Method and system for determining temperature of urban comprehensive pipe gallery operation environment

Abstract

The application provides a kind of urban comprehensive pipe gallery operating environment temperature determination method and system, it is related to electric digital data processing technical field, including obtaining first information, second information, pipe gallery heat source and structure data and third information;The historical data in first information is input into preset neural network and is trained, and prediction model is obtained;Real-time data in first information is input into prediction model, and predicted soil surface temperature is obtained;According to predicted soil surface temperature, second information and pipe gallery heat source and structure data, pipe gallery operation model is established;Heat exchange calculation is carried out to pipe gallery operation model, and predicted pipe gallery operating environment temperature is obtained and compared with third information, and pipe gallery operating state is obtained.The application predicts soil surface temperature in future pipe gallery construction area by using back propagation algorithm, simulates soil surface temperature and pipe gallery heat source and structure data obtained by prediction, can effectively evaluate the safe operation state of pipe gallery, and guarantee the normal operation of pipe gallery.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric digital data processing, in particular to a kind of urban comprehensive pipe gallery operating environment temperature determination method and system. BACKGROUND

[0002] Pipe gallery environment temperature is an important parameter of urban pipe gallery engineering operation and maintenance, and it plays a crucial role in pipe gallery safe operation and maintenance, and the pipe gallery contains a variety of cables and pipelines, among which cables and heat pipes emit a large amount of heat when working, and the pipe gallery is a closed underground space, so heat accumulates in the pipe gallery, causing the temperature in the gallery to rise, affecting the normal work of maintenance personnel and cables, and in severe cases, it can cause fire and other safety accidents. In the prior art, the influence of pipe gallery internal pipeline temperature and ventilation system on pipe gallery operating environment temperature is usually considered, but the influence of soil temperature on pipe gallery operating environment temperature is not considered. SUMMARY

[0003] The present application relates to the technical field of electric digital data processing, in particular to a kind of urban comprehensive pipe gallery operating environment temperature determination method and system.

[0004] In one aspect, the present application provides a kind of urban comprehensive pipe gallery operating environment temperature determination method, comprising:

[0005] Obtain first information, second information, pipe gallery heat source and structure data and third information;The first information is the soil surface temperature measured by at least the first group of soil temperature in the past year;The second information is the real-time temperature measured by the first group of soil temperature below soil surface to soil constant temperature layer;The third information is the real-time temperature measured by the second group of soil temperature from soil surface to soil constant temperature layer;The first group of soil temperature is located in pipe gallery construction area and is far away from heat source, and the second group of soil temperature is located in pipe gallery or one meter range within pipe gallery outside;

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

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

[0008] According to the predicted soil surface temperature, the second information and the pipe gallery heat source and structure data, a pipe gallery operation model is established;

[0009] Heat exchange calculation is carried out on the pipe gallery operation model to obtain the predicted pipe gallery operating environment temperature;

[0010] The predicted pipe gallery operating environment temperature is compared with the third information to obtain the pipe gallery operating state.

[0011] In another aspect, the application also provides a utility tunnel operation environment temperature determination system, comprising:

[0012] An acquisition unit is configured to acquire first information, second information, tunnel heat source and structure data, and third information; the first information is soil surface layer temperature measured by a first group of soil thermometers in the past year; the second information is real-time temperature below the soil surface layer to the soil constant temperature layer measured by the first group of soil thermometers; the third information is real-time temperature from the soil surface layer to the soil constant temperature layer measured by a second group of soil thermometers; the first group of soil thermometers is located in the tunnel construction area and away from the heat source, and the second group of soil thermometers is located in the tunnel or within one meter of the tunnel outside;

[0013] A neural network training unit is configured to input historical data in the first information into a preset neural network for training to obtain a prediction model;

[0014] A prediction unit is configured to input real-time data in the first information into the prediction model to obtain predicted soil surface layer temperature;

[0015] A modeling unit is configured to establish a tunnel operation model according to the predicted soil surface layer temperature, the second information, and the tunnel heat source and structure data;

[0016] A first calculation unit is configured to perform heat exchange calculation on the tunnel operation model to obtain predicted tunnel operation environment temperature;

[0017] A first discrimination unit is configured to compare the predicted tunnel operation environment temperature with the third information to obtain a tunnel operation state.

[0018] The application has the beneficial effects that the application predicts soil surface layer temperature in the tunnel construction area at future time by using the back propagation algorithm, simulates the predicted soil surface layer temperature and the tunnel heat source and structure data, obtains environment temperature when the tunnel is normally operated in the future, thereby proposes early warning standards for abnormal tunnel operation environment temperature, can effectively evaluate the operation state of the tunnel, helps the operation personnel to take corresponding measures in advance, and guarantees the normal operation of the tunnel.

[0019] Other features and advantages of the application will be set forth in the following description, and in part will become apparent to those skilled in the art upon examination of the following or can be learned by practice of the application. The objects and other advantages of the application can be realized and attained by means of the instrumentalities particularly pointed out in the written description and claims hereof, as well as the appended drawings. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced as follows. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be considered as limiting the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0021] Figure 1 Flowchart of the method for determining the operating environment temperature of the urban comprehensive pipe gallery according to the present application;

[0022] Figure 2 Schematic diagram of burying the second group of soil thermometers;

[0023] Figure 3 Schematic diagram of the pipe gallery cross section;

[0024] Figure 4 Structure schematic diagram of the system for determining the operating environment temperature of the urban comprehensive pipe gallery according to the present application.

[0025] Markings in the figure: 1, 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 application more clear, the technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, but not all the embodiments. The components of the embodiments of the present application 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 application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. All other embodiments obtained by those skilled in the art on the basis of the embodiments in the present application without creative labor are within the scope of protection of the present application.

[0027] It should be noted that: similar labels and letters represent similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, 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] As Figure 1As shown, the embodiment provides a method for determining the operating environment temperature of an urban utility tunnel, comprising steps S1, S2, S3, S4, S5 and S6.

[0030] S1, acquire first information, second information, tunnel heat source and structure data and third information; the first information is the soil surface temperature measured by the first group of soil thermometers in at least the past year; the second information is the real-time temperature below 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 is located in the tunnel construction area and away from the heat source, and the second group of soil thermometers 1 is located within one meter of the tunnel interior or the tunnel exterior, and the second group of soil thermometers 1 is buried as shown Figure 2 As shown, the first group of soil thermometers is buried in the same way as the second group of soil thermometers 1, but away from the heat source such as the tunnel.

[0031] It should be noted that the first group of soil thermometers and the second group of soil thermometers 1 each include multiple soil thermometers, wherein the first group of soil thermometers includes at least a soil thermometer at the soil surface. In general, the actual burial depth of the soil thermometer at the soil surface is about 5 cm underground to avoid the soil thermometer from being exposed to the ground, which affects the accuracy of the first information. The measurement interval of the first information, the second information and the third information is set according to the actual situation, which 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 the twenty-four hours is taken as the measured temperature.

[0032] Because the soil structure and composition of the newly built project will change, the soil temperature data before the construction of the tunnel cannot be used as the basis data for predicting future soil temperature data, therefore, the present application reacquires the soil temperature data after the construction of the tunnel 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 tunnel construction area, i.e., 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 steps S21, S22, S23, S24, S25 and S26.

[0035] S21, establish an input layer, a hidden layer and an output layer connected in turn, set the number of hidden layers, the learning rate, the total number of training rounds, and the weights and biases of each layer, and 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 of the input layer and the number of nodes of the output layer are set according to actual conditions, and in the embodiment, the number of nodes of the input layer and the number of nodes of the output layer are set to eight, the measurement interval is set to three hours, and the soil surface temperature in the next 24 hours is predicted. The input data of the input layer is 56 continuous soil surface temperatures (i.e., the soil surface temperature of one week), and the expected output data of the output layer is the next eight soil surface temperatures (i.e., the soil surface temperature of the next day one week later) after the input data. The number of hidden layers is selected to be a value between the number of nodes of the input layer and the number of nodes of the output layer, and in the 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 the embodiment, the historical data in the first information is divided into a data group every 56 continuous soil surface temperatures, and the data groups are sequentially input into the input layer for training according to the time sequence.

[0039] The mathematical expression of the input and output of any layer is:

[0040]

[0041] In the formula, (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, calculate the output error according to the output result and the actual soil surface temperature at the time corresponding to the output result.

[0044] The output error expression is:

[0045]

[0046] In the formula, E represents the output error; N represents the number of samples, i.e., the number of historical data in the first information; L t represents the actual soil surface temperature at the t-th time; o t represents the output result of the output layer at the t-th time.

[0047] In the 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, the output error is returned by back propagation, and gradient descent is performed using the partial derivative.

[0049] The gradient descent calculation formula is:

[0050]

[0051]

[0052]

[0053]

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

[0055] S25, updating 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, denotes the updated weight of the i-th layer; w(i) denotes the previous weight of the i-th layer; and a denotes the learning rate; denotes the partial derivative symbol; denotes the updated bias of the i-th layer; b i ) denotes the previous bias of the i-th layer.

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

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

[0062] It should be noted that whether the current training is overfitting or underfitting is obtained by the output error of the last round of training. When overfitting, the learning rate value is increased and the total training round number is reduced; when underfitting, the learning rate value is reduced and the total training round number is increased. The number of nodes of the input layer and the number of nodes of the output layer are adjusted between the number of nodes of the input layer and the number of nodes of the output layer of the hidden 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 (soil surface temperature one week before the current time), and the number of predicted soil surface temperatures is eight (soil surface temperature one day after the current time). Eight predicted soil surface temperatures are added to the 48 data closest to the current time to form new 56 soil surface temperatures, which are input into the output layer to obtain the soil surface temperature in the next 24 to 48 hours, and the process is repeated to obtain the soil surface temperature in the required future time period. These predicted soil surface temperatures are the environmental temperature during normal operation of the pipe gallery.

[0065] In some specific embodiments, it further includes a step S31 of updating the prediction model once every season, and the specific operation is:

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

[0067] It can be understood 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 season can make the prediction model more accurate for predicting the soil surface temperature in the current season.

[0068] S4, establishing a pipe gallery operation model according to the predicted soil surface temperature, the second information, and the pipe gallery heat source and structure data.

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

[0070] Specifically, step S4 includes:

[0071] S41, establishing a pipe gallery model in the simulation software fluent according to the pipe gallery heat source and structure data;

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

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

[0074] S44, taking the second information as the initial temperature of the soil around the pipe gallery model, to obtain a pipe gallery operation model.

[0075] It can be understood that, since the soil surface temperature is constantly changing, the predicted soil surface temperature is taken as a variable in the pipe gallery operation model, so as to simulate the pipe gallery operation environment.

[0076] S5, performing heat exchange calculation on the pipe gallery operation model to obtain a predicted pipe gallery operation environment temperature.

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

[0078] S51, dividing the pipe gallery operation model into a plurality of pipe gallery units and a plurality of soil units.

[0079] It can be understood that the divided pipe gallery operation model is in a grid shape, and the grid lines can be encrypted near the pipeline 3 and the pipe wall 2, so that the units at these positions are divided smaller, so that the subsequent heat exchange calculation is more accurate.

[0080] S52, performing heat exchange iteration on all pipe gallery units through 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 represents the convective heat transfer coefficient of the pipe wall 2 or the pipeline 3 wall surface; T w represents the surface temperature of the pipe wall 2 or the pipeline 3; T f represents the airflow temperature in the pipe gallery; λ f represents the thermal conductivity of the fluid molecules attached to the pipe wall 2 or the pipeline 3 wall surface; l represents the distance from the pipe gallery wall surface or the pipeline 3; T1 represents the temperature of the pipe gallery unit.

[0084] It can be understood that the heat exchange inside the pipe gallery mainly takes two forms, one is the convective heat transfer between the air in the gallery and the gallery wall surface, and the other is the heat dissipation of the internal conductor of the pipeline 3 through the high-temperature outer skin to the air, and the heat conduction mode is also convective heat transfer. According to the heat exchange form of different units, the first preset formula is substituted for calculation.

[0085] S53, performing heat exchange iteration on the pipe gallery unit adjacent to the soil unit by a second preset formula to obtain a 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, and z represent three-dimensional coordinates of the pipe gallery unit or the soil unit; Q represents heat; λ represents a thermal conductivity of the pipe wall 2 or the soil; and T2 represents a temperature of the pipe wall 2 or the soil.

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

[0092] S54, transmitting the temperature of each unit to an adjacent unit to perform heat exchange iteration again to obtain a predicted pipe gallery operating environment temperature.

[0093] S6, comparing the predicted pipe gallery operating environment temperature with third information to obtain a pipe gallery operating state.

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

[0095] S61, subtracting the third information corresponding to the predicted time from the predicted pipe gallery operating environment temperature to obtain a first value, and if the first value exceeds a first threshold value, the pipe gallery operating state is abnormal.

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

[0097] S62, calculating a temperature change rate of the predicted pipe gallery operating environment temperature and the third information in a preset time period respectively to obtain a first change rate and a second change rate.

[0098] S63, dividing the first change rate by the second change rate to obtain a second value, and if the second value exceeds a second threshold value, the pipe gallery operating state is abnormal.

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

[0100] S64, when the pipe gallery operating state is abnormal, performing safety investigation and early warning on the pipe gallery.

[0101] Step S6 gives the abnormal operation environment temperature warning standard of the pipe gallery, realizes the safety control of the pipe gallery, and effectively reduces the maintenance cost.

[0102] Embodiment 2:

[0103] Corresponding to the above method embodiment, the embodiment proposes a city comprehensive pipe gallery operation environment temperature determination system, as shown in Figure 4 The determination system 700 includes:

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

[0105] The neural network training unit 720 is 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 configured to input the real-time data in the first information into the prediction model to obtain a predicted soil surface layer temperature;

[0107] The modeling unit 740 is configured to establish a pipe gallery operation model according to the predicted soil surface layer temperature, the second information, and the pipe gallery heat source and structure data;

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

[0109] The first discrimination unit 760 is configured to compare the predicted pipe gallery operation environment temperature with the third information to obtain a pipe gallery operation state.

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

[0111] The first setting unit is configured to 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, and the weights and biases of each layer are generated by a random function, thereby obtaining a preset neural network;

[0112] The training unit is configured to forward propagate: input historical data in the first information into the input layer, and obtain an output result from the output layer; calculate the output error according to the output result and an actual soil surface temperature corresponding to the output result; and transmit the output error back through back propagation, and perform gradient descent using a partial derivative;

[0113] The updating unit is configured to update the weight and bias of each layer in the preset neural network according to a preset formula.

[0114] The circulating unit is configured to restart the forward propagation until a total training round number is reached and the forward propagation is stopped, and adjust the number of hidden layers, the learning rate and the total training round number according to the output error of the last round of training, so as to obtain the prediction model.

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

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

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

[0118] The fourth setting unit is configured to use the predicted soil surface temperature as the upper boundary condition of the soil material.

[0119] The fifth setting unit is configured to use the second information as the initial temperature of the soil around the pipe gallery model to obtain the pipe gallery operation model.

[0120] The first calculation unit 750 includes:

[0121] The dividing unit is configured 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 configured to perform heat exchange iteration on all pipe gallery units through a first preset formula to obtain the temperature of each pipe gallery unit.

[0123] The third calculation unit is configured to perform heat exchange iteration on the pipe gallery units adjacent to the soil units through a second preset formula to obtain the temperature of the pipe gallery units adjacent to the soil units.

[0124] The fourth calculation unit is configured to transfer the temperature of each unit to adjacent units to perform heat exchange iteration again to obtain the predicted pipe gallery operation environment temperature.

[0125] The first discrimination unit 760 includes:

[0126] The second discrimination unit is used to subtract the third information corresponding to the predicted time from the predicted operating temperature of the utility tunnel to obtain the first value. If the first value exceeds the first threshold, the operating status of the utility tunnel is abnormal.

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

[0128] The third discrimination unit is used to divide the first rate of change by the second rate of change to obtain the second value. If the second value exceeds the second threshold, the operation status of the utility tunnel is abnormal.

[0129] The early warning unit is used to conduct safety inspections and issue early warnings for the utility tunnel when its operational status is abnormal.

[0130] The model in this application is simple to use, easy to adjust parameters, and can accurately predict the ambient temperature and changes during the normal operation of the utility tunnel. It is realistic, safe and reliable.

[0131] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0132] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for determining the operating environment temperature of an urban integrated utility tunnel, characterized in that, The application relates to a pipeline gallery operation state prediction method. The method comprises the following steps: acquiring first information, second information, pipeline gallery heat source and structure data and third information; the first information is soil surface layer temperature measured by a first group of soil thermometers in the past year or so; the second information is real-time temperature below the soil surface layer to the soil constant temperature layer measured by the first group of soil thermometers; the third information is real-time temperature from the soil surface layer to the soil constant temperature layer measured by a second group of soil thermometers; the first group of soil thermometers are located in a pipeline gallery construction area and are far away from the heat source; the second group of soil thermometers are located in the pipeline gallery or within one meter of the pipeline gallery; inputting historical data in the first information into a preset neural network for training to obtain a prediction model; inputting real-time data in the first information into the prediction model to obtain predicted soil surface layer temperature; establishing a pipeline gallery operation model according to the predicted soil surface layer temperature, the second information and the pipeline gallery heat source and structure data; performing heat exchange calculation on the pipeline gallery operation model to obtain predicted pipeline gallery operation environment temperature; comparing the predicted pipeline gallery operation environment temperature with the third information to obtain a pipeline gallery operation state; wherein the heat exchange calculation on the pipeline gallery operation model to obtain the predicted pipeline gallery operation environment temperature comprises the following steps: dividing the pipeline gallery operation model into multiple pipeline gallery units and multiple soil units; performing heat exchange iteration on all pipeline gallery units through a first preset formula to obtain the temperature of each pipeline gallery unit; ； wherein represents the convective heat transfer coefficient of the pipe wall 2 or the pipeline 3 wall surface; represents the surface temperature of the pipe wall 2 or the pipeline 3; represents the temperature of the airflow in the pipe gallery; represents the thermal conductivity of the fluid molecules at the pipe wall 2 or the pipeline 3 wall surface; represents the distance from the pipe gallery wall surface or from the pipeline 3; represents the temperature of the pipe gallery unit; wherein the first preset formula is: performing heat exchange iteration on the pipeline gallery units adjacent to the soil units through a second preset formula to obtain the temperature of the pipeline gallery units adjacent to the soil units; ； ； ； wherein , , represents the three-dimensional coordinates of the pipe gallery unit or the soil unit; represents the heat; represents the thermal conductivity of the pipe wall 2 or the soil; represents the temperature of the pipe wall 2 or the soil; wherein the second preset formula is:

2. The urban utility tunnel operating environment temperature determination method according to claim 1, wherein, transferring the temperature of each unit to adjacent units to perform heat exchange iteration again to obtain the predicted pipeline gallery operation environment temperature. inputting historical data in the first information into a preset neural network for training to obtain a prediction model comprises the following steps: 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 training rounds and the weights and biases of each layer, and generating the weights and biases of each layer by a random function to obtain the preset neural network; forward propagation: inputting historical data in the first information into the input layer to obtain an output result from the output layer; calculating the output error according to the output result and the actual soil surface layer temperature at the time corresponding to the output result; transferring the output error back through back propagation and using partial derivatives for gradient descent; updating the weights and biases of each layer in the preset neural network according to a preset formula; 3. The urban utility tunnel operating environment temperature determining method according to claim 1, wherein the tunnel heat source and structure data include a tunnel shape, a size, a pipe wall thickness, shapes, sizes, and positions of an air inlet and an air outlet, and lengths, numbers, positions, and heat generation per meter of the pipelines. starting forward propagation again until the total training rounds are reached and stopping, and adjusting the number of hidden layers, the learning rate and the total training rounds according to the output error of the last round of training to obtain the prediction model. establishing a pipeline gallery operation model according to the predicted soil surface layer temperature, the second information and the pipeline gallery heat source and structure data comprises the following steps: establishing a pipeline gallery model in a simulation software according to the pipeline gallery heat source and structure data; setting soil material with a thickness greater than one meter around the pipeline gallery model and setting soil heat insulation on the side of the pipeline gallery model; taking the predicted soil surface layer temperature as the upper boundary condition of the soil material; taking the second information as the initial temperature of the soil around the pipeline gallery model to obtain the pipeline gallery operation model.

4. The urban utility tunnel operating environment temperature determining method according to claim 1, wherein, The predicted pipeline operation environment temperature is compared with the third information to obtain a pipeline operation state, including: The predicted pipeline operation environment temperature is subtracted from the third information corresponding to the time to obtain a first value, and if the first value exceeds a first threshold value, the pipeline operation state is abnormal; The temperature change rates of the predicted pipeline operation environment temperature and the third information in a preset time period are calculated 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, and if the second value exceeds a second threshold value, the pipeline operation state is abnormal; When the pipeline operation state is abnormal, a safety investigation and early warning is performed on the pipeline.

5. An urban utility tunnel operating environment temperature determining system characterized by, It includes: An acquisition unit is configured to acquire first information, second information, pipeline heat source and structure data, and third information; The first information is the soil surface temperature measured by a first group of soil thermometers in 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 a second group of soil thermometers; the first group of soil thermometers is located in the pipeline construction area and away from the heat source, and the second group of soil thermometers is located inside or within one meter of the pipeline outside; A neural network training unit is configured to input historical data in the first information into a preset neural network for training to obtain a prediction model; A prediction unit is configured to input real-time data in the first information into the prediction model to obtain a predicted soil surface temperature; A modeling unit is configured to establish a pipeline operation model according to the predicted soil surface temperature, the second information, and the pipeline heat source and structure data; A first calculation unit is configured to perform heat exchange calculation on the pipeline operation model to obtain a predicted pipeline operation environment temperature; A first discrimination unit is configured to compare the predicted pipeline operation environment temperature with the third information to obtain a pipeline operation state; The first calculation unit includes: A division unit is configured to divide the pipeline operation model into a plurality of pipeline units and a plurality of soil units; A second calculation unit is configured to perform heat exchange iteration on all pipeline units through a first preset formula to obtain the temperature of each pipeline unit; The first preset formula is: ； wherein represents the convective heat transfer coefficient of the pipe wall 2 or the pipeline 3 wall surface; represents the surface temperature of the pipe wall 2 or the pipeline 3; represents the temperature of the airflow in the pipe gallery; represents the thermal conductivity of the fluid molecules at the pipe wall 2 or the pipeline 3 wall surface; represents the distance from the pipe gallery wall surface or from the pipeline 3; represents the temperature of the pipe gallery unit; A third calculation unit is configured to perform heat exchange iteration on the pipeline units adjacent to the soil units through a second preset formula to obtain the temperature of the pipeline units adjacent to the soil units; The second preset formula is: ； ； ； wherein , , represents a three-dimensional coordinate of the pipe gallery unit or the soil unit; represents heat; represents a thermal conductivity of the pipe wall 2 or the soil; represents a temperature of the pipe wall 2 or the soil; A fourth calculation unit is configured to transfer the temperature of each unit to adjacent units to perform heat exchange iteration again to obtain the predicted pipeline operation environment temperature.

6. The urban utility tunnel operating environment temperature determining system of claim 5, wherein, The neural network training unit includes: A first setting unit is configured to establish an input layer, a hidden layer, and an output layer connected in sequence, set the number of hidden layers, a learning rate, a total number of training rounds, and the weights and biases of each layer, and generate the weights and biases of each layer by a random function to obtain a preset neural network; The training unit is configured to forward propagate: inputting historical data in the first information into the input layer to obtain an output result from the output layer; calculating an output error according to the output result and an actual soil surface temperature at a time corresponding to the output result; and performing gradient descent through back propagation of the output error and using a partial derivative. The updating unit is configured to update the weights and biases of each layer in the preset neural network according to a preset formula. The cycle unit is configured to restart the forward propagation until a total training round number is reached and the forward propagation is stopped, and adjust the number of hidden layers, a learning rate, and the total training round number according to the output error of the last round of training, so as to obtain a prediction model.

7. The urban utility tunnel operating environment temperature determining system according to claim 5, wherein the tunnel heat source and structure data include a tunnel shape, a size, a pipe wall thickness, shapes, sizes, and positions of an air inlet and an air outlet, and lengths, numbers, positions, and heat generation per meter of the pipelines. The modeling unit comprises: The second setting unit is configured to establish a pipe gallery model in simulation software according to pipe gallery heat sources and structure data. The third setting unit is configured to set soil material with a thickness greater than one meter around the pipe gallery model, and set soil heat insulation on a side of the pipe gallery model. The fourth setting unit is configured to set the predicted soil surface temperature as an upper boundary condition of the soil material. The fifth setting unit is configured to set the second information as an initial temperature of soil around the pipe gallery model to obtain a pipe gallery operation model.

8. The urban utility tunnel operating environment temperature determining system of claim 5, wherein, The first discrimination unit comprises: The second discrimination unit is configured to subtract the predicted pipe gallery operation environment temperature from the third information at a corresponding time to obtain a first value, and if the first value exceeds a first threshold value, the pipe gallery operation state is abnormal. The fifth calculation unit is configured to calculate a temperature change rate of the predicted pipe gallery operation environment temperature and the third information in a preset time period, respectively, to obtain a first change rate and a second change rate. The third discrimination unit is configured to divide the first change rate by the second change rate to obtain a second value, and if the second value exceeds a second threshold value, the pipe gallery operation state is abnormal. The early warning unit is configured to perform safety investigation and early warning of the pipe gallery when the pipe gallery operation state is abnormal.

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