Urban utility tunnel operation environment temperature determination method and system

Abstract

The invention provides an urban comprehensive pipe gallery operation environment temperature determination method and system, and relates to the technical field of electric digital data processing, and the method comprises the steps: obtaining first information, second information, pipe gallery heat source and structure data, and third information; inputting historical data in the first information into a preset neural network for training to obtain a prediction model; inputting the real-time data in the first information into a prediction model to obtain a predicted soil surface temperature; establishing a pipe gallery operation model according to the predicted soil surface layer temperature, the second information and the pipe gallery heat source and structural data; and performing heat exchange calculation on the pipe gallery operation model to obtain a predicted pipe gallery operation environment temperature, and comparing the predicted pipe gallery operation environment temperature with the third information to obtain a pipe gallery operation state. According to the invention, the back propagation algorithm is used to predict the soil surface temperature in the future pipe gallery construction area, analogue simulation is carried out on the predicted soil surface temperature and the pipe gallery heat source and structure data, the safe operation state of the pipe gallery can be effectively evaluated, and the normal operation of the pipe gallery is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular to a method and system for determining the operating environment temperature of an urban integrated pipe gallery. Background Art

[0002] The ambient temperature of the tunnel is an important parameter for the operation and maintenance of urban tunnel projects, and plays a vital role in the safe operation and maintenance of the tunnel. The tunnel contains a variety of cables and pipelines, among which cables and thermal pipelines emit a lot of heat when working. At the same time, the tunnel is a closed underground space, so heat will accumulate in the tunnel, causing the temperature in the tunnel to rise, affecting the normal work of maintenance personnel and cables, and in serious cases, causing safety accidents such as fires. In the prior art, the influence of the internal pipeline temperature and ventilation system of the tunnel on the operating ambient temperature of the tunnel is usually considered, but the influence of soil temperature on the operating ambient temperature of the tunnel is not considered. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for determining the operating environment temperature of an urban integrated pipe gallery to solve the above problems. In order to achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0004] On the one hand, the present application provides a method for determining the operating environment temperature of an urban integrated pipe gallery, comprising:

[0005] Obtaining first information, second information, heat source and structure data of the tunnel and third information; the first information is the soil surface temperature measured by the first group of soil thermometers for at least the past year; the second information is the real-time temperature from the soil surface to the soil constant temperature layer measured by the first group of soil thermometers; the third information is the real-time temperature from the soil surface to the soil constant temperature layer measured by the second group of soil thermometers; the first group of soil thermometers are located in the tunnel construction area and away from the heat source, and the second group of soil thermometers are located inside the tunnel or within one meter outside the tunnel;

[0006] Inputting historical data in the first information into a preset neural network for training to obtain a prediction model;

[0007] Inputting the real-time data in the first information into the prediction model to obtain the predicted soil surface temperature;

[0008] Establish a tunnel operation model based on the predicted soil surface temperature, the second information, and the tunnel heat source and structure data;

[0009] Perform heat exchange calculation on the tunnel operation model to obtain the predicted tunnel operation environment temperature;

[0010] The predicted tunnel operating environment temperature is compared with the third information to obtain the tunnel operating status.

[0011] On the other hand, the present application also provides a system for determining the operating environment temperature of an urban integrated pipe gallery, comprising:

[0012] An acquisition unit is used to acquire first information, second information, heat source and structure data of the pipeline corridor, and third information; the first information is the soil surface temperature measured by the first group of soil thermometers for at least the past year; the second information is the real-time temperature from the soil surface to the soil constant temperature layer measured by the first group of soil thermometers; the third information is the real-time temperature from the soil surface to the soil constant temperature layer measured by the second group of soil thermometers; the first group of soil thermometers are located in the pipeline corridor construction area and away from the heat source, and the second group of soil thermometers are located inside the pipeline corridor or within one meter outside the pipeline corridor;

[0013] A neural network training unit, used for inputting historical data in the first information into a preset neural network for training to obtain a prediction model;

[0014] A prediction unit, used for inputting the real-time data in the first information into a prediction model to obtain a predicted soil surface temperature;

[0015] A modeling unit, used for establishing a tunnel operation model according to the predicted soil surface temperature, the second information, and the tunnel heat source and structure data;

[0016] The first calculation unit is used to perform heat exchange calculation on the pipe gallery operation model to obtain the predicted pipe gallery operation environment temperature;

[0017] The first determination unit is used to compare the predicted operating environment temperature of the pipeline corridor with the third information to obtain the operating status of the pipeline corridor.

[0018] The beneficial effects of the present invention are as follows: the present invention predicts the soil surface temperature in the construction area of ​​the tunnel in the future by using the back propagation algorithm, simulates the predicted soil surface temperature and the heat source and structure data of the tunnel, and obtains the ambient temperature of the tunnel during normal operation in the future, thereby proposing an early warning standard for the abnormal operating ambient temperature of the tunnel, which can effectively evaluate the operating status of the tunnel, and help operators take corresponding measures in advance to ensure the normal operation of the tunnel.

[0019] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by implementing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 It is a flow chart of a method for determining the operating environment temperature of an urban integrated pipe gallery of the present invention;

[0022] Figure 2 This is the schematic diagram of burying the second set of soil thermometers;

[0023] Figure 3 This is a schematic diagram of the pipe gallery cross section;

[0024] Figure 4 It is a structural schematic diagram of the system for determining the operating environment temperature of the urban integrated pipe gallery of the present invention.

[0025] Markings in the figure: 1, the second group of soil thermometers; 2, pipe wall; 3, pipeline; 700, determination system; 710, acquisition unit; 720, neural network training unit; 730, prediction unit; 740, modeling unit; 750, first calculation unit; 760, first discrimination unit. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0027] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0028] Embodiment 1:

[0029] like Figure 1As shown, this embodiment provides a method for determining the operating environment temperature of an urban integrated pipe gallery, including step S1, step S2, step S3, step S4, step S5 and step S6.

[0030] S1. Obtain the first information, the second information, the heat source and structure data of the tunnel and the third information; the first information is the soil surface temperature measured by the first group of soil thermometers for at least the past year; the second information is the real-time temperature from the soil surface to the soil constant temperature layer measured by the first group of soil thermometers; the third information is the real-time temperature from the soil surface to the soil constant temperature layer measured by the second group of soil thermometers 1; the first group of soil thermometers are located in the tunnel construction area and away from the heat source, the second group of soil thermometers 1 are located inside the tunnel or within one meter outside the tunnel, and the second group of soil thermometers 1 are buried as follows Figure 2 As shown, the burial method of the first group of soil thermometers is the same as that of the second group of soil thermometers 1, but is far away from heat sources such as pipe corridors.

[0031] It should be noted that both the first group of soil thermometers and the second group of soil thermometers 1 include multiple soil thermometers, wherein the first group of soil thermometers includes at least soil thermometers on the soil surface. Normally, the actual burial depth of the soil thermometers on the soil surface is about 5 cm underground, so as to avoid the soil thermometers being buried too shallowly and exposed to the ground, which affects the accuracy of the first information. The measurement intervals of the first information, the second information and the third information are set according to the actual situation, and can be set to one hour, three hours or twenty-four hours; when the measurement interval is set to twenty-four hours, the average temperature in twenty-four hours is taken as the measured temperature.

[0032] Since the soil structure and composition of new construction projects will change, the soil temperature data before the construction of the pipeline corridor cannot be used as the basic data for predicting future soil temperature data. Therefore, the present application re-collects the soil temperature data after the construction of the pipeline corridor to obtain the first information, the second information and the third information, and considering that the soil surface temperature is easily affected by the environment and changes, the present application predicts the soil surface temperature in the pipeline corridor construction area, that is, performs steps S2 and S3.

[0033] S2. Input the historical data in the first information into a preset neural network for training to obtain a prediction model.

[0034] Specifically, step S2 includes step S21, step S22, step S23, step S24, step S25 and step S26.

[0035] S21, establish an input layer, a hidden layer and an output layer connected in sequence, set the number of hidden layers, the learning rate, the total number of training rounds, and the weights and biases of each layer, wherein the weights and biases of each layer are generated by a random function, thereby obtaining a preset neural network.

[0036] It should be noted that the number of nodes in the input layer and the number of nodes in the output layer are set according to the actual situation. In this embodiment, the number of nodes in the input layer and the number of nodes in the output layer are set to eight, and the measurement interval is set to three hours, which is used to predict the soil surface temperature in the next 24 hours. The input data of the input layer are 56 consecutive soil surface temperatures (i.e., the soil surface temperature of a week), and the expected output data of the output layer are the next eight soil surface temperatures that are consecutive to the input data (i.e., the soil surface temperature of the next day of a week). The number of hidden layers is selected from the value between the number of nodes in the input layer and the number of nodes in the output layer. In this embodiment, a value between 8 and 56 is randomly selected as the number of hidden layers.

[0037] S22, forward propagation: input the historical data in the first information into the input layer, and obtain the output result from the output layer.

[0038] In this embodiment, every 56 consecutive soil surface temperatures of the historical data in the first information are divided into a data group, and the data groups are input into the input layer in chronological order for training.

[0039] The mathematical expressions for the input and output of any layer are:

[0040]

[0041] Where (i) represents the i-th layer; represents the output of the i-th layer; represents the activation function of the i-th layer; represents the input of the i-th layer; w (i) represents the weight of the i-th layer; b (i) represents the bias of the i-th layer.

[0042] In the embodiment, the activation function is selected as the sigma function.

[0043] S23, calculating according to the output result and the actual soil surface temperature at the time corresponding to the output result to obtain an output error.

[0044] The output error expression is:

[0045]

[0046] Where, E represents the output error; N represents the number of samples, that is, the number of historical data in the first information; L t represents the actual soil surface temperature at time t; t Represents the output result of the output layer at the tth time.

[0047] In this embodiment, the actual soil surface temperature at the time corresponding to the output result is a data group after the current input data group.

[0048] S24. Output the error back through back propagation and perform gradient descent using partial derivatives.

[0049] The gradient descent calculation formula is:

[0050]

[0051]

[0052]

[0053]

[0054] In the formula, represents the sign of partial derivative; E represents the output error; w (i) represents the weight of the i-th layer; represents the transpose of the output of the i-1th layer; z(i) represents the intermediate function of the i-th layer, that is, b (i) represents the bias of the i-th layer; I T represents the unit matrix; k represents the last hidden layer; (oL) represents the difference between the predicted soil surface temperature and the actual soil surface temperature; h (k) Indicates; h (i) Indicates; * indicates the multiplication sign.

[0055] S25. Update the weights and biases of each layer in the preset neural network according to a preset formula.

[0056] The preset formula is:

[0057]

[0058]

[0059] In the formula, represents the weight of the i-th layer after the update; w(i) represents the weight of the i-th layer before; α represents the learning rate; represents the symbol of partial derivative; represents the updated bias of the i-th layer; b( i ) represents the bias of the previous i-th layer.

[0060] In this embodiment, the learning rate is 1e.

[0061] S26. Restart forward propagation until the total number of training rounds is reached and stop. According to the output error of the last round of training, adjust the number of hidden layers, learning rate, and total number of training rounds to obtain a prediction model.

[0062] It should be noted that the output error of the last round of training is used to determine whether the training is overfitting or underfitting. When overfitting occurs, increase the learning rate value and reduce the total number of training rounds; when underfitting occurs, reduce the learning rate value and increase the total number of training rounds. The number of hidden layers is adjusted between the number of nodes in the input layer and the number of nodes in the output layer.

[0063] S3. Input the real-time data in the first information into the prediction model to obtain the predicted soil surface temperature.

[0064] In this embodiment, the real-time data in the first information refers to the 56 data closest to the current time in the first information (the soil surface temperature one week before the current time), and the number of predicted soil surface temperatures is eight (the soil surface temperature one day after the current time). The eight predicted soil surface temperatures are added to the 48 data closest to the current time to form a new 56 soil surface temperatures, which are input and output layers to obtain the soil surface temperature from the next 24th to 48th hours, and are repeated continuously to obtain the soil surface temperature of the required future time period. These predicted soil surface temperatures are the ambient temperatures when the tunnel is in normal operation.

[0065] In some specific embodiments, step S31 is further included, in which the prediction model is updated once every quarter, and the specific operations are as follows:

[0066] The first information collected in the new quarter is input into the input layer, and steps S22 to S26 are repeated to complete the update of the prediction model.

[0067] It is understandable that the soil surface temperature is closely related to the air temperature, and the air temperature changes differently in different seasons. Updating the prediction model once every quarter can make the prediction model more accurate in predicting the soil surface temperature of the current season.

[0068] S4. Establish a tunnel operation model based on the predicted soil surface temperature, the second information, and the tunnel heat source and structure data.

[0069] The heat source and structure data of the pipe gallery include the shape, size, thickness of the pipe wall 2 (pipe gallery wall), the shape, size and position of the air inlet and outlet, and the length, number, position and heat per meter of the pipeline 3. In this embodiment, the cross section of the pipe gallery is as follows: Figure 3 shown.

[0070] Specifically, step S4 includes:

[0071] S41. Establish a pipe gallery model in the simulation software Fluent according to the pipe gallery heat source and structure data;

[0072] S42. Set soil material with a thickness greater than one meter around the pipe gallery model, and set soil insulation on the side of the pipe gallery model;

[0073] S43, using the predicted soil surface temperature as the upper boundary condition of the soil material;

[0074] S44. Using the second information as the initial temperature of the soil around the pipeline corridor model, and obtaining a pipeline corridor operation model.

[0075] It is understandable that since the soil surface temperature is constantly changing, the predicted soil surface temperature is used as a variable in the tunnel operation model to simulate the tunnel operation environment.

[0076] S5. Perform heat exchange calculation on the tunnel operation model to obtain the predicted tunnel operation environment temperature.

[0077] Specifically, step S5 includes step S51, step S52, step S53 and step S54.

[0078] S51. Divide the pipeline corridor operation model into multiple pipeline corridor units and multiple soil units.

[0079] It can be understood that the corridor operation model after the division is in a grid shape. The grid lines can be encrypted near the pipeline 3 and the pipe wall 2 to make the units at these locations smaller, making the subsequent calculation of heat exchange more accurate.

[0080] S52. Perform heat exchange iteration on all pipe gallery units using a first preset formula to obtain the temperature of each pipe gallery unit.

[0081] The first preset formula is:

[0082]

[0083] In the formula, h x Indicates the convection heat transfer coefficient of pipe wall 2 or pipe line 3; T w Indicates the surface temperature of the pipe wall 2 or pipeline 3; T f Indicates the air flow temperature in the pipe gallery; f represents the thermal conductivity of the fluid molecules near the pipe wall 2 or the pipe 3 wall; l represents the distance from the pipe gallery wall or the pipe 3; T 1 Indicates the temperature of the pipe gallery unit.

[0084] It can be understood that heat exchange inside the pipe gallery is mainly carried out in two forms, one is convection heat transfer between the air in the gallery and the wall of the pipe gallery, and the other is heat dissipation from the internal conductor of the pipeline 3 in the gallery to the air through the high-temperature outer skin, and the heat conduction method is also convection heat transfer. The heat exchange forms carried out by different units are substituted into the first preset formula for calculation.

[0085] S53, performing heat exchange iteration on the pipe gallery unit adjacent to the soil unit using a second preset formula to obtain the temperature of the pipe gallery unit adjacent to the soil unit.

[0086] The second preset formula is:

[0087]

[0088]

[0089]

[0090] In the formula, x, y, z represent the three-dimensional coordinates of the corridor unit or soil unit; Q represents the heat; λ represents the thermal conductivity of the pipe wall or soil; T 2 Indicates the temperature of the pipe wall 2 or the soil.

[0091] It can be understood that the pipe gallery is in direct contact with the external soil environment, and the heat exchange method between the outer wall of the pipe gallery and the external soil environment is heat conduction.

[0092] S54, the temperature of each unit is transferred to the adjacent unit, and the heat exchange iteration is performed again to obtain the predicted operating environment temperature of the tunnel.

[0093] S6. Compare the predicted tunnel operating environment temperature with the third information to obtain the tunnel operating status.

[0094] Specifically, step S6 includes step S61, step S62, step S63 and step S64.

[0095] S61. Subtract the third information of the corresponding time from the predicted operating environment temperature of the tunnel to obtain a first value. If the first value exceeds a first threshold, the operating status of the tunnel is abnormal.

[0096] In this embodiment, the first threshold is -5°C to +5°C.

[0097] S62, respectively calculating the predicted tunnel operating environment temperature and the temperature change rate of the third information within a preset time period to obtain a first change rate and a second change rate;

[0098] S63. Compare the first change rate with the second change rate to obtain a second value. If the second value exceeds a second threshold, the operation status of the pipeline corridor is abnormal.

[0099] In this embodiment, the preset time period is a single increase time period or a single decrease time period of the temperature, and the second threshold is -0.5 to +0.5.

[0100] S64. When the operation status of the tunnel is abnormal, conduct safety inspection and early warning of the tunnel.

[0101] Step S6 provides the early warning standard for abnormal operating environment temperature of the tunnel, realizes the safety control of the tunnel, and effectively reduces maintenance costs.

[0102] Embodiment 2:

[0103] Corresponding to the above method embodiment, this embodiment proposes a system for determining the operating environment temperature of an urban integrated pipe gallery, such as Figure 4 As shown, the determination system 700 includes:

[0104] The acquisition unit 710 is used to acquire the first information, the second information, the heat source and structure data of the pipeline corridor, and the third information; the first information is the soil surface temperature measured by the first group of soil thermometers for at least the past year; the second information is the real-time temperature from the soil surface to the soil constant temperature layer measured by the first group of soil thermometers; the third information is the real-time temperature from the soil surface to the soil constant temperature layer measured by the second group of soil thermometers 1; the first group of soil thermometers are located in the pipeline corridor construction area and away from the heat source, and the second group of soil thermometers 1 are located inside the pipeline corridor or within one meter outside the pipeline corridor;

[0105] A neural network training unit 720, configured to input the historical data in the first information into a preset neural network for training to obtain a prediction model;

[0106] The prediction unit 730 is used to input the real-time data in the first information into the prediction model to obtain the predicted soil surface temperature;

[0107] A modeling unit 740 is used to establish a pipeline corridor operation model according to the predicted soil surface temperature, the second information, and the pipeline corridor heat source and structure data;

[0108] The first calculation unit 750 is used to perform heat exchange calculation on the pipe gallery operation model to obtain a predicted pipe gallery operation environment temperature;

[0109] The first determination unit 760 is used to compare the predicted tunnel operation environment temperature with the third information to obtain the tunnel operation status.

[0110] In some specific embodiments, the neural network training unit 720 includes:

[0111] A first setting unit is used to establish an input layer, a hidden layer, and an output layer connected in sequence, and to set the number of hidden layers, a learning rate, a total number of training rounds, and weights and biases of each layer, wherein the weights and biases of each layer are generated by a random function, thereby obtaining a preset neural network;

[0112] A training unit is used for forward propagation: inputting the historical data in the first information into the input layer, and obtaining the output result from the output layer; calculating the output error based on the output result and the actual soil surface temperature at the time corresponding to the output result; transmitting the output error back through back propagation, and using partial derivatives for gradient descent;

[0113] An updating unit, used to update the weights and biases of each layer in a preset neural network according to a preset formula;

[0114] The recurrent unit is used to restart forward propagation until the total number of training rounds is reached and then stop. According to the output error of the last round of training, the number of hidden layers, learning rate, and total number of training rounds are adjusted to obtain a prediction model.

[0115] The heat source and structure data of the pipe gallery include the shape, size, thickness of the pipe wall 2, the shape, size and position of the air inlet and the air outlet, and the length, number, position and heat per meter of the pipeline 3. The modeling unit 740 includes:

[0116] The second setting unit is used to establish a pipe gallery model in the simulation software according to the pipe gallery heat source and structure data;

[0117] The third setting unit is used to set soil material with a thickness greater than one meter around the pipe gallery model and to set soil insulation on the side of the pipe gallery model;

[0118] a fourth setting unit, for using the predicted soil surface temperature as an upper boundary condition of the soil material;

[0119] The fifth setting unit is used to use the second information as the initial temperature of the soil around the pipeline corridor model to obtain the pipeline corridor operation model.

[0120] The first calculation unit 750 includes:

[0121] A division unit is used to divide the pipe gallery operation model into a plurality of pipe gallery units and a plurality of soil units;

[0122] The second calculation unit is used to perform heat exchange iteration on all pipe gallery units through the first preset formula to obtain the temperature of each pipe gallery unit;

[0123] A third calculation unit is used to perform heat exchange iteration on the pipe gallery unit adjacent to the soil unit by using a second preset formula to obtain the temperature of the pipe gallery unit adjacent to the soil unit;

[0124] The fourth calculation unit is used to transfer the temperature of each unit to the adjacent unit, perform heat exchange iteration again, and obtain the predicted operating environment temperature of the tunnel.

[0125] The first determination unit 760 includes:

[0126] The second determination unit is used to subtract the third information of the corresponding time from the predicted operating environment temperature of the pipe gallery to obtain a first value. If the first value exceeds a first threshold, the operating state of the pipe gallery is abnormal;

[0127] A fifth calculation unit is used to calculate the predicted temperature of the pipe gallery operation environment and the temperature change rate of the third information within a preset time period, respectively, to obtain a first change rate and a second change rate;

[0128] A third determination unit is used to obtain a second value by dividing the first change rate by the second change rate. If the second value exceeds a second threshold, the operation state of the pipe gallery is abnormal.

[0129] The early warning unit is used to conduct safety inspections and early warnings on the corridor when the corridor's operating status is abnormal.

[0130] The model applied for is easy to use and convenient for parameter adjustment. It can accurately predict the ambient temperature and changes during the normal operation of the tunnel. It is practical, safe and reliable.

[0131] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0132] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for determining the operating environment temperature of an urban integrated pipe gallery, characterized in that: include: Obtaining first information, second information, heat source and structure data of the pipe gallery, and third information; The first information is the soil surface temperature measured by the first group of soil thermometers for at least the past year; the second information is the real-time temperature from the soil surface to the soil constant temperature layer measured by the first group of soil thermometers; the third information is the real-time temperature from the soil surface to the soil constant temperature layer measured by the second group of soil thermometers; the first group of soil thermometers are located in the construction area of ​​the pipeline corridor and away from heat sources, and the second group of soil thermometers are located inside the pipeline corridor or within one meter outside the pipeline corridor; Inputting historical data in the first information into a preset neural network for training to obtain a prediction model; Inputting the real-time data in the first information into the prediction model to obtain the predicted soil surface temperature; Establish a tunnel operation model based on the predicted soil surface temperature, the second information, and the tunnel heat source and structure data; Perform heat exchange calculation on the tunnel operation model to obtain the predicted tunnel operation environment temperature; The predicted tunnel operating environment temperature is compared with the third information to obtain the tunnel operating status.

2. The method for determining the operating environment temperature of an urban integrated pipe gallery according to claim 1 is characterized in that: Inputting the historical data in the first information into a preset neural network for training to obtain a prediction model, including: Establishing an input layer, a hidden layer, and an output layer connected in sequence, setting the number of hidden layers, the learning rate, the total number of training rounds, and the weights and biases of each layer, wherein the weights and biases of each layer are generated by a random function, thereby obtaining a preset neural network; Forward propagation: input the historical data in the first information into the input layer and obtain the output result from the output layer; The output error is obtained by calculating the output result and the actual soil surface temperature at the time corresponding to the output result; Output the error by backpropagating it back and performing gradient descent using partial derivatives; Update the weights and biases of each layer in the preset neural network according to the preset formula; Restart forward propagation until the total number of training rounds is reached and stop. According to the output error of the last round of training, adjust the number of hidden layers, learning rate, and total number of training rounds to obtain the prediction model.

3. According to the method for determining the operating environment temperature of the urban integrated pipe gallery according to claim 1, the pipe gallery heat source and structure data include the pipe gallery shape, size, pipe wall thickness, shape, size and position of the air inlet and outlet, and the length, number, position and heat value per meter of the pipeline, characterized in that: The tunnel operation model is established based on the predicted soil surface temperature, the second information, and the tunnel heat source and structure data, including: Establish a corridor model in the simulation software based on the corridor heat source and structure data; Set soil material with a thickness of more than one meter around the pipeline gallery model, and set soil insulation on the side of the pipeline gallery model; The predicted soil surface temperature is used as the upper boundary condition of the soil material; The second information is used as the initial temperature of the soil around the pipeline gallery model to obtain the pipeline gallery operation model.

4. The method for determining the operating environment temperature of an urban integrated pipe gallery according to claim 1, characterized in that: Perform heat exchange calculation on the tunnel operation model to obtain the predicted tunnel operation environment temperature, including: The pipeline corridor operation model is divided into multiple pipeline corridor units and multiple soil units; Perform heat exchange iteration on all pipe gallery units through the first preset formula to obtain the temperature of each pipe gallery unit; Performing heat exchange iteration on the pipe gallery unit adjacent to the soil unit through a second preset formula to obtain the temperature of the pipe gallery unit adjacent to the soil unit; The temperature of each unit is transferred to the adjacent unit, and the heat exchange iteration is performed again to obtain the predicted operating environment temperature of the tunnel.

5. The method for determining the operating environment temperature of an urban integrated pipe gallery according to claim 1, characterized in that: The predicted operating environment temperature of the pipe gallery is compared with the third information to obtain the operating status of the pipe gallery, including: The predicted operating environment temperature of the pipe gallery is subtracted from the third information of the corresponding time of the prediction to obtain a first value. If the first value exceeds a first threshold, the operating state of the pipe gallery is abnormal. Calculate the predicted temperature change rate of the tunnel operation environment and the third information within a preset time period respectively to obtain a first change rate and a second change rate; The first change rate is divided by the second change rate to obtain a second value. If the second value exceeds a second threshold, the operation state of the pipe gallery is abnormal. When the operation status of the tunnel is abnormal, a safety inspection and early warning will be carried out on the tunnel.

6. A system for determining the operating environment temperature of an urban integrated pipe gallery, characterized in that: include: An acquisition unit, used to acquire first information, second information, heat source and structure data of the pipe gallery, and third information; The first information is the soil surface temperature measured by the first group of soil thermometers for at least the past year; the second information is the real-time temperature from the soil surface to the soil constant temperature layer measured by the first group of soil thermometers; the third information is the real-time temperature from the soil surface to the soil constant temperature layer measured by the second group of soil thermometers; the first group of soil thermometers are located in the construction area of ​​the pipeline corridor and away from heat sources, and the second group of soil thermometers are located inside the pipeline corridor or within one meter outside the pipeline corridor; A neural network training unit, used for inputting historical data in the first information into a preset neural network for training to obtain a prediction model; A prediction unit, used for inputting the real-time data in the first information into a prediction model to obtain a predicted soil surface temperature; A modeling unit, used for establishing a tunnel operation model according to the predicted soil surface temperature, the second information, and the tunnel heat source and structure data; The first calculation unit is used to perform heat exchange calculation on the pipe gallery operation model to obtain the predicted pipe gallery operation environment temperature; The first determination unit is used to compare the predicted operating environment temperature of the pipeline corridor with the third information to obtain the operating status of the pipeline corridor.

7. The system for determining the operating environment temperature of an urban integrated pipe gallery according to claim 6 is characterized in that: The neural network training unit comprises: A first setting unit is used to establish an input layer, a hidden layer, and an output layer connected in sequence, and to set the number of hidden layers, a learning rate, a total number of training rounds, and weights and biases of each layer, wherein the weights and biases of each layer are generated by a random function, thereby obtaining a preset neural network; A training unit is used for forward propagation: inputting the historical data in the first information into the input layer, and obtaining the output result from the output layer; calculating the output error based on the output result and the actual soil surface temperature at the time corresponding to the output result; transmitting the output error back through back propagation, and using partial derivatives for gradient descent; An updating unit, used to update the weights and biases of each layer in a preset neural network according to a preset formula; The recurrent unit is used to restart forward propagation until the total number of training rounds is reached and then stop. According to the output error of the last round of training, the number of hidden layers, learning rate, and total number of training rounds are adjusted to obtain a prediction model.

8. According to the system for determining the operating environment temperature of the urban integrated pipe gallery of claim 6, the pipe gallery heat source and structure data include the pipe gallery shape, size, pipe wall thickness, the shape, size and position of the air inlet and outlet, and the length, number, position and heat value per meter of the pipeline, characterized in that: The modeling unit comprises: The second setting unit is used to establish a pipe gallery model in the simulation software according to the pipe gallery heat source and structure data; The third setting unit is used to set soil material with a thickness greater than one meter around the pipe gallery model and to set soil insulation on the side of the pipe gallery model; a fourth setting unit, for using the predicted soil surface temperature as an upper boundary condition of the soil material; The fifth setting unit is used to use the second information as the initial temperature of the soil around the pipeline corridor model to obtain the pipeline corridor operation model.

9. The system for determining the operating environment temperature of an urban integrated pipe gallery according to claim 6 is characterized in that: The first computing unit comprises: A division unit is used to divide the pipe gallery operation model into a plurality of pipe gallery units and a plurality of soil units; The second calculation unit is used to perform heat exchange iteration on all pipe gallery units through the first preset formula to obtain the temperature of each pipe gallery unit; A third calculation unit is used to perform heat exchange iteration on the pipe gallery unit adjacent to the soil unit by using a second preset formula to obtain the temperature of the pipe gallery unit adjacent to the soil unit; The fourth calculation unit is used to transfer the temperature of each unit to the adjacent unit, perform heat exchange iteration again, and obtain the predicted operating environment temperature of the tunnel.

10. The system for determining the operating environment temperature of an urban integrated pipe gallery according to claim 6, characterized in that: The first determination unit comprises: The second determination unit is used to subtract the third information of the corresponding time from the predicted operating environment temperature of the pipe gallery to obtain a first value. If the first value exceeds a first threshold, the operating state of the pipe gallery is abnormal; A fifth calculation unit is used to calculate the predicted temperature of the pipe gallery operation environment and the temperature change rate of the third information within a preset time period, respectively, to obtain a first change rate and a second change rate; A third determination unit is used to obtain a second value by dividing the first change rate by the second change rate. If the second value exceeds a second threshold, the operation state of the pipe gallery is abnormal. The early warning unit is used to conduct safety inspections and early warnings on the corridor when the corridor's operating status is abnormal.

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