the target set.

[0141] Obtain one IMF component of each vibrator corresponding to all data in the target set as the traveling vibration signal when the lining trolley corresponding to each vibrator moves forward.

[0142] To facilitate the understanding of the above process, for example:

[0143] Suppose there are vibrators A, B, C, and D

[0144] IMF components corresponding to A: A1, A2

[0145] The form of the IMF components of BCD is analogous to that of A

[0146] The DTW set of AB is A1B1, A1B2, A2B1, A2B2

[0147] The DTW set of CD is C1D1, C1D2, C2D1, C2D2

[0148] Suppose an updated set with the minimum sum value is A1B1, C1D1, and take it as the target set

[0149] Therefore, the IMF components A1, B1, C1, and D1 in this target set are respectively the traveling vibration signals when the lining trolleys of vibrators A, B, C, and D move forward.

[0150] Suppose the target set is A1B2, C2D1 again. It can be seen that in this way, there will be a component of each of vibrators A, B, C, and D as the traveling vibration signal when the lining trolley moves forward.

[0151] Step S32, based on the traveling vibration signal, the humidity data, and the wind speed data, calculate the influence degree of external factors;

[0152] By comprehensively analyzing the data such as the traveling vibration signal, the humidity data, and the wind speed data, the external factors affecting the construction can be comprehensively covered, and the influence degree brought by the external factors can be further determined.

[0153] In a preferred embodiment, please refer to Figure 4 , step S32 includes:

[0154] Step S320, determining the peaks and valleys of the traveling vibration signal, the humidity data, and the wind speed data respectively;

[0155] Step S321, based on the respective peaks and valleys, determining the relative maximum vibration of the traveling vibration signal, the degree of humidity drop of the humidity data, and the degree of wind speed increase of the wind speed data respectively;

[0156] Step S322, using the relative maximum vibration, the degree of humidity drop, and the degree of wind speed increase to calculate the maximum mutation degree;

[0157] Step S323, determining the time nodes corresponding to the relative maximum vibration, the humidity drop, and the wind speed increase respectively, and determining the time difference from each time node to the current moment;

[0158] Step S324, using the maximum mutation degree, the time nodes, and the time difference to calculate the influence degree of external factors.

[0159] For the traveling vibration signal:

[0160] The mean sequence of the traveling vibration signals when the lining trolley corresponding to all vibrators moves forward, that is, after adding the traveling vibration data sequences of each vibrator and then taking the mean, is denoted as the final vibration signal F when the lining trolley moves forward.

[0161] Since if a vibration sensor is directly installed on the lining trolley to collect the vibration generated when the lining trolley travels, it will be affected by the vibration during the operation of the vibrator. Therefore, in this embodiment, the above method is used to obtain the traveling vibration signal generated when the lining trolley travels, so as to more effectively separate the above traveling vibration signal and the vibration signals of each vibrator.

[0162] Due to the dust, sand grains, and sundries floating into the track, it will only cause the vibration amplitude of F to increase. The larger the vibration amplitude, the greater the impact on the concrete leveling quality.

[0163] Therefore, the peaks and valleys in F are obtained, and the absolute value of the difference between adjacent peaks and valleys is calculated. , as the relative maximum vibration E in F, where is the largest absolute value of the difference, is the smallest absolute value of the difference. The time of the corresponding peak is denoted as the maximum vibration time node.

[0164] For the humidity data:

[0165] In the humidity data sequence, calculate the difference between each peak and the adjacent valley following it, as well as the time difference, in chronological order. , as the degree of humidity sudden drop G, where is the maximum difference, is the corresponding time difference. Denote the time of the valley corresponding to as the humidity sudden drop time. If there is no valley after the last peak, use the last data as the valley.

[0166] For wind speed data:

[0167] In the wind speed data sequence, calculate the absolute value of the difference between each valley and the adjacent peak following it, as well as the time difference, in chronological order. , as the degree of wind speed sudden increase H, where is the maximum absolute value of the difference, is the corresponding time difference. Denote the time of the peak corresponding to as the wind speed sudden increase time. If there is no peak after the last valley, use the last data as the peak.

[0168] Thus, the maximum mutation degree of external factors can be known. The larger K is, the more likely the quality problem is caused by external factors.

[0169] The process of obtaining the influence degree Q of external factors at the current moment is as follows:

[0170] Obtain the time differences from the maximum vibration time node, humidity sudden drop time node, and wind speed sudden increase time node to the current moment , and , and obtain the variance V of the maximum vibration time node, humidity sudden drop time node, and wind speed sudden increase time node. For the time differences , and , perform inverse proportional normalization on the product of the mean value and the variance V, obtain the inverse proportional normalization value, and use the function to normalize the product of the inverse proportional normalization value and the maximum mutation degree of external factors. Denote the obtained normalization value as the influence degree Q of external factors at the current moment. In this embodiment, use to perform inverse proportional normalization, and use linear normalization function to perform normalization processing. Among them, the smaller the variance of the maximum vibration time node, humidity sudden drop time node, and wind speed sudden increase time node is, the more unified the occurrence time of various mutation factors is, and the time differences , And The smaller the mean value of , the closer the mutation time of external factors is to the current moment. Therefore, the value of K is adjusted using the inverse proportional normalization value. The larger Q is, the more likely it is that the larger P (the degree of channel lining quality problem at the current moment) is caused by external factors.

[0171] Step S33: Determine whether the target cause information of the channel lining quality problem is affected by external factors according to the degree of influence of the external factors.

[0172] An external influence threshold can be set as needed, for example, 0.7. Then:

[0173] When Q is greater than 0.7, it is determined that the channel lining quality problem is caused by the mutation of external factors and is affected by external factors.

[0174] When Q is less than or equal to 0.7, the cause of the problem is further analyzed, and the smaller Q is, the greater the possibility of other reasons.

[0175] In addition, in another preferred embodiment, please refer to Figure 5 ; Step S3 further includes:

[0176] Step S300: Remove the traveling vibration signals in all the intrinsic mode function components corresponding to the vibration data sequence to obtain the remaining intrinsic mode function components;

[0177] Step S301: Reconstruct the remaining intrinsic mode function components into an updated vibration data sequence through empirical mode decomposition;

[0178] Step S302: Determine the corresponding fitting fluctuation curve based on the wave peaks and wave valleys in the updated vibration data sequence;

[0179] Step S303: Calculate the fault possibility at the current moment using the degree of influence of external factors, the fitting fluctuation curve, and the number of vibrators;

[0180] Step S304: Determine whether the target cause information of the channel lining quality problem is a construction equipment fault according to the fault possibility.

[0181] Among them, step S302 specifically includes:

[0182] Divide the updated vibration data sequence into multiple vibration fluctuations based on its wave peaks and wave valleys to obtain the amplitude and vibration period of each vibration fluctuation;

[0183] Calculate the instability value of the vibration fluctuation using the amplitude and vibration period of the vibration fluctuation and the amplitude and vibration period of adjacent vibration fluctuations;

[0184] Using the least squares method, the instability values of all vibration fluctuations are fitted to obtain a fitted fluctuation curve.

[0185] In this embodiment, the traveling vibration signal when the lining trolley advances is removed from all IMF components obtained by decomposing the vibration data sequence corresponding to each vibrator, and the remaining IMF components are used for EMD reconstruction to obtain the updated vibration data sequence corresponding to each vibrator.

[0186] Thus, the vibration influence when the lining trolley advances is removed.

[0187] The vibrators of the lining trolley are inserted into the concrete poured in the channel to work. At the beginning, there are air bubbles inside the concrete, making the inside of the concrete uneven, which will cause the vibration frequency and amplitude of the vibrators to be unstable. As the air bubbles are discharged, the inside of the concrete gradually becomes uniform, and the vibration frequency and amplitude of the vibrators will tend to be stable. Then, as the lining trolley advances, new concrete is continuously poured into the channel, and the vibrators perform this repetitive operation.

[0188] Since all vibrators operate synchronously, the change trends of the vibration frequencies and amplitudes of all vibrators from unstable to stable should be similar, and the stable states are similar (this is because the internal uniformity of the vibrated concrete is similar).

[0189] Using the wave valleys in the updated vibration data sequence, the updated vibration data sequence is divided into several vibration fluctuations, and the amplitude (the maximum value minus the minimum value in the vibration fluctuation) and vibration period (the duration of the vibration fluctuation) of each vibration fluctuation are obtained.

[0190] Thus, the instability of the t-th vibration fluctuation is obtained as follows:

[0191] Obtain the average value of the absolute values of the differences between the amplitude of the t-th vibration fluctuation and the amplitudes of the (t - 1)-th and (t + 1)-th vibration fluctuations as the first average value; obtain the average value of the absolute values of the differences between the vibration period of the t-th vibration fluctuation and the vibration periods of the (t - 1)-th and (t + 1)-th vibration fluctuations as the second average value; perform normalization processing on the product of the first average value and the second average value to obtain a normalized value, and use this normalized value as the instability of the t-th vibration fluctuation. . In this embodiment, a linear normalization function is used to perform normalization processing on the product. Among them, the greater the difference in the amplitude and vibration period between adjacent vibration fluctuations, the more unstable it indicates.

[0192] Using the least squares method, the instabilities of all vibration fluctuations in each updated vibration data sequence are fitted to obtain a fitted fluctuation curve.

[0193] Thus, the process for obtaining the fault possibility U at the current moment is:

[0194] Obtain the Pearson correlation coefficient of the fitting fluctuation curves corresponding to any two vibrators, calculate the mean value of the Pearson correlation coefficients of the fitting fluctuation curves corresponding to all pairs of any two vibrators, then calculate the difference between the value 1 and the mean value. Normalize the product of the difference, the reciprocal value of the external factor influence degree Q at the current moment, and the variance W of all peaks and valleys in all fitting fluctuation curves to obtain a normalized value, and use this normalized value as the fault possibility U at the current moment. In this embodiment, the reciprocal value of Q is 1 - Q, and the norm linear normalization function is used to normalize the product.

[0195] Among them, the smaller the external factor influence degree Q at the current moment, the more likely it is a non - external factor. The Pearson correlation coefficient is a well - known technology. The closer its value is to 1, the two curves are positively correlated. When there is no fault, it should be close to 1. And when there is no fault, the results after concrete vibration should be similar, so the stable vibration fluctuations should be similar, and the variance should be close to 0. Therefore, the larger U is, the more likely it is a fault.

[0196] Here, a fault - possibility threshold can be set as needed, for example, 0.6. Then:

[0197] When U is greater than 0.6, it is determined that the quality problem of the channel lining is caused by equipment failure.

[0198] When U is less than or equal to 0.6, it can be further determined as a problem with the feeding quality during the preparation work.

[0199] Based on the above steps and links, determine whether the target cause information of the channel lining quality problem is construction equipment failure or feeding quality problem.

[0200] Step S4, based on the target cause information, execute the corresponding target control instruction;

[0201] For the above three types of target cause information, after determining the location of the cause, execute their respective different target control instructions, so as to solve the channel lining quality problem in a timely and effective manner, greatly reduce the workload of manual comprehensive inspection, and improve construction efficiency and ensure production.

[0202] In a preferred embodiment, please refer to Figure 6 ; The step S4 includes:

[0203] Step S40, when the external factor influence degree is greater than the external influence threshold, execute the corresponding deceleration instruction; or

[0204] Step S41, when the external factor influence degree is less than or equal to the external influence threshold and the fault possibility is greater than the fault - possibility threshold, execute the shutdown instruction and the equipment - fault alarm instruction; or

[0205] Step S42, when the degree of influence of external factors is less than or equal to the external influence threshold and the probability of failure is less than or equal to the failure probability threshold, execute the shutdown instruction and the feeding problem alarm instruction.

[0206] Based on each of the above embodiments, using Q and U as the input reference data of the PID (PID control system) controller:

[0207] Taking the influence of external factors on Q being 0.7 and the failure probability threshold U being 0.6 as an example.

[0208] When Q is greater than or equal to 0.7, the PID controller outputs a deceleration instruction to slow down the lining trolley. The larger Q is, the slower the speed is, so as to use more time to better adapt to the sudden external environment and ensure the construction quality.

[0209] When Q is less than 0.7 and U is greater than 0.6, the PID controller outputs a shutdown instruction and issues an equipment failure alarm.

[0210] When Q is less than 0.7 and U is less than or equal to 0.6, the PID controller outputs a shutdown instruction and issues a feeding problem alarm.

[0211] Thus, automatic control of the channel lining construction of the water conservancy project is realized for different reasons for the occurrence of channel lining quality problems.

[0212] Based on the analysis of the elevation data of each monitoring point, the present invention can accurately determine whether there are channel lining quality problems. Also, based on the analysis of multi-dimensional data including the external environment and construction equipment, etc., when there are channel lining quality problems, it can accurately and efficiently find out the reasons for the problems and perform targeted adjustment and control, which not only helps the construction personnel quickly locate and solve the problems, but more importantly, effectively guarantees the efficiency of the automatic control of the channel lining construction of the water conservancy project.

[0213] Embodiment 2:

[0214] The embodiment of the present invention also proposes an automatic control device for the channel lining construction of a water conservancy project. The automatic control device for the channel lining construction of a water conservancy project can be a lining trolley.

[0215] As Figure 7 shown, Figure 7 is a schematic structural diagram of the hardware operating environment of the automatic control device for the channel lining construction of a water conservancy project involved in the embodiment of the present invention.

[0216] As Figure 7As shown in the figure, the automatic control device for the canal lining construction of the water conservancy project may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display (Display) and an input unit such as a control panel. Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WIFI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001. The memory 1005, as a computer storage medium, may include an automatic control program for canal lining construction.

[0217] Those skilled in the art can understand that Figure 7 the hardware structure shown in the figure does not constitute a limitation on the device, and it may include more or fewer components than shown in the figure, or combine some components, or have different component arrangements.

[0218] Continuing to refer to Figure 7 , Figure 7 the memory 1005, as a computer-readable storage medium, may include an operating system, a user interface module, a network communication module, and an automatic control program for canal lining construction.

[0219] In Figure 7 ,the network communication module is mainly used to connect to the server and can communicate with the server for data. And the processor 1001 can call the automatic control program for canal lining construction stored in the memory 1005 and execute the steps in each of the above embodiments.

[0220] Based on the hardware structure of the automatic control device for the canal lining construction of the water conservancy project, each embodiment for realizing the automatic control of the canal lining construction of the present invention is implemented.

[0221] In addition, the present invention also provides an automatic control device for the canal lining construction of the water conservancy project. Please refer to Figure 8 ,the automatic control device for the canal lining construction of the water conservancy project includes:

[0222] A data acquisition module A10, which is used to collect multi-dimensional data and the elevation data of each monitoring point in the canal in real time during the canal lining construction of the water conservancy project;

[0223] A quality determination module A20, which is used to determine whether there is a problem with the canal lining quality at the current moment based on the elevation data;

[0224] The problem analysis module A30 is used to determine the target cause information of the canal lining quality problem based on the multi-dimensional data when the canal lining quality problem occurs at the current moment;

[0225] The adjustment and control module A40 is used to execute the corresponding target control instruction based on the target cause information.

[0226] Furthermore, the quality determination module A20 is further used for:

[0227] Compare the target elevation data sequence with the standard elevation data sequence for each corresponding data to determine the problem data in the target elevation data sequence;

[0228] Based on the problem data, determine the degree of the canal lining quality problem at the current moment;

[0229] According to the degree of the canal lining quality problem, determine whether there is a canal lining quality problem at the current moment.

[0230] Furthermore, the quality determination module A20 is further used for:

[0231] Determine the problem degree, the first quantity of the problem data, and the second quantity of the total data in the target elevation data sequence;

[0232] Determine the sequence segment formed by adjacent problem data sequences in the target elevation data sequence, and determine the maximum length value in the sequence segment;

[0233] Use the problem degree, the first quantity, the second quantity, and the maximum length value to calculate the degree of the canal lining quality problem at the current moment.

[0234] Furthermore, the problem analysis module A30 is further used for:

[0235] Determine the vibration data sequence of each vibrator on the construction equipment;

[0236] Based on the vibration data sequence, determine the traveling vibration signal;

[0237] Based on the traveling vibration signal, the humidity data, and the wind speed data, calculate the influence degree of external factors;

[0238] According to the influence degree of external factors, determine whether the target cause information of the canal lining quality problem is the influence of external factors.

[0239] Furthermore, the problem analysis module A30 is further used for:

[0240] Decompose the vibration data sequence into multiple intrinsic mode function components by empirical mode decomposition;

[0241] Divide the intrinsic mode function components of two adjacent vibrators into component groups to obtain multiple non - repeating component groups;

[0242] Based on the intrinsic mode function components of the two vibrators in each component group, calculate the dynamic time warping set of all component groups;

[0243] Arbitrarily select any data in each dynamic time warping set and obtain multiple updated sets in a full - permutation manner;

[0244] Calculate the sum value in each updated set to determine the target set with the minimum sum value;

[0245] Based on the intrinsic mode function components corresponding to each data in the target set, obtain the traveling vibration signal corresponding to each vibrator.

[0246] Furthermore, the problem analysis module A30 is also used for:

[0247] Arbitrarily select any intrinsic mode function component of two vibrators in a component group and calculate the dynamic time warping distance between the two intrinsic mode function components;

[0248] Calculate the dynamic time warping distance between any two intrinsic mode function components from different vibrators in the same group without repetition to obtain the dynamic time warping set of all component groups.

[0249] Furthermore, the problem analysis module A30 is also used for:

[0250] Determine the peaks and valleys of the traveling vibration signal, the humidity data, and the wind speed data respectively;

[0251] Based on their respective peaks and valleys, determine the relative maximum vibration of the traveling vibration signal, the degree of humidity drop of the humidity data, and the degree of wind speed increase of the wind speed data respectively;

[0252] Use the relative maximum vibration, the degree of humidity drop, and the degree of wind speed increase to calculate the maximum mutation degree;

[0253] Determine the time nodes corresponding to the relative maximum vibration, the humidity drop, and the wind speed increase respectively, and determine the time difference from each time node to the current moment;

[0254] Use the maximum mutation degree, the time nodes, and the time difference to calculate the degree of influence of external factors.

[0255] Furthermore, the problem analysis module A30 is also used for:

[0256] Remove the traveling vibration signals in all the intrinsic mode function components corresponding to the vibration data sequence to obtain the remaining intrinsic mode function components;

[0257] Reconstruct the remaining intrinsic mode function components into an updated vibration data sequence through empirical mode decomposition;

[0258] Based on the wave peaks and wave valleys in the updated vibration data sequence, determine the corresponding fitted fluctuation curve;

[0259] Utilize the degree of influence of external factors, the fitted fluctuation curve, and the number of vibrators to calculate the fault probability at the current moment;

[0260] According to the fault probability, determine whether the target cause information of the canal lining quality problem is a construction equipment fault.

[0261] Furthermore, the problem analysis module A30 is further configured to:

[0262] Divide the updated vibration data sequence into multiple vibration fluctuations based on its wave peaks and wave valleys to obtain the amplitude and vibration period of each vibration fluctuation;

[0263] Utilize the amplitude and vibration period of the vibration fluctuation and the amplitude and vibration period of adjacent vibration fluctuations to calculate the instability value of the vibration fluctuation;

[0264] Use the least squares method to perform fluctuation curve fitting on the instability values of all vibration fluctuations to obtain the fitted fluctuation curve.

[0265] Furthermore, the adjustment control module A40 is further configured to:

[0266] Execute the corresponding deceleration instruction when the degree of influence of external factors is greater than the external influence threshold; or

[0267] Execute the shutdown instruction and the equipment fault alarm instruction when the degree of influence of external factors is less than or equal to the external influence threshold and the fault probability is greater than the fault probability threshold; or

[0268] Execute the shutdown instruction and the feeding problem alarm instruction when the degree of influence of external factors is less than or equal to the external influence threshold and the fault probability is less than or equal to the fault probability threshold.

[0269] The specific implementation manner of the automatic control device for canal lining construction in water conservancy projects of the present invention is basically the same as those of the above embodiments of the automatic control method for canal lining construction in water conservancy projects, and will not be elaborated herein.

[0270] In addition, the present invention also provides a computer-readable storage medium. A channel lining construction automation control program is stored on the computer-readable storage medium of the present invention. When the channel lining construction automation control program is executed by a processor, the steps of the water conservancy project channel lining construction automation control method as described above are implemented.

[0271] Among them, the method implemented when the channel lining construction automation control program is executed can refer to each embodiment of the water conservancy project channel lining construction automation control method of the present invention, which will not be elaborated here.

[0272] It should be noted that: the above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0273] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments.

[0274] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0275] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0276] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to work in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one or more of the procedures Figure 1 one or more procedures and / or blocks Figure 1 specified in the block or blocks.

[0277] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, such that a series of operational steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more of the procedures Figure 1 one or more procedures and / or blocks Figure 1 specified in the block or blocks.

[0278] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic inventive concept.

[0279] The foregoing are only preferred embodiments of the present invention and do not limit the scope of protection of the present invention. Any equivalent structural transformation made by using the specification and drawings of the present invention under the inventive concept of the present invention, or any direct / indirect application in other related technical fields, is included in the scope of protection of the present invention.

Claims

1. An automatic control method for the lining construction of a water conservancy project channel, characterized in that, The method includes: During the construction of the lining of the water conservancy project channel, multi-dimensional data and the elevation data of each monitoring point in the channel are collected in real time; Based on the elevation data, it is determined whether there is a problem with the quality of the channel lining at the current moment; In the case that there is a problem with the quality of the channel lining at the current moment, the target cause information of the channel lining quality problem is determined based on the multi-dimensional data; Based on the target cause information, the corresponding target control instruction is executed; Wherein, the multi-dimensional data includes vibration data, humidity data and wind speed data; the elevation data represents the distance value from the monitoring point in the channel to the uppermost surface of the channel; the target cause information includes construction equipment failure, external factor influence and feeding quality; the target control instructions include deceleration instruction, shutdown instruction and alarm instruction; The vibration data includes the traveling vibration signal when the construction equipment advances; The step of determining the target cause information of the channel lining quality problem based on the multi-dimensional data includes: Determining the vibration data sequence of each vibrator on the construction equipment; Based on the vibration data sequence, the traveling vibration signal is determined; Based on the traveling vibration signal, the humidity data and the wind speed data, the influence degree of external factors is calculated; According to the influence degree of external factors, it is determined whether the target cause information of the channel lining quality problem is the influence of external factors.

2. The automatic control method for the lining construction of a water conservancy project channel according to claim 1, characterized in that, The elevation data includes: a target elevation data sequence composed of the elevation data of multiple uniformly distributed monitoring points along the channel width direction at the current moment; The step of determining whether there is a problem with the quality of the channel lining at the current moment based on the elevation data includes: Comparing the target elevation data sequence with the standard elevation data sequence for each corresponding data to determine the problem data in the target elevation data sequence; Based on the problem data, the degree of the channel lining quality problem at the current moment is determined; According to the degree of the channel lining quality problem, it is determined whether there is a problem with the quality of the channel lining at the current moment.

3. The automatic control method for the lining construction of a water conservancy project channel according to claim 2, characterized in that, The step of determining the degree of the channel lining quality problem at the current moment based on the problem data includes: Determining the problem degree of the problem data, the first quantity and the second quantity of the total data in the target elevation data sequence; Determining the sequence segments formed by adjacent problem data sequences in the target elevation data sequence, and determining the maximum length value in the sequence segments; Using the problem degree, the first quantity, the second quantity and the maximum length value, the degree of the channel lining quality problem at the current moment is calculated.

4. The automatic control method for the lining construction of a water conservancy project channel according to claim 1, characterized in that, The step of determining the traveling vibration signal based on the vibration data sequence includes: Decomposing the vibration data sequence into multiple intrinsic mode function components by empirical mode decomposition; Dividing the intrinsic mode function components of adjacent two vibrators into component groups to obtain multiple non-repeating component groups; Based on the intrinsic mode function components of the two vibrators in the component group respectively, the dynamic time warping set of all component groups is calculated; Arbitrarily taking any data in each dynamic time warping set, multiple updated sets are obtained in a full permutation manner; Calculating the sum value in each updated set to determine the target set with the minimum sum value; Based on the intrinsic mode function components corresponding to the respective data in the target set, obtain the traveling vibration signals corresponding to each vibrator.

5. The automatic control method for the lining construction of a water conservancy project channel according to claim 4, wherein Based on the intrinsic mode function components of two respective vibrators in the component group, calculate the dynamic time warping set of all component groups, including: Arbitrarily select any intrinsic mode function component of two respective vibrators in the component group, and calculate the dynamic time warping distance between the two intrinsic mode function components; Calculate the dynamic time warping distances between any intrinsic mode function components of two different vibrators from the same group without repetition to obtain the dynamic time warping set of all component groups.

6. The automatic control method for lining construction of a water conservancy project channel according to claim 5, characterized in that, The steps of calculating the influence degree of external factors based on the traveling vibration signals, the humidity data, and the wind speed data include: Determine the peaks and valleys of the traveling vibration signals, the humidity data, and the wind speed data respectively; Based on the respective peaks and valleys, determine the relative maximum vibration of the traveling vibration signal, the degree of humidity drop of the humidity data, and the degree of wind speed increase of the wind speed data respectively; Use the relative maximum vibration, the degree of humidity drop, and the degree of wind speed increase to calculate the maximum mutation degree; Determine the time nodes corresponding to the relative maximum vibration, the humidity drop, and the wind speed increase respectively, and determine the time difference from each time node to the current moment; Use the maximum mutation degree, the time nodes, and the time difference to calculate the influence degree of external factors.

7. The automated control method for the lining construction of a water conservancy project channel according to claim 6, characterized in that, The steps of determining the target cause information of the channel lining quality problem based on the multi-dimensional data further include: Remove the traveling vibration signals from all the intrinsic mode function components corresponding to the vibration data sequence to obtain the remaining intrinsic mode function components; Reconstruct the remaining intrinsic mode function components into an updated vibration data sequence through empirical mode decomposition; Based on the peaks and valleys in the updated vibration data sequence, determine the corresponding fitting fluctuation curve; Use the influence degree of external factors, the fitting fluctuation curve, and the number of vibrators to calculate the failure possibility at the current moment; According to the failure possibility, determine whether the target cause information of the channel lining quality problem is a construction equipment failure.

8. The automatic control method for the lining construction of a water conservancy project channel according to claim 7, characterized in that, The steps of determining the corresponding fitting fluctuation curve based on the peaks and valleys in the updated vibration data sequence include: Divide the updated vibration data sequence into multiple vibration fluctuations based on the peaks and valleys therein to obtain the amplitude and vibration period of each vibration fluctuation; Use the amplitude and vibration period of the vibration fluctuation and the amplitude and vibration period of adjacent vibration fluctuations to calculate the instability value of the vibration fluctuation; Use the least squares method to perform fluctuation curve fitting on the instability values of all vibration fluctuations to obtain the fitting fluctuation curve.

9. The automatic control method for the lining construction of a water conservancy project channel according to claim 1, characterized in that, The steps of executing the corresponding target control instruction based on the target cause information include: In the case where the influence degree of external factors is greater than the external influence threshold, execute the corresponding deceleration instruction; or In the case where the influence degree of external factors is less than or equal to the external influence threshold and the failure possibility is greater than the failure possibility threshold, execute the shutdown instruction and the equipment failure alarm instruction; or In the case where the influence degree of external factors is less than or equal to the external influence threshold and the failure possibility is less than or equal to the failure possibility threshold, execute the shutdown instruction and the feeding problem alarm instruction.

Images