A method and device for constructing temperature field of large volume concrete

Abstract

The present application provides a method and device for constructing a temperature field of large-volume concrete, which relates to the field of construction engineering technology and includes the following steps: S100, setting sensor distribution rules: designing a sensor distribution rule based on the volume and shape of the concrete pouring; S200, embedding sensors: embedding corresponding sensors in the concrete according to the sensor distribution rules to perform data detection; S300, constructing a temperature field model: transmitting data to a central processing system via a wireless transmission device, and calculating and constructing a model of the temperature field of large-volume concrete based on the detected data. By setting sensor distribution rules and embedding sensors, the present application ensures that temperature data from all parts of the concrete can be collected, thereby improving the accuracy of the temperature field model. By constructing the temperature field model, the results of time series analysis and spatial interpolation are integrated to generate the final temperature field model, thereby improving the overall performance of the model.

Description

Technical Field

[0001] The present invention relates to the technical field of construction engineering, and in particular to a method and device for constructing a temperature field of large-volume concrete. Background Art

[0002] Due to its large pouring cross-section and poor thermal conductivity, the heat generated by hydration accumulates inside the concrete structure and is not easy to dissipate, which easily causes its internal temperature to rise. This temperature change may cause temperature stress in the concrete structure, and then cause cracks. Therefore, accurately obtaining the temperature field distribution inside the large-volume concrete structure is the key to controlling construction quality. Traditional temperature monitoring methods mostly use multiple temperature sensors to detect data. Since bubbles and internal voids will be generated during concrete pouring, this will affect the accuracy of the temperature detection of the sensor embedded in the concrete. In addition, the heat transfer efficiency of concrete of different materials is different, so the error of single temperature data in temperature field construction is large.

[0003] For example, the Chinese invention patent (CN117454655B) discloses a method, device, equipment and medium for constructing a temperature field for large-volume concrete. Its specification discloses that the advantage of traditional point thermometers is that they have high accuracy, but they can only measure the temperature of a single point, are complex to install, and have strict requirements on the working environment. The above patent can prove the defects of the existing technology.

[0004] Therefore, we made improvements to this and proposed a method and device for constructing the temperature field of large-volume concrete. Summary of the Invention

[0005] The purpose of the present invention is to address the current problem that bubbles and internal cavities are generated during concrete pouring, which affects the accuracy of temperature detection by sensors embedded in the concrete, so that single temperature data leads to large errors in temperature field construction.

[0006] In order to achieve the above-mentioned purpose of the invention, the present invention provides a method and device for constructing a temperature field of large-volume concrete to improve the above-mentioned problems.

[0007] The specific application is as follows:

[0008] The following steps are involved:

[0009] S100, setting sensor distribution rule: designing a sensor distribution rule according to the volume and shape of the concrete pouring;

[0010] S200, embedding sensors: embedding corresponding sensors in concrete according to the sensor distribution rule to perform data detection;

[0011] S300, constructing a temperature field model: transmitting data to a central processing system via a wireless transmission device, and calculating and constructing a model of the temperature field of a large volume of concrete based on the detected data.

[0012] As a preferred technical solution of this application, in S100, the sensor distribution rules specifically include:

[0013] S101. Divide the concrete into several sub-areas according to its shape and size, with each sub-area having a size of 10 m*10 m*1 m;

[0014] S102. In each sub-area, design a 5m*5m*0.5m three-dimensional grid, and place a temperature sensor, a humidity sensor, a vibration sensor, and a pressure sensor at each grid node;

[0015] S103. Add three additional temperature sensors, humidity sensors, and pressure sensors at each location, including the edge, center, and stress points of the concrete structure.

[0016] S104. Add a temperature sensor and a humidity sensor in the middle of each layer of layered pouring;

[0017] In areas where early-strength cement is used, add one temperature sensor and one humidity sensor for every 10 square meters;

[0018] In areas where humidity exceeds 80%, add one humidity sensor for every 10 square meters;

[0019] In areas where wind speed exceeds 5 m / s, add one wind speed sensor, temperature sensor, and humidity sensor for every 10 square meters.

[0020] As a preferred technical solution of this application, in S300, the specific steps of constructing the temperature field model include:

[0021] S301, preprocessing the collected temperature, humidity, wind speed, vibration and pressure data, including data cleaning, filling missing values ​​and smoothing;

[0022] S302, performing time series analysis on the preprocessed data, extracting time features, and predicting temperature values ​​at future time points;

[0023] S303, performing spatial interpolation on the pre-processed data to generate a continuous temperature field distribution;

[0024] S304, adjusting the weight of sensor data according to the sensor distribution rule and signal strength;

[0025] S305 , fusing the future temperature prediction value obtained by time series analysis with the temperature field distribution obtained by spatial interpolation to generate a final temperature field model.

[0026] As a preferred technical solution of this application, in S301, the specific steps of data preprocessing include:

[0027] S301a, Data cleaning: Use statistical methods to identify and remove outliers;

[0028] Outlier identification formula:

[0029] Here, x is the data point, μ is the mean of the data, and σ is the standard deviation of the data.

[0030] S301b, fill missing values: use interpolation method to fill missing values;

[0031] S301c, smoothing: using a filter to smooth the data and reduce noise;

[0032] Moving average filter formula: Where n is the window size, y(i) is the data point, is the smoothed data.

[0033] S301d, Data Standardization: Standardize all data to the same scale to facilitate subsequent model training and prediction;

[0034] Normalization formula:

[0035] Here, x is the data point, min(x) and max(x) are the minimum and maximum values ​​of the data, respectively.

[0036] As a preferred technical solution of this application, in S302, the specific steps of time series analysis include:

[0037] S302a, select the time series model: use the ARIMA model;

[0038] ARIMA model formula:

[0039] y t =c+φ1y t-1 +φ2y t-2 +…+φ p y t-p +∈ t +θ1∈ t-1 +θ2∈ t-2 +…+θ q ∈ t-q ;

[0040] Among them, y t is the observed value at time point t, c is a constant term, φ i is the autoregressive coefficient, θ iis the moving average coefficient, ∈ t It's white noise.

[0041] S302b, model training: using historical data to train a time series model;

[0042] S302c, model prediction: Use the trained model to predict the temperature value at a certain point in the future.

[0043] As a preferred technical solution of this application, in S303, the specific steps of spatial interpolation include:

[0044] S303a, select interpolation method: use Kriging interpolation method;

[0045] Kriging interpolation formula:

[0046] in, is the estimated value of the interpolation point s0, z(s i ) is a known data point, λ i is the weight, and n is the number of known data points.

[0047] S303b, generating a mesh: generating a three-dimensional mesh according to the shape and size of the concrete;

[0048] S303c, interpolation calculation: interpolate the discrete sensor data into the generated three-dimensional grid to generate a continuous temperature field distribution.

[0049] As a preferred technical solution of this application, in S304, the specific steps of adjusting the weight of sensor data according to the sensor distribution rule and signal strength include:

[0050] S304a, calculating an initial weight according to the position of the sensor;

[0051] Position importance weight: the edge position weight is 1.5, the center position weight is 1.5, the force point weight is 2, and the rest of the positions weight is 1;

[0052] S304b, according to the signal strength S i , adjust the weights;

[0053] Signal strength adjustment factor formula:

[0054] Among them, S i is the signal strength of the i-th sensor, T is the preset signal threshold, and the threshold is 1;

[0055] S304c, calculating the adjusted weight;

[0056] Adjusted weight formula: w′ i =wi ×α(S i );

[0057] Among them, w i is the initial weight, w′ i is the adjusted weight.

[0058] As the preferred technical solution of this application, in S305, the specific steps of fusion include:

[0059] S305a, weighted average: performing weighted average on the future temperature prediction value obtained by time series analysis and the temperature field distribution obtained by spatial interpolation to generate the final temperature field model;

[0060] Weighted average formula: T final (s, t) = w1T time (t)+w2T space (s);

[0061] Among them, T final (s, t) is the final temperature field model, T time (t) is the temperature forecast value obtained by time series analysis, T space (s) is the temperature field distribution obtained by spatial interpolation, and w1 and w2 are weights.

[0062] A device for constructing a temperature field of large-volume concrete, comprising the following modules:

[0063] Sensor module: including temperature sensor, humidity sensor, wind speed sensor, vibration sensor and pressure sensor, used to collect multivariate data inside the concrete;

[0064] Data acquisition module: used to collect sensor data in real time and transmit the data to the central processing system through wireless transmission equipment;

[0065] Central processing module: used to receive and process sensor data, perform time series analysis and spatial interpolation, and build temperature field models;

[0066] Storage module: used to store collected data and constructed temperature field models;

[0067] Display module: used to display the visualization results of the temperature field model.

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] In the scheme of this application:

[0070] 1. By setting sensor distribution rules and embedding sensors, we ensure that temperature data from all parts of the concrete can be collected, improving the accuracy of the temperature field model. By constructing the temperature field model, we fuse the results of time series analysis and spatial interpolation to generate the final temperature field model, thereby improving the overall performance of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 A schematic diagram of the method for constructing a temperature field of mass concrete provided in this application;

[0072] Figure 2 Schematic diagram of the mass concrete temperature field construction device provided in this application. DETAILED DESCRIPTION

[0073] In order to enable those skilled in the art to better understand the solutions of the present invention, 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 embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0074] As described in the background art, bubbles and internal cavities are generated during concrete pouring, which affects the accuracy of temperature detection by sensors embedded in the concrete. Therefore, a single temperature data may lead to large errors in temperature field construction.

[0075] In order to solve this technical problem, the present invention provides a method and device for constructing a temperature field of a large volume concrete, which is applied in the field of construction engineering technology.

[0076] Specifically, please refer to Figure 1 The method and device for constructing a temperature field of large-volume concrete specifically include:

[0077] S100, setting sensor distribution rule: designing a sensor distribution rule according to the volume and shape of the concrete pouring;

[0078] S200, embedding sensors: embedding corresponding sensors in concrete according to the sensor distribution rule to perform data detection;

[0079] S300, constructing a temperature field model: transmitting data to a central processing system via wireless transmission equipment (wireless signal transmission is an existing technology and will not be described in detail here), and calculating and constructing a temperature field model of a large volume of concrete based on the detected data.

[0080] By setting sensor distribution rules and embedding sensors, it is ensured that temperature data from all parts of the concrete can be collected, thereby improving the accuracy of the temperature field model. By constructing the temperature field model, the results of time series analysis and spatial interpolation are integrated to generate the final temperature field model, thereby improving the overall performance of the model.

[0081] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0082] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features and technical solutions therein may be combined with each other.

[0083] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0084] Example 1, please refer to Figure 1 A method and device for constructing a temperature field of a large volume concrete. In S100, the sensor distribution rules specifically include:

[0085] S101. Divide the concrete into several sub-areas according to its shape and size, with each sub-area having a size of 10 m*10 m*1 m;

[0086] S102. In each sub-area, design a 5m*5m*0.5m three-dimensional grid, and place a temperature sensor, a humidity sensor, a vibration sensor, and a pressure sensor at each grid node;

[0087] S103. Add three additional temperature sensors, humidity sensors, and pressure sensors at each key location, such as the edge, center, and stress points of the concrete structure. By arranging sensors at different locations of the concrete (such as the edge, center, and stress points), comprehensive coverage of the temperature field is ensured.

[0088] S104. Add a temperature sensor and a humidity sensor in the middle of each layer of layered pouring;

[0089] In areas where early-strength cement is used, add one temperature sensor and one humidity sensor for every 10 square meters;

[0090] In areas where humidity exceeds 80%, add one humidity sensor for every 10 square meters. Add sensors in areas where early-strength cement is used, areas where humidity exceeds 80%, and areas where wind speed exceeds 5 meters per second to ensure that temperature changes under these special conditions are accurately monitored.

[0091] In areas where wind speeds exceed 5 m / s, one wind speed sensor, temperature sensor, and humidity sensor are added for every 10 square meters. Before pouring concrete, the sensors are embedded in the designed grid positions to ensure that the sensors are tightly integrated with the concrete to avoid air gaps affecting measurement accuracy. A data acquisition system is used to collect sensor data in real time at a frequency of once every 10 minutes, and the data is transmitted to the central processing system via wireless transmission equipment.

[0092] In S300, the specific steps of constructing the temperature field model include:

[0093] S301, preprocessing the collected temperature, humidity, wind speed, vibration and pressure data, including data cleaning, filling missing values ​​and smoothing;

[0094] S302, performing time series analysis on the preprocessed data, extracting time features, and predicting temperature values ​​at future time points;

[0095] S303, performing spatial interpolation on the pre-processed data to generate a continuous temperature field distribution;

[0096] S304, adjusting the weight of sensor data according to the sensor distribution rule and signal strength;

[0097] S305 , fusing the future temperature prediction value obtained by time series analysis with the temperature field distribution obtained by spatial interpolation to generate a final temperature field model.

[0098] Example 2 further optimizes the method and device for constructing a temperature field of large-volume concrete provided in Example 1. Specifically, in S301, the specific steps of data preprocessing include:

[0099] S301a, Data cleaning: Use statistical methods to identify and remove outliers;

[0100] Outlier identification formula:

[0101] Where x is a data point, μ is the mean of the data, and σ is the standard deviation of the data. When |Z|>3, the data point is considered an outlier.

[0102] S301b, fill missing values: use interpolation method to fill missing values;

[0103] Linear interpolation formula:

[0104] Where y(t1) and y(t2) are known data points, and t is the time point that needs to be interpolated.

[0105] S301c, smoothing: using a filter to smooth the data and reduce noise;

[0106] Moving average filter formula: Where n is the window size, y(i) is the data point, is the smoothed data.

[0107] S301d, Data Standardization: Standardize all data to the same scale to facilitate subsequent model training and prediction;

[0108] Normalization formula:

[0109] Here, x is the data point, min(x) and max(x) are the minimum and maximum values ​​of the data, respectively.

[0110] In S302, the specific steps of time series analysis include:

[0111] S302a, select the time series model: use the ARIMA model;

[0112] ARIMA model formula:

[0113] y t =c+φ1y t-1 +φ2y t-2 +…+φ p y t-p +∈ t +θ1∈ t-1 +θ2∈ t-2 +…+θ q ∈ t-q ;

[0114] Among them, y t is the observed value at time point t, c is a constant term, φ i is the autoregressive coefficient, which indicates the influence of the observation value at the past i-th time point on the current observation value, θ i is the moving average coefficient, which indicates the influence of the error at the past i-th time point on the current error, ∈ t It is white noise, indicating random errors. By selecting a suitable ARIMA model or multivariate time series model, the prediction ability of the model is improved.

[0115] S302b, model training: using historical data to train a time series model;

[0116] S302c, model prediction: Use the trained model to predict the temperature value at a certain point in the future.

[0117] In S303, the specific steps of spatial interpolation include:

[0118] S303a, select interpolation method: use Kriging interpolation method;

[0119] Kriging interpolation formula:

[0120] in, is the estimated value of the interpolation point s0, z(s i ) is a known data point, λ i is the weight, and n is the number of known data points.

[0121] S303b, generating a mesh: generating a three-dimensional mesh according to the shape and size of the concrete;

[0122] S303c, interpolation calculation: interpolate the discrete sensor data into the generated three-dimensional grid to generate a continuous temperature field distribution.

[0123] The most direct method is to construct a temperature field model of concrete using temperature sensors. However, temperature sensors embedded in concrete are also easily affected by bubbles, voids and materials in the concrete when monitoring temperature data. Therefore, through the steps of S100-S303c, a variety of data can be collected, including temperature, humidity, wind speed, vibration and pressure (areas with high humidity and high wind speed have a great influence on temperature, affecting water evaporation and heat loss. The thermal expansion and contraction of concrete materials cause vibration and pressure changes, which are also caused by temperature changes. The factors that affect temperature, such as bubbles, voids and materials, have little effect on humidity, wind speed, vibration and pressure). The influence of these factors on the temperature field cannot be captured by temperature data alone, and temperature data is also It cannot reflect the stability or density of the concrete structure. This information needs to be obtained through vibration and pressure data. Humidity, wind speed, vibration and pressure data can provide complementary information to help more comprehensively describe the physical environment inside the concrete. For example, humidity data can reflect the moisture content of the concrete, wind speed data can reflect the impact of the external environment, vibration data can reflect the stability of the structure, and pressure data can reflect the density of the concrete. By integrating multivariate data, comprehensive analysis can be performed to discover patterns and trends that single variable data cannot reveal. For example, the joint analysis of temperature and humidity can more accurately predict the moisture evaporation rate of concrete, and the joint analysis of humidity and wind speed can more accurately predict the heat loss rate.

[0124] In S304, the specific steps of adjusting the weight of sensor data according to the sensor distribution rule and signal strength include:

[0125] S304a, calculating an initial weight according to the position of the sensor;

[0126] Position importance weight: the edge position weight is 1.5, the center position weight is 1.5, the force point weight is 2, and the rest of the positions weight is 1;

[0127] S304b, according to the signal strength Si , adjust the weights;

[0128] Signal strength adjustment factor formula:

[0129] Among them, S i is the signal strength of the i-th sensor, T is the preset signal threshold, and the threshold is 1;

[0130] S304c, calculating the adjusted weight;

[0131] Adjusted weight formula: w′ i =w i ×α(S i );

[0132] Among them, w i is the initial weight, w′ i is the adjusted weight.

[0133] In S305, the specific steps of fusion include:

[0134] S305a, weighted average: performing weighted average on the future temperature prediction value obtained by time series analysis and the temperature field distribution obtained by spatial interpolation to generate the final temperature field model;

[0135] Weighted average formula: T final (s, t) = w1T time (t)+w2T space (s);

[0136] Among them, T final (s, t) is the final temperature field model, T time (t) is the temperature forecast value obtained by time series analysis, T space (s) is the temperature field distribution obtained by spatial interpolation, and w1 and w2 are weights.

[0137] By assigning different weights to sensors in different locations, we ensure that temperature data from key areas have a higher proportion in the model. By adjusting the weights of sensors with lower signal strength, we ensure the reliability of the data and the accuracy of the model. By calculating the adjusted weights, we ensure the reasonable distribution of data and the accuracy of the model.

[0138] According to the sensor distribution rules, the temperature of concrete and other data can be well detected. However, large-volume concrete has a large volume, and the signals of sensors buried deeper will be affected, which also increases the difficulty of construction. Therefore, according to the distribution rules, different weights are given to sensors in different positions. This is very important for temperature field construction and is more reasonable.

[0139] Example 3, please refer to Figure 2 , a mass concrete temperature field construction device, including the following modules:

[0140] Sensor module: including temperature sensor, humidity sensor, wind speed sensor, vibration sensor and pressure sensor, used to collect multivariate data inside the concrete;

[0141] Data acquisition module: used to collect sensor data in real time and transmit the data to the central processing system through wireless transmission equipment;

[0142] Central processing module: used to receive and process sensor data, perform time series analysis and spatial interpolation, and build temperature field models;

[0143] Storage module: used to store collected data and constructed temperature field models;

[0144] Display module: used to display the visualization results of the temperature field model.

[0145] In the present invention, unless otherwise specified or limited, the terms "installed," "connected," "connect," "fixed," etc. should be understood in a broad sense. For example, they can refer to fixed connection, detachable connection, or integration; mechanical connection, electrical connection, or communication; direct connection or indirect connection through an intermediate medium; internal communication between two elements or interaction between two elements, unless otherwise specified. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0146] Obviously, the embodiments described above are only some embodiments of the present invention, rather than all embodiments. The preferred embodiments of the present invention are given in the accompanying drawings, but they do not limit the patent scope of the present invention. The present invention can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present invention more thorough and comprehensive. Although the present invention has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions described in the aforementioned specific embodiments, or to make equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the present invention specification and drawings, directly or indirectly used in other related technical fields, is also within the scope of patent protection of the present invention.

Claims

1. A method for constructing a temperature field of large volume concrete, characterized in that: The following steps are involved: S100, setting sensor distribution rule: designing a sensor distribution rule according to the volume and shape of the concrete pouring; S200, embedding sensors: embedding corresponding sensors in concrete according to the sensor distribution rule to perform data detection; S300, constructing a temperature field model: transmitting data to a central processing system via a wireless transmission device, and calculating and constructing a temperature field model of a large volume of concrete based on the detected data; In S100, the sensor distribution rules specifically include: S101. Divide the concrete into several sub-areas according to its shape and size, with each sub-area having a size of 10 m*10 m*1 m; S102. Design a 5m*5m*0.5m three-dimensional grid in each sub-area, and place a temperature sensor, humidity sensor, vibration sensor, and pressure sensor at each grid node; S103. Add three additional temperature sensors, humidity sensors, and pressure sensors at each location, including the edge, center, and stress points of the concrete structure. S104. Add a temperature sensor and a humidity sensor in the middle of each layer of layered pouring; In areas where early-strength cement is used, add one temperature sensor and one humidity sensor for every 10 square meters; In areas where humidity exceeds 80%, add one humidity sensor for every 10 square meters; In areas where wind speed exceeds 5 m / s, add one wind speed sensor, temperature sensor, and humidity sensor for every 10 square meters; In S300, the specific steps of constructing the temperature field model include: S301, preprocessing the collected temperature, humidity, wind speed, vibration and pressure data, including data cleaning, filling missing values ​​and smoothing; S302, performing time series analysis on the preprocessed data, extracting time features, and predicting temperature values ​​at future time points; S303, performing spatial interpolation on the pre-processed data to generate a continuous temperature field distribution; S304, adjusting the weight of sensor data according to the sensor distribution rule and signal strength; S305 , fusing the future temperature prediction value obtained by time series analysis with the temperature field distribution obtained by spatial interpolation to generate a final temperature field model.

2. The method for constructing a temperature field of a large volume concrete according to claim 1, characterized in that: In S301, the specific steps of data preprocessing include: S301a, Data cleaning: Use statistical methods to identify and remove outliers; Outlier identification formula: ; in, is a data point, is the mean of the data, is the standard deviation of the data; S301b, fill missing values: use interpolation method to fill missing values; S301c, smoothing: using a filter to smooth the data and reduce noise; Moving average filter formula: in, is the window size, is a data point, is the smoothed data; S301d, Data Standardization: Standardize all data to the same scale to facilitate subsequent model training and prediction; Normalization formula: ; in, is a data point, and are the minimum and maximum values ​​of the data respectively.

3. The method for constructing a temperature field of a large volume concrete according to claim 2, characterized in that: In S302, the specific steps of time series analysis include: S302a, select the time series model: use the ARIMA model; ARIMA model formula: ; in, It's time The observed value of is a constant term, is the autoregressive coefficient, is the moving average coefficient, It is white noise; S302b, model training: using historical data to train a time series model; S302c, model prediction: Use the trained model to predict the temperature value at a certain point in the future.

4. The method for constructing a temperature field of a large volume concrete according to claim 3, characterized in that: In S303, the specific steps of spatial interpolation include: S303a, select interpolation method: use Kriging interpolation method; Kriging interpolation formula: ; in, is the interpolation point The estimated value of are known data points, is the weight, is the number of known data points; S303b, generating a mesh: generating a three-dimensional mesh according to the shape and size of the concrete; S303c, interpolation calculation: interpolate the discrete sensor data into the generated three-dimensional grid to generate a continuous temperature field distribution.

5. The method for constructing a temperature field of a large volume concrete according to claim 4, characterized in that: In S304, the specific steps of adjusting the weight of sensor data according to the sensor distribution rule and signal strength include: S304a, calculating an initial weight according to the position of the sensor; Position importance weight: the edge position weight is 1.5, the center position weight is 1.5, the force point weight is 2, and the rest of the positions weight is 1; S304b, according to signal strength , adjust the weights; Signal strength adjustment factor formula: ; in, It is The signal strength of each sensor, is the preset signal threshold, the threshold is 1; S304c, calculating the adjusted weight; Adjusted weight formula: ; in, is the initial weight, is the adjusted weight.

6. The method for constructing a temperature field of a large volume concrete according to claim 5, characterized in that: In S305, the specific steps of fusion include: S305a, weighted average: performing weighted average on the future temperature prediction value obtained by time series analysis and the temperature field distribution obtained by spatial interpolation to generate the final temperature field model; Weighted average formula: ; in, is the final temperature field model, is the temperature forecast value obtained by time series analysis, is the temperature field distribution obtained by spatial interpolation, and is the weight.

7. A mass concrete temperature field construction device, used to implement the mass concrete temperature field construction method according to claim 6, characterized in that: Includes the following modules: Sensor module: including temperature sensor, humidity sensor, wind speed sensor, vibration sensor and pressure sensor, used to collect multivariate data inside the concrete; Data acquisition module: used to collect sensor data in real time and transmit the data to the central processing system through wireless transmission equipment; Central processing module: used to receive and process sensor data, perform time series analysis and spatial interpolation, and build temperature field models; Storage module: used to store the collected data and the constructed temperature field model; Display module: used to display the visualization results of the temperature field model.

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