Underground pipe network construction supervision system and method based on data analysis

Abstract

The invention discloses an underground pipe network construction supervision system and method based on data analysis, and belongs to the technical field of construction supervision. Uniformly numbering the underground pipeline facility information; a high-precision positioning sensor is installed on construction equipment; sound wave detection equipment is deployed, and position information of underground pipeline facilities is obtained; constructing a time node set, adding a period label, and obtaining position information data of all high-precision positioning sensors in a period time node; early warning is carried out by calculating the position information change of the high-precision positioning sensor and the distance between the underground pipeline facility and the high-precision positioning sensor; through unified numbering, accurate positioning and real-time monitoring of underground pipeline facilities and construction equipment, in combination with periodic data analysis, and through calculation of position information change and the distance between the equipment and the pipeline, a risk early warning index is designed, automatic early warning is triggered in real time, and the construction efficiency is improved. And the safety and the management efficiency in the underground pipe network construction process are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of construction supervision, and in particular to a system and method for underground pipe network construction supervision based on data analysis. Background Art

[0002] As an important part of urban infrastructure, the construction quality and safety of underground pipelines are directly related to the operation of the city and the lives of residents. In recent years, with the acceleration of urbanization, the scale and complexity of underground pipeline construction have gradually increased. However, the risks and challenges faced by underground pipeline construction are also becoming increasingly prominent. Traditional underground pipeline construction supervision methods mostly rely on manual experience and on-site monitoring. Although they can ensure construction safety to a certain extent, due to the lack of real-time and accurate data support, it is often difficult to effectively identify and warn of potential risks, resulting in damage to underground facilities and frequent safety accidents. Therefore, how to use advanced technical means to improve the supervision efficiency and safety of underground pipeline construction has become an urgent problem to be solved in the current urban construction field.

[0003] Existing underground pipeline construction supervision technologies have significant deficiencies in accuracy and real-time performance. First, traditional methods cannot capture the dynamic relative positions of construction equipment and underground pipeline facilities in real time, resulting in delayed risk identification. Second, the lack of comprehensive analysis and processing capabilities for a variety of data in the construction area makes it impossible to provide effective risk assessment and early warning. In addition, the environment during the construction process is complex and changeable, and underground pipeline facilities are unevenly distributed, making it difficult for existing technologies to be effectively adjusted for different situations. These deficiencies have seriously restricted the safety management level of underground pipeline construction and increased construction risks and costs. Summary of the invention

[0004] The purpose of the present invention is to provide an underground pipe network construction supervision system and method based on data analysis to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A method for supervising underground pipeline network construction based on data analysis, the method comprising the following steps: uniformly numbering all known underground pipeline facility information in a construction area, and constructing an underground pipeline facility database set; installing high-precision positioning sensors on construction equipment, and constructing a high-precision positioning sensor set; deploying acoustic wave detection equipment in the construction area, constructing an acoustic wave detection equipment set, and obtaining the location information of underground pipeline facilities; constructing a time node set, attaching a periodic label, recording it as a periodic time node, and obtaining the location information data of all high-precision positioning sensors in the periodic time node; calculating the location information change of the high-precision positioning sensor at different periodic time nodes, and constructing a high-precision positioning sensor anomaly set; calculating the distance between the underground pipeline facility and the high-precision positioning sensor at different periodic time nodes; and designing a risk warning index and a risk threshold based on the high-precision positioning sensor anomaly set and the distance.

[0007] As a preferred solution of the underground pipeline network construction supervision method based on data analysis described in the present invention, all known underground pipeline facility information in the construction area is uniformly numbered, and an underground pipeline facility database set UFD = {UFD i |i∈[1,I]}, where UFD i represents the i-th underground pipeline facility in the construction area, and I is the total number of underground pipeline facilities in the construction area; the construction area refers to the working scope of the ongoing underground pipeline network construction; the underground pipeline facilities refer to the underground infrastructure built and managed by the government and public institutions for providing public services and meeting social needs.

[0008] High-precision positioning sensors are installed on the construction equipment, where one high-precision positioning sensor is installed on each construction equipment, and a high-precision positioning sensor set HPS = {HPS a |a∈[1,A]}, where HPS a It means that a high-precision positioning sensor is installed on the a-th construction equipment, and A represents the total number of high-precision positioning sensors; the construction equipment refers to the machinery and tools used in the construction of underground pipelines to perform different construction tasks; the high-precision positioning sensor refers to the sensing equipment that provides accurate position data during the construction process.

[0009] As a preferred solution of the underground pipe network construction supervision method based on data analysis described in the present invention, acoustic wave detection equipment is deployed in the construction area to construct an acoustic wave detection equipment set SWD={SWD b |b∈[1,B]}, where SWD b represents the bth acoustic wave detection device, B represents the total number of acoustic wave detection devices; using the acoustic wave detection device set and the underground pipeline facility database set, the underground pipeline facility UFDi Perform an acoustic wave scan to obtain the UFD of the underground pipeline facilities i and constructs a location information set SLI = {SLI (UFD i )|i∈[1,I]}, where SLI(UFD i ) indicates underground pipeline facilities UFD i location information.

[0010] In a one-hour time range, the time range is divided into x time nodes at equal intervals of time Δt, and a time node set TN = {TN t |t∈[1,x]}, where TN t represents the tth time node, x represents the total number of time nodes; the time node TN t The cycle is hourly, with a cycle label added, recorded as the cycle time node TN t (k), where k represents the cycle number; get the cycle time node TN t (k) The location information data of all high-precision positioning sensors in the building to build the location information dataset LID[TN t (k)]={HPS a [TN t (k)]|a∈[1,A],t∈[1,x]}, where HPS a [TN t (k)] represents the periodic time node TN t (k) is the location information of the ath high-precision positioning sensor.

[0011] As a preferred solution of the underground pipe network construction supervision method based on data analysis described in the present invention, the calculation at the periodic time node TN t (k) to the cycle time node TN t+1 (k) High-precision positioning sensor HPS a The position information changes, and the calculation formula is as follows:

[0012] ΔHPS a [TN t (k)→TN t+1 (k)]=|HPS a [TN t (k)]-HPS a [TN t+1 (k)]|;

[0013] Preset the maximum allowable position change threshold β of the high-precision positioning sensor. If ΔHPS a [TN t (k)→TN t+1(k)]>β, then mark high-precision positioning sensor HPS a At cycle time node TN t (k) to the cycle time node TN t+1 (k) There is a position anomaly, and a high-precision positioning sensor anomaly set is constructed

[0014] Calculate UFD of underground pipeline facilities i With high-precision positioning sensor HPS a At cycle time node TN t (k) to the cycle time node TN t+1 The distance at (k) is calculated as follows:

[0015] ΔD[TN t (k)]=ID[TN t (k)]-D[TN t+1 (k)]|;

[0016] D[TN t (k)]=|SLI(UFD i )-HPS a [TN t (k)]|;

[0017] D[TN t+1 (k)]=|SLI(UFD i )-HPS a [TN t+1 (k)]|;

[0018] Among them, ΔD[TN t (k)] represents underground pipeline facilities UFD i With high-precision positioning sensor HPS a At cycle time node TN t (k) to the cycle time node TN t+1 (k) distance, D[TN t (k)] represents underground pipeline facilities UFD i With high-precision positioning sensor HPS a At cycle time node TN t (k) distance, D[TN t+1 (k)] represents underground pipeline facilities UFD i With high-precision positioning sensor HPS a At cycle time node TN t+1 (k) distance.

[0019] As a preferred solution of the underground pipe network construction supervision method based on data analysis described in the present invention, a risk warning index is designed, and the calculation formula is as follows:

[0020]

[0021] Among them, Risk represents the risk warning index. Indicates the number of high-precision positioning sensor anomalies, min(ΔD[TN t (k)]) means taking the minimum distance.

[0022] In the present invention, the risk status in the underground pipeline construction process is analyzed by monitoring the position information fed back by the high-precision positioning sensor and the position information of the underground pipeline facilities fed back by the acoustic detection equipment; in actual construction, the relative positions of the underground facilities and the construction equipment will change, and this change may bring potential risks, but the risk assessment and control can be carried out through continuous monitoring and analysis; the present invention combines the number of abnormalities of the high-precision positioning sensor and the minimum distance change between the underground pipeline facilities and the sensor to calculate the risk assessment index (Risk); if the risk assessment index value is large, it means that the impact of the number of abnormalities and the distance change is more significant; if the index value is small, it means that the construction status is relatively stable; the numerator reflects the number of abnormal situations detected by the high-precision positioning sensor, and the more abnormalities, the higher the potential risk; the denominator is the minimum value of the distance change among all time points, highlighting the importance of the minimum distance change, because the minimum distance often represents the situation with the highest potential risk, that is, the construction equipment is very close to the underground pipeline facilities, and there is a high probability of collision, resulting in serious consequences.

[0023] Set the risk threshold δ.

[0024] If Risk>δ, an early warning is triggered.

[0025] If Risk < δ, no warning is triggered and the high-precision positioning sensor HPS is tracked in real time a The location information of the risk warning index is recalculated.

[0026] A system for monitoring construction of underground pipelines based on data analysis, the system comprising: a data initialization module, a data acquisition module, a data collection module and a risk assessment module. The data initialization module is used to obtain the location information of underground pipeline facilities and construction equipment during the construction process. The data acquisition module is used to obtain the location information of underground pipeline facilities and construction equipment during the construction process. The anomaly analysis module is used to analyze the position changes of construction equipment, detect anomalies and assess potential risks during the construction process. The risk assessment module is used to assess the risks during the construction process and trigger corresponding early warning mechanisms.

[0027] Furthermore, the data initialization module also includes an underground pipeline facility numbering unit and a high-precision positioning sensor installation unit.

[0028] The underground pipeline facility numbering unit uniformly numbers all known underground pipeline facility information in the construction area and constructs an underground pipeline facility database set UFD = {UFD i |i∈[1,I]}, where UFD i represents the i-th underground pipeline facility in the construction area, and I is the total number of underground pipeline facilities in the construction area; the construction area refers to the working scope of the ongoing underground pipeline network construction; the underground pipeline facilities refer to the underground infrastructure built and managed by the government and public institutions for providing public services and meeting social needs.

[0029] The high-precision positioning sensor installation unit installs a high-precision positioning sensor on the construction equipment, wherein one high-precision positioning sensor is installed corresponding to one construction equipment, and a high-precision positioning sensor set HPS={HPS a |a∈[1,A]}, where HPS a It means that a high-precision positioning sensor is installed on the a-th construction equipment, and A represents the total number of high-precision positioning sensors; the construction equipment refers to the machinery and tools used in the construction of underground pipelines to perform different construction tasks; the high-precision positioning sensor refers to the sensing equipment that provides accurate position data during the construction process.

[0030] Furthermore, the data acquisition module also includes an acoustic wave detection equipment deployment unit and a time node division unit.

[0031] The acoustic wave detection equipment deployment unit deploys acoustic wave detection equipment in the construction area, constructing an acoustic wave detection equipment set SWD={SWD b |b∈[1,B]}, where SWD b represents the bth wave detection device, B represents the total number of wave detection devices; using the acoustic wave detection device set and the underground pipeline facility database set, the underground pipeline facility UFD i Perform an acoustic wave scan to obtain the UFD of the underground pipeline facilities i and constructs a location information set SLI = {SLI (UFD i )|i∈[1,I]}, where SLI(UFD i ) indicates underground pipeline facilities UFD i location information.

[0032] The time node division unit divides the time range into x time nodes at equal intervals of time Δt within a one-hour time range, and constructs a time node set TN = {TN t |t∈[1,x]}, where TN trepresents the tth time node, x represents the total number of time nodes; the time node TN t The cycle is hourly, with a cycle label added, recorded as the cycle time node TN t (k), where k represents the cycle number; get the cycle time node TN t (k) The location information data of all high-precision positioning sensors in the building to build the location information dataset LID[TN t (k)]={HPS a [TN t (k)]|a∈[1,A],t∈[1,x]}, where HPS a [TN t (k)] represents the periodic time node TN t (k) is the location information of the ath high-precision positioning sensor.

[0033] Furthermore, the abnormality analysis module also includes a position change calculation unit and a distance calculation unit.

[0034] The position change calculation unit calculates the periodic time node TN t (k) to the cycle time node TN t+1 (k) High-precision positioning sensor HPS a Position information change ΔHPS a [TN t (k)→TN t+1 (k)], the maximum allowable position change threshold β of the high-precision positioning sensor is preset. If ΔHPS a [TN t (k)→TN t+1 (k)]>β, then mark high-precision positioning sensor HPS a At cycle time node TN t (k) to the cycle time node TN t+1 (k) There is a position anomaly, and a high-precision positioning sensor anomaly set is constructed

[0035] The distance calculation unit calculates the underground pipeline facility UFD i With high-precision positioning sensor HPS a At cycle time node TN t (k) to the cycle time node TN t+1 (k) The distance ΔD[TN t (k)].

[0036] Furthermore, the risk assessment module also includes a risk warning index calculation unit and a warning mechanism triggering unit.

[0037] The risk warning index calculation unit is designed to have a risk warning index Risk.

[0038] The warning mechanism trigger unit sets a risk threshold δ; if Risk>δ, a warning is triggered; if Risk<δ, no warning is triggered, and the high-precision positioning sensor HPS is tracked in real time a The location information of the risk warning index is recalculated.

[0039] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention ensures the systematic management and traceability of information by uniformly numbering all known underground pipeline facilities in the construction area and constructing a database set of underground pipeline facilities. Secondly, high-precision positioning sensors are installed on the construction equipment, and acoustic wave detection equipment is deployed to obtain accurate underground pipeline location information and location data of construction equipment, thereby realizing real-time monitoring of underground facilities and construction equipment. On this basis, a set of time nodes is constructed, periodic monitoring is performed, the changes in the location information of high-precision positioning sensors are analyzed, abnormal situations are identified, and the distance between underground pipeline facilities and construction equipment is calculated, thereby effectively assessing construction risks. Finally, by designing a risk warning index and setting a risk threshold, quantitative assessment and automated warning of construction risks are achieved, ensuring the safety and management efficiency of the construction site. Overall, the present invention effectively reduces the potential risks in underground pipeline construction and significantly improves construction safety through comprehensive analysis and real-time monitoring of data. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0041] in:

[0042] Figure 1 The present invention is a flowchart of an underground pipe network construction supervision method based on data analysis according to an embodiment of the present invention.

[0043] Figure 2 It is a structural schematic diagram of an underground pipe network construction supervision system based on data analysis of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] See also Figure 1-Figure 2 , the present invention provides a technical solution:

[0046] See also Figure 1 In the first embodiment, a method for monitoring underground pipe network construction based on data analysis is provided, and the method comprises the following steps:

[0047] Step S1: uniformly number all known underground pipeline facility information in the construction area and construct an underground pipeline facility database set; install high-precision positioning sensors on construction equipment and construct a high-precision positioning sensor set.

[0048] Specifically, all known underground pipeline facilities information in the construction area is uniformly numbered, and an underground pipeline facility database set UFD = {UFD i |i∈[1,I]}, where UFD i represents the i-th underground pipeline facility in the construction area, and I is the total number of underground pipeline facilities in the construction area; the construction area refers to the working scope of the ongoing underground pipeline network construction; the underground pipeline facilities refer to the underground infrastructure built and managed by the government and public institutions for providing public services and meeting social needs.

[0049] Furthermore, high-precision positioning sensors are installed on the construction equipment, wherein one high-precision positioning sensor is installed on one construction equipment, and a high-precision positioning sensor set HPS = {HPS a |a∈[1,A]}, where HPS a It means that a high-precision positioning sensor is installed on the a-th construction equipment, and A represents the total number of high-precision positioning sensors; the construction equipment refers to the machinery and tools used in the construction of underground pipelines to perform different construction tasks; the high-precision positioning sensor refers to the sensing equipment that provides accurate position data during the construction process.

[0050] Step S2: deploy acoustic wave detection equipment in the construction area, build an acoustic wave detection equipment set, and obtain the location information of underground pipeline facilities; build a time node set, attach periodic labels, record them as periodic time nodes, and obtain the location information data of all high-precision positioning sensors within the periodic time nodes.

[0051] Specifically, deploy acoustic wave detection equipment in the construction area to construct an acoustic wave detection equipment set SWD={SWD b |b∈[1,B]}, where SWD b represents the bth acoustic wave detection device, B represents the total number of acoustic wave detection devices; using the acoustic wave detection device set and the underground pipeline facility database set, the underground pipeline facility UFD i Perform an acoustic wave scan to obtain the UFD of the underground pipeline facilities i and constructs a location information set SLI = {SLI (UFD i )|i∈[1,I]), where SLI(UFD i ) indicates underground pipeline facilities UFD i location information.

[0052] Furthermore, within a one-hour time range, the time range is divided into x time nodes at equal intervals of time Δt, and a time node set TN = {TN t |t∈[1,x]}, where TN t represents the tth time node, x represents the total number of time nodes; the time node TN t The cycle is hourly, with a cycle label added, recorded as the cycle time node TN t (k), where k represents the cycle number; get the cycle time node TN t (k) The location information data of all high-precision positioning sensors in the building to build the location information dataset LID[TN t (k)]={HPS a [TN t (k)]|a∈[1,A],t∈[1,x]}, where HPS a [TN t (k)] represents the periodic time node TN t (k) is the location information of the ath high-precision positioning sensor.

[0053] Step S3: Calculate the location information changes of the high-precision positioning sensor at different periodic time nodes, and construct a high-precision positioning sensor anomaly set; calculate the distance between the underground pipeline facilities and the high-precision positioning sensor at different periodic time nodes.

[0054] Specifically, the calculation is done at the cycle time node TN t (k) to the cycle time node TN t+1 (k) High-precision positioning sensor HPS a The position information changes, and the calculation formula is as follows:

[0055] ΔHPS a [TNt (k)→TN t+1 (k)]=|HPS a [TN t (k)]-HPS a [TN t+1 (k)]|;

[0056] Preset the maximum allowable position change threshold β of the high-precision positioning sensor. If ΔHPS a [TN t (k)→TN t+1 (k)]>β, then mark high-precision positioning sensor HPS a At cycle time node TN t (k) to the cycle time node TN t+1 (k) There is a position anomaly, and a high-precision positioning sensor anomaly set is constructed

[0057] For example, assuming that there is only one high-precision positioning sensor HPS1, and the cycle number k is 1, the time node is 1, the positions of the high-precision positioning sensor HPS1 at the cycle time node TN1(1) to the cycle time node TN2(1) are 10 and 12 respectively, and the maximum allowable position change threshold β is 1.5. Substituting into the formula, we get ΔHPS a [TN1(1)→TN2(1)]=|10-12|=2, then ΔHPS a [TN1(1)→TN2(1)]>β, the high-precision positioning sensor HPS1 is marked as abnormal and added to the high-precision positioning sensor abnormal set.

[0058] Calculate UFD of underground pipeline facilities i With high-precision positioning sensor HPS a At cycle time node TN t (k) to the cycle time node TN t+1 The distance at (k) is calculated as follows:

[0059] ΔD[TN t (k)]=ID[TN t (k)]-D[TN t+1 (k)]|;

[0060] D[TN t (k)]=|SLI(UFD i )-HPS a [TN t (k)]|;

[0061] D[TN t+1 (k)]=|SLI(UFD i )-HPSa [TN t+1 (k)]|;

[0062] Among them, ΔD[TN t (k)] represents underground pipeline facilities UFD i With high-precision positioning sensor HPS a At cycle time node TN t (k) to the cycle time node TN t+1 (k) distance, D[TN t (k)] represents underground pipeline facilities UFD i With high-precision positioning sensor HPS a At cycle time node TN t (k) distance, D[TN t+1 (k)] represents underground pipeline facilities UFD i With high-precision positioning sensor HPS a At cycle time node TN t+1 (k) distance.

[0063] For example, assuming that there is only one underground pipeline facility UFD1, and the position of the underground pipeline facility UFD1 is 60, the distance between the underground pipeline facility UFD1 and the high-precision positioning sensor HPS1 at the periodic time node TN1(1) is D[TN1(1)]=|60-10|=50, and the distance at the periodic time node TN2(1) is D[TN2(1)]=|60-12|=48. Substituting into the formula, ΔD[TN t (k)]=2.

[0064] Step S4: Based on the high-precision positioning sensor anomaly set and the distance, a risk warning index and a risk threshold are designed.

[0065] Specifically, the design risk warning index is calculated as follows:

[0066]

[0067] Among them, Risk represents the risk warning index. Indicates the number of high-precision positioning sensor anomalies, min(ΔD[TN t (k)]) means taking the minimum distance.

[0068] Set the risk threshold6.

[0069] If Risk>δ, an early warning is triggered.

[0070] If Risk < δ, no warning is triggered and the high-precision positioning sensor HPS is tracked in real time a The location information of the risk warning index is recalculated.

[0071] For example, assuming there is only one time node and the risk threshold δ is 3, based on the above calculation results, we can substitute it into the formula to get If Risk < δ, no warning will be triggered.

[0072] See also Figure 2 In the second embodiment of the present invention, a system for monitoring construction of underground pipelines based on data analysis is provided, and the system includes: a data initialization module, a data acquisition module, a data collection module and a risk assessment module. The data initialization module is used to obtain the location information of underground pipeline facilities and construction equipment during the construction process. The data acquisition module is used to obtain the location information of underground pipeline facilities and construction equipment during the construction process. The anomaly analysis module is used to analyze the position changes of construction equipment, detect anomalies and assess potential risks during the construction process. The risk assessment module is used to assess the risks during the construction process and trigger corresponding early warning mechanisms.

[0073] Furthermore, the data initialization module also includes an underground pipeline facility numbering unit and a high-precision positioning sensor installation unit.

[0074] The underground pipeline facility numbering unit uniformly numbers all known underground pipeline facility information in the construction area and constructs an underground pipeline facility database set UFD = {UFD i |i∈[1,I]}, where UFD i represents the i-th underground pipeline facility in the construction area, and I is the total number of underground pipeline facilities in the construction area; the construction area refers to the working scope of the ongoing underground pipeline network construction; the underground pipeline facilities refer to the underground infrastructure built and managed by the government and public institutions for providing public services and meeting social needs.

[0075] The high-precision positioning sensor installation unit installs a high-precision positioning sensor on the construction equipment, wherein one high-precision positioning sensor is installed corresponding to one construction equipment, and a high-precision positioning sensor set HPS={HPS a |a∈[1,A]}, where HPS a It means that a high-precision positioning sensor is installed on the a-th construction equipment, and A represents the total number of high-precision positioning sensors; the construction equipment refers to the machinery and tools used in the construction of underground pipelines to perform different construction tasks; the high-precision positioning sensor refers to the sensing equipment that provides accurate position data during the construction process.

[0076] Furthermore, the data acquisition module also includes an acoustic wave detection equipment deployment unit and a time node division unit.

[0077] The acoustic wave detection equipment deployment unit deploys acoustic wave detection equipment in the construction area, constructing an acoustic wave detection equipment set SWD={SWD b |b∈[1,B]}, where SWD b represents the bth wave detection device, B represents the total number of wave detection devices; using the acoustic wave detection device set and the underground pipeline facility database set, the underground pipeline facility UFD i Perform an acoustic wave scan to obtain the UFD of the underground pipeline facilities i and constructs a location information set SLI = {SLI (UFD i )|i∈[1,I]}, where SLI(UFD i ) indicates underground pipeline facilities UFD i location information.

[0078] The time node division unit divides the time range into x time nodes at equal intervals of time Δt within a one-hour time range, and constructs a time node set TN = {TN t |t∈[1,x]}, where TN t represents the tth time node, x represents the total number of time nodes; the time node TN t The cycle is hourly, with a cycle label added, recorded as the cycle time node TN t (k), where k represents the cycle number; get the cycle time node TN t (k) The location information data of all high-precision positioning sensors in the building to build the location information dataset LID[TN t (k)]={HPS a [TN t (k)]|a∈[1,A],t∈[1,x]}, where HPS a [TN t (k)] represents the periodic time node TN t (k) is the location information of the ath high-precision positioning sensor.

[0079] Furthermore, the abnormality analysis module also includes a position change calculation unit and a distance calculation unit.

[0080] The position change calculation unit calculates the periodic time node TN t (k) to the cycle time node TN t+1 (k) High-precision positioning sensor HPS a Position information change ΔHPS a [TN t (k)→TN t+1(k)], the maximum allowable position change threshold β of the high-precision positioning sensor is preset. If ΔHPS a [TN t (k)→TN t+1 (k)]>β, then mark high-precision positioning sensor HPS a At cycle time node TN t (k) to the cycle time node TN t+1 (k) There is a position anomaly, and a high-precision positioning sensor anomaly set is constructed

[0081] The distance calculation unit calculates the underground pipeline facility UFD i With high-precision positioning sensor HPS a At cycle time node TN t (k) to the cycle time node TN t+1 (k) The distance ΔD[TN t (k)].

[0082] Furthermore, the risk assessment module also includes a risk warning index calculation unit and a warning mechanism triggering unit.

[0083] The risk warning index calculation unit is configured to design a risk warning index Risk;

[0084] The warning mechanism trigger unit sets a risk threshold δ; if Risk>δ, a warning is triggered; if Risk<δ, no warning is triggered, and the high-precision positioning sensor HPS is tracked in real time a The location information of the risk warning index is recalculated.

[0085] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0086] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for supervising underground pipe network construction based on data analysis, characterized in that: The method comprises the following steps: Step S1: uniformly number all known underground pipeline facility information in the construction area and construct an underground pipeline facility database set; install high-precision positioning sensors on construction equipment and construct a high-precision positioning sensor set; Step S2: deploy acoustic wave detection equipment in the construction area, build an acoustic wave detection equipment set, and obtain the location information of underground pipeline facilities; build a time node set, attach a periodic label, record it as a periodic time node, and obtain the location information data of all high-precision positioning sensors in the periodic time node; Step S3: Calculate the location information change of the high-precision positioning sensor at different periodic time nodes, and construct a high-precision positioning sensor anomaly set; calculate the distance between the underground pipeline facilities and the high-precision positioning sensor at different periodic time nodes; Step S4: Based on the high-precision positioning sensor anomaly set and the distance, a risk warning index and a risk threshold are designed.

2. According to claim 1, a method for supervising underground pipe network construction based on data analysis is characterized in that: The step S1 comprises the following steps: All known underground pipeline facilities in the construction area are uniformly numbered, and an underground pipeline facility database set UFD = {UFD i |i∈[1,I]}, where UFD i represents the i-th underground pipeline facility in the construction area, and I is the total number of underground pipeline facilities in the construction area; the construction area refers to the scope of work where the underground pipeline network construction is ongoing; the underground pipeline facilities refer to underground infrastructure built and managed by the government and public institutions for providing public services and meeting social needs; High-precision positioning sensors are installed on the construction equipment, where one high-precision positioning sensor is installed on each construction equipment, and a high-precision positioning sensor set HPS = {HPS a |a∈[1,A]}, where HPS a It means that a high-precision positioning sensor is installed on the a-th construction equipment, and A represents the total number of high-precision positioning sensors; the construction equipment refers to the machinery and tools used in the construction of underground pipelines to perform different construction tasks; the high-precision positioning sensor refers to the sensing equipment that provides accurate position data during the construction process.

3. The underground pipe network construction supervision method based on data analysis according to claim 2 is characterized in that: The step S2 comprises the following steps: Deploy acoustic wave detection equipment in the construction area and construct an acoustic wave detection equipment set SWD={SWD b |b∈[1,B]}, where SWD b represents the bth acoustic wave detection device, B represents the total number of acoustic wave detection devices; using the acoustic wave detection device set and the underground pipeline facility database set, the underground pipeline facility UFD i Perform an acoustic wave scan to obtain the UFD of the underground pipeline facilities i and constructs a location information set SLI = {SLI (UFD i )|i∈[1,I]}, where SLI(UFD i ) indicates underground pipeline facilities UFD i location information; In a one-hour time range, the time range is divided into x time nodes at equal intervals of time Δt, and a time node set TN = {TN t |t∈[1,x]}, where TN t represents the tth time node, x represents the total number of time nodes; the time node TN t The cycle is hourly, with a cycle label added, recorded as the cycle time node TN t (k), where k represents the cycle number; get the cycle time node TN t (k) The location information data of all high-precision positioning sensors in the building to build the location information dataset LID[TN t (k)]={HPS a [TN t (k)]|a∈[1,A],t∈[1,x]}, where HPS a [TN t (k)] represents the periodic time node TN t (k) is the location information of the ath high-precision positioning sensor.

4. The underground pipe network construction supervision method based on data analysis according to claim 3 is characterized in that: The step S3 comprises the following steps: Calculate at cycle time node TN t (k) to the cycle time node TN t+1 (k) High-precision positioning sensor HPS a The position information changes, and the calculation formula is as follows: ΔHPS a [TN t (k)→TN t+1 (k)]=|HPS a [TN t (k)]-HPS a [TN t+1 (k)]|; Preset the maximum allowable position change threshold β of the high-precision positioning sensor. If ΔHPS a [TN t (k)→TN t+1 (k)]>β, then mark the high-precision positioning sensor HPS a At cycle time node TN t (k) to the cycle time node TN t+1 (k) There is a position anomaly, and a high-precision positioning sensor anomaly set is constructed Calculate UFD of underground pipeline facilities i With high-precision positioning sensor HPS a At cycle time node TN t (k) to the cycle time node TN t+1 The distance at (k) is calculated as follows: ΔD[TN t (k)]=ID[TN t (k)]-D[TN t+1 (k)]|; D[TN t (k)]=|SLI(UFD i )-HPS a [TN t (k)]|; D[TN t+1 (k)]=|SLI(UFD i )-HPS a [TN t+1 (k)]|; Among them, ΔD[TN t (k)] represents underground pipeline facilities UFD i With high-precision positioning sensor HPS a At cycle time node TN t (k) to the cycle time node TN t+1 (k) distance, D[TN t (k)] represents underground pipeline facilities UFD i With high-precision positioning sensor HPS a At cycle time node TN t (k) distance, D[TN t+1 (k)] represents underground pipeline facilities UFD i With high-precision positioning sensor HPS a At cycle time node TN t+1 (k) distance.

5. The underground pipe network construction supervision method based on data analysis according to claim 4 is characterized in that: The step S4 comprises the following steps: Design risk warning index, the calculation formula is as follows: Among them, Risk represents the risk warning index. Indicates the number of high-precision positioning sensor anomalies, min(ΔD[TN t (k)]) means taking the minimum distance; Set the risk threshold δ; If Risk>δ, an early warning is triggered; If Risk < δ, no warning is triggered and the high-precision positioning sensor HPS is tracked in real time a The location information of the risk warning index is recalculated.

6. A data analysis-based underground pipe network construction supervision system, which executes a data analysis-based underground pipe network construction supervision method as described in any one of claims 1 to 5, characterized in that: The system includes: a data initialization module, a data acquisition module, a data collection module and a risk assessment module; The data initialization module is used to obtain the location information of underground pipeline facilities and construction equipment during the construction process; The data acquisition module is used to obtain the location information of underground pipeline facilities and construction equipment during the construction process; The abnormality analysis module is used to analyze the position changes of construction equipment, detect abnormalities and evaluate potential risks in the construction process; The risk assessment module is used to assess the risks during the construction process and trigger corresponding early warning mechanisms.

7. The underground pipe network construction supervision system based on data analysis according to claim 6 is characterized by: The data initialization module also includes an underground pipeline facility numbering unit and a high-precision positioning sensor installation unit; The underground pipeline facility numbering unit uniformly numbers all known underground pipeline facility information in the construction area and constructs an underground pipeline facility database set UFD = {UFD i |i∈[1,I]}, where UFD i represents the i-th underground pipeline facility in the construction area, and I is the total number of underground pipeline facilities in the construction area; the construction area refers to the scope of work where the underground pipeline network construction is ongoing; the underground pipeline facilities refer to underground infrastructure built and managed by the government and public institutions for providing public services and meeting social needs; The high-precision positioning sensor installation unit installs a high-precision positioning sensor on the construction equipment, wherein one high-precision positioning sensor is installed corresponding to one construction equipment, and a high-precision positioning sensor set HPS={HPS a |a∈[1,A]}, where HPS a It means that a high-precision positioning sensor is installed on the a-th construction equipment, and A represents the total number of high-precision positioning sensors; the construction equipment refers to the machinery and tools used in the construction of underground pipelines to perform different construction tasks; the high-precision positioning sensor refers to the sensing equipment that provides accurate position data during the construction process.

8. The underground pipe network construction supervision system based on data analysis according to claim 7 is characterized by: The data acquisition module also includes an acoustic wave detection equipment deployment unit and a time node division unit; The acoustic wave detection equipment deployment unit deploys acoustic wave detection equipment in the construction area, constructing an acoustic wave detection equipment set SWD={SWD b |b∈[1,B]}, where SWD b represents the bth wave detection device, B represents the total number of wave detection devices; using the acoustic wave detection device set and the underground pipeline facility database set, the underground pipeline facility UFD i Perform an acoustic wave scan to obtain the UFD of the underground pipeline facilities i and constructs a location information set SLI = {SLI (UFD i )|i∈[1,I]}, where SLI(UFD i ) indicates underground pipeline facilities UFD i location information; The time node division unit divides the time range into x time nodes at equal intervals of time Δt within a one-hour time range, and constructs a time node set TN = {TN t |t∈[1,x]}, where TN t represents the tth time node, x represents the total number of time nodes; the time node TN t The cycle is hourly, with a cycle label added, recorded as the cycle time node TN t (k), where k represents the cycle number; get the cycle time node TN t (k) The location information data of all high-precision positioning sensors in the building to build the location information dataset LID[TN t (k)]={HPS a [TN t (k)]|a∈[1,A],t∈[1,x]}, where HPS a [TN t (k)] represents the periodic time node TN t (k) is the location information of the ath high-precision positioning sensor.

9. The underground pipe network construction supervision system based on data analysis according to claim 8 is characterized by: The abnormality analysis module also includes a position change calculation unit and a distance calculation unit; The position change calculation unit calculates the periodic time node TN t (k) to the cycle time node TN t+1 (k) High-precision positioning sensor HPS a Position information change ΔHPS a [TN t (k)→TN t+1 (k)], the maximum allowable position change threshold β of the high-precision positioning sensor is preset. If ΔHPS a [TN t (k)→TN t+1 (k)]>β, then mark the high-precision positioning sensor HPS a At cycle time node TN t (k) to the cycle time node TN t+1 (k) There is a position anomaly, and a high-precision positioning sensor anomaly set is constructed The distance calculation unit calculates the underground pipeline facility UFD i With high-precision positioning sensor HPS a At cycle time node TN t (k) to the cycle time node TN t+1 (k) The distance ΔD[TN t (k)].

10. The underground pipe network construction supervision system based on data analysis according to claim 9 is characterized in that: The risk assessment module also includes a risk warning index calculation unit and a warning mechanism triggering unit; The risk warning index calculation unit is configured to design a risk warning index Risk; The early warning mechanism triggering unit sets a risk threshold δ; If Risk>δ, an early warning is triggered; If Risk < δ, no warning is triggered and the high-precision positioning sensor HPS is tracked in real time a The location information of the risk warning index is recalculated.

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