Pipe gallery intelligent design method and system based on multi-technology fusion

Abstract

The invention provides a pipe gallery intelligent design method and system based on multi-technology fusion, and the method comprises the steps: collecting and analyzing the comprehensive data of a pipe gallery region, and constructing a related database and a map; obtaining and analyzing the production demand information of the service object, and predicting the production scale change and the material demand of the service object; the operation states of the pipe gallery under different working conditions are simulated, and an optimal pipe gallery wiring scheme is evaluated and screened; physical exploration is carried out on underground conditions along the line, underground space information is obtained, pipeline space conflicts are checked, and an adjustment scheme is generated; pipeline operation parameters are collected, a data transmission network is constructed, a monitoring strategy is adjusted in a self-adaptive mode, and pipeline operation abnormity early warning is conducted; and constructing a digital twinborn model, and carrying out pipe gallery design optimization and construction process management and control. The system has the advantages that integration of pipe gallery design from data collection to full life cycle management is achieved, basic data accuracy, demand prediction perspectiveness and wiring scheme reliability are improved, construction hidden dangers and operation risks are reduced, and full-stage management and control efficiency is optimized.

Description

Technical Field

[0001] This invention belongs to the field of intelligent engineering design technology, and in particular relates to an intelligent design method and system for utility tunnels based on the integration of multiple technologies. Background Technology

[0002] Integrated utility tunnels are core infrastructure in chemical industrial parks that connect various enterprises, upstream and downstream facilities, and public works. They are mainly used to lay process material pipelines, public works pipelines, heating pipelines, and data communication optical cables. They are erected in the form of overhead or underground in areas such as green belts on both sides of roads, and undertake the transmission of gas, liquid materials and energy. They are the "arteries" that ensure the efficient operation of the park.

[0003] As chemical industrial parks expand and enterprise needs diversify, existing utility tunnel design technologies are increasingly revealing limitations: basic data collection relies on manual surveys, and environmental data such as site topography, geology, and hydrology lag behind park planning data, resulting in incomplete collection of enterprise needs and frequent design modifications, increasing renovation costs; traditional drafting methods lack intelligent optimization tools, leading to frequent pipeline collisions, especially at critical locations such as road intersections and overhead elevation adjustments, impacting construction progress and operational safety; underground exploration methods are limited, resulting in unclear information about underground pipelines and obstacles along the route, easily causing safety hazards during excavation and wasting time and money on readjustment. Therefore, there is an urgent need for an intelligent design method and system that integrates advanced digital technologies and covers the entire design process to solve these problems. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method and system for intelligent design of utility tunnels based on the integration of multiple technologies, which is particularly suitable for the intelligent design of integrated utility tunnels.

[0005] The technical solution adopted in this invention is as follows: Firstly, a method for intelligent design of utility tunnels based on multi-technology integration is provided, comprising the following steps:

[0006] Collect and analyze site environmental data and park planning data of the area to be built, and construct a three-dimensional geographic database and a geological hazard probability map;

[0007] Acquire and analyze the production demand information of the utility tunnel service objects, and predict the changes in the production scale and material demand of the service objects within a preset period;

[0008] Multiple sets of utility tunnel routing schemes are generated to simulate the operation status of the utility tunnel under different working conditions, and the optimal utility tunnel routing scheme is evaluated and selected.

[0009] Physical exploration is conducted along the underground conditions of the utility tunnel to obtain underground space information, and pipeline space conflicts are investigated. If conflicts exist, a utility tunnel routing adjustment plan is generated.

[0010] Collect pipeline operation parameters, build a data transmission network, and adaptively adjust monitoring strategies based on pipeline corridor operation status and environmental changes to provide early warning of pipeline operation anomalies;

[0011] Construct a digital twin model that maps to the actual utility tunnel to optimize tunnel design, control construction process, monitor operational status, and predict faults.

[0012] Furthermore, the site environment data includes topographic data, geological data, hydrological data, climate data, and data related to surrounding buildings; the park planning data includes overall park layout data and regional planning change data; the three-dimensional geographic database is constructed by integrating high-precision topographic data, pipeline survey data, and satellite remote sensing topographic data; and the geological hazard probability map is generated by modeling historical geological hazard data based on a preset algorithm.

[0013] Furthermore, the production demand information includes material type information, transmission parameter information, and waste recycling information; the transmission parameters include pressure value, temperature value, and corrosion level.

[0014] Furthermore, multiple candidate schemes for utility tunnel routing are generated based on intelligent optimization algorithms. The intelligent optimization algorithms take the construction cost, operating energy consumption, maintenance convenience, and service coverage benefits of the utility tunnel as optimization objectives. Fluid dynamics simulation technology is used to simulate the operating status of the utility tunnel under extreme weather and peak production conditions. The operating status includes the fluid dynamic parameters and structural stress parameters of the utility tunnel.

[0015] Furthermore, the intelligent optimization algorithm is a fusion algorithm of genetic algorithm and particle swarm optimization algorithm; extreme weather includes typhoons; structural stress parameters include wind pressure load of the overhead pipe gallery.

[0016] Secondly, a multi-technology integrated intelligent design system for utility tunnels is provided, including:

[0017] The data acquisition and analysis unit, by deploying IoT sensors and cooperating with satellite remote sensing technology and geographic information systems, collects site environmental data and park planning data related to the construction of the utility tunnel, and constructs a three-dimensional geographic database and a geological hazard probability map along the utility tunnel route;

[0018] The demand processing and forecasting unit is used to analyze the production demand information of the utility tunnel service objects and combine it with the forecasting model to predict the changes in the production scale and material demand of the service objects within a preset period.

[0019] The wiring optimization simulation unit is used to generate multiple sets of candidate wiring schemes for utility tunnels, evaluate the suitability of each candidate scheme, and select the optimal wiring scheme for the utility tunnel.

[0020] The exploration and adjustment unit is used to obtain underground space information along the utility tunnel, identify pipeline conflicts, and generate utility tunnel routing adjustment plans.

[0021] The intelligent monitoring unit, by deploying monitoring elements and building a self-organizing data transmission network, adjusts the monitoring strategy according to the operating status of the utility tunnel and environmental changes, and provides early warning of abnormal pipeline operation.

[0022] The digital twin management unit is used to build a digital twin model that maps to the actual utility tunnel. Based on the digital twin model, the design of the utility tunnel can be optimized, the construction process can be controlled, and the operation status can be monitored and faults can be predicted.

[0023] Furthermore, the IoT sensors are LoRaWAN low-power sensors, deployed at a density of one group every 50 meters, with a sampling frequency of 10Hz, used to collect data on soil moisture, geological subsidence rate, and groundwater level; satellite remote sensing technology is used to acquire DEM topographic data with a resolution of 0.5m; the geographic information system is used to fuse 1:500 high-precision digital topographic maps, centimeter-level precision municipal pipeline survey data, and DEM data to construct a three-dimensional geographic database; and the geological hazard probability map is generated by modeling historical earthquake and flood data based on the XGBoost algorithm.

[0024] Furthermore, monitoring elements are deployed along pipelines within the utility tunnel that transport toxic, hazardous, flammable, explosive, or high-pressure materials. These monitoring elements employ pressure sensors, flow sensors, and leak detection sensors compatible with the ZigBee self-organizing network. The deployment density along high-risk pipelines is one group every 30-50 meters, with denser deployment at critical nodes prone to leaks and parameter fluctuations, such as pipeline valves and interfaces, with a spacing of 10-15 meters between each group. The installation location of all monitoring elements is at least 0.3 meters away from other pipelines and structural components within the utility tunnel.

[0025] The advantages and positive effects of this invention are as follows: By adopting the above-mentioned technical solution, the design of utility tunnels is integrated from data collection to full life cycle management, improving the accuracy of basic data, the foresight of demand forecasting, and the reliability of wiring schemes, reducing construction hazards and operational risks, and optimizing the efficiency of full-stage management and control; the wiring scheme is generated by a multi-objective optimization algorithm, improving the efficiency and rationality of scheme generation; and the adaptability of the scheme is verified in advance by simulating complex working conditions through fluid dynamics, ensuring the reliable operation of the utility tunnel and reducing the cost of later adjustments. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating an embodiment of the intelligent design method for utility tunnels according to the present invention. Detailed Implementation

[0027] The present disclosure will now be described more fully with reference to the accompanying drawings, which illustrate exemplary embodiments of the present disclosure. The technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present disclosure, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present disclosure without creative effort are within the scope of protection of the present disclosure.

[0028] like Figure 1 As shown, this invention provides a smart design method for utility tunnels based on multi-technology integration, comprising the following steps:

[0029] S100. Collect and analyze site environmental data and park planning data of the area to be built, and construct a three-dimensional geographic database and a geological hazard probability map.

[0030] An intelligent data acquisition system is employed to conduct on-site investigations and topographic mapping of the project; to fully understand and record the surrounding geology, hydrology, climate, and architectural conditions; and to survey the overall layout of the park. This system integrates IoT sensors, satellite remote sensing technology, and a Geographic Information System (GIS). IoT sensors are deployed on-site, using LoRaWAN low-power sensors at a density of one group every 50 meters, to collect real-time data on soil moisture, geological subsidence rate, and groundwater level at a sampling frequency of 10Hz. Satellite remote sensing technology acquires large-scale topographic data, compensating for the limitations of on-site surveys; the GIS system integrates and analyzes multi-source data to construct a dynamic geographic environment database. A three-dimensional geographic environment database is constructed by fusing 1:500 high-precision digital topographic maps, municipal pipeline survey data (accuracy down to the centimeter level), and satellite remote sensing DEM data (resolution 0.5m). Simultaneously, big data analytics are used to deeply mine historical geological disaster data and surrounding area planning change data, providing more comprehensive and accurate basic data support for the utility tunnel design. Risk modeling is performed on historical earthquake and flood data based on the XGBoost algorithm to generate a probability map of geological hazards along the utility tunnel.

[0031] S200: Obtain and parse the production demand information of the utility tunnel service objects, and predict the changes in the production scale and material demand of the service objects within a preset period;

[0032] A smart interactive platform for enterprise demand was developed to survey existing and prospective enterprises in the industrial park, understanding their production processes, the types and quantities of materials (including liquids and gases) required for production, and the status of waste and reusable materials generated during production. The platform employs Natural Language Processing (NLP) technology, allowing enterprises to submit their needs via voice or text, which the system automatically parses and categorizes. A BERT pre-trained model is used to parse enterprise demand text, achieving a 95% entity recognition accuracy rate and supporting multi-turn dialogue to complete material parameters (such as pressure, temperature, and corrosivity). Simultaneously, based on historical production data and industry development trend data, an enterprise demand prediction model is constructed. Machine learning algorithms are used to predict changes in production scale and material demand fluctuations for the next 3-5 years. Based on an LSTM-GARCH neural network, input parameters include the enterprise's production data for the past 5 years, industry GDP growth rate, and policy planning documents, with a prediction error rate ≤8%. This allows for more forward-looking utility tunnel design, avoiding frequent modifications due to enterprise development.

[0033] S300: Generate multiple sets of utility tunnel routing schemes, simulate the operation status of the utility tunnel under different working conditions, evaluate and select the optimal utility tunnel routing scheme.

[0034] The utility tunnel cabling design includes: main tunnel routing, branch line selection, and inter-enterprise specialized pipeline cabling schemes. During the cabling scheme design phase, an intelligent cabling optimization algorithm is introduced. This algorithm combines genetic algorithms and particle swarm optimization algorithms, using multiple objectives such as tunnel construction cost, operating energy consumption, maintenance convenience, and coverage enterprise benefits as optimization functions to automatically generate multiple cabling schemes. Furthermore, a dynamic simulation system is established to simulate the operational status of the utility tunnel under different conditions (such as extreme weather and peak production periods). ANSYS Fluent is used to simulate the fluid dynamics characteristics within the utility tunnel under extreme weather conditions, such as wind pressure load analysis of overhead utility tunnels during typhoons. The reliability and adaptability of each cabling scheme are evaluated, providing a more scientific basis for scheme comparison.

[0035] S400: Conduct physical exploration of the underground conditions along the utility tunnel to obtain underground space information, check for pipeline space conflicts, and generate a utility tunnel routing adjustment plan if conflicts exist.

[0036] In practice, to avoid incomplete or inaccurate data on underground pipelines collected in the early stages, which could lead to damage to unknown pipelines during excavation or the discovery of obstacles that could hinder project implementation, on-site physical exploration should be conducted at this stage. This involves conducting a physical exploration of the underground conditions along the utility tunnel in Step 3 of the wiring plan. This includes detecting underground pipelines along the route to understand their location, material, and diameter; and detecting underground obstacles to understand their type, location, and scale. Augmented Reality (AR) technology is used during the on-site physical exploration. Geophysical data is fused with a 3D model in real time. Using HoloLens 2 AR glasses, geophysical data (such as radar scan images of underground pipelines) is overlaid with the BIM model in real time, with a spatial registration accuracy of ≤2mm. Designers can intuitively see the spatial relationship between underground pipelines, obstacles, and the design scheme. When the detected pipeline spacing is less than the safety threshold (e.g., 0.5m), the AR system automatically generates three adjustment schemes, including parameters such as pipeline detour angle and elevation rise, and previews the adjustment effect through virtual construction, thereby improving the efficiency and accuracy of scheme adjustment.

[0037] S500 collects pipeline operating parameters, builds a data transmission network, and adaptively adjusts monitoring strategies based on the pipeline corridor's operating status and environmental changes to provide early warnings of pipeline operation anomalies.

[0038] For pipelines posing a safety hazard in the event of a leak, detection sensors should be deployed to monitor pipeline flow, pressure, and leakage status. The data should be transmitted in real-time to the park's central control room to ensure the safe operation of the utility tunnel. The sensor deployment scheme incorporates an intelligent sensor network. This network utilizes a ZigBee self-organizing network with node communication distances ≥100m, data transmission latency ≤50ms, and supports 99.99% network availability. It enables multi-hop data transmission and automatic routing, improving data transmission reliability. Simultaneously, an adaptive monitoring algorithm is introduced to automatically adjust the monitoring strategy based on the utility tunnel's operating status and environmental changes. Based on reinforcement learning (Q-Learning), the monitoring strategy is dynamically adjusted. When abnormal pressure fluctuations are detected in a certain area, such as pressure fluctuations exceeding ±10% of the rated value, the system automatically increases the sampling frequency of sensors in that area from 1 time / minute to 1 time / second, and triggers the activation of leak detection sensors within a 50-meter radius, achieving accurate early warning and rapid response to safety hazards.

[0039] S600: Construct a digital twin model that maps to the actual utility tunnel, and optimize the tunnel design, control the construction process, monitor the operation status, and predict faults.

[0040] Digital twin technology was incorporated throughout the design process. A 1:1 digital twin, mapped to the actual utility tunnel, was constructed based on a 3D model. During the design phase, the digital twin was used for scheme optimization and clash detection. Built using Unity 3D, the digital twin enabled clash detection for over 100 pipeline types, achieving a 98% accuracy rate. During construction, construction progress and quality data were synchronized in real-time, enabling visualized management of the construction process. BIM+GPS positioning technology was integrated to synchronize construction progress in real-time, with a progress deviation warning threshold set at ±3 days. During operation, sensor data was used to simulate the utility tunnel's operating status and predict equipment failures. Combined with an AI-powered fault prediction model, the remaining lifespan prediction error for equipment such as pumps and valves was ≤15%, issuing maintenance warnings 72 hours in advance. This provided support for maintenance decisions and enabled intelligent management of the utility tunnel throughout its entire lifecycle.

[0041] By employing the above methods, advanced technologies such as the Internet of Things (IoT), big data, artificial intelligence (AI), augmented reality (AR), and digital twins are deeply integrated into the design of integrated utility tunnels in chemical industrial parks. This breaks through the limitations of traditional design models and provides a completely new technological path for utility tunnel design. Compared to traditional design schemes, this system increases the update frequency of IoT sensor data from once a week through manual inspections to real-time collection, improving data integrity by 90% and reducing design rework rate by 75%. It achieves integrated management of utility tunnel design from data collection to the entire lifecycle, improving the accuracy of basic data, the foresight of demand forecasting, and the reliability of wiring schemes, reducing construction hazards and operational risks, and optimizing the efficiency of all-stage control.

[0042] In one embodiment, the site environment data includes topographic data, geological data, hydrological data, climate data, and data related to surrounding buildings; the park planning data includes overall park layout data and regional planning change data; the three-dimensional geographic database is constructed by integrating high-precision topographic data, pipeline survey data, and satellite remote sensing topographic data; and the geological hazard probability map is generated by modeling historical geological hazard data based on a preset algorithm.

[0043] In one embodiment, the production demand information includes material type information, transmission parameter information, and waste recycling information; the transmission parameters include pressure value, temperature value, and corrosion level.

[0044] In one embodiment, multiple candidate schemes for utility tunnel routing are generated based on an intelligent optimization algorithm. The intelligent optimization algorithm takes the construction cost, operating energy consumption, maintenance convenience, and service coverage benefits of the utility tunnel as optimization objectives. Fluid dynamics simulation technology is used to simulate the operating status of the utility tunnel under extreme weather and peak production conditions. The operating status includes the fluid dynamic parameters and structural stress parameters of the utility tunnel.

[0045] In one embodiment, the intelligent optimization algorithm is a fusion algorithm of genetic algorithm and particle swarm optimization algorithm; extreme weather includes typhoons; structural stress parameters include wind pressure load of the overhead pipe gallery.

[0046] To facilitate the use of the intelligent pipe gallery design method based on multi-technology fusion provided in this disclosure, this disclosure also provides an intelligent pipe gallery design system based on multi-technology fusion, including:

[0047] The data acquisition and analysis unit, by deploying IoT sensors and cooperating with satellite remote sensing technology and geographic information systems, collects site environmental data and park planning data related to the construction of the utility tunnel, and constructs a three-dimensional geographic database and a geological hazard probability map along the utility tunnel route;

[0048] The demand processing and forecasting unit is used to analyze the production demand information of the utility tunnel service objects and combine it with the forecasting model to predict the changes in the production scale and material demand of the service objects within a preset period.

[0049] The wiring optimization simulation unit is used to generate multiple sets of candidate wiring schemes for utility tunnels, evaluate the suitability of each candidate scheme, and select the optimal wiring scheme for the utility tunnel.

[0050] The exploration and adjustment unit is used to obtain underground space information along the utility tunnel, identify pipeline conflicts, and generate utility tunnel routing adjustment plans.

[0051] The intelligent monitoring unit, by deploying monitoring elements and building a self-organizing data transmission network, adjusts the monitoring strategy according to the operating status of the utility tunnel and environmental changes, and provides early warning of abnormal pipeline operation.

[0052] The digital twin management unit is used to build a digital twin model that maps to the actual utility tunnel. Based on the digital twin model, the design of the utility tunnel can be optimized, the construction process can be controlled, and the operation status can be monitored and faults can be predicted.

[0053] By employing the above-mentioned setup, intelligent algorithms and predictive models enable the intelligent generation, dynamic optimization, and adaptive adjustment of design schemes, improving their scientific rigor and rationality, and reducing cost waste and safety hazards caused by unreasonable designs. The intelligent cabling algorithm can generate 10 optimized schemes within 2 hours, increasing efficiency by 20 times compared to manual design and reducing construction costs by 15%-20%. Based on the digital twin-based full lifecycle management concept, the design, construction, and operation stages are organically integrated to achieve intelligent and refined management of utility tunnels, improving their overall efficiency and service level.

[0054] In one embodiment, the IoT sensor is a LoRaWAN low-power sensor, deployed at a density of one group every 50 meters, with a sampling frequency of 10Hz, used to collect data on soil moisture, geological subsidence rate, and groundwater level; satellite remote sensing technology is used to acquire DEM topographic data with a resolution of 0.5m; a geographic information system is used to fuse 1:500 high-precision digital topographic maps, centimeter-level precision municipal pipeline survey data, and DEM data to construct a three-dimensional geographic database; and a geological hazard probability map is generated by modeling historical earthquake and flood data based on the XGBoost algorithm.

[0055] In one embodiment, monitoring elements are deployed along pipelines within the utility tunnel that transport toxic, hazardous, flammable, explosive, or high-pressure materials. The monitoring elements employ pressure sensors, flow sensors, and leak detection sensors compatible with ZigBee self-organizing networks. The deployment density along high-risk pipelines is one group every 30-50 meters, with denser deployment at critical nodes prone to leaks and parameter fluctuations, such as pipeline valves and interfaces, with a spacing of 10-15 meters. The installation location of all monitoring elements is ≥0.3m away from other pipelines and structural components within the utility tunnel.

[0056] The following description, in conjunction with a preferred embodiment, illustrates the content involved in the above embodiments.

[0057] In the area of ​​the planned utility tunnel within the chemical industrial park, a set of 200 LoRaWAN low-power sensors is deployed every 50 meters to collect real-time data on soil moisture, geological subsidence rate, and groundwater level. The data is transmitted to a central server at a sampling frequency of 10Hz. Simultaneously, satellite remote sensing technology is used to acquire 0.5m resolution DEM data for the entire park, which is integrated with a 1:500 high-precision digital topographic map and municipal pipeline survey data. A three-dimensional geographic environment database is constructed using a GIS system. Based on the XGBoost algorithm, historical geological disaster data such as earthquakes and floods in the area over the past 30 years are analyzed to generate a probability map of geological hazards along the utility tunnel route. For example, in the southeast corner of the park, the algorithm predicts a 12% probability of flooding in the area within the next 5 years, providing data support for the selection of flood protection height and materials in the subsequent utility tunnel design.

[0058] A smart interactive platform for enterprise needs has been established, allowing 80 existing and 20 prospective enterprises in the park to submit their production requirements. Enterprises can submit their needs via voice or text, such as "Our company needs to transport 50 tons of ethylene gas at a pressure of 3 MPa daily." The platform uses a BERT pre-trained model to parse the text, achieving a 95% entity recognition accuracy, and automatically extracts and categorizes relevant parameters. Based on an LSTM-GARCH neural network, and inputting parameters such as the enterprise's production data for the past 5 years, industry GDP growth rate, and local chemical industry policy planning documents, the platform predicts changes in the enterprise's production scale and material demand fluctuations over the next 3-5 years. For example, if a chemical company currently uses 100 tons of methanol per day, the model predicts that its methanol consumption will increase to 140 tons per day in 3 years due to capacity expansion, with an error rate controlled within 8%. Based on this, the corresponding diameter methanol transport pipeline is reserved in the pipeline corridor design.

[0059] For the design of utility tunnel cabling schemes, an intelligent cabling optimization algorithm combines genetic algorithms and particle swarm optimization algorithms to construct an optimization function with multiple objectives such as construction cost, operating energy consumption, maintenance convenience, and coverage enterprise benefits. In this chemical industrial park project, the algorithm generated 10 cabling schemes within 2 hours. ANSYS Fluent was used to dynamically simulate each scheme, simulating the wind pressure load on the overhead utility tunnel under typhoon conditions. For example, the simulation of one scheme revealed that under a level 10 typhoon, the wind pressure of a certain section of the overhead utility tunnel exceeded the safety threshold, and this scheme was eliminated, thus selecting the most scientifically sound and reasonable cabling scheme.

[0060] Smart sensors are deployed along pipelines transporting toxic and hazardous gases within the park, forming a ZigBee self-organizing network. Sensor nodes can communicate over a distance of up to 120 meters, with data transmission latency controlled within 50 milliseconds and network availability reaching 99%. When pressure fluctuations in a section of the chlorine pipeline exceed ±10% of the rated value, an adaptive monitoring algorithm based on reinforcement learning (Q-Learning) is activated, increasing the sampling frequency of sensors in that area from once per minute to once per second, and triggering the activation of leak detection sensors within a 50-meter radius. Within 30 seconds of detecting an abnormal pressure, the system sends an early warning to the park's central control room for timely intervention.

[0061] During the design phase, a digital twin of the actual utility tunnel was constructed using Unity 3D, mapping it 1:1. Collision checks were performed on 120 types of pipelines, achieving an accuracy rate of 98%, and 23 pipeline collision issues were identified and resolved in advance. During the construction phase, BIM + GPS positioning technology was integrated, and construction personnel uploaded daily construction progress data via handheld terminals. When the construction progress of a section of the utility tunnel was two days behind schedule, the system automatically issued an early warning, reminding the construction team to adjust the construction plan. During the operation phase, real-time sensor data was combined with an AI fault prediction model to monitor the pumps and valves within the utility tunnel. For example, if a centrifugal pump was predicted to have 70 hours of remaining life, the system would issue a maintenance warning 72 hours in advance, notifying maintenance personnel to carry out repairs and avoiding production interruptions caused by equipment failure.

[0062] Based on embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0063] An electronic device includes at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute the intelligent design method for utility tunnels based on multi-technology fusion provided in this disclosure.

[0064] Electronic devices are intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0065] A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the intelligent design method for utility tunnels based on multi-technology integration provided in this disclosure.

[0066] The various embodiments of this disclosure can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0067] A computer program product includes a computer program / instructions, which are executed by a processor. This disclosure provides a method for intelligent design of utility tunnels based on multi-technology fusion.

[0068] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0069] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0070] The embodiments of the present invention have been described in detail above, but the content described is only a preferred embodiment of the present invention and should not be considered as limiting the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the patent coverage of the present invention.

Claims

1. A method for intelligent design of utility tunnels based on multi-technology integration, characterized in that, Includes the following steps: Collect and analyze site environmental data and park planning data of the area to be built, and construct a three-dimensional geographic database and a geological hazard probability map; Acquire and analyze the production demand information of the utility tunnel service objects, and predict the changes in the production scale and material demand of the service objects within a preset period; Multiple sets of utility tunnel routing schemes are generated to simulate the operation status of the utility tunnel under different working conditions, and the optimal utility tunnel routing scheme is evaluated and selected. Physical exploration is conducted along the underground conditions of the utility tunnel to obtain underground space information, and pipeline space conflicts are investigated. If conflicts exist, a utility tunnel routing adjustment plan is generated. Collect pipeline operation parameters, build a data transmission network, and adaptively adjust monitoring strategies based on pipeline corridor operation status and environmental changes to provide early warning of pipeline operation anomalies; Construct a digital twin model that maps to the actual utility tunnel to optimize tunnel design, control construction process, monitor operational status, and predict faults.

2. The intelligent design method for utility tunnels based on multi-technology fusion according to claim 1, characterized in that: Site environmental data includes topographic data, geological data, hydrological data, climate data, and data related to surrounding buildings; park planning data includes overall park layout data and regional planning change data; the three-dimensional geographic database is constructed by integrating high-precision topographic data, pipeline survey data, and satellite remote sensing topographic data; the geological hazard probability map is generated by modeling historical geological hazard data based on a preset algorithm.

3. The intelligent design method for utility tunnels based on multi-technology fusion according to claim 1, characterized in that: Production demand information includes material type information, transmission parameter information, and waste recycling information; transmission parameters include pressure value, temperature value, and corrosion level.

4. The intelligent design method for utility tunnels based on multi-technology fusion according to claim 1, characterized in that: Multiple candidate schemes for utility tunnel routing are generated based on intelligent optimization algorithms. The intelligent optimization algorithms take the construction cost, operation energy consumption, maintenance convenience and service coverage benefits of the utility tunnel as optimization objectives. Fluid dynamics simulation technology is used to simulate the operation status of the utility tunnel under extreme weather and peak production conditions. The operation status includes the fluid dynamic parameters and structural stress parameters of the utility tunnel.

5. The intelligent design method for utility tunnels based on multi-technology fusion according to claim 4, characterized in that: The intelligent optimization algorithm is a fusion of genetic algorithm and particle swarm optimization algorithm; extreme weather includes typhoons; structural stress parameters include wind pressure load on the overhead pipe gallery.

6. A utility tunnel intelligent design system based on multi-technology integration, characterized in that, include: The data acquisition and analysis unit, by deploying IoT sensors and cooperating with satellite remote sensing technology and geographic information systems, collects site environmental data and park planning data related to the construction of the utility tunnel, and constructs a three-dimensional geographic database and a geological hazard probability map along the utility tunnel route; The demand processing and forecasting unit is used to analyze the production demand information of the utility tunnel service objects and combine it with the forecasting model to predict the changes in the production scale and material demand of the service objects within a preset period. The wiring optimization simulation unit is used to generate multiple sets of candidate wiring schemes for utility tunnels, evaluate the suitability of each candidate scheme, and select the optimal wiring scheme for the utility tunnel. The exploration and adjustment unit is used to obtain underground space information along the utility tunnel, identify pipeline conflicts, and generate utility tunnel routing adjustment plans. The intelligent monitoring unit, by deploying monitoring elements and building a self-organizing data transmission network, adjusts the monitoring strategy according to the operating status of the utility tunnel and environmental changes, and provides early warning of abnormal pipeline operation. The digital twin management unit is used to build a digital twin model that maps to the actual utility tunnel. Based on the digital twin model, the design of the utility tunnel can be optimized, the construction process can be controlled, and the operation status can be monitored and faults can be predicted.

7. The intelligent pipe gallery design system based on multi-technology fusion according to claim 6, characterized in that: The IoT sensors are LoRaWAN low-power sensors, deployed at a density of one group every 50 meters, with a sampling frequency of 10Hz, used to collect data on soil moisture, geological subsidence rate, and groundwater level; satellite remote sensing technology is used to acquire DEM topographic data with a resolution of 0.5m; the geographic information system is used to fuse 1:500 high-precision digital topographic maps, centimeter-level precision municipal pipeline survey data, and DEM data to construct a three-dimensional geographic database; the geological hazard probability map is generated by modeling historical earthquake and flood data based on the XGBoost algorithm.

8. The intelligent pipe gallery design system based on multi-technology fusion according to claim 6, characterized in that: The monitoring elements are deployed along pipelines within the utility tunnel that transport toxic, hazardous, flammable, explosive, or high-pressure materials. The monitoring elements are pressure sensors, flow sensors, and leak detection sensors compatible with the ZigBee self-organizing network. The deployment density along high-risk pipelines is one group every 30-50 meters, with denser deployment at critical nodes such as pipeline valves and interfaces where leaks and parameter fluctuations are likely to occur, with a spacing of 10-15 meters. The installation location of all monitoring elements is ≥0.3m away from other pipelines and structural components within the utility tunnel.

Images