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

Abstract

The application discloses a kind of intelligent underground pipeline segment joint push and integrity monitoring method and system, the method comprises: in the push construction process of target underground pipeline segment joint, the regional sensing data of push region is acquired;According to the regional sensing data, at least one construction risk of the push region is predicted based on the prediction model of multiple risk types;According to the regional sensing data, the construction integrity corresponding to the current time period is predicted based on the integrity prediction model;According to the construction risk of multiple push regions and the construction integrity of multiple time periods, the overall integrity risk corresponding to the target underground pipeline segment is determined.The application can realize more real-time and accurate risk analysis and integrity analysis by combining sensing data in the push process, provide more accurate data basis for the planning of push construction, improve construction efficiency and construction effect.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and particularly relates to a method and system for intelligent underground pipeline segment joint jacking and integrity monitoring. BACKGROUND

[0002] With the development of the national economy of China, the demand for infrastructure is increasing, and the number of urban underground pipelines is also increasing. How to improve the construction efficiency of underground pipelines has become an important technical problem. Among them, the underground pipeline segment construction based on jacking process has always been a typical construction technology, which has attracted widespread attention due to its small external environment interference, small influence on the surrounding environment, and high construction automation. However, in the existing jacking construction process, the sensing technology is not fully combined to identify and monitor the risks and integrity of various construction conditions in the jacking process, so the construction efficiency and construction effect cannot be guaranteed. Therefore, the existing technology has defects and needs to be solved. SUMMARY

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

[0004] In order to solve the above technical problems, the first aspect of the present application discloses a method for intelligent underground pipeline segment joint jacking and integrity monitoring, which comprises:

[0005] In the jacking construction process of the target underground pipeline segment joint, the regional sensing data of the jacking area is obtained;

[0006] According to the regional sensing data, at least one construction risk of the jacking area is predicted based on a prediction model of multiple risk types;

[0007] According to the regional sensing data, the construction integrity corresponding to the current time period is predicted based on an integrity prediction model;

[0008] According to the construction risks of multiple jacking areas and the construction integrities of multiple time periods, the overall integrity risk corresponding to the target underground pipeline segment is determined.

[0009] As an optional implementation, in the first aspect of the present application, the target underground pipeline 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 humidity, soil pressure and soil settlement parameters.

[0010] As an optional implementation, in the first aspect of the present application, the at least one construction risk of the jacking region is predicted based on a prediction model of a plurality of risk types according to the regional sensing data, which includes:

[0011] For each risk type, a prediction model corresponding to the risk type and an input data type are determined; the prediction model is trained by a training data set including a plurality of training sensing data corresponding to the input data type and risk labels corresponding to the risk type;

[0012] According to the input data type, type sensing data of the same type is screened out from the regional sensing data;

[0013] The type sensing data is input into the prediction model corresponding to the risk type to obtain a risk probability corresponding to the risk type;

[0014] According to the risk probability, at least one construction risk of the jacking region is determined from a plurality of risk types.

[0015] As an optional implementation, in the first aspect of the present application, the at least one construction risk of the jacking region is determined from a plurality of risk types according to the risk probability, which includes:

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

[0017] 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 is calculated to obtain an association parameter corresponding to the two risk types;

[0018] The objective function is set to be the minimum number of all type sets and the minimum number of types in each type set;

[0019] The constraint condition includes:

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

[0021] The association 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 constraint condition, iteratively clustering and calculating the plurality of risk types according to a dynamic programming algorithm to obtain at least one type set;

[0023] Determining the risk types in the type set as at least one construction risk of the jacking area.

[0024] As an optional implementation, in the first aspect of the present application, 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.

[0025] As an optional implementation, in the first aspect of the present application, the construction integrity corresponding to a current time period is predicted based on an integrity prediction model according to the area sensing data, including:

[0026] All data in the area sensing data is sorted from early to late based on 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 purpose, and a construction engineering type;

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

[0030] inputting 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 obtained by training a training data set including a plurality of training sensing sequence data corresponding to the construction characteristic parameters and corresponding construction integrity labels.

[0031] As an optional implementation, in the first aspect of the present application, the determining of the overall integrity risk corresponding to the target underground pipeline section according to the construction risks of the plurality of jacking zones and the construction integrities of the plurality of time periods comprises:

[0032] For each time period, a plurality of jacking zones in the construction process in the time period are determined from the plurality of jacking zones;

[0033] An average value of the risk probability corresponding to the construction risk of the plurality of jacking zones is calculated to obtain a risk parameter;

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

[0035] The integrity risk parameters of the plurality of time periods are sorted from early to late based on the corresponding time periods to obtain a risk parameter sequence;

[0036] It is judged whether the risk parameter sequence meets 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 that have a plurality of parameter values greater than a preset parameter threshold value in succession;

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

[0038] When the judgment result is no, the overall integrity risk corresponding to the target underground pipeline section is determined to be a parameter average value of the integrity risk parameters in the risk parameter sequence that have all parameter values greater than a preset parameter threshold value.

[0039] As an optional implementation, in the first aspect of the present application, the method further comprises:

[0040] For each construction risk of the jacking zone, a correction control parameter corresponding to the construction risk is determined; 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 jacking angle, changing jacking angle rate, accelerating jacking speed, or slowing down jacking speed;

[0041] Based on the correction control parameter, a driving equipment control instruction corresponding to the construction risk is generated.

[0042] sending all the driving device control instructions to the pushing driving device for execution;

[0043] monitoring in real time whether the overall integrity risk is greater than a preset risk threshold or whether the target underground pipeline segment has the serious defect risk, and if so, sending a shutdown instruction to the pushing driving device and sending a warning inspection instruction to a construction monitoring terminal.

[0044] The second aspect of the embodiment of the present application discloses an intelligent underground pipeline segment joint pushing and integrity monitoring system, which comprises:

[0045] an acquisition module configured to acquire regional sensing data of a pushing region during pushing construction of a target underground pipeline segment joint;

[0046] a first prediction module configured to predict at least one construction risk of the pushing region based on a prediction model of a plurality of risk types according to the regional sensing data;

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

[0048] a determination module configured to determine an overall integrity risk corresponding to the target underground pipeline segment according to the construction risks of a plurality of pushing regions and the construction integrities of a plurality of time periods.

[0049] As an optional implementation, in the second aspect of the present application, a sensing acquisition device for acquiring the regional sensing data is arranged on the target underground pipeline segment joint; the regional sensing data comprises 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 comprises at least one of soil temperature, soil humidity, soil pressure and soil settlement parameters.

[0050] As an optional implementation, in the second aspect of the present application, the specific manner in which the first prediction module predicts at least one construction risk of the pushing region based on a prediction model of a plurality of risk types according to the regional sensing data comprises:

[0051] for each risk type, determining a prediction model corresponding to the risk type and an input data type; the prediction model is obtained by training a training data set comprising a plurality of training sensing data corresponding to the input data type and risk labels corresponding to the risk type;

[0052] according to the input data type, screening type sensing data of the same type from the regional sensing data;

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

[0054] determining at least one construction risk of the jacking area from the plurality of risk types according to the risk probability.

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

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

[0057] calculating a proportion of a number of records in which the two risk types appear simultaneously in the historical monitoring records in a total number of all records to obtain an association parameter corresponding to the two risk types;

[0058] setting a target function as a minimum number of all type sets and a minimum number of types in each type set;

[0059] the restriction conditions include:

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

[0061] the association 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;

[0062] based on the target function and the restriction conditions, performing iterative clustering calculation on the plurality of risk types according to a dynamic programming algorithm to obtain at least one type set;

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

[0064] As an optional implementation, in the second aspect of the present application, 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; and 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 optional implementation, in the second aspect of the present application, the second prediction module predicts the specific manner of the construction integrity corresponding to the current time period according to the regional sensing data based on an integrity prediction model, which includes:

[0066] sequencing all data in the regional sensing data from early to late based on corresponding acquisition time points to obtain a sensing data sequence;

[0067] performing feature extraction on the sensing data sequence to obtain sequence features;

[0068] 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 a construction soil type, a pipeline type, a pipeline purpose, and a construction engineering type;

[0069] filtering out an integrity verification model corresponding to the construction feature parameters in a preset integrity verification model library;

[0070] inputting 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 labels.

[0071] As an optional implementation, in the second aspect of the present application, the determination module determines the specific manner of the overall integrity risk corresponding to the target underground pipeline segment according to the construction risks of a plurality of jacking regions and the construction integrities of a plurality of time periods, which includes:

[0072] for each time period, determining a plurality of jacking regions in the plurality of jacking regions that are in the construction process in the time period;

[0073] calculating an average value of the risk probability corresponding to the construction risk corresponding to the plurality of jacking regions in time to obtain a risk parameter;

[0074] calculating a 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;

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

[0076] judging whether the risk parameter sequence meets a preset risk parameter continuity rule, to obtain a judgment result; the risk parameter continuity rule is used to limit that there are a plurality of parameter values greater than a preset parameter threshold in the integrity risk parameters in the risk parameter sequence;

[0077] when the judgment result is yes, determining that the target underground pipeline section has a serious defect risk;

[0078] when the judgment result is no, determining that an overall integrity risk corresponding to the target underground pipeline section is a parameter average value of the integrity risk parameters with all parameter values greater than a preset parameter threshold in the risk parameters.

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

[0080] for each construction risk corresponding to the jacking area, determining a correction control parameter corresponding to the construction risk; the correction control parameter is used to limit parameter values corresponding to different control instruction types; the control instruction types are increasing power, decreasing power, changing jacking angle, changing jacking angle rate, increasing jacking speed or decreasing jacking speed;

[0081] generating a driving equipment control instruction corresponding to the construction risk based on the correction control parameter;

[0082] sending all the driving equipment control instructions to the jacking driving equipment for execution;

[0083] monitoring in real time whether the overall integrity risk is greater than a preset risk threshold or whether the target underground pipeline section has the serious defect risk, and if yes, sending a shutdown instruction to the jacking driving equipment and sending a warning inspection instruction to a construction monitoring terminal.

[0084] The third aspect of the present application discloses another intelligent underground pipeline section joint jacking and integrity monitoring system, which comprises:

[0085] a memory storing executable program codes;

[0086] a processor coupled with the memory;

[0087] The processor invokes the executable program code stored in the memory to execute part or all of the steps of the intelligent underground pipeline segment joint jacking and integrity monitoring method disclosed in the first aspect of the application.

[0088] The fourth aspect of the application discloses a computer storage medium, which stores computer instructions, and when the computer instructions are invoked, part or all of the steps of the intelligent underground pipeline segment joint jacking and integrity monitoring method disclosed in the first aspect of the application are executed.

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

[0090] The application can predict the construction risk of the jacking area according to the regional sensing data and the prediction model of multiple risk types, and 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 of the pipeline segment corresponding to the multiple jacking areas and the multiple time periods of construction integrity, so as to realize more real-time and accurate risk analysis and integrity analysis combined with the sensing data in the jacking process, provide more accurate data basis for the planning of jacking construction, and improve the construction efficiency and construction effect. BRIEF DESCRIPTION OF DRAWINGS

[0091] In order to more clearly illustrate the technical solutions in the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can also be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0092] Figure 1 is a flowchart of an intelligent underground pipeline segment joint jacking and integrity monitoring method disclosed by the embodiments of the application.

[0093] Figure 2 is a structural schematic diagram of an intelligent underground pipeline segment joint jacking and integrity monitoring system disclosed by the embodiments of the application.

[0094] Figure 3 is a structural schematic diagram of another intelligent underground pipeline segment joint jacking and integrity monitoring system disclosed by the embodiments of the application. DETAILED DESCRIPTION

[0095] In the following, the technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the drawings in the embodiments of the present application, so that those skilled in the art can better understand the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0096] The terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish different objects, and are not used to describe a particular order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product, or equipment including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product, or equipment.

[0097] In this document, the term "embodiment" means that the specific features, structures, or characteristics described in connection with the embodiment can be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily mean the same embodiment, nor is it independent or alternative to other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0098] The present application discloses a kind of intelligent underground pipeline segment joint jacking and integrity monitoring method and system, can be according to regional sensing data and the prediction model of multiple risk types Prediction of construction risk in jacking area, then based on the construction integrity of the corresponding time period predicted by integrity prediction model, finally, the overall integrity risk of pipeline segment corresponding is accurately determined through the construction risk of multiple jacking areas and the construction integrity of multiple time periods, so that more real-time and accurate risk analysis and integrity analysis can be realized in combination with sensing data in jacking process, provide more accurate data basis for jacking construction planning, improve construction efficiency and construction effect. The following are described in detail respectively.

[0099] Embodiment one

[0100] Please refer to Figure 1 , Figure 1 It is a flowchart of the intelligent underground pipeline segment joint jacking and integrity monitoring method disclosed in the embodiments of the present application. Among them, Figure 1 The intelligent underground pipeline segment joint jacking and integrity monitoring method described can be applied in a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). For example,Figure 1 The intelligent underground pipeline segment joint jacking and integrity monitoring method can include the following operations:

[0101] 101. During the jacking construction of a target underground pipeline segment joint, regional sensing data of a jacking region is acquired.

[0102] 102. According to the regional sensing data, at least one construction risk of the jacking region is predicted based on a prediction model of multiple risk types.

[0103] 103. According to the regional sensing data, construction integrity corresponding to a current time period is predicted based on an integrity prediction model.

[0104] 104. According to the construction risks of multiple jacking regions and the construction integrities of multiple time periods, an overall integrity risk corresponding to the target underground pipeline segment is determined.

[0105] It can be seen that the above-mentioned embodiments can predict the construction risk of the jacking region according to the regional sensing data and the prediction model of multiple risk types, 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 regions and the construction integrities of multiple time periods, so as to realize more real-time and accurate risk analysis and integrity analysis combined with the sensing data in the jacking process, provide 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 acquiring 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 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 can 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 water level measuring pipe for monitoring underground water level data;

[0111] an axial force gauge or a steel stress gauge for monitoring joint pressure data such as axial force;

[0112] an electronic level or an indium steel ruler for monitoring soil sensing data such as settlement conditions;

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

[0114] As can be seen from the above optional embodiments, the details of the sensing acquisition device and the content of the regional sensing data are limited to comprehensively characterize the characteristics of the jacking region, so as to assist in realizing more real-time and accurate risk analysis and integrity analysis combined with the sensing data in the jacking process, and to provide a more accurate data basis for the planning of jacking construction, thereby improving the construction efficiency and construction effect.

[0115] As an optional embodiment, in the above step, predicting at least one construction risk of the jacking region according to the regional sensing data based on a prediction model of a plurality of risk types comprises:

[0116] For each risk type, determine the prediction model corresponding to the risk type and the input data type; optionally, the prediction model is trained by a training data set comprising a plurality of training sensing data corresponding to the input data type and risk labels corresponding to the risk type;

[0117] According to the input data type, type sensing data conforming to the type is screened out from the regional sensing data;

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

[0119] According to the risk probability, at least one construction risk of the jacking region is determined from the plurality of risk types.

[0120] As can be seen from the above optional embodiments, the regional sensing data can be screened and probabilistically predicted based on the prediction model corresponding to each risk type and the input data type, so as to determine at least one construction risk of the jacking region, thereby assisting in realizing more real-time and accurate risk analysis and integrity analysis combined with the sensing data in the jacking process, and providing a more accurate data basis for the planning of jacking construction, thereby improving the construction efficiency and construction effect.

[0121] As an optional embodiment, in the above step, at least one construction risk of the jacking region is determined from the plurality of risk types according to the risk probability, comprising:

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

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

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

[0125] The constraint conditions include:

[0126] The association probability parameter between any two risk types in each type set is greater than a first parameter threshold; optionally, the association probability parameter is the product of the average 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;

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

[0128] Based on the objective function and the constraint conditions, the multiple risk types are iteratively clustered and calculated according to a dynamic programming algorithm to obtain at least one type set;

[0129] The risk types in the type set are determined as at least one construction risk of the jacking area.

[0130] As can be seen, through the above optional embodiments, the process of calculating the proportion of the number of historical simultaneous occurrences between two risk types to obtain the association parameter and the details of clustering and grouping based on the association parameter and the dynamic algorithm to obtain more associated and high-risk construction risks are limited, thereby assisting in realizing more real-time and accurate risk analysis and integrity analysis combined with the sensing data in the jacking process, providing a more accurate data basis for the planning of jacking construction, and improving construction efficiency and construction effect.

[0131] As an optional embodiment, in the above steps, 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.

[0132] As can be seen, through the above optional embodiments, the risk types are limited, which can comprehensively represent the risks that may exist in the jacking construction process, and subsequent accurate risk analysis and integrity analysis are realized, thereby assisting in realizing more real-time and accurate risk analysis and integrity analysis combined with the sensing data in the jacking process, providing a more accurate data basis for the planning of jacking construction, and improving construction efficiency and construction effect.

[0133] As an optional embodiment, in the above step, the construction integrity corresponding to the current time period is predicted based on the integrity prediction model according to the regional sensing data, including:

[0134] All data in the regional sensing data is sorted from early to late based on the corresponding acquisition time point to obtain a sensing data sequence;

[0135] Feature extraction is performed on the sensing data sequence to obtain sequence features;

[0136] The sequence features are input into the trained construction feature parameter prediction model to obtain corresponding construction feature parameters; optionally, the construction feature parameters include at least one of a construction soil type, a pipeline type, a pipeline purpose, and a construction engineering type;

[0137] An integrity verification model corresponding to the construction feature parameters is selected from a preset integrity verification model library;

[0138] The sensing 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 sensing sequence data corresponding to the construction feature parameters and corresponding construction integrity labels.

[0139] As can be seen, through the above optional embodiment, the construction feature prediction and the determination of the integrity verification model can be realized based on the sequencing and feature extraction of the sensing data, so as to realize accurate construction integrity prediction, determine the construction integrity of the current jacking pipeline, and thus assist in realizing more real-time and accurate risk analysis and integrity analysis combined with the sensing data in the jacking process, providing a more accurate data basis for jacking construction planning, and improving construction efficiency and construction effect.

[0140] As an optional embodiment, in the above step, the overall integrity risk corresponding to the target underground pipeline section is determined according to the construction risks of the plurality of jacking regions and the construction integrities of the plurality of time periods, including:

[0141] For each time period, a plurality of jacking regions in the plurality of time periods are determined from the plurality of jacking regions;

[0142] The average value of the risk probability corresponding to the construction risk of the plurality of jacking regions is calculated to obtain a risk parameter;

[0143] The product between the risk parameter and the construction integrity corresponding to the time period is calculated to obtain an integrity risk parameter corresponding to the time period;

[0144] The integrity risk parameters of the plurality of time periods are sorted from early to late based on the corresponding time periods to obtain a risk parameter sequence;

[0145] determining whether the sequence of risk parameters meets a preset risk parameter continuity rule to obtain a determination result; optionally, the risk parameter continuity rule is used to limit the integrity risk parameter in the sequence of risk parameters, in which a plurality of parameter values are greater than a preset parameter threshold value;

[0146] when the determination result is yes, it is determined that the target underground pipeline section has a serious defect risk;

[0147] when the determination result is no, it is determined that the overall integrity risk of the target underground pipeline section is the parameter average value of the integrity risk parameters in which all parameter values are greater than the preset parameter threshold value.

[0148] It can be seen that, through the above optional embodiments, the sequence of risk parameters can be calculated by combining the construction risk and the integrity parameter of the corresponding jacking area in the time period, so as to effectively represent the continuous construction risk of the underground pipeline section, and determine whether the underground pipeline section has a serious defect risk or calculate an effective risk representation value based on the preset risk parameter continuity rule, thereby realizing more real-time and accurate risk analysis and integrity analysis in combination with 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.

[0149] As an optional embodiment, in the above step, the method further comprises:

[0150] for each construction risk corresponding to the jacking area, determining a correction control parameter corresponding to the construction risk; optionally, the correction control parameter is used to limit the parameter value corresponding to different control instruction types; the control instruction type is to increase power, decrease power, change jacking angle, change jacking angle rate, speed up jacking speed or slow down jacking speed;

[0151] based on the correction control parameter, generating a driving equipment control instruction corresponding to the construction risk;

[0152] sending all driving equipment control instructions to the jacking driving equipment for execution;

[0153] real-time monitoring whether the overall integrity risk is greater than a preset risk threshold value or whether the target underground pipeline section has a serious defect risk; if yes, sending a stop instruction to the jacking driving equipment and sending a warning inspection instruction to the construction monitoring end.

[0154] It can be seen that, through the above optional embodiments, the control instructions of the jacking driving device such as the pipe jacking machine device can be generated based on the predicted construction risks, to correct the jacking construction errors in real time, improve the construction effect, and can realize stopping and warning when there is a serious defect in the overall construction integrity of the pipeline stage, to realize more real-time and accurate risk analysis and integrity analysis combined with the sensing data in the jacking process, provide more accurate data basis for the planning of jacking construction, and improve the construction efficiency and construction effect.

[0155] Embodiment two

[0156] Please refer to Figure 2 , Figure 2 is a structural schematic diagram of an intelligent underground pipeline segment joint jacking and integrity monitoring system disclosed by an embodiment of the application. Wherein, Figure 2 The intelligent underground pipeline segment joint jacking and integrity monitoring system described can be applied in 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 indicated, the intelligent underground pipeline segment joint jacking and integrity monitoring system can include:

[0157] The acquisition module 201 is configured to acquire regional sensing data of a jacking region in a jacking construction process of a target underground pipeline segment joint.

[0158] The first prediction module 202 is configured to predict at least one construction risk of the jacking region based on a plurality of risk type prediction models according to the regional sensing data.

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

[0160] The determination module 204 is configured to determine an overall integrity risk corresponding to the target underground pipeline segment according to the construction risks of a plurality of jacking regions and the construction integrities of a plurality of time periods.

[0161] It can be seen that the above application embodiments can predict the construction risks of the jacking region according to the regional sensing data and the plurality of risk type prediction models, 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 the plurality of jacking regions and the construction integrities of the plurality of time periods, so as to realize more real-time and accurate risk analysis and integrity analysis combined with the sensing data in the jacking process, provide more accurate data basis for the planning of jacking construction, and improve the construction efficiency and construction effect.

[0162] As an optional embodiment, the target underground pipeline segment joint is provided with a sensing acquisition device for acquiring 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 humidity, soil pressure and soil settlement parameters.

[0163] 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 limited to comprehensively characterize the features of the jacking region, and subsequent accurate risk analysis and integrity analysis are realized, thereby assisting in realizing more real-time and accurate risk analysis and integrity analysis combined with 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.

[0164] As an optional embodiment, the first prediction module predicts the specific manner of at least one construction risk of the jacking region based on the regional sensing data and a prediction model of multiple risk types, including:

[0165] For each risk type, a prediction model corresponding to the risk type and an input data type are determined; optionally, 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;

[0166] According to the input data type, type sensing data of the same type is screened out from the regional sensing data;

[0167] The type sensing data is input into the prediction model corresponding to the risk type to obtain a risk probability corresponding to the risk type;

[0168] According to the risk probability, at least one construction risk of the jacking region is determined from the multiple risk types.

[0169] It can be seen that through the above optional embodiments, the regional sensing data can be screened and probabilistically predicted based on the prediction model corresponding to each risk type and the input data type, so as to determine at least one construction risk of the jacking region, thereby assisting in realizing more real-time and accurate risk analysis and integrity analysis combined with 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.

[0170] As an optional embodiment, the first prediction module determines the specific manner of at least one construction risk of the jacking region from the multiple risk types according to the risk probability, including:

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

[0172] a proportion of the total number of records in the history monitoring records in which the two risk types appear simultaneously, to obtain a corresponding association parameter between the two risk types;

[0173] set the target function to be the minimum number of all type sets and the minimum number of types in each type set;

[0174] The restriction conditions include:

[0175] The association probability parameter between any two risk types in each type set is greater than a first parameter threshold; optionally, the association probability parameter is the product of the average 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;

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

[0177] Based on the target function and the restriction conditions, the multiple risk types are iteratively clustered and calculated according to a dynamic programming algorithm to obtain at least one type set;

[0178] The risk types in the type set are determined as at least one construction risk of the jacking area.

[0179] As can be seen, through the above optional embodiments, the process of calculating the proportion of the number of times of the simultaneous occurrence of two risk types in history to obtain the association parameter and the clustering grouping based on the association parameter and the dynamic algorithm to obtain more associated and high-risk construction risks are limited, thereby assisting to realize more real-time and accurate risk analysis and integrity analysis combined with the sensing data in the jacking process, to provide a more accurate data basis for the planning of jacking construction, and to improve the construction efficiency and construction effect.

[0180] As an optional embodiment, the risk type or 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.

[0181] It can be seen that by the above optional embodiments, the risk types are limited, the risks that can exist in the jacking construction process can be comprehensively characterized, and subsequent accurate risk analysis and integrity analysis are realized, thereby assisting in realizing more real-time and accurate risk analysis and integrity analysis combined with the sensing data in the jacking process, providing a more accurate data basis for the planning of jacking construction, and improving construction efficiency and construction effect.

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

[0183] All data in the regional sensing data is sorted from early to late based on the corresponding acquisition time points to obtain a sensing data sequence;

[0184] Feature extraction is performed on the sensing data sequence to obtain sequence features;

[0185] The sequence features are input into the trained construction feature parameter prediction model to obtain corresponding construction feature parameters; optionally, the construction feature parameters include at least one of a construction soil type, a pipe type, a pipe purpose, and a construction engineering type;

[0186] An integrity verification model corresponding to the construction feature parameters is selected from a preset integrity verification model library;

[0187] The sensing 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 sensing sequence data corresponding to the construction feature parameters and corresponding construction integrity labels.

[0188] It can be seen that by the above optional embodiments, the construction feature prediction and the determination of the integrity verification model can be realized based on the serialization and feature extraction of the sensing data, to realize accurate construction integrity prediction and determine the construction integrity of the current jacking pipe, thereby assisting in realizing more real-time and accurate risk analysis and integrity analysis combined with the sensing data in the jacking process, providing a more accurate data basis for the planning of jacking construction, and improving construction efficiency and construction effect.

[0189] As an optional embodiment, the determination module determines a specific way of the overall integrity risk of the target underground pipeline section according to the construction risks of the plurality of jacking regions and the construction integrities of the plurality of time periods, which includes:

[0190] For each time period, a plurality of jacking regions in the plurality of jacking regions that are in the jacking process for a plurality of times corresponding to the time period are determined;

[0191] calculate an average value of the risk probabilities corresponding to the construction risks of the jacking area in multiple times, to obtain a risk parameter;

[0192] calculate a 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;

[0193] sort 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;

[0194] determine whether the risk parameter sequence meets a preset risk parameter continuity rule, to obtain a determination result; optionally, the risk parameter continuity rule is used to limit that there are a plurality of integrity risk parameters with parameter values greater than a preset parameter threshold in the risk parameter sequence;

[0195] when the determination result is yes, determine that the target underground pipeline section has a serious defect risk;

[0196] when the determination result is no, determine that the overall integrity risk of the target underground pipeline section corresponds to a parameter average value of the integrity risk parameters with all parameter values greater than the preset parameter threshold in the risk parameter.

[0197] It can be seen that, through the above optional embodiments, the risk parameter sequence can be calculated by combining the construction risk and the integrity parameter of the corresponding jacking area in the time period, so as to effectively represent the continuous construction risk of the underground pipeline section, and based on the preset risk parameter continuity rule, it is determined whether the underground pipeline section has a serious defect risk or an effective risk representation value is calculated, so that more real-time and accurate risk analysis and integrity analysis are realized by combining the sensing data in the jacking process, more accurate data basis is provided for the planning of jacking construction, and the construction efficiency and construction effect are improved.

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

[0199] for each construction risk corresponding to the jacking area, determine a correction control parameter corresponding to the construction risk; optionally, the correction control parameter is used to limit the parameter values corresponding to different control instruction types; the control instruction type is to increase power, reduce power, change jacking angle, change jacking angle rate, speed up jacking speed or slow down jacking speed;

[0200] based on the correction control parameter, generate a driving equipment control instruction corresponding to the construction risk;

[0201] send all driving equipment control instructions to the jacking driving equipment for execution;

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

[0203] It can be seen that through the above optional embodiments, control instructions for the jacking driving device such as a pipe jacking machine device can be generated based on the predicted construction risk to correct jacking construction errors in real time, improve construction effect, and can stop and warn when the overall construction integrity of the pipeline stage has a serious defect, so as to realize more real-time and accurate risk analysis and integrity analysis combined with sensing data in the jacking process, provide more accurate data basis for jacking construction planning, and improve construction efficiency and construction effect.

[0204] Embodiment three

[0205] Please refer to Figure 3 , Figure 3 It is another intelligent underground pipeline segment joint jacking and integrity monitoring system disclosed by the embodiments of the present application. Figure 3 The intelligent underground pipeline segment joint jacking and integrity monitoring system described is applied in 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 can include:

[0206] a memory 301 storing executable program codes;

[0207] a processor 302 coupled with the memory 301;

[0208] The processor 302 calls the executable program codes stored in the memory 301, which are used to execute the steps of the intelligent underground pipeline segment joint jacking and integrity monitoring method described in embodiment one.

[0209] Embodiment four

[0210] The embodiments of the present application disclose a computer readable storage medium storing a computer program for electronic data exchange, wherein the computer program causes a computer to execute the steps of the intelligent underground pipeline segment joint jacking and integrity monitoring method described in embodiment one.

[0211] Embodiment five

[0212] The embodiments of the present application 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 cause a computer to execute the steps of the intelligent underground pipeline segment joint jacking and integrity monitoring method described in embodiment one.

[0213] The above-described embodiments of the present specification are described with reference to particular embodiments. Other embodiments are within the scope of the following claims. In some cases, actions recited in the claims can be performed in a different order and still achieve desirable results. Additionally, the processes depicted in the accompanying figures do not necessarily require the particular order shown or sequential order to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous.

[0214] The systems, apparatuses, modules, or units illustrated by the above-described embodiments can be specifically realized 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 a combination of any of these devices.

[0215] For the convenience of description, the above apparatuses are described in various units by functions respectively when described. Of course, the functions of each unit can be realized in the same or more software and / or hardware when implementing the present specification.

[0216] Those skilled in the art will understand that the embodiments of the present specification can be provided as a method, a system, or a computer program product. Therefore, the embodiments of the present specification can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of the present 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 storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0217] The present specification is described with reference to flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present specification. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a result for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one flow or multiple flows and / or blocks Figure 1 The apparatuses specified in 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 function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0219] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

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

[0221] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) and / or cache memory, non-volatile memory, such as read-only memory (ROM), EPROM, and / or flash memory, etc. The memory is an example of computer readable media.

[0222] Computer readable media includes permanent and non-permanent, moveable and non- moveable media that can be implemented in any method or technology for storage of information such as 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 technology, compact disc read-only memory (CD-ROM), digital versatile disks (DVDs) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that is accessible to a computing device. According to the definition provided herein, computer readable media does not include transitory media, such as modulated data signals and carrier waves.

[0223] It is also to be noted that the terms "comprising", "comprises" or other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

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

[0225] The various embodiments in the specification are described in progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the difference from other embodiments. In particular, for the system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiments.

[0226] Finally, it should be noted that the disclosed intelligent underground pipeline segment joint pushing and integrity monitoring method and system disclosed by the embodiments of the present application are only the preferred embodiments of the present application, and are only used to illustrate the technical solutions of the present application, but not to limit the present application. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalent ones. The modification or replacement does not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for intelligent underground pipeline segment joint jacking and integrity monitoring, characterized in that, The method includes: During the jacking construction of the target underground pipeline segment joint, regional sensing data of the jacking area is acquired; the target underground pipeline segment joint is equipped 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; Based on the regional sensing data, and using a prediction model for multiple risk types, at least one construction risk in the jacking area is predicted. Based on the area sensing data and an integrity prediction model, the construction integrity for the current time period is predicted, including: All data in the area sensor data are sorted from morning to night based on their corresponding acquisition time points to obtain a sensor data sequence; Feature extraction is performed on the sensor data sequence to obtain sequence features; The sequence features are input into a trained construction feature parameter prediction model to obtain the corresponding construction feature parameters; the construction feature parameters include at least one of construction soil type, pipeline type, pipeline purpose and construction project type; Select the integrity verification model corresponding to the construction feature parameters from the 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 using a training dataset that includes multiple training sensor sequence data corresponding to the construction feature parameters and corresponding construction integrity annotations. 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 is determined.

2. The method for jacking and integrity monitoring of intelligent underground pipeline segment joints according to claim 1, characterized in that, The step of predicting at least one construction risk in the jacking area based on the regional sensing data and a prediction model for multiple risk types includes: For each risk type, a prediction model and input data type corresponding to that risk type are determined; the prediction model is trained using a training dataset that includes training sensor data corresponding to multiple input data types and risk labels corresponding to that risk type. Based on the input data type, filter out the type-matching sensing data from the area sensing data; The sensor data of the aforementioned type is input into the prediction model corresponding to the risk type to obtain the risk probability corresponding to the risk type. Based on the risk probability, at least one construction risk in the jacking area is determined from among the multiple risk types.

3. The method for jacking and integrity monitoring of intelligent underground pipeline segment joints according to claim 2, characterized in that, The step of determining at least one construction risk in the jacking area from multiple risk types based on the risk probability includes: For any two of the aforementioned risk types, obtain the historical monitoring records corresponding to each of the two risk types; Calculate the percentage of records in the historical monitoring records that simultaneously exhibit both of the two risk types out of the total number of records, and obtain the corresponding correlation parameters between the two risk types. The objective function is set to minimize the number of all type sets and the number of types in each type set. Setting restrictions includes: The correlation probability parameter between any two risk types in each set of types is greater than a first parameter threshold; the correlation probability parameter is the product of the average risk probability 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. The correlation probability parameter between any two risk types belonging to different sets of the aforementioned types 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 constraints, iterative clustering calculations are performed on multiple risk types using a dynamic programming algorithm to obtain at least one type set. The risk type in the set of types is identified as at least one construction risk in the jacking area.

4. The method for jacking and integrity monitoring of intelligent underground pipeline segment joints according to claim 3, characterized in that, The risk type or construction risk is the risk of soil collapse, the risk of harmful environment, the risk of damage to existing pipelines, the risk of damage to building foundations, or the risk of human error; the risk of harmful environment includes at least one of the risks of toxic gases, toxic liquids, and toxic organisms. The risks of damage to existing pipelines include at least one of the risks of damage to water supply pipelines, damage to power supply lines, and damage to gas supply pipelines; The risks of human error include at least one of the following: guidance error risk, timing error risk, and instruction error risk.

5. The method for jacking and integrity monitoring of intelligent underground pipeline segment joints according to claim 2, characterized in that, The determination of 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: For each time period, identify the jacking areas within the construction process corresponding to that time period from multiple jacking areas; Calculate the average value of the risk probabilities corresponding to the construction risks of the jacking areas within the multiple time periods to obtain risk parameters; Calculate the product between the risk parameter and the construction integrity corresponding to the time period to obtain the integrity risk parameter corresponding to the time period; The integrity risk parameters for multiple time periods are sorted from morning to night based on the corresponding time periods to obtain a risk parameter sequence; Determine whether the risk parameter sequence conforms to a preset risk parameter continuity rule, and obtain a determination result; the risk parameter continuity rule is used to limit the integrity risk parameters in the risk parameter sequence to have multiple consecutive parameter values ​​greater than a preset parameter threshold; When the judgment result is yes, it is determined that the target underground pipeline segment has a serious defect risk; If the judgment result is negative, the overall integrity risk corresponding to the target underground pipeline segment is determined to be the average value of all integrity risk parameters whose parameter values ​​are greater than the preset parameter threshold.

6. The method for jacking and integrity monitoring of intelligent underground pipeline segment joints according to claim 5, characterized in that, The method further includes: For each construction risk corresponding to the jacking area, a corrective control parameter corresponding to the construction risk is determined; the corrective control parameter is used to limit the parameter value corresponding to different control command types; the control command 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. Based on the corrective control parameters, a drive equipment control command corresponding to the construction risk is generated; All control commands for the drive devices are sent to the push drive devices for execution. Real-time monitoring is conducted to determine whether the overall integrity risk exceeds a preset risk threshold or whether the target underground pipeline segment has the risk of serious defects. If so, a shutdown command is sent to the jacking drive equipment and a warning inspection command is sent to the construction monitoring terminal.

7. A smart underground pipeline segment joint jacking and integrity monitoring system, characterized in that, The system includes: The acquisition module is used to acquire area sensing data of the jacking area during the jacking construction of the target underground pipeline segment joint; The first prediction module is used to predict at least one construction risk in the jacking area based on the regional sensing data and a prediction model for multiple risk types. The second prediction module is used to predict the construction integrity corresponding to the current time period based on the regional sensing data and the integrity prediction model. The determination module is used to determine 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; the system is used to implement the intelligent underground pipeline segment joint jacking and integrity monitoring method as described in any one of claims 1-6.

8. A smart underground pipeline segment joint jacking and integrity monitoring system, characterized in that, The system includes: Memory containing 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 1-6.

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