Temperature monitoring method and device for rockfill concrete integrated pouring dam body and medium

Abstract

The invention provides a rockfill concrete integrated pouring dam body temperature monitoring method. The method comprises the steps that before concrete is poured, a multivariate regression model of in-bin temperature field distribution of integrated pouring rockfill concrete under various conditions is built; obtaining related parameters of a to-be-monitored construction warehouse surface, setting a key characteristic moment in a hydration heat temperature rise process, and obtaining an in-warehouse temperature field distribution function of the to-be-monitored construction warehouse surface based on the multiple regression model; utilizing the function to predict the in-bin temperature of the construction bin surface to be monitored and the maximum value of the gradient of the in-bin temperature to obtain an in-bin temperature field distribution function meeting the construction requirements, selecting a plurality of points in the function as representative temperature measurement points, and embedding thermometers in the representative temperature measurement points; during cast-in-place concrete, dynamically updating the distribution function of the temperature field in the bin according to the monitoring result of each temperature measuring point; and monitoring the temperature in the warehouse according to the dynamically updated distribution function of the temperature field in the warehouse. According to the invention, the cracking risk of the dam caused by abnormal temperature or sudden environment change can be obviously reduced.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of large-volume concrete, and in particular relates to a temperature monitoring method, device and medium for an integrated rockfill concrete pouring dam body. Background Art

[0002] Rockfill concrete is a new dam-building technology independently developed in my country. By reducing cement usage and introducing large amounts of rockfill, it effectively lowers the temperature of the dam concrete, alleviating the problem of cracking in concrete dams. Integrated casting, a rapid construction method that integrally casts an impermeable or impact-resistant layer with the rockfill concrete, significantly speeds up the construction of complex rockfill concrete dams.

[0003] However, due to the introduction of a large amount of rockfill, the temperature variation within the rockfill concrete initially exhibited significant heterogeneity. The integrated casting process also resulted in high local temperature rises and large stress gradients. Furthermore, the large local temperature gradients and strong heterogeneity of the temperature and temperature gradient fields made temperature control and crack prevention and early warning for integrated rockfill concrete dams challenging. These characteristics and challenges are particularly prominent in high-altitude, cold regions with large ambient temperature fluctuations.

[0004] At present, the temperature monitoring and control methods for conventional concrete dams are relatively mature, but they do not take into account the characteristics of the heterogeneous temperature distribution inside and outside the rockfill concrete dam. The monitoring points and methods lack representativeness and cannot effectively reflect the temperature changes and distribution patterns inside the rockfill concrete dam. Therefore, (1) how to select representative temperature monitoring points, (2) how to infer the temperature field and temperature gradient field of the impact and wear-resistant layer and the dam body rockfill concrete based on the monitoring results of representative measurement points, and (3) how to carry out early warning and alarm of temperature control and crack prevention based on the results of preliminary on-site testing have become urgent problems to be solved. Summary of the Invention

[0005] The present disclosure aims to solve at least one of the technical problems in the related art.

[0006] To this end, the present invention provides a temperature monitoring method, device and medium for an integrated rockfill concrete dam body. The method, device and medium reflect the actual temperature rise of the impact-resistant and wear-resistant layer and the dam body rockfill concrete during storage and hydration heat based on the measured data of several representative measuring points, infer the temperature field changes and distribution patterns of the overall structure, and based on this, provide temperature early warning and alarm, and provide feedback to guide the on-site implementation of necessary and reasonable temperature control and crack prevention measures, effectively reducing the risk of dam body cracking, improving the quality of dam construction, and ensuring the safe and stable operation of the dam.

[0007] In order to achieve the above objectives, the present disclosure adopts the following technical solutions:

[0008] A first aspect of the present disclosure provides a method for monitoring the temperature of a rockfill concrete integrated casting dam body, comprising:

[0009] Before pouring concrete, based on the design data of different dam bodies and historical field data, an inverse analysis of the finite element non-homogeneous temperature stress simulation model and a batch parameter sensitivity analysis were performed to construct a multivariate regression model for the temperature field distribution in the silo of integrated rockfill concrete pouring under different concrete parameters, rockfill parameters, and environmental parameters.

[0010] Acquire concrete parameters, rockfill parameters, and environmental parameters of the construction chamber surface of the integrated rockfill concrete to be monitored, set key characteristic moments in the hydration heat temperature rise process, and bring them into the multivariate regression model to obtain the temperature field distribution function of the construction chamber surface to be monitored at the key characteristic moments;

[0011] The maximum values ​​of the in-bin temperature and its gradient at key characteristic moments during the hydration heat temperature rise process of the concrete on the monitored construction bin surface are predicted using the in-bin temperature field distribution function. When the predicted value exceeds the construction requirements, an early warning is issued and the proposed on-site temperature control measures or pouring plan are adjusted to obtain adjusted concrete parameters, rockfill parameters, and environmental parameters. The in-bin temperature field distribution function is updated using the adjusted parameters, and the maximum values ​​of the in-bin temperature and its gradient are continuously predicted until the predicted value meets the construction requirements. Several points in the in-bin temperature field distribution function are selected as representative temperature measurement points, and thermometers are respectively embedded in the bin at positions corresponding to the representative temperature measurement points.

[0012] During the on-site concrete pouring process, the temperature field distribution function in the warehouse is dynamically updated according to the monitoring results of each representative temperature measurement point at the key characteristic moment to obtain the temperature field distribution function in the warehouse based on the on-site measured data;

[0013] According to the temperature field distribution function in the silo based on the field measured data, it is judged whether the maximum temperature and temperature gradient of the concrete in the silo meet the construction requirements at different times; if not, an alarm is issued to remind the site to take temperature control measures.

[0014] In some embodiments, the design data include geometric parameters, concrete parameters and rockfill parameters of each bin surface of the dam body. The geometric parameters include the length, width and thickness of the bin surface and the thickness of the anti-seepage layer. The concrete parameters include the adiabatic temperature rise parameters, thermal conductivity, specific heat capacity and linear expansion coefficient of various types of concrete in the bin. The rockfill parameters include the rockfill surface temperature and rockfill rate. The historical measured data on site include the concrete entry temperature, environmental parameters and dam body temperature. The environmental parameters include solar thermal radiation, ambient temperature, ambient humidity and wind speed.

[0015] In some embodiments, the multivariate regression model is T(ti,x,z,P en ,P con ,P rock ):

[0016]

[0017] Among them, ti is the key characteristic moment in the hydration heat temperature rise process; the upstream and downstream directions of the dam body, the dam axis direction, and the plumb bob height direction are defined as the x-axis, y-axis, and z-axis respectively; the coordinate origin O is the center point of the intersection line of the bottom surface of the silo and the upstream surface along the dam axis direction; x and z are the distances to the upstream surface and the distance to the bottom of the silo in space, respectively; T0 is the temperature of the concrete area on the upstream surface of the anti-seepage layer, T p is the peak temperature of the concrete in the impermeable layer, T s is the stable temperature value of the rockfill concrete area in the warehouse, and is also the asymptotic value of the temperature in the upstream and downstream directions in space; x p T p The distance from the corresponding position to the upstream surface, x s T s The distance from the corresponding position to the upstream surface, β and n describe the temperature drop rate in the upstream and downstream directions near the interface between the anti-seepage layer and the rockfill concrete; and T0, T p 、T s 、x p 、x s The mapping relationship between P and input parameters; con 、P rock and P en They are concrete parameter set, rockfill parameter set and environmental parameter set, and the parameters they contain are:

[0018]

[0019] Among them, the environmental parameter set P en The parameter Q in s 、T A , RH, U w They are solar thermal radiation, ambient temperature, ambient humidity and wind speed respectively, and the concrete parameter set P con Parameters in λ j , κ j , α j They are the entry temperature, final adiabatic temperature rise, adiabatic temperature rise single exponential model parameters, thermal conductivity, specific heat capacity and linear expansion coefficient of the j-th type of concrete in the silo, and the rockfill parameter set P rock Parameter T in r ,λ r , κr , α r , Rv are the surface temperature, thermal conductivity, specific heat capacity, linear expansion coefficient and average rockfill rate of the rockfill, respectively.

[0020] In some embodiments, the multivariate regression model satisfies the requirement of continuous differentiability in the entire space, and only considers the temperature changes along the upstream and downstream directions of the dam body and the plumb bob height direction in terms of spatial position.

[0021] In some embodiments, the key characteristic moments ti in the hydration heat temperature rise process include the peak time point t1 of the concrete temperature in the warehouse and the stable time point t2 of the concrete temperature field in the warehouse; the environmental parameter set P en , concrete parameter set P con and rockfill parameter set P rock Bring into the multivariate regression model T(ti,x,z,P en ,P con ,P rock ), the temperature field distribution function of the construction warehouse to be monitored along the upstream and downstream directions and the plumb height direction at the key characteristic moment is obtained as follows:

[0022]

[0023] Among them, T ti (x, z) is the temperature field distribution function of the construction warehouse to be monitored along the upstream and downstream directions and the plumb height direction at the key characteristic moment, and T0, T p 、T s 、x p 、x s The mapping relationship between the input parameters.

[0024] In some embodiments, the temperature control measures include adding ice to mix and controlling the temperature of concrete out of the machine, heating and cooling raw materials, and heating and cooling rockfill materials. Adjusting the pouring plan is to adjust the pouring time period.

[0025] In some embodiments, the selected representative temperature measurement points include at least a first representative measurement point P0, a second representative measurement point P1, and a third representative measurement point P2.

[0026] The first representative measuring point P0 is the position corresponding to the temperature T0 of the concrete area on the upstream surface of the dam body anti-seepage layer. It is usually located near the surface of the upstream anti-seepage layer or the center of the impact and wear-resistant layer concrete or the warehouse surface;

[0027] The second representative measuring point P1 is the peak temperature T of the concrete of the dam body anti-seepage layer. p The corresponding position It is usually located at the junction of the anti-seepage layer or the anti-impact and wear-resistant layer and the rockfill concrete or near the center of the warehouse surface;

[0028] The third representative measuring point P2 is the stable temperature of the rockfill concrete in the warehouse T s The corresponding position

[0029] In some embodiments, the T corresponding to the third representative temperature measurement point P2 is calculated using the following formula: s Make corrections:

[0030] T s =C(Rv,Rv local )*T s *

[0031] Among them, T s * is the monitoring value measured by the thermometer buried at the third representative temperature measuring point P2, C(Rv, Rv local ) is the correction coefficient, which is related to the average rockfill rate Rv and the local rockfill rate Rv local Related functions.

[0032] A second aspect of the present disclosure provides a temperature monitoring device for an integrated rockfill concrete dam, comprising:

[0033] The first module is used to perform inverse analysis and batch parameter sensitivity analysis on the finite element non-homogeneous temperature stress simulation model before concrete pouring, based on the design data of different dam bodies and historical field measurement data. It also constructs a multivariate regression model for the temperature field distribution in the silo of integrated rockfill concrete pouring under different concrete parameters, rockfill parameters, and environmental parameters.

[0034] The second module is used to obtain the concrete parameters, rockfill parameters, and environmental parameters of the construction chamber surface of the integrated rockfill concrete to be monitored, and set the key characteristic moments in the hydration heat temperature rise process, bring them into the multivariate regression model, and obtain the temperature field distribution function of the construction chamber surface to be monitored at the key characteristic moments;

[0035] The third module is used to predict the maximum value of the in-storage temperature and its gradient at each key characteristic moment during the hydration heat temperature rise process of the concrete on the monitored construction warehouse surface using the in-storage temperature field distribution function. When the predicted value exceeds the construction requirements, an early warning is issued and the proposed on-site temperature control measures or pouring plan are adjusted to obtain adjusted concrete parameters, rockfill parameters, and environmental parameters. The in-storage temperature field distribution function is updated using the adjusted parameters, and the maximum value of the in-storage temperature and its gradient is continuously predicted until the predicted value meets the construction requirements. Several points in the in-storage temperature field distribution function are then selected as representative temperature measurement points, and thermometers are respectively embedded in the warehouse at positions corresponding to the representative temperature measurement points.

[0036] The fourth module is used to dynamically update the temperature field distribution function in the warehouse according to the monitoring results of each representative temperature measurement point at the key characteristic moment during the on-site concrete pouring process, so as to obtain the temperature field distribution function in the warehouse based on the on-site measured data;

[0037] The fifth module is used to determine whether the maximum temperature and temperature gradient of the concrete in the warehouse meet the construction requirements at different times based on the temperature field distribution function in the warehouse based on the on-site measured data; if not, an alarm is issued to remind the site to take temperature control measures.

[0038] A third aspect of the present disclosure provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the dam body temperature monitoring method according to any embodiment of the first aspect of the present disclosure.

[0039] Compared with the prior art, the present invention has the following characteristics and beneficial effects:

[0040] (1) Based on a large amount of numerical calculation results and field measurement data, the functional relationship between the temperature distribution and temperature gradient distribution in the bin under the condition of integrated rockfill concrete pouring is given, which solves the problem of estimating the temperature field and temperature gradient field in the bin and the problem of simplified expression. It has a strong guiding significance for the selection and design of dam temperature control and crack prevention measures in engineering practice.

[0041] (2) Based on the above functional relationship, the positions of representative temperature measuring points (including the point with the maximum temperature in the bin, the point with the maximum temperature gradient in the bin, and the point with the stable temperature of the anti-seepage layer and the dam body rockfill concrete under the stable temperature field) can be deduced and the temperature measuring points can be arranged. This solves the problem of selecting the positions of representative temperature measuring points in the integrated rockfill concrete bin and provides suggestions and guidance for on-site monitoring of the dam body temperature.

[0042] (3) Based on the monitoring results of representative temperature measurement points, the parameters in the above expressions can be dynamically updated to obtain more accurate temperature fields and temperature gradient fields. This solves the problem of optimizing the estimation results of the temperature and temperature gradient field in the warehouse based on on-site measured data, and provides a more accurate decision-making basis for the anti-freeze temperature control of the on-site dam.

[0043] (4) Based on the estimated results of the temperature and temperature gradient distribution in the warehouse integrated with the field measured data, and in accordance with the anti-cracking temperature control requirements in the engineering construction plan, a warning and alarm method for the temperature and temperature gradient distribution in the warehouse is given, which solves the problem of temperature control and anti-cracking warning and alarm for integrated cast rockfill concrete, and reduces the risk of cracking of the dam due to abnormal temperature or sudden environmental changes.

[0044] In summary, the present invention can effectively solve the problems of estimating the temperature and temperature gradient distribution in the warehouse, solving the location of representative temperature measuring points, improving the estimation accuracy of the temperature field and temperature gradient field in the warehouse, temperature control and crack prevention warning and alarm, etc., guides the layout of on-site temperature measuring points, provides an effective basis for the design and selection of dam anti-freeze and temperature control measures, and significantly reduces the risk of dam cracking caused by temperature anomalies or sudden environmental changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is an overall flow chart of a temperature monitoring method for a rockfill concrete integrated casting dam body provided by an embodiment of the first aspect of the present disclosure.

[0046] Figure 2 It is a schematic diagram of the warehouse surface coordinate system involved in the multivariate regression model constructed by the temperature monitoring method provided in the embodiment of the first aspect of the present disclosure.

[0047] Figure 3 The temperature monitoring method provided by the embodiment of the first aspect of the present disclosure is applied to the test results of a construction warehouse in Dongzhuang, wherein (a) is the constructed non-homogeneous finite element temperature simulation model and its inversion analysis results, and (b) is the comparison result of the inversion analysis and the temperature field predicted by formula (5).

[0048] Figure 4 Schematic diagram of the position coordinates of representative temperature measurement points calculated according to an embodiment of the present disclosure.

[0049] Figure 5 It is a structural diagram of an electronic device provided in an embodiment of the third aspect of the present disclosure. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of this application more clearly understood, this application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0051] On the contrary, this application covers any alternatives, modifications, equivalents, and solutions made within the spirit and scope of this application as defined by the claims. Furthermore, to facilitate a better understanding of this application, certain specific details are described in detail below in the detailed description of this application. Those skilled in the art will be able to fully understand this application without these details.

[0052] See also Figure 1 In a first aspect of the present disclosure, an embodiment provides a method for monitoring the temperature of a dam body during integrated rockfill concrete casting. Integrated rockfill concrete casting refers to a rapid construction method in which an impermeable layer or an impact-resistant and wear-resistant layer is integrally cast with the dam body rockfill concrete. The internal structure includes the impermeable layer and the dam body rockfill concrete. The temperature monitoring method of this embodiment includes the following steps:

[0053] S1. Before pouring concrete, based on the design data of different dam bodies and historical on-site measured data, a finite element non-homogeneous temperature stress simulation model was constructed through inverse analysis and its model parameters were determined. Based on this simulation model, a batch parameter sensitivity analysis was conducted to construct a multivariate regression model for the temperature field distribution in the silo of integrated rockfill concrete pouring under different concrete parameters, rockfill parameters, and environmental parameters.

[0054] S2. Acquire concrete parameters, rockfill parameters, and environmental parameters of the construction chamber surface of the integrated rockfill concrete to be monitored, set key characteristic moments in the hydration heat temperature rise process, and apply these parameters to the multivariate regression model constructed in step S1 to obtain the temperature field distribution function within the chamber surface to be monitored at the key characteristic moments.

[0055] S3. Based on the in-bin temperature field distribution function obtained in step S2, the maximum values ​​of the in-bin temperature and its gradient at each key characteristic moment during the hydration heat temperature rise process of the concrete on the monitored construction bin surface are predicted. When the predicted value exceeds the threshold value specified in the construction requirements, the proposed on-site temperature control measures or pouring plan need to be adjusted to obtain adjusted concrete parameters, rockfill parameters, and environmental parameters. The in-bin temperature field distribution function is updated using the adjusted parameters, and the maximum values ​​of the in-bin temperature and its gradient are continuously predicted until the predicted value is less than the threshold value specified in the construction requirements. Several points in the resulting in-bin temperature field distribution function are selected as representative temperature measurement points, and thermometers are respectively embedded in the bin at positions corresponding to the representative temperature measurement points.

[0056] S4, pouring concrete on site, dynamically updating the in-warehouse temperature field distribution function finally obtained in step S3 according to the monitoring results of each representative temperature measurement point at the key characteristic moment, to obtain the in-warehouse temperature field distribution function based on the on-site measured data;

[0057] S5. According to the temperature field distribution function in the silo based on the field measured data obtained in step S4, determine whether the maximum temperature and temperature gradient of the concrete in the silo meet the construction requirements at different times; if not, issue an alarm in time to remind the site to take temperature control measures.

[0058] In some embodiments, in step S1, the finite element heterogeneous temperature stress simulation model is constructed using design data and historical field data. The design data includes geometric parameters, concrete parameters, and rockfill parameters of each dam body compartment. Geometric parameters include the length, width, and thickness of the compartment, as well as the thickness of the anti-seepage layer. Concrete parameters include the adiabatic temperature rise parameters, thermal conductivity, specific heat capacity, and linear expansion coefficient of each type of concrete in the compartment. Rockfill parameters include the rockfill surface temperature and rockfill ratio. Historical field data include concrete entry temperature, environmental parameters, and dam body temperature. Environmental parameters include solar thermal radiation, ambient temperature, ambient humidity, and wind speed.

[0059] In some embodiments, see Figure 2 In step S1, the plane where the warehouse surface is located is taken as the xy plane, and the x-axis is set along the upstream and downstream directions of the dam body in the warehouse, with the positive direction pointing downstream; the y-axis is set along the axis of the dam body in the warehouse, with the positive direction pointing to the right bank; the height direction of the plumb bob is taken as the z-axis, with the positive direction vertically upward; the center point of the intersection of the bottom surface of the warehouse surface and the upstream surface of the dam body along the axis of the dam body is taken as the coordinate origin O, thereby constructing the warehouse surface coordinate system O-xyz. Based on the heterogeneous finite element temperature stress simulation model, inverse analysis and batch parameter sensitivity analysis are carried out; finally, based on the numerical analysis results, a multivariate regression model of the temperature field distribution in the warehouse under different concrete parameters, rockfill parameters, and environmental parameters is constructed. The mathematical expression of this model is:

[0060]

[0061] Among them, ti is the key characteristic moment, including the time point when the concrete temperature in the bin reaches the peak and the time point when the concrete temperature field in the bin stabilizes; the upstream and downstream directions of the dam body, the dam axis direction, and the plumb bob height direction are defined as the x-axis (positive direction pointing downstream), the y-axis (positive direction pointing to the right bank), and the z-axis (positive direction vertically upward), respectively. The coordinate origin O is the center point of the intersection line of the bottom surface of the bin and the upstream surface along the dam axis direction, and x and z are the spatial distances from the upstream surface and the distance from the bottom of the bin, respectively. T0 is the temperature of the concrete area on the upstream surface of the anti-seepage layer, T p is the peak temperature of the concrete in the impermeable layer, T s is the stable temperature value of the rockfill concrete area in the warehouse, and is also the asymptotic value of the temperature in the upstream and downstream directions in space; x p T p The distance from the corresponding position to the upstream surface, x s The temperature decreases along the x-axis to Ts The inflection point position, that is, T s The distance from the corresponding position to the upstream surface; β and n describe the temperature drop rate in the upstream and downstream directions near the interface between the anti-seepage layer and the rockfill concrete. β is usually related to the "steepness" or "rate" of the transition region. A larger β value will make the transition region steeper, that is, the physical quantity changes from T p Change to T s The speed of the transition is faster, and a smaller β value will make the transition region smoother; n determines the degree of curvature of the curve near the peak. A larger n value will make the curve more curved near the peak (that is, more "rounded" but sharp), while a smaller n value will make the curve more linear. and T0, T p 、T s 、x p 、x s With the input parameters (ie ti, z, P en ,P con ,P rock ) mapping relationship between them; con 、P rock and P en They are concrete parameter set, rockfill parameter set and environmental parameter set. Generally, the parameters they contain are:

[0062]

[0063] Among them, the environmental parameter set P en The parameter Q in s 、T A , RH, U w They are solar thermal radiation, ambient temperature, ambient humidity and wind speed respectively, and the concrete parameter set P con Parameters in λ j , κ j , α j They are the entry temperature, final adiabatic temperature rise, adiabatic temperature rise single exponential model parameters, thermal conductivity, specific heat capacity and linear expansion coefficient of the j-th type of concrete in the silo, and the rockfill parameter set P rock Parameter T in r ,λ r , κ r , α r , Rv are the surface temperature, thermal conductivity, specific heat capacity, linear expansion coefficient and average rockfill rate of the rockfill, respectively.

[0064] Furthermore, the multivariate regression model of the temperature field distribution in the warehouse constructed in the embodiment of the present disclosure must be continuous and differentiable in the entire space (including x p), from which the temperature and temperature gradient value at any point in the warehouse can be calculated; in addition, the multiple regression model takes into account the changes in temperature along the upstream and downstream directions of the dam body (i.e., the x-axis direction) and the plumb bob height direction (i.e., the z-axis direction) in terms of spatial position, but does not consider the changes along the dam body axis direction (i.e., the y-axis direction). This is because according to the field measurement data and simulation analysis results, along the dam axis direction, except for the edges at both ends (1 to 2 meters away from the boundary), the temperature gradient of the dam body concrete temperature in this direction is small, and its changes in this direction can be ignored.

[0065] In some embodiments, in step S2, the environmental parameters of the construction chamber surface of the integrated rockfill concrete to be monitored include solar thermal radiation Q s 、Ambient temperature T A , ambient humidity RH and wind speed U w , concrete parameters include the temperature of each type of concrete entering the warehouse Final adiabatic temperature rise Adiabatic temperature rise single exponential model parameters Thermal conductivity λ j , specific heat capacity κ j and linear expansion coefficient α j , rockfill parameters include the surface temperature T r , thermal conductivity λ r , specific heat capacity κ r , linear expansion coefficient α r and the average rockfill rate Rv. Subsequent on-site monitoring can be used to obtain the measured solar thermal radiation Q s * 、Ambient temperature T A * , ambient humidity RH * and wind speed U w * , Warehouse temperature The surface temperature of the rockfill is T r * and average rockfill rate Rv * All can be obtained through on-site monitoring data. Other parameter values ​​( λ j , κ j , α j ,λ r , κ r , α r) can be determined based on the parameter inversion in S1. The key characteristic moment ti of the monitored construction warehouse surface during the hydration heat temperature rise process can generally be taken as the time point t1 when the concrete temperature in the warehouse reaches the peak and the time point t2 when the concrete temperature field in the warehouse stabilizes; the former is the moment when the temperature peak in the warehouse is the largest, and the latter is the moment when the temperature field in the warehouse stabilizes. Substituting the above parameters into formula (1), the distribution function T of the temperature field in the warehouse of the monitored construction warehouse surface at the key characteristic moment along the upstream and downstream directions and the plumb bob height direction can be obtained. ti (x,z), as shown in formula (3), we can then determine any point (x k ,y k ) temperature gradient on Temperature gradient The calculation formula is as follows:

[0066]

[0067] in, and T0, T p 、T s 、x p 、x s The mapping relationship between and the input parameter z.

[0068] In some embodiments, in step S3, the temperature field distribution function in the warehouse obtained in step S2 is used to predict the maximum temperature or the maximum temperature gradient corresponding to each key characteristic moment (in this embodiment, the time point t1 when the temperature of the concrete in the warehouse reaches the peak and the time point t2 when the temperature field of the concrete in the warehouse stabilizes) during the hydration heat temperature rise process of the concrete on the construction warehouse surface to be monitored. When the predicted maximum temperature exceeds the temperature limit threshold T max Or the predicted maximum temperature gradient exceeds the temperature gradient limit threshold When the temperature reaches a certain level, a timely warning is required to remind the site to adjust the proposed temperature control measures or pouring plan. Among them, temperature control measures include controlling the concrete outlet temperature by adding ice to mix, heating and cooling raw materials, and heating and cooling rockfill materials. Adjusting the pouring plan mainly involves adjusting the pouring time period. Based on the adjusted temperature control measures or pouring plan, the concrete parameters, rockfill parameters, and environmental parameters of the construction warehouse surface are re-acquired, and the warehouse temperature field distribution function is updated according to the operation of step S2. The updated warehouse temperature field distribution function is used to continue predicting the maximum value of the warehouse temperature and its gradient until the predicted temperature and the maximum value of the temperature gradient meet the construction requirements. Then, several points in the final warehouse temperature field distribution function are selected as representative temperature measurement points, and thermometers are embedded in the warehouse at the positions corresponding to each representative temperature measurement point.

[0069] Furthermore, the number of representative temperature measurement points selected is at least 3, which are respectively recorded as the first representative measurement point P0, the second representative measurement point P1 and the third representative measurement point P2. Each representative temperature measurement point can be calculated according to formula (3), where:

[0070] The first representative measuring point P0 is the position corresponding to the temperature T0 of the concrete area on the upstream surface of the dam body anti-seepage layer. It is usually located near the surface of the upstream anti-seepage layer or the impact and wear-resistant layer concrete or the center of the warehouse surface. Its temperature monitoring value T0 * Represents the current size of T0;

[0071] The second representative measuring point P1 is the peak temperature T of the concrete of the dam body anti-seepage layer p The corresponding position It is usually located at the junction of the anti-seepage layer or the anti-impact and wear-resistant layer and the rockfill concrete or near the center of the warehouse surface. Its temperature monitoring value T p * Represents the current T p size;

[0072] The third representative measuring point P2 is the stable temperature of the rockfill concrete in the warehouse T s The corresponding position Generally, it is located at the center of the rockfill voids 3 to 5 times the thickness of the anti-seepage layer or the anti-impact and wear-resistant layer upstream of the dam body or near the center of the warehouse surface. The temperature monitoring value T s * Represents the current T s It should be noted that the third representative temperature measurement point P2 corresponds to T s The value of the local rockfill ratio Rv local Therefore, T s The correction value considering the local rockfill rate is:

[0073] T s =C(Rv,Rv local )*T s * (4)

[0074] Among them, T s * is the monitoring value measured by the thermometer buried at the third representative temperature measuring point P2, C(Rv, Rv local ) is the correction factor, which is related to Rv and Rv local The related functions can be obtained through indoor tests or numerical simulations; the local rockfill rate Rv local It can be calculated based on the existing image recognition algorithm according to the rock pile photos near the representative temperature measurement point P2.

[0075] In some embodiments, in step S4, first, the environmental parameter P measured on site is en* , concrete parameters P con* and rockfill parameter P rock* Substituting formula (1) into formula (3) can obtain the formula that meets the on-site conditions; then, based on the coordinates of the representative measuring points Compared with the measured temperature data T0 * 、T p * 、T s * Substituting into formula (3) can realize T ti Dynamic update of (x, z) to form the temperature field distribution function T in the warehouse based on the field measured data ti * (x, z), correct the deviation between the predicted value and the actual value, realize the accurate reconstruction of the actual temperature field, and then provide alarm feedback that conforms to the actual situation on site.

[0076] It should be noted that the temperature monitoring method proposed in the embodiment of the present disclosure is also applicable to other types of mass concrete in addition to rockfill concrete.

[0077] In order to verify the effectiveness of the temperature monitoring method provided by the embodiment of the first aspect of the present disclosure, the temperature simulation results of a construction warehouse in Dongzhuang during the construction period are used as an example. The constructed heterogeneous finite element temperature simulation model and its inverse analysis results are shown in Figure 3 In (a), the environmental parameter P obtained by field measurement and inversion analysis is input into formula (1): en* , concrete parameters P con* and rockfill parameter P rock* , the temperature field distribution function of the concrete temperature field in this warehouse after the stable time point t2 is obtained as shown in formula (5):

[0078]

[0079] The temperature distribution curve obtained by calculating the temperature field of the concrete in the silo at the stable time point t2 using the existing finite element model FEM (referred to as FEM in the figure) and the temperature distribution curve obtained by formula (5) in the embodiment of the present disclosure (referred to as Eq in the figure) are shown as follows: Figure 3 As shown in (b). Figure 3 As can be seen from (b), the multivariate regression model shown in formula (5) constructed in the embodiment of the present disclosure predicts the temperature field distribution well, which is basically consistent with the calculation results of the finite element model.

[0080] The calculated maximum temperature and temperature gradient meet the construction requirements; then, the position coordinates of the representative temperature measurement points can be calculated according to formula (5), see Figure 4 , the expression is as follows:

[0081]

[0082] After burying the measuring points at the designated locations on site, the T ti (x,z) is dynamically updated and an alarm is issued.

[0083] It can be seen that the embodiments of the present disclosure can effectively solve the problems of estimating the temperature and temperature gradient distribution in the warehouse, solving the positions of representative temperature measurement points, improving the estimation accuracy of the temperature field and temperature gradient field in the warehouse, temperature control and crack prevention warning and alarm, etc., and guide the layout of on-site temperature measurement points, provide an effective basis for the design and selection of dam anti-freeze and temperature control measures, and significantly reduce the risk of dam cracking due to temperature anomalies or sudden environmental changes.

[0084] A second embodiment of the present disclosure provides a temperature monitoring device for an integrated rockfill concrete dam, comprising:

[0085] The first module is used to perform inverse analysis and batch parameter sensitivity analysis on the finite element non-homogeneous temperature stress simulation model before concrete pouring, based on the design data of different dam bodies and historical field measurement data. It also constructs a multivariate regression model for the temperature field distribution in the silo of integrated rockfill concrete pouring under different concrete parameters, rockfill parameters, and environmental parameters.

[0086] The second module is used to obtain the concrete parameters, rockfill parameters, and environmental parameters of the construction chamber surface of the integrated rockfill concrete to be monitored, and set the key characteristic moments in the hydration heat temperature rise process, bring them into the multivariate regression model, and obtain the temperature field distribution function of the construction chamber surface to be monitored at the key characteristic moments;

[0087] The third module is used to predict the maximum value of the in-storage temperature and its gradient at each key characteristic moment during the hydration heat temperature rise process of the concrete on the monitored construction warehouse surface using the in-storage temperature field distribution function. When the predicted value exceeds the construction requirements, it is necessary to adjust the proposed on-site temperature control measures or pouring plan to obtain adjusted concrete parameters, rockfill parameters, and environmental parameters. The in-storage temperature field distribution function is updated using the adjusted parameters, and the maximum value of the in-storage temperature and its gradient is continuously predicted until the predicted value meets the construction requirements. Several points in the in-storage temperature field distribution function are then selected as representative temperature measurement points, and thermometers are respectively embedded in the warehouse at positions corresponding to the representative temperature measurement points.

[0088] The fourth module is used to dynamically update the temperature field distribution function in the warehouse according to the monitoring results of each representative temperature measurement point at the key characteristic moment during the on-site concrete pouring process, so as to obtain the temperature field distribution function in the warehouse based on the on-site measured data;

[0089] The fifth module is used to determine whether the maximum temperature and temperature gradient of the concrete in the warehouse meet the construction requirements at different times based on the temperature field distribution function in the warehouse based on the on-site measured data; if not, an alarm is issued to remind the site to take temperature control measures.

[0090] It should be noted that the above explanations of the embodiment of the dam body temperature monitoring method are also applicable to the dam body temperature monitoring device of this embodiment, and will not be repeated here.

[0091] In order to implement the above embodiment, the embodiment of the present disclosure further proposes a computer-readable storage medium on which a computer program is stored. The program is executed by a processor to execute the dam body temperature monitoring method of the above embodiment.

[0092] Reference below Figure 5 , which shows a schematic structural diagram of an electronic device suitable for implementing the embodiments of the present disclosure. It should be noted that the electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs, desktop computers, and servers. Figure 5 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0093] like Figure 5 As shown, the electronic device may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 101, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 102 or a program loaded from a storage device 108 into a random access memory (RAM) 103. Various programs and data required for the operation of the electronic device are also stored in the RAM 103. The processing device 101, the ROM 102, and the RAM 103 are connected to each other via a bus 104. An input / output (I / O) interface 105 is also connected to the bus 104.

[0094] Typically, the following devices may be connected to the I / O interface 105: an input device 106 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, etc.; an output device 107 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 108 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 109. The communication device 109 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Figure 5The electronic device is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead.

[0095] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, this embodiment includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication device 109, or installed from the storage device 108, or installed from the ROM 102. When the computer program is executed by the processing device 101, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed.

[0096] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0097] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0098] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the dam body temperature monitoring method.

[0099] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, Python, and conventional procedural programming languages ​​such as "C-" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0100] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0101] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0102] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0103] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection having one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing it in a suitable manner if necessary, and then storing it in a computer memory.

[0104] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0105] Those skilled in the art will understand that all or part of the steps carried out in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the developed program can be stored in a computer-readable storage medium. When the program is executed, it includes one of the steps of the method embodiment or a combination thereof.

[0106] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0107] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for monitoring the temperature of a rockfill concrete integrated dam, characterized in that: include: Before pouring concrete, based on the design data of different dam bodies and historical measured data on site, the finite element non-homogeneous temperature stress simulation model was subjected to inverse analysis and batch parameter sensitivity analysis to construct a multivariate regression model for the temperature field distribution in the silo of integrated rockfill concrete pouring under different concrete parameters, rockfill parameters, and environmental parameters. Acquire concrete parameters, rockfill parameters and environmental parameters of the construction warehouse surface of the integrated rockfill concrete to be monitored, set key characteristic moments in the hydration heat temperature rise process, bring them into the multivariate regression model, and obtain the warehouse temperature field distribution function of the construction warehouse surface to be monitored at the key characteristic moments; The maximum values ​​of the temperature in the warehouse and its gradient at each key characteristic moment during the hydration heat temperature rise process of the concrete on the monitored construction warehouse surface are predicted by using the temperature field distribution function in the warehouse. When the predicted value exceeds the construction requirement, an early warning is issued and the proposed on-site temperature control measures or pouring plan are adjusted to obtain adjusted concrete parameters, rockfill parameters and environmental parameters. The temperature field distribution function in the warehouse is updated by using the adjusted parameters, and the maximum values ​​of the temperature in the warehouse and its gradient are continuously predicted until the predicted value meets the construction requirement. Then, several points in the temperature field distribution function in the warehouse are selected as representative temperature measurement points, and thermometers are respectively buried at positions corresponding to the representative temperature measurement points in the warehouse. During the on-site concrete pouring process, the temperature field distribution function in the warehouse is dynamically updated according to the monitoring results of each representative temperature measuring point at the key characteristic moment to obtain the temperature field distribution function in the warehouse based on the on-site measured data; According to the temperature field distribution function in the warehouse based on the field measured data, it is judged whether the maximum temperature and temperature gradient of the concrete in the warehouse meet the construction requirements at different times; if not, an alarm is issued to remind the site to take temperature control measures.

2. The dam body temperature monitoring method according to claim 1, characterized in that: The design data include geometric parameters, concrete parameters and rockfill parameters of each bin surface of the dam body. The geometric parameters include the length, width and thickness of the bin surface and the thickness of the anti-seepage layer. The concrete parameters include the adiabatic temperature rise parameters, thermal conductivity, specific heat capacity and linear expansion coefficient of various types of concrete in the bin. The rockfill parameters include the rockfill surface temperature and rockfill rate. The historical measured data on site include the concrete entry temperature, environmental parameters and dam body temperature. The environmental parameters include solar thermal radiation, ambient temperature, ambient humidity and wind speed.

3. The dam body temperature monitoring method according to claim 1 is characterized in that The multivariate regression model is T(ti,x,z,P en ,P con ,P rock ): Among them, ti is the key characteristic moment in the process of hydration heat temperature rise; the upstream and downstream directions of the dam body, the dam axis direction, and the plumb height direction are defined as the x-axis, y-axis, and z-axis respectively, the coordinate origin O is the center point of the intersection line between the bottom surface of the warehouse surface and the upstream surface along the dam axis direction, and x and z are the distance to the upstream surface and the distance to the bottom of the warehouse surface in space respectively; T0 is the temperature of the concrete area on the upstream surface of the anti-seepage layer, T p is the peak temperature of the concrete in the impermeable layer, T s is the stable temperature value of the rockfill concrete area in the warehouse, and is also the asymptotic value of the temperature in the upstream and downstream directions in space; x p T p The distance from the corresponding position to the upstream surface, x s T s The distance from the corresponding position to the upstream surface, β and n describe the temperature drop rate in the upstream and downstream directions near the interface between the impermeable layer and the rockfill concrete; and T0, T p , T s 、x p 、x s The mapping relationship between P and input parameters; con , P rock and P en They are concrete parameter set, rockfill parameter set and environmental parameter set, and the parameters they contain are: Among them, the environmental parameter set P en The parameter Q in s , T A RH, U w They are solar thermal radiation, ambient temperature, ambient humidity and wind speed, respectively, and the concrete parameter set P con Parameters in λ j , κ j , α j They are the entry temperature, final adiabatic temperature rise, adiabatic temperature rise single exponential model parameters, thermal conductivity, specific heat capacity and linear expansion coefficient of the jth type of concrete in the silo, and the rockfill parameter set P rock The parameter T in r , r , κ r , α r , Rv are the surface temperature, thermal conductivity, specific heat, linear expansion coefficient and average rockfill rate of the rockfill, respectively.

4. The dam body temperature monitoring method according to claim 3, characterized in that: The multivariate regression model satisfies the requirement of continuous differentiability in the entire space, and only considers the temperature changes along the upstream and downstream directions of the dam body and the plumb bob height direction in terms of spatial position.

5. The dam body temperature monitoring method according to claim 3, characterized in that: The key characteristic moments ti in the hydration heat temperature rise process include the peak time point t1 of the concrete temperature in the warehouse and the stable time point t2 of the concrete temperature field in the warehouse; the environmental parameter set P en , concrete parameter set P con and the rockfill parameter set P rock Substitute into the multivariate regression model T(ti, x, z, P en , P con , P rock ), the temperature field distribution function of the construction warehouse surface to be monitored along the upstream and downstream directions and the plumb height direction at the key characteristic moment is obtained as follows: Among them, T ti (x,z) is the temperature field distribution function of the construction warehouse surface to be monitored along the upstream and downstream directions and the plumb height direction at the key characteristic moment, and T0, T p , T s 、x p 、x s The mapping relationship between the input parameters.

6. The dam body temperature monitoring method according to claim 1, characterized in that: The temperature control measures include adding ice to mix and controlling the temperature of concrete out of the machine, heating and cooling the raw materials and heating and cooling the rockfill materials, and adjusting the pouring plan is to adjust the pouring time period.

7. The dam body temperature monitoring method according to claim 1, characterized in that: The selected representative temperature measuring points at least include a first representative measuring point P0, a second representative measuring point P1 and a third representative measuring point P2, The first representative measuring point P0 is the position corresponding to the temperature T0 of the concrete area on the upstream surface of the dam body anti-seepage layer. Generally located near the surface of the upstream anti-seepage layer or the impact and wear-resistant layer concrete or the center of the warehouse surface; The second representative measuring point P1 is the peak temperature T of the concrete of the dam body anti-seepage layer. p The corresponding position Generally located near the junction of the anti-seepage layer or the impact and wear-resistant layer and the rockfill concrete or near the center of the warehouse surface; The third representative measuring point P2 is the stable temperature T of the rockfill concrete in the warehouse s The corresponding position 8. The dam body temperature monitoring method according to claim 7, characterized in that: The T corresponding to the third representative temperature measurement point P2 is calculated using the following formula: s To make corrections: T s =C(Rv,Rv local )*T s * Among them, T s * is the monitoring value measured by the thermometer buried at the third representative temperature measuring point P2, C(Rv, Rv local ) is the correction coefficient, which is related to the average rockfill rate Rv and the local rockfill rate Rv local Related functions.

9. A temperature monitoring device for rockfill concrete integrated casting dam, characterized in that: include: The first module is used to perform inverse analysis and batch parameter sensitivity analysis on the finite element non-homogeneous temperature stress simulation model before pouring concrete, based on the design data of different dam bodies and historical measured data on site, to build a multivariate regression model for the temperature field distribution in the silo of integrated rockfill concrete pouring under different concrete parameters, rockfill parameters, and environmental parameters; The second module is used to obtain the concrete parameters, rockfill parameters and environmental parameters of the construction warehouse surface of the integrated cast rockfill concrete to be monitored, and set the key characteristic moments in the hydration heat temperature rise process, bring them into the multivariate regression model, and obtain the temperature field distribution function of the construction warehouse surface to be monitored at the key characteristic moments; The third module is used to predict the maximum value of the temperature in the warehouse and its gradient at each key characteristic moment of the concrete on the monitored construction warehouse surface during the hydration heat temperature rise process through the temperature field distribution function in the warehouse. When the predicted value exceeds the construction requirement, an early warning is issued and the proposed on-site temperature control measures or pouring plan are adjusted to obtain adjusted concrete parameters, rockfill parameters and environmental parameters. The temperature field distribution function in the warehouse is updated with the adjusted parameters, and the maximum value of the temperature in the warehouse and its gradient is continuously predicted until the predicted value meets the construction requirement. Then, several points in the temperature field distribution function in the warehouse are selected as representative temperature measurement points, and thermometers are respectively buried at positions corresponding to the representative temperature measurement points in the warehouse. The fourth module is used to dynamically update the temperature field distribution function in the warehouse according to the monitoring results of each representative temperature measurement point at the key characteristic moment during the on-site concrete pouring process, so as to obtain the temperature field distribution function in the warehouse based on the on-site measured data; The fifth module is used to determine whether the maximum temperature and temperature gradient of the concrete in the warehouse meet the construction requirements at different times according to the warehouse temperature field distribution function based on the field measured data; if not, an alarm is issued to remind the site to take temperature control measures.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the dam body temperature monitoring method according to any one of claims 1 to 8.

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