Intelligent underground pipeline segment joint pushing and integrity monitoring method and system

Abstract

The invention discloses an intelligent underground pipeline segment joint pushing and integrity monitoring method and system, and the method comprises the steps: obtaining the region sensing data of a pushing region in the pushing construction process of a target underground pipeline segment joint; according to the area sensing data, predicting at least one construction risk of the pushing area based on a prediction model of multiple risk types; predicting the construction integrity corresponding to the current time period based on an integrity prediction model according to the area sensing data; and determining the overall integrity risk corresponding to the target underground pipeline section according to the construction risks of the plurality of pushing areas and the construction integrity of the plurality of time periods. Therefore, more real-time and accurate risk analysis and integrity analysis can be realized in combination with the sensing data in the incremental launching process, a more accurate data basis is provided for planning of incremental launching construction, and the construction efficiency and the construction effect are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an intelligent underground pipeline segment joint jacking and integrity monitoring method and system. Background Art

[0002] With the development of my country's national economy, the demand for infrastructure is constantly increasing, and the number of urban underground pipelines is also increasing day by day. How to improve the construction efficiency of underground pipelines has become an important technical issue. Among them, the segmented construction of underground pipelines based on the jacking process has always been a typical construction process. It has attracted widespread attention because of its small interference from the external environment, small impact on the surrounding environment, and high degree of construction automation. However, in the existing jacking construction process, there is no full combination of sensing technology to identify dangers and monitor integrity of various construction conditions during the jacking process, so its construction efficiency and construction effect cannot be guaranteed. It can be seen that the existing technology has defects that need to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide an intelligent underground pipeline segment joint jacking and integrity monitoring method and system, which can realize more real-time and accurate risk analysis and integrity analysis by combining the sensor data during the jacking process, provide a more accurate data basis for the planning of jacking construction, and improve construction efficiency and construction effect.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses an intelligent underground pipeline segment joint jacking and integrity monitoring method, the method comprising:

[0005] During the jacking construction of the target underground pipeline segment joint, the regional sensor data of the jacking area is obtained;

[0006] Predicting at least one construction risk in the jacking area based on the regional sensor data and prediction models of multiple risk types;

[0007] According to the regional sensor data, based on the integrity prediction model, predict the construction integrity corresponding to the current time period;

[0008] The overall integrity risk corresponding to the target underground pipeline segment is determined based on the construction risks of multiple jacking areas and the construction integrity in multiple time periods.

[0009] As an optional implementation manner, in the first aspect of the present invention, a sensing acquisition device for acquiring the regional sensing data is provided on the joint of the target underground pipeline segment; the regional sensing data includes at least one of sound data, joint displacement data, soil sensing data, joint pressure-bearing data, water level data, and joint deformation data; the soil sensing data includes at least one of soil temperature, soil humidity, soil pressure, and soil settlement parameters.

[0010] As an optional implementation manner, in the first aspect of the present invention, predicting at least one construction risk of the jacking area according to the regional sensing data based on prediction models of multiple risk types includes:

[0011] For each risk type, determine the prediction model and input data type corresponding to this risk type; the prediction model is trained by a training data set including training sensing data corresponding to multiple input data types and risk labels corresponding to this risk type;

[0012] According to the input data type, screen out the type sensing data that meets the type in the regional sensing data;

[0013] Input the type sensing data into the prediction model corresponding to this risk type to obtain the risk probability corresponding to this risk type;

[0014] According to the risk probability, determine at least one construction risk of the jacking area from multiple risk types.

[0015] As an optional implementation manner, in the first aspect of the present invention, determining at least one construction risk of the jacking area from multiple risk types according to the risk probability includes:

[0016] For any two risk types, obtain the historical monitoring records corresponding to these two risk types respectively;

[0017] Calculate the proportion of the number of records in which these two risk types appear simultaneously in the historical monitoring records to the total number of all records to obtain the correlation parameter corresponding to these two risk types;

[0018] Set the objective function to minimize the number of all type sets and the number of types in each type set;

[0019] Set the constraint conditions to include:

[0020] The correlation probability parameter between any two of the risk types in each of the type sets is greater than a first parameter threshold; the correlation probability parameter is the product of the average of the risk probabilities of the two risk types and the correlation weight; wherein, the correlation weight is proportional to the corresponding correlation parameter between the two risk types;

[0021] The correlation probability parameter between any two of the risk types belonging to different type sets is less than a second parameter threshold; the second parameter threshold is less than the first parameter threshold;

[0022] Based on the objective function and the constraints, iterative clustering calculations are performed on multiple risk types according to the dynamic programming algorithm to obtain at least one type set;

[0023] The risk types in the type set are determined as at least one construction risk in the jacking area.

[0024] As an optional implementation manner, in the first aspect of the present invention, the risk type or the construction risk is a soil layer collapse risk, a harmful environment risk, an existing pipeline damage risk, a building foundation damage risk, or a human operation error risk; the harmful environment risk includes at least one of a toxic gas risk, a toxic liquid risk, and a toxic biological risk; the existing pipeline damage risk includes at least one of a water supply pipeline damage risk, a power supply line damage risk, and a gas supply pipeline damage risk; the human operation error risk includes at least one of a guiding error risk, a timing error risk, and an instruction error risk.

[0025] As an optional implementation manner, in the first aspect of the present invention, the predicting the construction integrity corresponding to the current time period based on the integrity prediction model according to the regional sensing data includes:

[0026] All the data in the regional sensing data are sorted from earliest to latest based on the corresponding acquisition time points to obtain a sensing data sequence;

[0027] Feature extraction is performed on the sensing data sequence to obtain sequence features;

[0028] The sequence features are input into a trained construction feature parameter prediction model to obtain corresponding construction feature parameters; the construction feature parameters include at least one of a construction soil type, a pipeline type, a pipeline use, and a construction project type;

[0029] An integrity verification model corresponding to the construction feature parameters is screened out from a preset integrity verification model library;

[0030] Input the sensing data sequence into the integrity verification model to obtain the construction integrity corresponding to the current time period; the integrity verification model is trained by a training data set including a plurality of training sensing sequence data corresponding to the construction characteristic parameters and corresponding construction integrity annotations.

[0031] As an optional implementation manner, in the first aspect of the present invention, determining the overall integrity risk corresponding to the target underground pipeline segment according to the construction risks of multiple jacking areas and the construction integrity of multiple time periods includes:

[0032] For each time period, determine multiple in-time jacking areas in the construction process corresponding to the time period from multiple jacking areas;

[0033] Calculate the average value of the risk probabilities corresponding to the construction risks of the multiple in-time jacking areas to obtain a risk parameter;

[0034] Calculate the product between the risk parameter and the construction integrity corresponding to the time period to obtain an integrity risk parameter corresponding to the time period;

[0035] Sort the integrity risk parameters of multiple time periods based on the corresponding time periods from early to late to obtain a risk parameter sequence;

[0036] Judge whether the risk parameter sequence conforms to a preset risk parameter continuity rule to obtain a judgment result; the risk parameter continuity rule is used to limit the integrity risk parameters in the risk parameter sequence with multiple consecutive parameter values greater than a preset parameter threshold;

[0037] When the judgment result is yes, determine that the target underground pipeline segment has a serious defect risk;

[0038] When the judgment result is no, determine that the overall integrity risk corresponding to the target underground pipeline segment is the parameter average value of the integrity risk parameters in the risk parameters with all parameter values greater than the preset parameter threshold.

[0039] As an optional implementation manner, in the first aspect of the present invention, the method further includes:

[0040] For each construction risk corresponding to the jacking area, determine a correction control parameter corresponding to the construction risk; the correction control parameter is used to limit the parameter values corresponding to different control instruction types; the control instruction types are increasing power, decreasing power, changing the jacking angle, changing the rate of the jacking angle, increasing the jacking speed or slowing down the jacking speed;

[0041] Generate a drive device control instruction corresponding to the construction risk based on the correction control parameter;

[0042] Send all the driving device control instructions to the jacking driving device for execution;

[0043] Real-time monitor whether the overall integrity risk is greater than a preset risk threshold or whether the target underground pipeline segment has the severe defect risk. If so, send a shutdown instruction to the jacking driving device and send a warning inspection instruction to the construction monitoring end.

[0044] A second aspect of an embodiment of the present invention discloses an intelligent underground pipeline segment joint jacking and integrity monitoring system, and the system includes:

[0045] An acquisition module, configured to acquire regional sensing data of a jacking area during the jacking construction of a target underground pipeline segment joint;

[0046] A first prediction module, configured to predict at least one construction risk of the jacking area based on a prediction model of multiple risk types according to the regional sensing data;

[0047] A second prediction module, configured to predict the construction integrity corresponding to the current time period based on an integrity prediction model according to the regional sensing data;

[0048] A determination module, configured to determine the overall integrity risk corresponding to the target underground pipeline segment according to the construction risks of multiple jacking areas and the construction integrity of multiple time periods.

[0049] As an optional implementation manner, in the second aspect of the present invention, a sensing acquisition device for acquiring the regional sensing data is arranged on the target underground pipeline segment joint; the regional sensing data includes at least one of sound data, joint displacement data, soil sensing data, joint pressure-bearing data, water level data, and joint deformation data; the soil sensing data includes at least one of soil temperature, soil humidity, soil pressure, and soil settlement parameters.

[0050] As an optional implementation manner, in the second aspect of the present invention, the specific manner in which the first prediction module predicts at least one construction risk of the jacking area based on a prediction model of multiple risk types according to the regional sensing data includes:

[0051] For each risk type, determine the prediction model and input data type corresponding to the risk type; the prediction model is trained by a training data set including training sensing data corresponding to multiple input data types and risk labels corresponding to the risk type;

[0052] According to the input data type, filter out type sensing data that meets the type in the regional sensing data;

[0053] Input the type of sensing data into the prediction model corresponding to the risk type to obtain the risk probability corresponding to the risk type;

[0054] According to the risk probability, determine at least one construction risk in the jacking area from multiple risk types.

[0055] As an optional implementation manner, in the second aspect of the present invention, the specific manner in which the first prediction module determines at least one construction risk in the jacking area from multiple risk types according to the risk probability includes:

[0056] For any two of the risk types, obtain the historical monitoring records corresponding to the two risk types respectively;

[0057] Calculate the proportion of the number of records in which the two risk types appear simultaneously in the historical monitoring records to the total number of all records, and obtain the correlation parameter corresponding to the two risk types;

[0058] Set the objective function to minimize the number of all type sets and the number of types in each type set;

[0059] Set the constraint conditions to include:

[0060] The correlation probability parameter between any two risk types in each type set is greater than the first parameter threshold; the correlation probability parameter is the product of the average value of the risk probabilities of the two risk types and the correlation weight; wherein, the correlation weight is proportional to the correlation parameter corresponding to the two risk types;

[0061] The correlation probability parameter between any two risk types belonging to different type sets is less than the second parameter threshold; the second parameter threshold is less than the first parameter threshold;

[0062] Based on the objective function and the constraint conditions, perform iterative clustering calculation on multiple risk types according to the dynamic programming algorithm to obtain at least one type set;

[0063] Determine the risk types in the type set as at least one construction risk in the jacking area.

[0064] As an alternative implementation, in the second aspect of the present invention, the risk type or the construction risk is a soil collapse risk, a harmful environment risk, an existing pipeline damage risk, a building foundation damage risk, or a human operation error risk; the harmful environment risk includes at least one of a toxic gas risk, a toxic liquid risk, and a toxic biological risk; the existing pipeline damage risk includes at least one of a water supply pipeline damage risk, a power supply line damage risk, and a gas supply pipeline damage risk; the human operation error risk includes at least one of a guidance error risk, a timing error risk, and an instruction error risk.

[0065] As an alternative implementation, in the second aspect of the present invention, the specific manner in which the second prediction module predicts the construction integrity corresponding to the current time period based on the integrity prediction model according to the regional sensing data includes:

[0066] Sort all the data in the regional sensing data from earliest to latest based on the corresponding acquisition time points to obtain a sensing data sequence;

[0067] Extract features from the sensing data sequence to obtain sequence features;

[0068] Input the sequence features into the trained construction feature parameter prediction model to obtain the corresponding construction feature parameters; the construction feature parameters include at least one of a construction soil type, a pipeline type, a pipeline use, and a construction project type;

[0069] Select the integrity verification model corresponding to the construction feature parameters from the preset integrity verification model library;

[0070] Input the sensing data sequence into the integrity verification model to obtain the construction integrity corresponding to the current time period; the integrity verification model is trained by a training data set including a plurality of training sensing sequence data corresponding to the construction feature parameters and corresponding construction integrity annotations.

[0071] As an alternative implementation, in the second aspect of the present invention, the specific manner in which the determination module determines the overall integrity risk corresponding to the target underground pipeline segment based on the construction risks of multiple jacking areas and the construction integrity of multiple time periods includes:

[0072] For each time period, determine multiple in-time jacking areas in the multiple jacking areas that are in the construction process corresponding to this time period;

[0073] Calculate the average value of the risk probabilities corresponding to the construction risks of the multiple in-time jacking areas to obtain a risk parameter;

[0074] Calculate the product of the risk parameter and the construction integrity corresponding to the time period to obtain the integrity risk parameter corresponding to the time period;

[0075] Sort the integrity risk parameters for multiple time periods from early to late based on the corresponding time periods to obtain a risk parameter sequence;

[0076] Determine whether the risk parameter sequence conforms to a preset risk parameter continuity rule to obtain a judgment result; the risk parameter continuity rule is used to limit the integrity risk parameters in the risk parameter sequence with consecutive multiple parameter values greater than a preset parameter threshold;

[0077] When the judgment result is yes, determine that there is a serious defect risk in the target underground pipeline segment;

[0078] When the judgment result is no, determine that the overall integrity risk corresponding to the target underground pipeline segment is the parameter average value of the integrity risk parameters with all parameter values greater than the preset parameter threshold among the risk parameters.

[0079] As an optional implementation manner, in the second aspect of the present invention, the system is further configured to perform the following steps:

[0080] For each construction risk corresponding to the jacking area, determine the corrective control parameter corresponding to the construction risk; the corrective control parameter is used to limit the parameter values corresponding to different control instruction types; the control instruction types are increasing power, decreasing power, changing the jacking angle, changing the rate of the jacking angle, increasing the jacking speed or slowing down the jacking speed;

[0081] Generate a control instruction for the driving device corresponding to the construction risk based on the corrective control parameter;

[0082] Send all the control instructions for the driving device to the jacking driving device for execution;

[0083] Real-time monitor whether the overall integrity risk is greater than a preset risk threshold or whether there is the serious defect risk in the target underground pipeline segment. If so, send a shutdown instruction to the jacking driving device and send a warning inspection instruction to the construction monitoring end.

[0084] The third aspect of the present invention discloses another intelligent underground pipeline segment joint jacking and integrity monitoring system, and the system includes:

[0085] A memory storing executable program code;

[0086] A processor coupled to the memory;

[0087] The processor calls the executable program code stored in the memory and executes some or all of the steps in the intelligent underground pipeline segment joint jacking and integrity monitoring method disclosed in the first aspect of the present invention.

[0088] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which are used to execute some or all of the steps in the intelligent underground pipeline segment joint jacking and integrity monitoring method disclosed in the first aspect of the present invention when the computer instructions are called.

[0089] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0090] The present invention can predict the construction risks in the jacking area based on the regional sensing data and prediction models of various risk types, then predict the construction integrity corresponding to the current time period based on the integrity prediction model, and finally accurately determine the overall integrity risk corresponding to the pipeline segment through the construction risks in multiple jacking areas and the construction integrity in multiple time periods. Therefore, it can realize more real-time and accurate risk analysis and integrity analysis by combining the sensing data during the jacking process, provide a more accurate data basis for the planning of jacking construction, and improve the construction efficiency and construction effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0092] Figure 1 It is a schematic flow chart of an intelligent underground pipeline segment joint jacking and integrity monitoring method disclosed in an embodiment of the present invention.

[0093] Figure 2 It is a schematic structural diagram of an intelligent underground pipeline segment joint jacking and integrity monitoring system disclosed in an embodiment of the present invention.

[0094] Figure 3 It is a schematic structural diagram of another intelligent underground pipeline segment joint jacking and integrity monitoring system disclosed in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0095] To enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solution in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work belong to the scope of protection of the present invention.

[0096] The terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or equipment.

[0097] Referring to "embodiment" herein means that a specific feature, structure or characteristic described in connection with the embodiment can be included in at least one embodiment of the present invention. The phrase appears in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0098] The present invention discloses an intelligent underground pipeline segment joint jacking and integrity monitoring method and system, which can predict the construction risks in the jacking area according to the regional sensing data and prediction models of multiple risk types, and then predict the construction integrity corresponding to the current time period based on the integrity prediction model. Finally, the overall integrity risk corresponding to the pipeline segment can be accurately determined through the construction risks in multiple jacking areas and the construction integrity in multiple time periods, so as to realize more real-time and accurate risk analysis and integrity analysis by combining the sensing data in the jacking process, provide a more accurate data basis for the planning of jacking construction, and improve the construction efficiency and construction effect. The following will be described in detail respectively.

[0099] Embodiment 1

[0100] Please refer to Figure 1 , Figure 1 which is a schematic flow chart of an intelligent underground pipeline segment joint jacking and integrity monitoring method disclosed in an embodiment of the present invention. Among them, Figure 1 the described intelligent underground pipeline segment joint jacking and integrity monitoring method can be applied to a data processing system / data processing device / data processing server (wherein, the server includes a local processing server or a cloud processing server). AsFigure 1 As shown in Figure 1 , the intelligent underground pipeline segment joint jacking and integrity monitoring method may include the following operations:

[0101] 101. During the jacking construction process of the target underground pipeline segment joint, obtain the regional sensing data of the jacking area.

[0102] 102. Based on the regional sensing data and the prediction models of multiple risk types, predict at least one construction risk in the jacking area.

[0103] 103. Based on the regional sensing data and the integrity prediction model, predict the construction integrity corresponding to the current time period.

[0104] 104. Determine the overall integrity risk corresponding to the target underground pipeline segment according to the construction risks of multiple jacking areas and the construction integrity of multiple time periods.

[0105] It can be seen that the above-mentioned invention embodiments can predict the construction risks in the jacking area according to the regional sensing data and the prediction models of multiple risk types, then predict the construction integrity corresponding to the current time period based on the integrity prediction model, and finally accurately determine the overall integrity risk corresponding to the pipeline segment through the construction risks of multiple jacking areas and the construction integrity of multiple time periods. Therefore, it can realize more real-time and accurate risk analysis and integrity analysis by combining the sensing data during the jacking process, provide a more accurate data basis for the planning of jacking construction, and improve the construction efficiency and construction effect.

[0106] As an optional embodiment, in the above steps, a sensing acquisition device for obtaining regional sensing data is provided on the target underground pipeline segment joint; the regional sensing data includes at least one of sound data, joint displacement data, soil sensing data, joint bearing pressure data, water level data, and joint deformation data; the soil sensing data includes at least one of soil temperature, soil humidity, soil pressure, and soil settlement parameters.

[0107] Specifically, the sensing acquisition device may include:

[0108] A miniature microphone: for monitoring sound data;

[0109] A total station or an inclinometer: for monitoring joint displacement data such as horizontal displacement;

[0110] A water level gauge or a piezometer tube: for monitoring underground water level data;

[0111] An axial force meter or a steel bar stress meter: for monitoring joint bearing pressure data such as axial force;

[0112] An electronic level or an invar tape: for monitoring soil sensing data such as settlement conditions;

[0113] Electronic convergence meter: used for monitoring joint deformation data such as convergence inside the pipe jacking.

[0114] It can be seen that through the above optional embodiments, the details of the sensing acquisition device and the content of the regional sensing data are defined to comprehensively characterize the characteristics of the jacking area, and subsequent accurate risk analysis and integrity analysis are realized, so as to assist in realizing more real-time and accurate risk analysis and integrity analysis by combining the sensing data during the jacking process, providing a more accurate data basis for the planning of the jacking construction, and improving the construction efficiency and construction effect.

[0115] As an optional embodiment, in the above steps, according to the regional sensing data, based on the prediction models of multiple risk types, at least one construction risk of the jacking area is predicted, including:

[0116] For each risk type, determine the prediction model and input data type corresponding to this risk type; optionally, the prediction model is trained through a training data set including the training sensing data corresponding to multiple input data types and the risk labels corresponding to this risk type.

[0117] According to the input data type, filter out the type sensing data that meets the type in the regional sensing data.

[0118] Input the type sensing data into the prediction model corresponding to this risk type to obtain the risk probability corresponding to this risk type.

[0119] According to the risk probability, determine at least one construction risk of the jacking area from multiple risk types.

[0120] It can be seen that through the above optional embodiments, it is possible to screen and predict the probability of the regional sensing data based on the prediction model and input data type corresponding to each risk type to determine at least one construction risk of the jacking area, so as to assist in realizing more real-time and accurate risk analysis and integrity analysis by combining the sensing data during the jacking process, providing a more accurate data basis for the planning of the jacking construction, and improving the construction efficiency and construction effect.

[0121] As an optional embodiment, in the above steps, according to the risk probability, determine at least one construction risk of the jacking area from multiple risk types, including:

[0122] For any two risk types, obtain the historical monitoring records corresponding to these two risk types respectively.

[0123] Calculate the proportion of the number of records in which these two risk types appear simultaneously in the historical monitoring records to the total number of all records to obtain the correlation parameter corresponding to these two risk types.

[0124] Set the objective function to minimize the number of all types of sets and the number of types in each type of set;

[0125] Set the constraints including:

[0126] The correlation probability parameter between any two risk types in each type of set is greater than the first parameter threshold; optionally, the correlation probability parameter is the product of the average of the risk probabilities of the two risk types and the correlation weight; wherein, the correlation weight is proportional to the corresponding correlation parameter between the two risk types;

[0127] The correlation probability parameter between any two risk types belonging to different type sets is less than the second parameter threshold; optionally, the second parameter threshold is less than the first parameter threshold;

[0128] Based on the objective function and the constraints, perform iterative clustering calculations on multiple risk types according to the dynamic programming algorithm to obtain at least one type of set;

[0129] Determine the risk types in the type set as at least one construction risk in the jacking area.

[0130] It can be seen that through the above optional embodiments, the process of calculating the historical simultaneous occurrence ratio between two risk types to obtain the correlation parameter and the details of clustering and grouping based on the correlation parameter and the dynamic algorithm to obtain more correlated and high-risk construction risks are defined, so as to assist in realizing more real-time and accurate risk analysis and integrity analysis by combining the sensing data in the jacking process, providing a more accurate data basis for the planning of jacking construction, and improving the construction efficiency and construction effect.

[0131] As an optional embodiment, in the above steps, the risk type or construction risk is soil layer collapse risk, harmful environment risk, existing pipeline damage risk, building foundation damage risk or human operation error risk; the harmful environment risk includes at least one of toxic gas risk, toxic liquid risk and toxic biological risk; the existing pipeline damage risk includes at least one of water supply pipeline damage risk, power supply line damage risk and gas supply pipeline damage risk; the human operation error risk includes at least one of orientation error risk, timing error risk and instruction error risk.

[0132] It can be seen that through the above optional embodiments, the risk types are defined, which can comprehensively characterize the possible risks in the jacking construction process, and subsequent accurate risk analysis and integrity analysis can be realized, so as to assist in realizing more real-time and accurate risk analysis and integrity analysis by combining the sensing data in the jacking process, providing a more accurate data basis for the planning of jacking construction, and improving the construction efficiency and construction effect.

[0133] As an optional embodiment, in the above steps, predicting the construction integrity corresponding to the current time period based on the integrity prediction model according to the regional sensing data includes:

[0134] Sort all the data in the regional sensing data from earliest to latest based on the corresponding acquisition time points to obtain a sensing data sequence;

[0135] Extract features from the sensing data sequence to obtain sequence features;

[0136] Input the sequence features into the trained construction feature parameter prediction model to obtain the corresponding construction feature parameters; optionally, the construction feature parameters include at least one of construction soil type, pipeline type, pipeline use, and construction project type;

[0137] Screen out the integrity verification model corresponding to the construction feature parameters in the preset integrity verification model library;

[0138] Input the sensing data sequence into the integrity verification model to obtain the construction integrity corresponding to the current time period; the integrity verification model is trained through a training data set including multiple training sensing sequence data corresponding to the construction feature parameters and the corresponding construction integrity annotations.

[0139] It can be seen that through the above optional embodiment, it is possible to realize the prediction of construction features and the determination of the integrity verification model based on the serialization and feature extraction of sensing data, so as to realize accurate construction integrity prediction, determine the construction integrity degree of the currently jacked pipeline, and thus assist in realizing more real-time and accurate risk analysis and integrity analysis by combining the sensing data during the jacking process, providing a more accurate data basis for the planning of jacking construction, and improving construction efficiency and construction effect.

[0140] As an optional embodiment, in the above steps, determining the overall integrity risk corresponding to the target underground pipeline segment according to the construction risks of multiple jacking regions and the construction integrity of multiple time periods includes:

[0141] For each time period, determine multiple in-time jacking regions in the construction process corresponding to the time period from multiple jacking regions;

[0142] Calculate the average value of the risk probabilities corresponding to the construction risks of multiple in-time jacking regions to obtain a risk parameter;

[0143] Calculate the product of the risk parameter and the construction integrity corresponding to the time period to obtain the integrity risk parameter corresponding to the time period;

[0144] Sort the integrity risk parameters of multiple time periods from earliest to latest based on the corresponding time periods to obtain a risk parameter sequence;

[0145] Determine whether the risk parameter sequence conforms to a preset risk parameter continuity rule to obtain a judgment result; optionally, the risk parameter continuity rule is used to define the integrity risk parameters in the risk parameter sequence where there are continuously multiple parameter values greater than a preset parameter threshold;

[0146] When the judgment result is yes, determine that there is a serious defect risk in the target underground pipeline segment;

[0147] When the judgment result is no, determine that the overall integrity risk corresponding to the target underground pipeline segment is the parameter average value of the integrity risk parameters in the risk parameters where all parameter values are greater than the preset parameter threshold.

[0148] It can be seen that through the above optional embodiments, it is possible to calculate the risk parameter sequence by combining the construction risk and integrity parameters of the corresponding jacking area in the time period to effectively characterize the continuous construction risk of the underground pipeline segment, and based on the preset risk parameter continuity rule, determine whether there is a serious defect risk in the underground pipeline segment or calculate an effective risk characterization value, so as to realize more real-time and accurate risk analysis and integrity analysis by combining the sensing data in the jacking process, provide a more accurate data basis for the planning of the jacking construction, and improve the construction efficiency and construction effect.

[0149] As an optional embodiment, in the above steps, the method further includes:

[0150] For each construction risk corresponding to the jacking area, determine the correction control parameter corresponding to the construction risk; optionally, the correction control parameter is used to define the parameter values corresponding to different control instruction types; the control instruction types are increasing power, decreasing power, changing the jacking angle, changing the rate of the jacking angle, accelerating the jacking speed or slowing down the jacking speed;

[0151] Based on the correction control parameter, generate a control instruction for the drive device corresponding to the construction risk;

[0152] Send all the drive device control instructions to the jacking drive device for execution;

[0153] Real-time monitor whether the overall integrity risk is greater than a preset risk threshold or whether there is a serious defect risk in the target underground pipeline segment. If so, send a shutdown instruction to the jacking drive device and send a warning inspection instruction to the construction monitoring end.

[0154] It can be seen that through the above optional embodiments, control instructions for a jacking drive device such as a pipe jacking machine device can be generated based on the predicted construction risks, so as to correct jacking construction errors in real time, improve the construction effect, and it is possible to implement shutdown and warning when there are serious defects in the overall construction integrity at the pipeline stage, so as to realize more real-time and accurate risk analysis and integrity analysis by combining the sensing data during the jacking process, provide a more accurate data basis for the planning of jacking construction, and improve the construction efficiency and construction effect.

[0155] Embodiment 2

[0156] Please refer to Figure 2 , Figure 2 which is a schematic structural diagram of an intelligent underground pipeline segment joint jacking and integrity monitoring system disclosed in an embodiment of the present invention. Among them, Figure 2 the described intelligent underground pipeline segment joint jacking and integrity monitoring system can be applied to a data processing system / data processing device / data processing server (wherein, the server includes a local processing server or a cloud processing server). As Figure 2 shown, the intelligent underground pipeline segment joint jacking and integrity monitoring system may include:

[0157] An acquisition module 201, configured to acquire regional sensing data of a jacking area during the jacking construction process of a target underground pipeline segment joint.

[0158] A first prediction module 202, configured to predict at least one construction risk of the jacking area based on a prediction model of multiple risk types according to the regional sensing data.

[0159] A second prediction module 203, configured to predict the construction integrity corresponding to the current time period based on an integrity prediction model according to the regional sensing data.

[0160] A determination module 204, configured to determine the overall integrity risk corresponding to the target underground pipeline segment according to the construction risks of multiple jacking areas and the construction integrity of multiple time periods.

[0161] It can be seen that the above embodiments of the invention can predict the construction risks of the jacking area according to the regional sensing data and the prediction model of multiple risk types, then predict the construction integrity corresponding to the current time period based on the integrity prediction model, and finally accurately determine the overall integrity risk corresponding to the pipeline segment through the construction risks of multiple jacking areas and the construction integrity of multiple time periods, so as to realize more real-time and accurate risk analysis and integrity analysis by combining the sensing data during the jacking process, provide a more accurate data basis for the planning of jacking construction, and improve the construction efficiency and construction effect.

[0162] As an alternative embodiment, a sensing acquisition device for obtaining regional sensing data is provided on the joint of the target underground pipeline segment; the regional sensing data includes at least one of sound data, joint displacement data, soil sensing data, joint pressure data, water level data, and joint deformation data; the soil sensing data includes at least one of soil temperature, soil humidity, soil pressure, and soil settlement parameters.

[0163] It can be seen that through the above alternative embodiments, the details of the sensing acquisition device and the content of the regional sensing data are defined to comprehensively characterize the characteristics of the jacking area, and subsequent accurate risk analysis and integrity analysis are realized, so as to assist in realizing more real-time and accurate risk analysis and integrity analysis by combining the sensing data during the jacking process, providing a more accurate data basis for the planning of the jacking construction, and improving the construction efficiency and construction effect.

[0164] As an alternative embodiment, the specific manner in which the first prediction module predicts at least one construction risk of the jacking area based on the regional sensing data and multiple risk type prediction models includes:

[0165] For each risk type, determine the prediction model and input data type corresponding to the risk type; optionally, the prediction model is trained through a training data set including training sensing data corresponding to multiple input data types and risk labels corresponding to the risk type;

[0166] According to the input data type, filter out the type sensing data that meets the type in the regional sensing data;

[0167] Input the type sensing data into the prediction model corresponding to the risk type to obtain the risk probability corresponding to the risk type;

[0168] According to the risk probability, determine at least one construction risk of the jacking area from multiple risk types.

[0169] It can be seen that through the above alternative embodiments, it is possible to screen and predict the probability of the regional sensing data based on the prediction model and input data type corresponding to each risk type to determine at least one construction risk of the jacking area, so as to assist in realizing more real-time and accurate risk analysis and integrity analysis by combining the sensing data during the jacking process, providing a more accurate data basis for the planning of the jacking construction, and improving the construction efficiency and construction effect.

[0170] As an alternative embodiment, the specific manner in which the first prediction module determines at least one construction risk of the jacking area from multiple risk types according to the risk probability includes:

[0171] For any two risk types, obtain the historical monitoring records corresponding to the two risk types respectively;

[0172] Calculate the proportion of the number of records in the historical monitoring records where these two risk types appear simultaneously to the total number of all records, and obtain the corresponding correlation parameter between these two risk types;

[0173] Set the objective function to minimize the number of all type sets and the number of types in each type set;

[0174] Set the constraint conditions including:

[0175] The correlation probability parameter between any two risk types in each type set is greater than the first parameter threshold; optionally, the correlation probability parameter is the product of the average of the risk probabilities of these two risk types and the correlation weight; wherein, the correlation weight is proportional to the corresponding correlation parameter between these two risk types;

[0176] The correlation probability parameter between any two risk types belonging to different type sets is less than the second parameter threshold; optionally, the second parameter threshold is less than the first parameter threshold;

[0177] Based on the objective function and the constraint conditions, perform iterative clustering calculations on multiple risk types according to the dynamic programming algorithm to obtain at least one type set;

[0178] Determine the risk types in the type set as at least one construction risk in the jacking area.

[0179] It can be seen that through the above optional embodiments, the process of calculating the historical co-occurrence ratio between two risk types to obtain the correlation parameter and the details of clustering and grouping based on the correlation parameter and the dynamic algorithm to obtain more correlated and high-risk construction risks are defined, so as to assist in realizing more real-time and accurate risk analysis and integrity analysis by combining the sensing data in the jacking process, providing a more accurate data basis for the planning of jacking construction, and improving the construction efficiency and construction effect.

[0180] As an optional embodiment, the risk type or construction risk is soil layer collapse risk, harmful environment risk, existing pipeline damage risk, building foundation damage risk or human operation error risk; the harmful environment risk includes at least one of toxic gas risk, toxic liquid risk and toxic biological risk; the existing pipeline damage risk includes at least one of water supply pipeline damage risk, power supply line damage risk and gas supply pipeline damage risk; the human operation error risk includes at least one of guidance error risk, timing error risk and instruction error risk.

[0181] It can be seen that through the above optional embodiments, the risk types are defined, which can comprehensively represent the possible risks in the incremental launching construction process. Subsequently, accurate risk analysis and integrity analysis can be realized, so as to assist in realizing more real-time and accurate risk analysis and integrity analysis in combination with the sensing data during the incremental launching process, providing a more accurate data basis for the planning of incremental launching construction, and improving the construction efficiency and construction effect.

[0182] As an optional embodiment, the specific manner in which the second prediction module predicts the construction integrity corresponding to the current time period based on the regional sensing data and the integrity prediction model includes:

[0183] Sort all the data in the regional sensing data from the earliest to the latest based on the corresponding acquisition time points to obtain a sensing data sequence;

[0184] Extract features from the sensing data sequence to obtain sequence features;

[0185] Input the sequence features into the trained construction feature parameter prediction model to obtain the corresponding construction feature parameters; optionally, the construction feature parameters include at least one of construction soil type, pipeline type, pipeline use, and construction project type;

[0186] Select the integrity verification model corresponding to the construction feature parameters from the preset integrity verification model library;

[0187] Input the sensing data sequence into the integrity verification model to obtain the construction integrity corresponding to the current time period; the integrity verification model is trained through a training data set including multiple training sensing sequence data corresponding to the construction feature parameters and the corresponding construction integrity annotations.

[0188] It can be seen that through the above optional embodiments, the prediction of construction features and the determination of the integrity verification model can be realized based on the serialization and feature extraction of sensing data, so as to realize accurate prediction of construction integrity, determine the construction integrity degree of the currently launched pipeline, and thus assist in realizing more real-time and accurate risk analysis and integrity analysis in combination with the sensing data during the incremental launching process, providing a more accurate data basis for the planning of incremental launching construction, and improving the construction efficiency and construction effect.

[0189] As an optional embodiment, the specific manner in which the determination module determines the overall integrity risk corresponding to the target underground pipeline segment according to the construction risks of multiple incremental launching areas and the construction integrity of multiple time periods includes:

[0190] For each time period, determine multiple in-time incremental launching areas in the multiple incremental launching areas that are in the construction process corresponding to the time period;

[0191] Calculate the average value of the risk probabilities corresponding to the construction risks in the jacking area over multiple time periods to obtain a risk parameter;

[0192] Calculate the product of the risk parameter and the construction integrity corresponding to this time period to obtain the integrity risk parameter corresponding to this time period;

[0193] Sort the integrity risk parameters of multiple time periods from early to late based on the corresponding time periods to obtain a risk parameter sequence;

[0194] Determine whether the risk parameter sequence conforms to a preset risk parameter continuity rule to obtain a judgment result; Optionally, the risk parameter continuity rule is used to limit the integrity risk parameters in the risk parameter sequence with multiple consecutive parameter values greater than a preset parameter threshold;

[0195] When the judgment result is yes, determine that there is a serious defect risk in the target underground pipeline segment;

[0196] When the judgment result is no, determine that the overall integrity risk corresponding to the target underground pipeline segment is the parameter average of the integrity risk parameters with all parameter values greater than the preset parameter threshold among the risk parameters.

[0197] It can be seen that through the above optional embodiments, it is possible to calculate a risk parameter sequence by combining the construction risk and integrity parameters of the corresponding jacking area in the time period to effectively characterize the continuous construction risk of the underground pipeline segment, and determine whether there is a serious defect risk in the underground pipeline segment or calculate an effective risk characterization value based on the preset risk parameter continuity rule, so as to realize more real-time and accurate risk analysis and integrity analysis by combining the sensing data in the jacking process, provide a more accurate data basis for the planning of the jacking construction, and improve the construction efficiency and construction effect.

[0198] As an optional embodiment, the system is further configured to perform the following steps:

[0199] For each construction risk corresponding to the jacking area, determine the corrective control parameter corresponding to this construction risk; Optionally, the corrective control parameter is used to limit the parameter values corresponding to different control instruction types; The control instruction types are increasing power, decreasing power, changing the jacking angle, changing the rate of the jacking angle, increasing the jacking speed or slowing down the jacking speed;

[0200] Generate a control instruction for the drive device corresponding to this construction risk based on the corrective control parameter;

[0201] Send all the drive device control instructions to the jacking drive device for execution;

[0202] Real-time monitor whether the overall integrity risk is greater than a preset risk threshold or whether there is a serious defect risk in the target underground pipeline segment. If so, send a shutdown instruction to the jacking drive device and send a warning inspection instruction to the construction monitoring end.

[0203] It can be seen that through the above optional embodiments, control instructions for jacking drive devices such as pipe jacking machines can be generated based on the predicted construction risks to correct jacking construction errors in real time, improve the construction effect, and can achieve shutdown and warning when there are serious defects in the overall construction integrity at the pipeline stage, so as to realize more real-time and accurate risk analysis and integrity analysis by combining the sensing data in the jacking process, provide a more accurate data basis for the planning of jacking construction, and improve the construction efficiency and construction effect.

[0204] Embodiment III

[0205] Please refer to Figure 3 , Figure 3 which is another intelligent underground pipeline segment joint jacking and integrity monitoring system disclosed in the embodiments of the present invention. Figure 3 The described intelligent underground pipeline segment joint jacking and integrity monitoring system is applied to a data processing system / data processing device / data processing server (wherein, the server includes a local processing server or a cloud processing server). As Figure 3 shown, the intelligent underground pipeline segment joint jacking and integrity monitoring system may include:

[0206] A memory 301 storing executable program code;

[0207] A processor 302 coupled to the memory 301;

[0208] Among them, the processor 302 calls the executable program code stored in the memory 301 to execute the steps of the intelligent underground pipeline segment joint jacking and integrity monitoring method described in Embodiment I.

[0209] Embodiment IV

[0210] The embodiments of the present invention disclose a computer-readable storage medium, which stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the intelligent underground pipeline segment joint jacking and integrity monitoring method described in Embodiment I.

[0211] Embodiment V

[0212] The embodiments of the present invention disclose a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the intelligent underground pipeline segment joint jacking and integrity monitoring method described in Embodiment I.

[0213] The above describes specific embodiments of this specification, and other embodiments are within the scope of the appended claims. In some cases, the acts or steps recited in the claims can be performed in a different order than in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0214] The systems, devices, modules, or units illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0215] For convenience of description, when describing the above devices, they are described separately as various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0216] Those skilled in the art should understand that the embodiments of this specification can be provided as a method, a system, or a computer program product. Therefore, the embodiments of this specification can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0217] This specification is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of this specification. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors 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.

[0218] 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 process Figure 1 one process or multiple processes and / or blocks Figure 1 specified in one block or multiple blocks.

[0219] 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 generate a computer-implemented process, and thus the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 specified in one block or multiple blocks.

[0220] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0221] The memory may include non-permanent memory in the computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0222] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology for information storage. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0223] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, commodity or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising said element.

[0224] This specification can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification can also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0225] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and reference can be made to the relevant parts of the method embodiments for the relevant content.

[0226] Finally, it should be noted that: The intelligent underground pipeline segment joint jacking and integrity monitoring method and system disclosed in the embodiments of the present invention only disclose the preferred embodiments of the present invention, and are only used to illustrate the technical solutions of the present invention, rather than to limit them; Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: They can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent underground pipeline segment joint jacking and integrity monitoring method, characterized in that: The method comprises: During the jacking construction of the target underground pipeline segment joint, the regional sensor data of the jacking area is obtained; Predicting at least one construction risk in the jacking area based on the regional sensor data and prediction models of multiple risk types; According to the regional sensor data, based on the integrity prediction model, predict the construction integrity corresponding to the current time period; The overall integrity risk corresponding to the target underground pipeline segment is determined based on the construction risks of multiple jacking areas and the construction integrity in multiple time periods.

2. The intelligent underground pipeline segment joint jacking and integrity monitoring method according to claim 1 is characterized in that: The target underground pipeline segment joint is provided with a sensing acquisition device for acquiring the regional sensing data; the regional sensing data includes at least one of sound data, joint displacement data, soil sensing data, joint pressure data, water level data and joint deformation data; the soil sensing data includes at least one of soil temperature, soil moisture, soil pressure and soil settlement parameters.

3. The intelligent underground pipeline segment joint jacking and integrity monitoring method according to claim 2 is characterized in that: The predicting of at least one construction risk in the jacking area based on the regional sensor data and prediction models of multiple risk types includes: For each risk type, determining a prediction model and an input data type corresponding to the risk type; the prediction model is obtained by training a training data set including a plurality of training sensor data corresponding to the input data type and a risk annotation corresponding to the risk type; According to the input data type, filter out type sensor data that matches the type in the area sensor data; Inputting the type of sensor data into a prediction model corresponding to the risk type to obtain a risk probability corresponding to the risk type; At least one construction risk of the jacking area is determined from a plurality of risk types according to the risk probability.

4. The intelligent underground pipeline segment joint jacking and integrity monitoring method according to claim 3 is characterized in that: Determining at least one construction risk of the jacking area from a plurality of risk types according to the risk probability includes: For any two of the risk types, obtaining historical monitoring records corresponding to the two risk types respectively; Calculate the ratio of the number of records in which the two risk types appear simultaneously in the historical monitoring records to the total number of all records, and obtain the corresponding correlation parameter between the two risk types; The objective function is set to minimize the number of all type sets and minimize the number of types in each type set; Setting restrictions includes: The association probability parameter between any two risk types in each of the type sets is greater than a first parameter threshold; the association probability parameter is the product of an average value of the risk probabilities of the two risk types and an association weight; wherein the association weight is proportional to the corresponding association parameter between the two risk types; The association probability parameter between any two risk types belonging to different type sets is less than a second parameter threshold; the second parameter threshold is less than the first parameter threshold; Based on the objective function and the constraint condition, performing iterative clustering calculation on the plurality of risk types according to a dynamic programming algorithm to obtain at least one type set; The risk type in the type set is determined as at least one construction risk in the jacking area.

5. The intelligent underground pipeline segment joint jacking and integrity monitoring method according to claim 4 is characterized in that: The risk type or the construction risk is soil collapse risk, harmful environmental risk, existing pipeline damage risk, building foundation damage risk or manual operation error risk; the harmful environmental risk includes at least one of toxic gas risk, toxic liquid risk and toxic biological risk; The existing pipeline damage risk includes at least one of a water supply pipeline damage risk, a power supply line damage risk and a gas supply pipeline damage risk; The risk of manual operation error includes at least one of a guidance error risk, a timing error risk and an instruction error risk.

6. The intelligent underground pipeline segment joint jacking and integrity monitoring method according to claim 1 is characterized in that: The predicting the construction integrity corresponding to the current time period based on the regional sensor data and the integrity prediction model includes: Sorting all the data in the regional sensor data from early to late based on the corresponding acquisition time points to obtain a sensor data sequence; Extracting features from the sensor data sequence to obtain sequence features; Inputting the sequence features into a trained construction feature parameter prediction model to obtain corresponding construction feature parameters; the construction feature parameters include at least one of construction soil type, pipeline type, pipeline use and construction project type; Selecting the integrity verification model corresponding to the construction characteristic parameter from a preset integrity verification model library; The sensor data sequence is input into the integrity verification model to obtain the construction integrity corresponding to the current time period; the integrity verification model is trained by a training data set including a plurality of training sensor sequence data corresponding to the construction characteristic parameters and corresponding construction integrity annotations.

7. The intelligent underground pipeline segment joint jacking and integrity monitoring method according to claim 3 is characterized in that: The determining, based on the construction risks of multiple jacking areas and the construction integrity of multiple time periods, the overall integrity risk corresponding to the target underground pipeline segment includes: For each time period, determining, from the plurality of jacking areas, a plurality of time jacking areas in the construction process corresponding to the time period; Calculate the average value of the risk probabilities corresponding to the construction risks corresponding to the jacking areas within the multiple time periods to obtain a risk parameter; Calculate the product of the risk parameter and the construction integrity corresponding to the time period to obtain the integrity risk parameter corresponding to the time period; Sorting the integrity risk parameters of the multiple time periods from early to late based on the corresponding time periods to obtain a risk parameter sequence; Determine whether the risk parameter sequence meets a preset risk parameter continuity rule to obtain a determination result; the risk parameter continuity rule is used to limit the integrity risk parameter having multiple consecutive parameter values ​​greater than a preset parameter threshold in the risk parameter sequence; When the judgment result is yes, determining that the target underground pipeline segment has a serious defect risk; When the judgment result is no, the overall integrity risk corresponding to the target underground pipeline segment is determined to be the parameter average value of the integrity risk parameters whose parameter values ​​in the risk parameters are greater than a preset parameter threshold.

8. The intelligent underground pipeline segment joint jacking and integrity monitoring method according to claim 7 is characterized in that: The method further comprises: For each of the construction risks corresponding to the jacking area, a corrective control parameter corresponding to the construction risk is determined; the corrective control parameter is used to limit parameter values ​​corresponding to different control instruction types; the control instruction types are increasing power, reducing power, changing the jacking angle, changing the rate of the jacking angle, speeding up the jacking speed, or slowing down the jacking speed; Based on the corrective control parameters, generating a driving equipment control instruction corresponding to the construction risk; Sending all the drive device control instructions to the push drive device for execution; Real-time monitoring is performed to determine whether the overall integrity risk is greater than a preset risk threshold or whether the target underground pipeline segment has the serious defect risk. If so, a shutdown command is sent to the jacking drive device and a warning inspection command is sent to the construction monitoring end.

9. An intelligent underground pipeline segment joint jacking and integrity monitoring system, characterized in that: The system comprises: An acquisition module, used to acquire regional sensor data of a jacking area during the jacking construction of a target underground pipeline segment joint; A first prediction module is used to predict at least one construction risk of the jacking area based on the regional sensor data and prediction models of multiple risk types; A second prediction module is used to predict the construction integrity corresponding to the current time period based on the regional sensor data and the integrity prediction model; A determination module is used to determine the overall integrity risk corresponding to the target underground pipeline segment according to the construction risks of multiple jacking areas and the construction integrity of multiple time periods.

10. An intelligent underground pipeline segment joint jacking and integrity monitoring system, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the intelligent underground pipeline segment joint jacking and integrity monitoring method as described in any one of claims 1-8.

Images