A chemical park display system based on GIS and digital panoramic industrial chain

Abstract

The present invention relates to the field of industrial pipe network management technology, and more specifically, to a chemical park display system based on GIS and a digital panoramic industry chain. The system comprises: a pipeline life monitoring unit that establishes a pipeline life model based on a long short-term memory network combined with an XGBoost algorithm; a waste resource processing unit that formulates a preliminary waste recovery pipeline transportation plan based on liquid waste recycling industry chain data, using the Dijkstra algorithm and introducing the impact of liquid waste on the inner wall of the pipeline; a pipeline transportation scheduling unit that uses a non-dominated sorting genetic algorithm to comprehensively consider the effects of the acidity and alkalinity of liquid waste and the acidity and alkalinity of soil to obtain a final waste recovery pipeline transportation plan; and an underground panoramic monitoring unit that constructs panoramic display data for an underground panoramic three-dimensional model. This chemical park display system based on GIS and a digital panoramic industry chain comprehensively considers the interference of the acidity and alkalinity of liquid waste and the impact of impact on pipelines, thereby achieving safe utilization of liquid waste in chemical parks.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial pipe network management, and in particular to a chemical park display system based on GIS and a digital panoramic industrial chain. Background Art

[0002] The underground chemical park display system is designed to visualize pipeline network structure and operating status information and assist in hazardous material scheduling and management decisions within the park. By integrating GIS, BIM and multi-source monitoring technologies, it controls the transportation path and scheduling method of underground liquid waste, and realizes the safe, efficient and low-risk recovery and utilization of chemical waste resources throughout the entire process.

[0003] Existing chemical park display systems often have difficulty in finely managing the classified transportation routes of liquid waste. Furthermore, due to the impact force generated at the bends in the liquid waste transportation scheduling and the possibility of liquid waste penetrating the soil through microcracks during transportation, low-viscosity, high-impact waste liquid flows through aging pipes, causing local wear and tear, as well as local environmental acid-base imbalance, leading to corrosion spread or environmental pollution. Therefore, a chemical park display system based on GIS and a digital panoramic industry chain is designed. Summary of the Invention

[0004] The purpose of the present invention is to provide a chemical park display system based on GIS and a digital panoramic industrial chain, so as to solve the problems raised in the above-mentioned background technology, namely, the impact force generated at the bends in the liquid waste transportation scheduling, and the possibility that the liquid waste may penetrate the soil through micro-cracks during transportation, resulting in low-viscosity and high-impact waste liquid flowing through aging pipelines, causing local wear to increase, and the local environmental acid-base imbalance causing corrosion spread or environmental pollution.

[0005] To achieve the above objectives, the present invention provides a chemical park display system based on GIS and digital panoramic industrial chain, comprising:

[0006] A pipeline life monitoring unit uses sensors to monitor damage data on the inner and outer walls of pipelines in real time, and establishes a pipeline life model based on a long-short-term memory network combined with the XGBoost algorithm to monitor pipeline health risks in real time.

[0007] A waste resource processing unit collects liquid waste data from the chemical park and classifies the liquid waste according to its chemical pH value and physical viscosity. Based on the liquid waste recycling industry chain data and the pipeline life model, the unit uses the Dijkstra algorithm and introduces the impact of liquid waste on the inner wall of the pipeline to develop a preliminary waste recycling pipeline transportation plan for the liquid waste.

[0008] A pipeline transportation scheduling unit, which uses a non-dominated sorting genetic algorithm to comprehensively consider the interaction between the acidity and alkalinity of liquid waste and soil based on the preliminary waste recycling pipeline transportation plan to optimize the final waste recycling pipeline transportation plan;

[0009] The underground panoramic monitoring unit is based on GIS+BIM+LiDAR to build an underground panoramic three-dimensional model for panoramic display of data from the pipeline life monitoring unit, waste resource processing unit, and pipeline transportation scheduling unit.

[0010] As a further improvement of the present technical solution, the damage data of the inner and outer walls of the pipeline include thickness data, crack data and corrosion data, and the damage data is used as a data basis for establishing a pipeline life model.

[0011] As a further improvement of this technical solution, the pipeline life monitoring unit includes a pipeline structure monitoring module and a pipeline life prediction module;

[0012] The pipeline structure monitoring module monitors the location and health information of all pipelines in the chemical park in real time based on damage data of the inner and outer walls of the pipelines and in combination with the engineering pipeline construction drawings, and displays the information on the underground panoramic monitoring unit.

[0013] The pipeline life prediction module is based on the damage data of the inner and outer walls of the pipeline, and uses a long short-term memory network combined with the XGBoost algorithm to establish a pipeline life model. The pipeline life model is used to monitor pipeline health risks in real time.

[0014] As a further improvement of this technical solution, the waste resource processing unit includes a liquid waste analysis and classification module and a liquid waste recovery and scheduling module;

[0015] The liquid waste analysis and classification module collects liquid waste data from the chemical park and classifies the liquid waste according to its chemical pH value and physical viscosity, classifying it into pH-low viscosity liquid waste and pH-high viscosity liquid waste.

[0016] The liquid waste recovery scheduling module is based on the liquid waste recycling industry chain data and combined with the pipeline life model. It uses the Dijkstra algorithm and introduces the impact of liquid waste on the inner wall of the pipeline to formulate a preliminary waste recovery pipeline transportation plan for liquid waste.

[0017] As a further improvement to this technical solution, the liquid waste recycling scheduling module is based on liquid waste recycling industry chain data and combined with the pipeline life model. It uses the Dijkstra algorithm and introduces the impact of liquid waste on the inner wall of the pipeline to formulate a preliminary waste recycling pipeline transportation plan for liquid waste. The specific method and steps are as follows:

[0018] S2.2.1. Obtain data on the liquid waste recycling industry chain in chemical parks;

[0019] S2.2.2. Calculate the impact of liquid waste on pipe bends on pipe life.

[0020] S2.2.3. Combined with the pipeline health score subjected to impact forces, use the Dijkstra algorithm to develop a preliminary waste recovery pipeline transportation plan for liquid waste.

[0021] As a further improvement to this technical solution, in S2.2.2, the impact of the impact force of liquid waste on the curved pipe on the pipe life is calculated as follows:

[0022] ;

[0023] in, Index for the pipeline; For time No. Impact force at the bend of the pipe section; is the liquid waste density; is the volume flow rate; is the flow velocity in the pipe; For the The radius of the bend in the pipe section; For time;

[0024] ;

[0025] in, For pipelines in time health score; The running time of liquid waste in the pipeline; is the yield strength of the pipeline material in the first section; For pipelines in time Pipeline health score after cumulative impact;

[0026] like , then The section of pipeline cannot transport the liquid waste;

[0027] like , then The liquid waste can be transported by a pipeline segment.

[0028] As a further improvement to this technical solution, in S2.2.3, the Dijkstra algorithm is used to formulate a preliminary waste recovery pipeline transportation plan for liquid waste in combination with the pipeline health score subjected to impact force. The specific method is as follows:

[0029] ;

[0030] in, For the Segment pipeline in time Pipeline health score after cumulative impact; For the Fatigue coefficient of pipeline segment; is the transport time step, For the Cross-sectional area of ​​the pipe segment;

[0031] ;

[0032] in, The running time of liquid waste in the pipeline; Integral for the historical cumulative impact of impact force on the pipeline;

[0033] Based on the pipeline in time The pipeline health score after the cumulative impact of the impact force, the historical cumulative impact score of the impact force on the pipeline, and the transportation cost are used to construct the pipeline health weight:

[0034] ;

[0035] in, is the integral weight coefficient of the historical cumulative impact of the impact force on the pipeline; For pipelines in time The weight coefficient of pipeline health score after the cumulative impact of impact force; is the transportation cost weight coefficient; For transportation costs; For the Segment pipeline in time The pipeline health weight;

[0036] The Dijkstra algorithm is used to find the optimal transportation path for liquid waste under the influence of impact force, and a preliminary waste recycling pipeline transportation plan is obtained:

[0037] ;

[0038] in, A preliminary waste recovery pipeline transportation plan for liquid waste under impact forces; For the Segment pipeline; Index for the pipeline; For the Segment pipeline.

[0039] As a further improvement of this technical solution, the pipeline transportation scheduling unit includes a soil acid-base monitoring module and an acid-base balance control module;

[0040] The soil acid-base monitoring module calculates the change in soil acidity and alkalinity based on the impact of liquid waste on soil pH changes, and is used to determine whether the pipeline meets the transportation requirements. The specific method is as follows:

[0041] S3.1.1. Calculate the change in soil pH based on the impact of the transported liquid waste on soil pH and determine whether the pipeline meets the transportation requirements:

[0042] ;

[0043] in, is the change in soil acidity and alkalinity; The amount of liquid waste material; is the buffer capacity of the soil, is the theoretical maximum pH change of liquid waste;

[0044] Set the soil acidity and alkalinity change threshold to ;

[0045] like , then the environmental acid-base changes exceed the standard, and the transportation path needs to be adjusted. Execute S3.2.1;

[0046] like , the environmental acid-base changes are stable and the pipeline meets the transportation requirements.

[0047] As a further improvement to this technical solution, the acid-base balance control module uses a non-dominated sorting genetic algorithm to comprehensively consider the interaction between the acidity and alkalinity of liquid waste and the acidity and alkalinity of soil to optimize the preliminary waste recycling pipeline transportation plan, and obtains a final waste recycling pipeline transportation plan with acid-base compatibility. The specific method is as follows:

[0048] S3.2.1. Use the non-dominated sorting genetic algorithm to optimize the preliminary waste recycling pipeline transportation plan, construct the acid-base optimization objective function, and obtain the final waste recycling pipeline transportation plan:

[0049] ;

[0050] in, is the maximum value of soil acidity and alkalinity change;

[0051] Solve the acid-base optimization objective function and obtain the final waste recycling pipeline transportation plan based on acid-base compatibility:

[0052] ;

[0053] in, a pipeline transportation plan for final waste recovery; The optimized Segment pipeline; The optimized Segment pipeline.

[0054] As a further improvement to this technical solution, the underground panoramic monitoring unit builds an underground panoramic 3D model based on GIS+BIM+LiDAR to comprehensively display the data of the pipeline life monitoring unit, waste resource processing unit, and pipeline transportation scheduling unit. The specific method is as follows:

[0055] S4.1. Use GIS to locate the geographic coordinates, spatial orientation, and depth distribution of underground pipelines. Based on BIM, integrate pipeline construction drawings, structural models, and material properties. Use LiDAR to perform high-density 3D point cloud scanning of the underground pipeline network structure to construct a panoramic 3D underground model of the chemical park.

[0056] S4.2. Based on the underground panoramic 3D model, display the data of the pipeline life monitoring unit, including: damage data of the inner and outer walls of the pipeline, pipeline life model, and pipeline health status data set;

[0057] Displays data from the waste resource processing unit, including: pH value for low-viscosity liquid waste and pH value for high-viscosity liquid waste, impact force of liquid waste on curved pipes, health score of pipes subjected to impact force, and preliminary waste recycling pipeline transportation plan;

[0058] Displays data from the pipeline transportation scheduling unit, including changes in soil acidity and alkalinity and the final waste recycling pipeline transportation plan.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] 1. This chemical park display system, based on GIS and a digital panoramic industrial chain, collects the physical and chemical properties of waste, such as acidity, alkalinity, and viscosity, to rationally classify different types of liquid waste. Based on the differences in their impact on pipelines, appropriate delivery pipelines can be selected in advance to avoid transporting waste liquids that are prone to damage to pipelines into old or curved pipelines.

[0061] 2. This chemical park display system based on GIS and digital panoramic industry chain takes into account the interference of waste liquid on the acid-base environment of underground soil. It uses a non-dominated sorting genetic algorithm to construct the optimal pipeline transportation path for liquid waste under the constraints of multiple risk factors, achieving a coordinated balance among waste resource utilization efficiency, pipeline service life and underground ecological environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is the overall flow chart of the present invention;

[0063] The meaning of each number in the figure is:

[0064] 1. Pipeline life monitoring unit; 11. Pipeline structure monitoring module; 12. Pipeline life prediction module; 2. Waste resource processing unit; 21. Liquid waste analysis and classification module; 22. Liquid waste recovery and scheduling module; 3. Pipeline transportation scheduling unit; 31. Soil acid-base monitoring module; 32. Acid-base balance control module; 4. Underground panoramic monitoring unit. DETAILED DESCRIPTION

[0065] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0066] See also Figure 1 As shown, a chemical park display system based on GIS and digital panoramic industrial chain is provided, including:

[0067] Pipeline life monitoring unit 1, which uses sensors to monitor damage data of the inner and outer walls of the pipeline in real time, and establishes a pipeline life model based on a long short-term memory network combined with the XGBoost algorithm to monitor pipeline health risks in real time;

[0068] In this embodiment, the damage data of the inner and outer walls of the pipeline include thickness data, crack data, and corrosion data. The damage data is used as a data basis for establishing a pipeline life model.

[0069] In this embodiment, sensors are used to monitor damage data of the inner and outer walls of the pipeline in real time, specifically as follows: acoustic / ultrasonic detection is used to detect pipeline cracks; fiber optic sensing technology is used to measure pipeline stress and deformation in real time to detect whether there is structural damage. The collected data is used for panoramic display; electromagnetic induction sensors are used to detect pipeline surface and internal corrosion, including corrosion rate data, coating data, etc.

[0070] In this embodiment, the pipeline life monitoring unit 1 includes a pipeline structure monitoring module 11 and a pipeline life prediction module 12;

[0071] The pipeline structure monitoring module 11 monitors the location and health information of all pipelines in the chemical park in real time based on the damage data of the inner and outer walls of the pipelines and in combination with the engineering pipeline construction drawings, and displays the information on the underground panoramic monitoring unit 4;

[0072] In this embodiment, the engineering pipeline construction drawing is one of the technical documents for the construction of the chemical park. It is a technical drawing used to guide the construction, installation, maintenance, and inspection of the pipeline. It describes in detail the design, layout, installation method, specifications, materials, welding methods, safety requirements, etc. of the pipeline. It is an important reference material for pipeline monitoring, maintenance, and management. In this system, the engineering pipeline construction drawing is used to locate the pipeline position, analyze the pipeline structure, and optimize the maintenance plan.

[0073] The pipeline life prediction module 12 establishes a pipeline life model based on the damage data of the inner and outer walls of the pipeline using a long short-term memory network combined with the XGBoost algorithm. The pipeline life model is used to monitor pipeline health risks in real time.

[0074] In this embodiment, based on the damage data of the inner and outer walls of the pipeline, a pipeline life model is established using a long short-term memory network combined with the XGBoost algorithm. The specific method is as follows:

[0075] The pipeline health status dataset is defined based on the damage data of the inner and outer walls of the pipeline as follows:

[0076] ;

[0077] in, Index for the pipeline; For time; is the length of the time series; For the Segment pipeline health status dataset; For the Segment pipeline in time The feature vector of Damage data of the inner and outer walls of the pipeline section; For the Segment pipeline in time health score;

[0078] Normalize the pipeline health status dataset and preprocess it. Each feature in the interval [0,1]; set the window length to , construct input samples;

[0079] Calculate the forget gate, input gate, and output gate of the long short-term memory network to obtain the predicted value ;

[0080] The predicted value There is a nonlinear error, and the XGBoost algorithm is used to correct the error to obtain the error correction value ;

[0081] Use weighted average composite forecast value and error correction value , and get the final pipeline in time Health score The value range is 0-1, 1 indicates that the pipeline is healthy. A value of 0 indicates that the pipe needs repair.

[0082] Waste resource processing unit 2, which collects liquid waste data from the chemical park and classifies the liquid waste according to chemical pH value and physical viscosity. Based on the liquid waste recycling industry chain data and the pipeline life model, it uses the Dijkstra algorithm and introduces the impact of liquid waste on the inner wall of the pipeline to develop a preliminary waste recycling pipeline transportation plan for the liquid waste;

[0083] In this embodiment, the waste resource processing unit 2 includes a liquid waste analysis and classification module 21 and a liquid waste recovery and scheduling module 22;

[0084] The liquid waste analysis and classification module 21 collects liquid waste data from the chemical park and classifies the liquid waste according to its chemical pH value and physical viscosity, into pH-low viscosity liquid waste and pH-high viscosity liquid waste.

[0085] In this embodiment, liquid waste data from a chemical park is collected and classified according to chemical compatibility. The specific steps are as follows:

[0086] Collect physical and chemical property data of various liquid wastes in the chemical park:

[0087] Composition, reactivity, acidity, alkalinity, corrosivity, and fluidity; toxicity assessments classified according to environmental standards; storage and generation of liquid waste, source companies, storage time, and temperature impacts;

[0088] Based on chemical compatibility analysis, liquid wastes are grouped and their compatibility is calculated: the chemical compatibility matrix between different liquid wastes is calculated to avoid violent reactions when incompatible liquid wastes are mixed and treated;

[0089] Determine whether co-pipeline transportation is feasible. For example, acidic wastewater should not be co-pipelined with alkaline wastewater to avoid violent neutralization reactions.

[0090] Oxidants should not be mixed with organic waste liquids to avoid the risk of combustion caused by oxidation reactions.

[0091] Combined with pipeline material data, the interaction between liquid waste and the inner wall of the pipeline is analyzed:

[0092] For example, strong acidic waste liquids may corrode metal pipes, requiring the use of acid-resistant lined pipes or plastic composite pipes;

[0093] Finally, all liquid wastes in the chemical park are divided into pH-low viscosity liquid wastes and pH-high viscosity liquid wastes;

[0094] The pH value-low viscosity liquid waste is named according to the characteristics of the liquid waste. For example, the characteristic name of waste ethanol is 7-low viscosity liquid waste, which means that the pH of the waste ethanol is 7 and it is a low viscosity liquid, which means that the waste ethanol is a neutral liquid and has a greater impact force on the pipeline.

[0095] The pH value-high viscosity liquid waste, such as the characteristic of waste concentrated sulfuric acid, is named 1-high viscosity liquid waste, which means that the pH of waste concentrated sulfuric acid is 1 and it is a high viscosity liquid, which means that the waste concentrated sulfuric acid is an acidic liquid and has little impact on the pipeline;

[0096] The pH value of the liquid waste is measured by a glass electrode pH meter, and the viscosity is measured by a rotational viscometer;

[0097] The liquid waste recycling scheduling module 22 is based on the liquid waste recycling industry chain data and combined with the pipeline life model, using the Dijkstra algorithm and introducing the impact of liquid waste on the inner wall of the pipeline to formulate a preliminary waste recycling pipeline transportation plan for liquid waste.

[0098] In this embodiment, the specific reasons for the impact of liquid waste on the inner wall of the pipeline are as follows:

[0099] When liquid waste flows through curved pipes such as 90° and 45° elbows, the fluid is subjected to centrifugal force, resulting in increased impact force, which will cause pipe wall wear, abnormal increase in local pressure in the pipeline, and stratified flow of waste affecting the transportation efficiency: low-viscosity liquid waste has a greater centrifugal impact force, and eddy current erosion is prone to occur at the bends, resulting in increased wear of the local pipe wall. High-viscosity liquid waste has a higher viscosity, a slower flow rate, and a relatively small centrifugal impact force.

[0100] The liquid waste recycling scheduling module 22 is based on the liquid waste recycling industry chain data and the pipeline life model, uses the Dijkstra algorithm and introduces the impact of liquid waste on the inner wall of the pipeline to formulate a preliminary waste recycling pipeline transportation plan for liquid waste. The specific method steps are as follows:

[0101] S2.2.1. Obtain data on the liquid waste recycling industry chain in chemical parks;

[0102] The liquid waste recycling industry chain data is obtained from each chemical company to determine whether the liquid waste is reused. It analyzes which liquid wastes need to be recycled and which need to be harmlessly treated, and obtains the wastes that need to be transported through pipelines, such as: waste concentrated sulfuric acid 1 - high-viscosity liquid waste needs to be transported to the acid recovery device; waste ethanol 7 - low-viscosity liquid waste needs to be transported to the solvent recovery workshop;

[0103] The data of the liquid waste recycling industry chain is used as data input, as shown in the following example:

[0104] Company name: Company A;

[0105] Waste generated: waste concentrated sulfuric acid;

[0106] Waste characteristics: 1-High viscosity liquid waste;

[0107] Resource utilization method: need to be recycled;

[0108] Transportation requirements: need to be transported through pipelines;

[0109] Transportation starting point: Workshop B;

[0110] Transport destination: treatment device C;

[0111] Special requirements: Acid-resistant pipes are required;

[0112] S2.2.2. Calculate the impact of liquid waste on pipe bends on pipe life.

[0113] S2.2.3. Combined with the pipeline health score subjected to impact forces, use the Dijkstra algorithm to develop a preliminary waste recovery pipeline transportation plan for liquid waste.

[0114] In this embodiment S2.2.2, the impact of the impact force of liquid waste on the bend in the pipeline on the pipeline life is calculated as follows:

[0115] ;

[0116] in, Index for the pipeline; For time No. Impact force at the bend of the pipe section; is the liquid waste density; is the volume flow rate; is the flow velocity in the pipe; For the The radius of the bend in the pipe section; For time;

[0117] ;

[0118] in, For pipelines in time health score; The running time of liquid waste in the pipeline; For the Yield strength of the pipe material; For pipelines in time Pipeline health score after cumulative impact;

[0119] like , then The section of pipeline cannot transport the liquid waste;

[0120] like , then The liquid waste can be transported by a pipeline segment.

[0121] In this embodiment, the pipeline is Pipeline health score after cumulative impact To preliminarily calculate whether the pipeline can transport the liquid waste, time The subsequent impact force changes will be calculated and analyzed in S2.2.3;

[0122] In this embodiment S2.2.3, the Dijkstra algorithm is used to formulate a preliminary waste recycling pipeline transportation plan for liquid waste in combination with the pipeline health score subjected to the impact force. The specific method is as follows:

[0123] After time 𝑡, the health score at the bent pipe is dynamically affected by the impact force and is updated as follows:

[0124] ;

[0125] in, For the Segment pipeline in time Pipeline health score after cumulative impact; For the Fatigue coefficient of pipeline segment; is the transport time step, For the Cross-sectional area of ​​the pipe segment;

[0126] Since pipeline damage accumulates over a long period of time, we need to calculate the historical cumulative impact of the impact force using the integral method:

[0127] ;

[0128] in, The running time of liquid waste in the pipeline; Integral for the historical cumulative impact of impact force on the pipeline;

[0129] Based on the pipeline in time The pipeline health score after the cumulative impact of the impact force, the historical cumulative impact score of the impact force on the pipeline, and the transportation cost are used to construct the pipeline health weight:

[0130] ;

[0131] in, is the integral weight coefficient of the historical cumulative impact of the impact force on the pipeline; For pipelines in time The weight coefficient of pipeline health score after the cumulative impact of impact force; is the transportation cost weight coefficient; For transportation costs; For the Segment pipeline in time The pipeline health weight;

[0132] The Dijkstra algorithm is used to find the optimal transportation path for liquid waste under the influence of impact force, and a preliminary waste recycling pipeline transportation plan is obtained:

[0133] ;

[0134] in, A preliminary waste recovery pipeline transportation plan for liquid waste under impact forces; For the Segment pipeline; Index for the pipeline; For the Segment pipeline.

[0135] In this embodiment, the Dijkstra algorithm is a graph-based algorithm. Therefore, the start and end point numbers of all pipelines are recorded, an adjacency matrix of the pipeline graph structure is established, and the Dijkstra algorithm is used to find the optimal transportation path for liquid waste under the influence of impact force, thereby obtaining a preliminary waste recycling pipeline transportation plan.

[0136] The pipeline transportation scheduling unit 3 uses a non-dominated sorting genetic algorithm to comprehensively consider the interaction between the acidity and alkalinity of the liquid waste and the acidity and alkalinity of the soil based on the preliminary waste recycling pipeline transportation plan to optimize the final waste recycling pipeline transportation plan;

[0137] In this embodiment, the pipeline transportation scheduling unit 3 includes a soil acid-base monitoring module 31 and an acid-base balance control module 32;

[0138] The soil acid-base monitoring module 31 calculates the change in soil acidity and alkalinity based on the impact of liquid waste on soil pH changes, and is used to determine whether the pipeline meets the transportation requirements. The specific method is as follows:

[0139] S3.1.1. Calculate the change in soil pH based on the impact of the transported liquid waste on soil pH and determine whether the pipeline meets the transportation requirements:

[0140] ;

[0141] in, is the change in soil acidity and alkalinity; The amount of liquid waste material; is the buffer capacity of the soil, is the theoretical maximum pH change of liquid waste;

[0142] Set the soil acidity and alkalinity change threshold to ;

[0143] like , then the environmental acid-base changes exceed the standard, and the transportation path needs to be adjusted. Execute S3.2.1;

[0144] like , the environmental acid-base changes are stable and the pipeline meets the transportation requirements.

[0145] The acid-base balance control module 32 uses a non-dominated sorting genetic algorithm to comprehensively consider the interaction between the acidity and alkalinity of liquid waste and the acidity and alkalinity of soil to optimize the preliminary waste recycling pipeline transportation plan, and obtains a final waste recycling pipeline transportation plan with acid-base compatibility. The specific method is as follows:

[0146] S3.2.1. Use the non-dominated sorting genetic algorithm to optimize the preliminary waste recycling pipeline transportation plan, construct the acid-base optimization objective function, and obtain the final waste recycling pipeline transportation plan:

[0147] ;

[0148] in, is the maximum value of soil acidity and alkalinity change;

[0149] Solve the acid-base optimization objective function and obtain the final waste recycling pipeline transportation plan based on acid-base compatibility:

[0150] ;

[0151] in, a pipeline transportation plan for final waste recovery; The optimized Segment pipeline; The optimized Segment pipeline.

[0152] In this embodiment, during the transportation process, some liquid waste may penetrate into the soil through micro-cracks in the pipeline, causing changes in the acidity and alkalinity of the environment. Considering the interaction between the acidity and alkalinity of the liquid waste and the soil, it is ensured that the pipeline or surrounding soil will not be contaminated by the acid-base neutralization reaction during transportation. The preliminary waste recycling pipeline transportation plan is further optimized to form a final waste recycling pipeline transportation plan that is acid-base compatible.

[0153] In this embodiment, the acid-base optimization objective function is solved by the following method:

[0154] Generate multiple possible delivery paths and calculate the time required for each section of the pipeline The pipeline health weight, soil acidity and alkalinity change, and transportation cost are calculated; according to the Pareto optimization principle, the path solutions are sorted and the optimal solution is selected; the pipeline path is mutated, a new solution is generated, and a new optimization target value is calculated; the path with the best comprehensive performance is selected as the final optimized path, that is, the final waste recycling pipeline transportation plan. .

[0155] An underground panoramic monitoring unit 4, which builds an underground panoramic three-dimensional model based on GIS, BIM, and lidar to comprehensively display data from the pipeline life monitoring unit 1, the waste resource processing unit 2, and the pipeline transportation scheduling unit 3;

[0156] In this embodiment, the underground panoramic monitoring unit 4 constructs an underground panoramic three-dimensional model based on GIS, BIM, and lidar to comprehensively display the data of the pipeline life monitoring unit 1, the waste resource processing unit 2, and the pipeline transportation scheduling unit 3. The specific method is as follows:

[0157] S4.1. Use GIS to locate the geographic coordinates, spatial orientation, and depth distribution of underground pipelines. Based on BIM, integrate pipeline construction drawings, structural models, and material properties. Use LiDAR to perform high-density 3D point cloud scanning of the underground pipeline network structure to construct a panoramic 3D underground model of the chemical park.

[0158] S4.2. Based on the underground panoramic 3D model, display the data of pipeline life monitoring unit 1, including: damage data of the inner and outer walls of the pipeline, pipeline life model, and pipeline health status data set;

[0159] Displays data from waste resource processing unit 2, including: pH values ​​for low-viscosity liquid waste and high-viscosity liquid waste, impact forces of liquid waste on curved pipes, health scores of pipes subjected to impact forces, and preliminary waste recovery pipeline transportation plans;

[0160] Displays the data of pipeline transportation scheduling unit 3, including: changes in soil acidity and alkalinity and the final waste recycling pipeline transportation plan.

[0161] Application examples:

[0162] Provides a chemical park display system based on GIS and digital panoramic industrial chain, including:

[0163] Pipeline life monitoring unit 1, which uses sensors to monitor damage data of the inner and outer walls of the pipeline in real time, and establishes a pipeline life model based on a long short-term memory network combined with the XGBoost algorithm to monitor pipeline health risks in real time;

[0164] In this embodiment, the damage data of the inner and outer walls of the pipeline include thickness data, crack data, and corrosion data. The damage data is used as a data basis for establishing a pipeline life model.

[0165] In this embodiment, sensors are used to monitor damage data of the inner and outer walls of the pipeline in real time, specifically as follows: acoustic / ultrasonic detection is used to detect pipeline cracks; fiber optic sensing technology is used to measure pipeline stress and deformation in real time to detect whether there is structural damage. The collected data is used for panoramic display; electromagnetic induction sensors are used to detect pipeline surface and internal corrosion, including corrosion rate data, coating data, etc.

[0166] In this embodiment, the pipeline life monitoring unit 1 includes a pipeline structure monitoring module 11 and a pipeline life prediction module 12;

[0167] The pipeline structure monitoring module 11 monitors the location and health information of all pipelines in the chemical park in real time based on the damage data of the inner and outer walls of the pipelines and in combination with the engineering pipeline construction drawings, and displays the information on the underground panoramic monitoring unit 4;

[0168] In this embodiment, the engineering pipeline construction drawing is one of the technical documents for the construction of the chemical park. It is a technical drawing used to guide the construction, installation, maintenance, and inspection of the pipeline. It describes in detail the design, layout, installation method, specifications, materials, welding methods, safety requirements, etc. of the pipeline. It is an important reference material for pipeline monitoring, maintenance, and management. In this system, the engineering pipeline construction drawing is used to locate the pipeline position, analyze the pipeline structure, and optimize the maintenance plan.

[0169] The pipeline life prediction module 12 establishes a pipeline life model based on the damage data of the inner and outer walls of the pipeline using a long short-term memory network combined with the XGBoost algorithm. The pipeline life model is used to monitor pipeline health risks in real time.

[0170] In this embodiment, based on the damage data of the inner and outer walls of the pipeline, a pipeline life model is established using a long short-term memory network combined with the XGBoost algorithm. The specific method is as follows:

[0171] The pipeline health status dataset is defined based on the damage data of the inner and outer walls of the pipeline as follows:

[0172] ;

[0173] in, Index for the pipeline; For time; is the length of the time series; For the Segment pipeline health status dataset; For the Segment pipeline in time The feature vector of Damage data of the inner and outer walls of the pipeline section; For the Segment pipeline in time health score;

[0174] Set time series length (i.e., observation for 5 days); pipeline index is 1; the damage characteristic of each time step : Contains three dimensional features: pipe thickness remaining rate (Thickness, unit mm); surface corrosion rate (CorrosionRatio, unit %) and crack length (Crack Length, unit mm);

[0175] Time 𝑡 is 1, thickness (mm) is 6.0, corrosion rate (%) is 12, and crack length (mm) is 0.5;

[0176] Time 𝑡 is 2, thickness (mm) is 5.8, corrosion rate (%) is 14, and crack length (mm) is 0.6;

[0177] Time 𝑡 is 3, thickness (mm) is 5.5, corrosion rate (%) is 17, and crack length (mm) is 0.8;

[0178] Time 𝑡 is 4, thickness (mm) is 5.3, corrosion rate (%) is 20, and crack length (mm) is 1.1;

[0179] Time 𝑡 is 5, thickness (mm) is 5.1, corrosion rate (%) is 24, and crack length (mm) is 1.4;

[0180] Normalize the pipeline health status dataset and preprocess it. Each feature in to the interval [0,1];

[0181] The normal range of thickness is [5.0, 7.0] mm; the normal range of corrosion rate is [10%, 30%]; the normal range of crack length is [0.0 mm, 2.0 mm];

[0182] The normalized result is:

[0183] The time 𝑡 is 1, the thickness is normalized to 0.5, the corrosion rate is normalized to 0.1, and the crack length is normalized to 0.25;

[0184] The time 𝑡 is 2, the thickness is normalized to 0.4, the corrosion rate is normalized to 0.2, and the crack length is normalized to 0.3;

[0185] The time 𝑡 is 3, the thickness is normalized to 0.25, the corrosion rate is normalized to 0.35, and the crack length is normalized to 0.4;

[0186] The time 𝑡 is 4, the thickness is normalized to 0.15, the corrosion rate is normalized to 0.5, and the crack length is normalized to 0.55;

[0187] The time 𝑡 is 5, the thickness is normalized to 0.05, the corrosion rate is normalized to 0.7, and the crack length is normalized to 0.7;

[0188] Set the window length to 3, construct the input sample for time 3 prediction, and get the prediction ;

[0189] Similarly, use time 2 to 4 to predict the health score at time 5; use time 3 to 5 to predict time 6;

[0190] Calculate the forget gate, input gate, and output gate of the long short-term memory network to obtain the predicted value :

[0191] Through training, we can get the following prediction value on the 3rd day: ; Forecast value on the 4th day ; Forecast value on the 3rd day ;

[0192] The predicted value There is a nonlinear error, and the XGBoost algorithm is used to correct the error to obtain the error correction value : ; ; ;

[0193] Use weighted average composite forecast value and error correction value , and get the final pipeline in time Health score The value range is 0-1, 1 indicates that the pipeline is healthy. A value of 0 indicates that the pipeline needs maintenance: Set the weighting factor to 0.7 (LSTM takes the main weight, XGBoost does the correction), and calculate the forecast for day 5:

[0194] ;

[0195] The final health score on day 5 was 0.599;

[0196] Waste resource processing unit 2, which collects liquid waste data from the chemical park and classifies the liquid waste according to chemical pH value and physical viscosity. Based on the liquid waste recycling industry chain data and the pipeline life model, it uses the Dijkstra algorithm and introduces the impact of liquid waste on the inner wall of the pipeline to develop a preliminary waste recycling pipeline transportation plan for the liquid waste;

[0197] In this embodiment, the waste resource processing unit 2 includes a liquid waste analysis and classification module 21 and a liquid waste recovery and scheduling module 22;

[0198] The liquid waste analysis and classification module 21 collects liquid waste data from the chemical park and classifies the liquid waste according to its chemical pH value and physical viscosity, into pH-low viscosity liquid waste and pH-high viscosity liquid waste.

[0199] In this embodiment, liquid waste data from a chemical park is collected and classified according to chemical compatibility. The specific steps are as follows:

[0200] Collect physical and chemical property data of various liquid wastes in the chemical park:

[0201] Composition, reactivity, acidity, alkalinity, corrosivity, and fluidity; toxicity assessments classified according to environmental standards; storage and generation of liquid waste, source companies, storage time, and temperature impacts;

[0202] Based on chemical compatibility analysis, liquid wastes are grouped and their compatibility is calculated: the chemical compatibility matrix between different liquid wastes is calculated to avoid violent reactions when incompatible liquid wastes are mixed and treated;

[0203] Determine whether co-pipeline transportation is feasible. For example, acidic wastewater should not be co-pipelined with alkaline wastewater to avoid violent neutralization reactions.

[0204] Oxidants should not be mixed with organic waste liquids to avoid the risk of combustion caused by oxidation reactions.

[0205] Combined with pipeline material data, the interaction between liquid waste and the inner wall of the pipeline is analyzed:

[0206] For example, strong acidic waste liquids may corrode metal pipes, requiring the use of acid-resistant lined pipes or plastic composite pipes;

[0207] Finally, all liquid wastes in the chemical park are divided into pH-low viscosity liquid wastes and pH-high viscosity liquid wastes;

[0208] The pH value-low viscosity liquid waste is named according to the characteristics of the liquid waste. For example, the characteristic name of waste ethanol is 7-low viscosity liquid waste, which means that the pH of the waste ethanol is 7 and it is a low viscosity liquid, which means that the waste ethanol is a neutral liquid and has a greater impact force on the pipeline.

[0209] The pH value-high viscosity liquid waste, such as the characteristic of waste concentrated sulfuric acid, is named 1-high viscosity liquid waste, which means that the pH of waste concentrated sulfuric acid is 1 and it is a high viscosity liquid, which means that the waste concentrated sulfuric acid is an acidic liquid and has little impact on the pipeline;

[0210] The pH value of the liquid waste is measured by a glass electrode pH meter, and the viscosity is measured by a rotational viscometer;

[0211] The liquid waste recycling scheduling module 22 is based on the liquid waste recycling industry chain data and combined with the pipeline life model, using the Dijkstra algorithm and introducing the impact of liquid waste on the inner wall of the pipeline to formulate a preliminary waste recycling pipeline transportation plan for liquid waste.

[0212] In this embodiment, the specific reasons for the impact of liquid waste on the inner wall of the pipeline are as follows:

[0213] When liquid waste flows through curved pipes such as 90° and 45° elbows, the fluid is subjected to centrifugal force, resulting in increased impact force, which will cause pipe wall wear, abnormal increase in local pressure in the pipeline, and stratified flow of waste affecting the transportation efficiency: low-viscosity liquid waste has a greater centrifugal impact force, and eddy current erosion is prone to occur at the bends, resulting in increased wear of the local pipe wall. High-viscosity liquid waste has a higher viscosity, a slower flow rate, and a relatively small centrifugal impact force.

[0214] The liquid waste recycling scheduling module 22 is based on the liquid waste recycling industry chain data and the pipeline life model, uses the Dijkstra algorithm and introduces the impact of liquid waste on the inner wall of the pipeline to formulate a preliminary waste recycling pipeline transportation plan for liquid waste. The specific method steps are as follows:

[0215] S2.2.1. Obtain data on the liquid waste recycling industry chain in chemical parks;

[0216] The liquid waste recycling industry chain data is obtained from each chemical company to determine whether the liquid waste is reused. It analyzes which liquid wastes need to be recycled and which need to be harmlessly treated, and obtains the wastes that need to be transported through pipelines, such as: waste concentrated sulfuric acid 1 - high-viscosity liquid waste needs to be transported to the acid recovery device; waste ethanol 7 - low-viscosity liquid waste needs to be transported to the solvent recovery workshop;

[0217] The data of the liquid waste recycling industry chain is used as data input, as shown in the following example:

[0218] Company name: Company A;

[0219] Waste generated: waste concentrated sulfuric acid;

[0220] Waste characteristics: 1-High viscosity liquid waste;

[0221] Resource utilization method: need to be recycled;

[0222] Transportation requirements: need to be transported through pipelines;

[0223] Transportation starting point: Workshop B;

[0224] Transport destination: treatment device C;

[0225] Special requirements: Acid-resistant pipes are required;

[0226] S2.2.2. Calculate the impact of liquid waste on pipe bends on pipe life.

[0227] S2.2.3. Combined with the pipeline health score subjected to impact forces, use the Dijkstra algorithm to develop a preliminary waste recovery pipeline transportation plan for liquid waste.

[0228] Current time , i.e., the evaluation was conducted on the 5th day; the waste was: waste ethanol, classified as 7-low viscosity liquid waste; the density of the liquid waste Volume flow rate ; Flow velocity in the pipe v = 1.6m / s; Radius of the bending pipe section 0.3m; the pipe material is stainless steel, with a yield strength of ; Pipeline running time Seconds (i.e. one hour of delivery); the health score of the pipeline at the current time ;

[0229] in, For pipelines in time health score; The running time of liquid waste in the pipeline; For the Yield strength of the pipe material; For pipelines in time Pipeline health score after cumulative impact;

[0230] like , then The section of pipeline cannot transport the liquid waste;

[0231] like , then The section of pipeline can transport the liquid waste;

[0232] Here , the conditions are met, and the pipeline can continue to transport the waste. Although waste ethanol is a low-viscosity liquid with relatively high impact, its current pipeline health score is good (0.65) and it has only been in operation for one hour, so its impact on pipeline life is minimal. Subsequently, this pipeline will be input into the Dijkstra path optimization as a candidate path.

[0233] In this embodiment, the pipeline is Pipeline health score after cumulative impact To preliminarily calculate whether the pipeline can transport the liquid waste, time The subsequent impact force changes will be calculated and analyzed in S2.2.3;

[0234] In this embodiment S2.2.3, the Dijkstra algorithm is used to formulate a preliminary waste recycling pipeline transportation plan for liquid waste in combination with the pipeline health score subjected to the impact force. The specific method is as follows:

[0235] After time 𝑡, the health score at the bent pipe is dynamically affected by the impact force and is updated as follows:

[0236] ;

[0237] Since pipeline damage accumulates over a long period of time, we need to calculate the historical cumulative impact of the impact force using the integral method:

[0238] ;

[0239] in, The running time of liquid waste in the pipeline; Integral for the historical cumulative impact of impact force on the pipeline;

[0240] Based on the pipeline in time The pipeline health score after the cumulative impact of the impact force, the historical cumulative impact score of the impact force on the pipeline, and the transportation cost are used to construct the pipeline health weight:

[0241] ;

[0242] in, is the integral weight coefficient of the historical cumulative impact of the impact force on the pipeline; For pipelines in time The weight coefficient of pipeline health score after the cumulative impact of impact force; is the transportation cost weight coefficient; For transportation costs; For the The pipeline is in time The pipeline health weight of the day;

[0243] The Dijkstra algorithm is used to find the optimal transportation path for liquid waste under the influence of impact force, and a preliminary waste recycling pipeline transportation plan is obtained:

[0244] ;

[0245] in, A preliminary waste recovery pipeline transportation plan for liquid waste under impact forces; For the Segment pipeline; Index for the pipeline; For the Segment pipeline.

[0246] In this embodiment, the Dijkstra algorithm is a graph-based algorithm. Therefore, the start and end point numbers of all pipelines are recorded, and an adjacency matrix of the pipeline graph structure is established. The Dijkstra algorithm is then used to find the optimal transportation path for liquid waste under the influence of impact forces, thereby obtaining a preliminary waste recycling pipeline transportation plan.

[0247] The Dijkstra algorithm is used to find the optimal transportation path for liquid waste under the influence of impact force based on the calculated pipeline health weight;

[0248] The pipeline transportation scheduling unit 3 uses a non-dominated sorting genetic algorithm to comprehensively consider the interaction between the acidity and alkalinity of the liquid waste and the acidity and alkalinity of the soil based on the preliminary waste recycling pipeline transportation plan to optimize the final waste recycling pipeline transportation plan;

[0249] In this embodiment, the pipeline transportation scheduling unit 3 includes a soil acid-base monitoring module 31 and an acid-base balance control module 32;

[0250] The soil acid-base monitoring module 31 calculates the change in soil acidity and alkalinity based on the impact of liquid waste on soil pH changes, and is used to determine whether the pipeline meets the transportation requirements. The specific method is as follows:

[0251] Set the influence coefficient of liquid waste on soil pH change 0.1; the mass of liquid waste transported 500kg; the buffering capacity of the soil in the target area 2000kg;

[0252] S3.1.1. Calculate the change in soil pH based on the impact of the transported liquid waste on soil pH and determine whether the pipeline meets the transportation requirements:

[0253] ;

[0254] Set the soil acidity and alkalinity change threshold to ;

[0255] like , then the environmental acid-base changes exceed the standard, and the transportation path needs to be adjusted. Execute S3.2.1;

[0256] like , then the environmental acid-base changes are stable and the pipeline meets the transportation requirements;

[0257] because The soil acidity and alkalinity change threshold is less than the set value. , so the pipeline meets the transportation requirements;

[0258] The acid-base balance control module 32 uses a non-dominated sorting genetic algorithm to comprehensively consider the interaction between the acidity and alkalinity of liquid waste and the acidity and alkalinity of soil to optimize the preliminary waste recycling pipeline transportation plan, and obtains a final waste recycling pipeline transportation plan with acid-base compatibility. The specific method is as follows:

[0259] S3.2.1. Use the non-dominated sorting genetic algorithm to optimize the preliminary waste recycling pipeline transportation plan, construct the acid-base optimization objective function, and obtain the final waste recycling pipeline transportation plan:

[0260] ;

[0261] in, is the maximum value of soil acidity and alkalinity change;

[0262] Solve the acid-base optimization objective function, generate multiple candidate waste transportation routes, calculate the health weight, soil acid-base change, and transportation cost of each route. Then, according to the Pareto optimal solution principle, select the optimal route:

[0263] The candidate waste transportation routes are:

[0264] path , health weight 46542.472, maximum acid-base change 0.0244, delivery cost 100;

[0265] path , health weight 46000.000, maximum acid-base change 0.0200, delivery cost 90;

[0266] path , health weight 47000.000, maximum acid-base change 0.0300, delivery cost 110;

[0267] path , health weight 46800.000, maximum acid-base change 0.0150, delivery cost 95;

[0268] Final waste recycling pipeline transportation plan to obtain acid-base compatibility:

[0269] ;

[0270] in, a pipeline transportation plan for final waste recovery;

[0271] In this embodiment, during the transportation process, some liquid waste may penetrate into the soil through micro-cracks in the pipeline, causing changes in the acidity and alkalinity of the environment. Considering the interaction between the acidity and alkalinity of the liquid waste and the soil, it is ensured that the pipeline or surrounding soil will not be contaminated by the acid-base neutralization reaction during transportation. The preliminary waste recycling pipeline transportation plan is further optimized to form a final waste recycling pipeline transportation plan that is acid-base compatible.

[0272] In this embodiment, the acid-base optimization objective function is solved by the following method:

[0273] Generate multiple possible delivery paths and calculate the time required for each section of the pipeline The pipeline health weight, soil acidity and alkalinity change, and transportation cost are calculated; according to the Pareto optimization principle, the path solutions are sorted and the optimal solution is selected; the pipeline path is mutated, a new solution is generated, and a new optimization target value is calculated; the path with the best comprehensive performance is selected as the final optimized path, that is, the final waste recycling pipeline transportation plan. .

[0274] An underground panoramic monitoring unit 4, which builds an underground panoramic three-dimensional model based on GIS, BIM, and lidar to comprehensively display data from the pipeline life monitoring unit 1, the waste resource processing unit 2, and the pipeline transportation scheduling unit 3;

[0275] In this embodiment, the underground panoramic monitoring unit 4 constructs an underground panoramic three-dimensional model based on GIS, BIM, and lidar to comprehensively display the data of the pipeline life monitoring unit 1, the waste resource processing unit 2, and the pipeline transportation scheduling unit 3. The specific method is as follows:

[0276] S4.1. Use GIS to locate the geographic coordinates, spatial orientation, and depth distribution of underground pipelines. Based on BIM, integrate pipeline construction drawings, structural models, and material properties. Use LiDAR to perform high-density 3D point cloud scanning of the underground pipeline network structure to construct a panoramic 3D underground model of the chemical park.

[0277] S4.2. Based on the underground panoramic 3D model, display the data of pipeline life monitoring unit 1, including: damage data of the inner and outer walls of the pipeline, pipeline life model, and pipeline health status data set;

[0278] Displays data from waste resource processing unit 2, including: pH values ​​for low-viscosity liquid waste and high-viscosity liquid waste, impact forces of liquid waste on curved pipes, health scores of pipes subjected to impact forces, and preliminary waste recovery pipeline transportation plans;

[0279] Displays the data of pipeline transportation scheduling unit 3, including: changes in soil acidity and alkalinity and the final waste recycling pipeline transportation plan.

[0280] The basic principles, main features, and advantages of the present invention are shown and described above. It should be understood by those skilled in the art that the present invention is not limited to the above-described embodiments. The above-described embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention claimed.

Claims

1. A chemical park display system based on GIS and digital panoramic industrial chain, characterized by: include: A pipeline life monitoring unit (1), wherein the pipeline life monitoring unit (1) uses sensors to monitor damage data of the inner and outer walls of the pipeline in real time, and establishes a pipeline life model based on a long short-term memory network combined with an XGBoost algorithm, for real-time monitoring of pipeline health risks; A waste resource processing unit (2) collects liquid waste data from the chemical park and classifies the liquid waste according to chemical pH value and physical viscosity, and formulates a preliminary waste recycling pipeline transportation plan for the liquid waste based on the liquid waste recycling industry chain data and in combination with the pipeline life model, using the Dijkstra algorithm and introducing the impact of the liquid waste on the inner wall of the pipeline; A pipeline transportation scheduling unit (3) is configured to optimize a final waste recycling pipeline transportation plan based on a preliminary waste recycling pipeline transportation plan by using a non-dominated sorting genetic algorithm to comprehensively consider the effects of the acidity and alkalinity of the liquid waste and the acidity and alkalinity of the soil; An underground panoramic monitoring unit (4), wherein the underground panoramic monitoring unit (4) constructs an underground panoramic three-dimensional model based on GIS+BIM+lidar, and is used to panoramically display data of the pipeline life monitoring unit (1), the waste resource processing unit (2), and the pipeline transportation scheduling unit (3); The waste resource processing unit (2) includes a liquid waste analysis and classification module (21) and a liquid waste recovery and scheduling module (22); The liquid waste analysis and classification module (21) collects liquid waste data from the chemical park and classifies the liquid waste according to the chemical pH value and physical viscosity, and classifies the liquid waste into pH value-low viscosity liquid waste and pH value-high viscosity liquid waste; The liquid waste recycling scheduling module (22) is based on the liquid waste recycling industry chain data and combined with the pipeline life model, using the Dijkstra algorithm and introducing the impact of the liquid waste on the inner wall of the pipeline to formulate a preliminary waste recycling pipeline transportation plan for the liquid waste; The liquid waste recycling scheduling module (22) formulates a preliminary waste recycling pipeline transportation plan for liquid waste, and the specific method and steps are as follows: S2.2.

1. Obtain data on the liquid waste recycling industry chain in chemical parks; S2.2.

2. Calculate the impact of liquid waste on pipe bends on pipe life. S2.2.

3. Combined with the pipeline health score subjected to impact forces, use the Dijkstra algorithm to develop a preliminary waste recovery pipeline transportation plan for liquid waste.

2. The chemical park display system based on GIS and digital panoramic industrial chain according to claim 1 is characterized by: The damage data of the inner and outer walls of the pipeline include thickness data, crack data and corrosion data, and the damage data is used as the data basis for establishing a pipeline life model.

3. The chemical park display system based on GIS and digital panoramic industrial chain according to claim 2 is characterized by: The pipeline life monitoring unit (1) comprises a pipeline structure monitoring module (11) and a pipeline life prediction module (12); The pipeline structure monitoring module (11) monitors the location and health information of all pipelines in the chemical park in real time based on the damage data of the inner and outer walls of the pipelines and in combination with the engineering pipeline construction drawings, and displays the information on the underground panoramic monitoring unit (4); The pipeline life prediction module (12) is based on the damage data of the inner and outer walls of the pipeline, and uses a long short-term memory network combined with an XGBoost algorithm to establish a pipeline life model. The pipeline life model is used to monitor pipeline health risks in real time.

4. The chemical park display system based on GIS and digital panoramic industrial chain according to claim 3 is characterized by: In S2.2.2, the impact of the impact of liquid waste on the pipe bend on the pipe life is calculated as follows: ； in, Index for the pipeline; For time No. Impact force at the bend of the pipe section; is the liquid waste density; is the volume flow rate; is the flow velocity in the pipe; For the The radius of the bend in the pipe section; For time; ； in, For pipelines in time health score; The running time of liquid waste in the pipeline; For the Yield strength of the pipe material; For pipelines in time Pipeline health score after cumulative impact; like , then The section of pipeline cannot transport the liquid waste; like , then The liquid waste can be transported by a pipeline segment.

5. The chemical park display system based on GIS and digital panoramic industrial chain according to claim 4 is characterized by: In S2.2.3, the Dijkstra algorithm is used to formulate a preliminary waste recovery pipeline transportation plan for liquid waste in combination with the pipeline health score subjected to impact forces. The specific method is as follows: ； in, For the Segment pipeline in time Pipeline health score after cumulative impact; For the Fatigue coefficient of pipeline segment; is the transport time step, For the Cross-sectional area of ​​the pipe segment; ； in, The running time of liquid waste in the pipeline; Integral for the historical cumulative impact of impact force on the pipeline; Based on the pipeline in time The pipeline health score after the cumulative impact of the impact force, the historical cumulative impact score of the impact force on the pipeline, and the transportation cost are used to construct the pipeline health weight: ； in, is the integral weight coefficient of the historical cumulative impact of the impact force on the pipeline; For pipelines in time The weight coefficient of pipeline health score after the cumulative impact of impact force; is the transportation cost weight coefficient; For transportation costs; For the Segment pipeline in time Pipeline health weight; The Dijkstra algorithm is used to find the optimal transportation path for liquid waste under the influence of impact force, and a preliminary waste recycling pipeline transportation plan is obtained: ； in, A preliminary waste recovery pipeline transportation plan for liquid waste under impact forces; For the Segment pipeline; Index for the pipeline; For the Segment pipeline.

6. The chemical park display system based on GIS and digital panoramic industrial chain according to claim 5 is characterized by: The pipeline transportation scheduling unit (3) includes a soil acid-base monitoring module (31) and an acid-base balance control module (32); The soil acid-base monitoring module (31) calculates the change in soil acidity and alkalinity based on the impact of liquid waste on soil pH changes, and is used to determine whether the pipeline meets the transportation requirements. The specific method is as follows: S3.1.

1. Calculate the change in soil pH based on the impact of the transported liquid waste on soil pH and determine whether the pipeline meets the transportation requirements: ； in, is the change in soil acidity and alkalinity; The amount of liquid waste material; is the buffer capacity of the soil, is the theoretical maximum pH change of liquid waste; Set the soil acidity and alkalinity change threshold to ; like , then the environmental acid-base changes exceed the standard, and the transportation path needs to be adjusted. Execute S3.2.1; like , the environmental acid-base changes are stable and the pipeline meets the transportation requirements.

7. The chemical park display system based on GIS and digital panoramic industrial chain according to claim 6 is characterized by: The acid-base balance control module (32) uses a non-dominated sorting genetic algorithm to comprehensively consider the effects of the acidity and alkalinity of liquid waste and soil acidity and alkalinity to optimize the preliminary waste recycling pipeline transportation plan, and obtains the final waste recycling pipeline transportation plan with acid-base compatibility. The specific method is as follows: S3.2.

1. Use a non-dominated sorting genetic algorithm to optimize the preliminary waste recycling pipeline transportation plan, construct the acid-base optimization objective function, and obtain the final waste recycling pipeline transportation plan: ； in, is the maximum value of soil acidity and alkalinity change; Solve the acid-base optimization objective function and obtain the final waste recycling pipeline transportation plan based on acid-base compatibility: ； in, a pipeline transportation plan for final waste recovery; The optimized Segment pipeline; The optimized Segment pipeline.

8. The chemical park display system based on GIS and digital panoramic industrial chain according to claim 7 is characterized by: The underground panoramic monitoring unit (4) is based on GIS+BIM+LiDAR to construct an underground panoramic three-dimensional model for panoramic display of the data of the pipeline life monitoring unit (1), the waste resource processing unit (2) and the pipeline transportation scheduling unit (3). The specific method is as follows: S4.

1. Use GIS to locate the geographic coordinates, spatial orientation, and depth distribution of underground pipelines. Based on BIM, integrate pipeline construction drawings, structural models, and material properties. Use LiDAR to perform high-density 3D point cloud scanning of the underground pipeline network structure to construct a panoramic 3D underground model of the chemical park. S4.

2. Based on the underground panoramic three-dimensional model, display the data of the pipeline life monitoring unit (1), including: damage data of the inner and outer walls of the pipeline, pipeline life model and pipeline health status data set; Display data of the waste resource processing unit (2), including: pH value - low viscosity liquid waste and pH value - high viscosity liquid waste, impact force of liquid waste at the bend pipe, health score of the pipe subjected to impact force, and preliminary waste recycling pipeline transportation plan; Display the data of pipeline transportation scheduling unit (3), including: the change of soil acidity and alkalinity and the final waste recycling pipeline transportation plan.

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