Intelligent temperature control processing method and system for mass concrete

Abstract

The invention particularly relates to an intelligent temperature control processing method and system for mass concrete, and the method comprises the steps: carrying out the modeling of a three-dimensional finite element, effectively mapping the data collected by a temperature sensor to a three-dimensional finite element model, and reconstructing the data of a temperature field, and predicting temperature change from the current acquisition moment to the next acquisition moment based on the temperature data, calculating and optimizing the corresponding cooling water control temperature and flow rate under the current condition, and generating a corresponding control instruction. Intelligent temperature control can be effectively carried out on mass concrete, the development condition of the concrete temperature can be mastered in real time, the maximum internal temperature difference of the concrete is ensured to be within a safe range by dynamically adjusting the flow speed and the water temperature of the water pipe, temperature cracks are effectively prevented, and therefore the durability of the concrete is improved, and the quality of the concrete is guaranteed.

Description

Technical Field

[0001] The present application relates to the field of intelligent data processing technology, and specifically to a method and system for intelligent temperature control processing of large-volume concrete. Background Art

[0002] With the rapid development of the economy, the scale of projects is expanding, which has promoted the widespread application of mass concrete in the construction industry. This construction technology meets the high-level construction requirements of engineering projects. However, the biggest feature of mass concrete is that the structural section is thick and it also has a small surface coefficient. This makes it difficult for the hydration heat generated during the pouring process to be dissipated in time and accumulates inside, causing the internal temperature to be too high. In addition, if the external ambient temperature is too low and insulation and other protective measures are not done well, the temperature difference between the inside and outside of the concrete will increase. The excessive stress caused by the temperature difference will cause cracks. The generation of cracks reduces the stability and durability of the structure, burying safety hazards for the project.

[0003] Traditional temperature control measures for large-volume concrete use cooling water to adjust the internal temperature of concrete. The cooling water is controlled and adjusted manually or through experience. The intelligent control efficiency is not high and it consumes a lot of manpower, energy consumption, fresh water and other resources. At the same time, traditional temperature control measures cannot timely feedback the internal temperature of the structure to adjust the circulating water temperature and flow, and the crack control effect on large-volume concrete is not ideal. Summary of the invention

[0004] Based on this, the present application proposes a method and system for intelligent temperature control treatment of large-volume concrete to address the above-mentioned problems, aiming to automatically and efficiently achieve temperature control treatment of large-volume concrete while effectively saving resources.

[0005] On the one hand, the present application provides a method for intelligent temperature control of large-volume concrete, the method comprising:

[0006] Establish a three-dimensional finite element model of mass concrete;

[0007] Controlling each temperature sensor to collect temperature data at a first preset frequency;

[0008] Mapping the temperature data to the three-dimensional finite element model and reconstructing temperature field data;

[0009] Predict the thermal change from the current acquisition time to the next acquisition time based on each temperature data, calculate and optimize the corresponding cooling water control temperature and flow rate under the current conditions, and generate corresponding control instructions;

[0010] The three-dimensional finite element model data and the temperature control data are rendered and displayed.

[0011] Furthermore, the method further comprises:

[0012] Generate three-dimensional finite element model data of different resolutions and corresponding temperature curve display data;

[0013] Receiving access or query instructions from a remote target terminal;

[0014] According to the type of the target terminal, the corresponding resolution finite element model data and temperature curve data are sent.

[0015] Furthermore, the method further comprises: monitoring the temperature variation trend of the mass concrete;

[0016] If the temperature change trend meets the preset condition, each temperature sensor is controlled to collect temperature data at a second preset frequency.

[0017] Preferably, mapping the temperature data to the three-dimensional finite element model and reconstructing the temperature field data comprises:

[0018] Establishing an association mapping relationship between each temperature sensor and the grid nodes of the three-dimensional finite element model;

[0019] Mapping each temperature sensor data to a corresponding grid node according to the mapping relationship;

[0020] According to the temperature data of each mapped grid node, the temperature field data of each node of the three-dimensional finite element is reconstructed:

[0021]

[0022] Among them, T p is the temperature data of any reconstructed grid node P(x,y,z), d ppi Map node P to i grid nodes p i The distance is , and k is the corresponding adjustment coefficient.

[0023] Preferably, the establishing of the association mapping relationship between each temperature sensor and the grid node of the three-dimensional finite element model includes:

[0024] Dividing the space of the three-dimensional finite element model into a plurality of sub-regions;

[0025] Define a set of boundary coordinates for each sub-region, and record the boundary coordinates of each sub-region and the included grid node information;

[0026] Traverse the location coordinate information of all temperature sensors to determine the sub-area to which each temperature sensor belongs;

[0027] Calculate the distance between the sensor location and all grid nodes in the sub-area, and search for the grid node closest to the sensor location;

[0028] The information of the successfully matched grid nodes is recorded, and the associated mapping relationship is established and stored.

[0029] Preferably, the calculation and optimization of the cooling water control temperature and flow rate corresponding to the current condition includes:

[0030] According to the data collected by each temperature sensor, the change rate k of the heat of the mass concrete at the current collection time is predicted Q :

[0031]

[0032] Among them, C c 、m c , C w 、m w are the specific heat capacity and mass of the current concrete and water respectively, are the average values ​​of the temperature collected in the concrete at the current collection time and the previous collection time, are the cooling water inlet and outlet temperature differences at the current collection time and the previous collection time, respectively, and Δt is the current collection interval;

[0033] Construct the calculation optimization objective function J:

[0034]

[0035] Among them, T w , v are the inlet temperature and flow rate of the cooling water to be optimized, h and α are the corresponding heat exchange parameters of the cooling water under the current working conditions, s is the heat exchange area of ​​the cooling water pipe, ΔT target P(T W ) is the target cooling water temperature T that meets the current flow rate under the current working conditions w The energy consumption required, λ and η are the corresponding adjustment coefficients;

[0036] Set optimization constraints: in, is the highest temperature of the concrete currently collected, v min 、v max are the minimum and maximum flow rates allowed under the current working conditions respectively;

[0037] Continuously update T through a preset iterative algorithm w Iterate the values ​​of and v to minimize the value min(J) of the objective function J;

[0038] Output the optimal water inlet temperature T that minimizes the objective function value w and flow velocity v.

[0039] Furthermore, the method further comprises:

[0040] Receive the user's preset switching instructions and switch to display the corresponding on-site monitoring data.

[0041] A second aspect of the present application provides a large volume concrete intelligent temperature control processing system, the system comprising:

[0042] 3D modeling unit, used to build 3D finite element model of large volume concrete;

[0043] A sensor control unit controls each temperature sensor to collect temperature data at a first preset frequency;

[0044] A temperature field reconstruction unit, used for mapping temperature data to a finite element model and reconstructing temperature field data;

[0045] A calculation control unit is used to predict the thermal change from the current acquisition moment to the next acquisition moment, calculate and optimize the corresponding cooling water control temperature and flow rate under the current conditions, and generate corresponding control instructions;

[0046] The rendering and display unit is used to render and display the three-dimensional finite element model data and temperature control data.

[0047] The third aspect of the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to perform the steps of any one of the above methods.

[0048] A fourth aspect of the present application provides a computer terminal device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of any one of the above methods.

[0049] The intelligent temperature control processing scheme for large-volume concrete provided in the present application is based on three-dimensional finite element modeling, and the data collected by the temperature sensor is effectively mapped to the three-dimensional finite element model, and the temperature field data is reconstructed, and then the temperature change from the current collection time to the next collection time is predicted based on each temperature data, and the corresponding cooling water control temperature and flow rate under the current condition are calculated and optimized, and the corresponding control instructions are generated. It can effectively perform intelligent temperature control on large-volume concrete, and realize real-time grasp of the development of concrete temperature. By dynamically adjusting the water pipe flow rate and water temperature, it ensures that the maximum internal temperature difference of the concrete is within a safe range, effectively preventing the generation of temperature cracks, thereby improving the durability of concrete and ensuring the quality of concrete.

[0050] Furthermore, the present application is also equipped with a cooling water intelligent circulation control system to perform intelligent automatic control and management of the cooling water pipe flow and temperature, which not only realizes the automatic control of the cooling water temperature, but also saves a lot of manpower, energy consumption, fresh water and other resources. At the same time, the real-time online monitoring of the concrete intelligent monitoring platform of the present application can be displayed based on the mobile client and PC website respectively through the design of a corresponding friendly interface. Users can receive on-site monitoring data online in real time through mobile terminals, and can query the temperature difference, maximum temperature, change curve and other data of the concrete surface in real time, and perform remote command control, which greatly facilitates users to grasp the temperature status of the concrete at any time and take corresponding emergency response measures in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0052] in:

[0053] Figure 1 A flowchart of a method for intelligent temperature control of large volume concrete in one embodiment;

[0054] Figure 2 A rendering of a three-dimensional modeling of a large volume of concrete in an embodiment;

[0055] Figure 3 A three-dimensional model of the corresponding resolution of large-volume concrete and a temperature curve effect diagram in an embodiment;

[0056] Figure 4 This is a diagram showing the effect of switching on-site monitoring of large-volume concrete in an embodiment;

[0057] Figure 5 It is a structural block diagram of a large volume concrete intelligent temperature control processing system in one embodiment;

[0058] Figure 6 FIG. 4 is a structural block diagram of a computer device in one embodiment. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments 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 creative work are within the scope of protection of the present invention.

[0060] The terms "include", "comprising" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices. In the claims, specification and drawings of the present application, relational terms such as "first" and "second" are merely used to distinguish one entity / operation / object from another entity / operation / object, and do not necessarily require or imply any such actual relationship or order between these entities / operations / objects.

[0061] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The occurrence of the phrase at various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0062] It should be noted that the steps shown in the embodiments of the present application and the flowcharts in the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the embodiments and the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0063] In one embodiment, if Figure 1 The flowchart of the intelligent temperature control method for mass concrete of the present application is shown, and the method comprises:

[0064] S100. Establish a three-dimensional finite element model of large-volume concrete.

[0065] Specifically, the three-dimensional finite element modeling of large-volume concrete in this application is mainly divided into three steps: finite element modeling, meshing, and attribute definition, specifically including: Finite element modeling: According to the actual size and shape of the large-volume concrete object, use CAD software or specialized finite element pre-processing software (such as ANSYS, Abaqus, etc.) to establish a geometric model.

[0066] Meshing: Divide the geometric model into multiple small, interconnected units (i.e., finite elements). These units can be three-dimensional surfaces, tetrahedrons, hexahedrons, etc. The density of the mesh can be adjusted according to the expected changes in the temperature gradient to ensure the accuracy of the simulation. Meshing can be done by uniform meshing, adaptive meshing based on node density, and other methods.

[0067] Material property definition: Input the thermal conductivity, heat capacity and other thermophysical properties of the bulk object material based on the actual material.

[0068] like Figure 2 Shown is a rendering of a three-dimensional model built for a large volume of concrete of a bridge pier in one embodiment of the present application.

[0069] S11, controlling each temperature sensor to collect temperature data at a first preset frequency.

[0070] Specifically, in an example scheme of the present application, a certain number of temperature sensor monitoring points are reasonably arranged in the mass concrete to ensure that the temperature distribution inside the concrete can be fully reflected. The monitoring points should be located at different depths, different positions and key areas, such as the center of the concrete, the surface and the junction of the new and old concrete. The arrangement of the temperature measuring points is representatively arranged according to the actual situation, so as to highlight the key points and take the overall situation into consideration. Furthermore, corresponding cooling water pipes are arranged inside the mass concrete to cool the concrete. Preferably, in an embodiment of the present application, the pouring layer is arranged with 3 layers of cooling water pipes in a crisscross pattern; the horizontal pipe spacing of the water pipes is 75cm, the vertical pipe spacing is 75cm, and the distance between the water pipes and the concrete surface / side is not less than 50cm; there are 10 to 12 sets of water pipes in a single layer; each set of water pipes is provided with an inlet and outlet, and the length of each set of water pipes does not exceed 200m, and corresponding sensors are arranged at corresponding positions to sense the inlet and outlet temperatures.

[0071] Furthermore, in the implementation scheme of the present application, each sensor is calibrated and assigned a unique identifier (such as a number or barcode) so that it can be accurately identified in subsequent data processing, and then the corresponding wiring testing and debugging are performed, and a communication connection is established with the data processing and control center through wireless or wired transmission.

[0072] The data processing center can control the data acquisition frequency of each temperature sensor through control instructions and transmit the data to the data processing center. The data includes the sensor's identification, specific location information (including coordinates or relative position relative to a fixed point) and corresponding collected temperature data and other information. In one embodiment, the present application scheme performs temperature acquisition by setting multiple acquisition frequencies in different stages and / or current temperature changes. During the concrete pouring process and after the completion of the pouring to the hydration heat heating stage, the temperature sensor is controlled to collect at a first frequency, such as measuring the temperature once every 0.5-2h. Furthermore, by monitoring the temperature change trend of the mass concrete, if the temperature change trend meets the preset conditions (such as within the preset time of the hydration heat cooling stage or within a relatively small temperature change stage), each temperature sensor is controlled to collect temperature data at a second preset frequency, such as collecting and measuring once every 2-4h.

[0073] S12, mapping the temperature data to the three-dimensional finite element model, and reconstructing the temperature field data.

[0074] Specifically, in the embodiment of the present application, the raw data collected from the sensor network is cleaned, denoised and calibrated to ensure the accuracy and consistency of the data. Then, the sensor data is mapped to the corresponding nodes or units of the finite element model, and the concrete temperature field data is reconstructed.

[0075] Preferably, mapping the temperature data to the three-dimensional finite element model and reconstructing the temperature field data comprises:

[0076] S121, establishing an association mapping relationship between each temperature sensor and the grid nodes of the three-dimensional finite element model.

[0077] S122. Map each temperature sensor data to a corresponding grid node according to the mapping relationship.

[0078] S123, reconstructing the temperature field data of each node of the three-dimensional finite element according to the temperature data of each mapped grid node:

[0079]

[0080] Among them, T p is the temperature data of any reconstructed grid node P(x,y,z), d ppi Map node P to the i-th grid node p i Distance k is the corresponding adjustment coefficient.

[0081] S124. Dynamically update the finite element model according to the latest sensor data through the real-time data stream channel.

[0082] Further preferably, the establishing of the association mapping relationship between each temperature sensor and the grid node of the three-dimensional finite element model includes:

[0083] S1211. Divide the space of the three-dimensional finite element model into multiple sub-areas.

[0084] Preferably, in one embodiment of the present application, a uniform grid partitioning strategy is adopted to divide the space of the finite element model into a plurality of small cubic sub-region nodes.

[0085] S1212: define a set of boundary coordinates for each sub-region, and record the boundary coordinates and mesh node information of each sub-region. If the coordinate systems of the finite element model and the sensor are inconsistent, perform coordinate conversion.

[0086] S1213. Traverse the position coordinate information of all temperature sensors to determine the sub-area to which each temperature sensor belongs.

[0087] S1214. Calculate the distance between the sensor position and all grid nodes in the sub-area, and search for the grid node closest to the sensor position.

[0088] S1215. Record the information of the successfully matched grid nodes, establish an associated mapping relationship and store it.

[0089] S13. Predict the thermal change from the current acquisition time to the next acquisition time based on each temperature data, calculate and optimize the corresponding cooling water control temperature and flow rate under the current condition, and generate corresponding control instructions.

[0090] Specifically, the present application predicts the thermal changes from the current acquisition time to the next acquisition time through various temperature data to calculate and optimize the corresponding target cooling water temperature and flow rate, and then sends control instructions to the corresponding equipment to adjust the cooling water of the corresponding temperature and control the corresponding flow rate. Specifically, the PID control algorithm and / or the control method based on fuzzy logic can be combined to adjust the output of the heater / cooler or water pump and other equipment through the error between the current temperature / flow rate and the set temperature / flow rate to achieve rapid stabilization of the temperature and flow rate. Preferably, the calculation optimizes the corresponding cooling water control temperature and flow rate under the current conditions, including:

[0091] S131, based on the data collected by each temperature sensor, predict the change rate k of the heat of the mass concrete at the current collection time Q .

[0092] Specifically, the temperature collected by the sensor in the concrete represents the temperature result obtained after various heat exchanges in the concrete under the current situation. According to the principle of conservation of heat, the heat increment generated by the current concrete water thermalization is approximately equal to the sum of the heat change inside the concrete between the current moment and the previous moment and the difference in heat taken away by the cooling water between the current moment and the previous moment. The heat change inside the concrete between the current moment and the previous moment and the difference in heat taken away by the cooling water between the current moment and the previous moment can be represented by the difference in concrete temperature data between the current moment and the previous moment and the difference in inlet and outlet temperature of the cooling water, respectively.

[0093] Assuming that the incremental rate of heat generated by concrete hydrothermal in a relatively short period of time (relative to the entire hydrothermal cycle of concrete) is relatively constant, combined with the heat equation based on thermodynamic principles, the rate of change k of heat generated by large-volume concrete from the current collection time to the next collection time under the current working conditions can be estimated. Q :

[0094]

[0095] Among them, C c 、m c , C w 、m w are the specific heat capacity and mass of the current concrete and water respectively, are the average values ​​of the temperature collected in the concrete at the current collection time and the previous collection time, are the cooling water inlet and outlet temperature differences at the current collection time and the previous collection time, respectively, and Δt is the current collection interval.

[0096] S132, constructing a calculation optimization objective function J.

[0097] The optimization objective function constructed in this application, which includes water inlet temperature and flow rate parameters, aims to optimize the control of water inlet temperature and flow rate, so that the temperature change rate in the concrete is within a preset controllable safety range and save energy resources as much as possible. Specifically, the process of constructing the optimization objective function is as follows:

[0098] The heat removed by the cooling water heat exchange is positively correlated with the current temperature difference, the corresponding parameters of the water flow rate, and the surface area of ​​the heat exchange surface. Since the heat exchange surface area of ​​the pre-buried cooling water pipe and the concrete is relatively fixed s = π·d·l, d is the pipe diameter, and l is the length, the corresponding fitting function can be established based on this: The corresponding coefficients of the established fitting function can be fitted by calculating the heat by measuring different data such as the inlet water temperature, the outlet water temperature, the concrete temperature and the flow rate.

[0099] Further based on the heat equation of concrete thermodynamics principle, the current instantaneous temperature-heat change equation can be obtained:

[0100] Where Q = Q j +k Q t,

[0101] Solving the above equations, we can get the current temperature T under the current working conditions. w The cooling water can be controlled by the flow rate v to estimate the temperature change rate of the concrete from the current sampling time to the next sampling time.

[0102] Optionally, in one embodiment of the present application, a certain power of the flow rate is used to establish a fitting function for characterization. In the current case, the corresponding function can be fitted to characterize the target cooling water inlet temperature T w and flow rate v, the heat removed by heat exchange is: Among them, the convection heat transfer parameters h and α can be fitted by measuring different water inlet temperature, water outlet temperature, concrete temperature, flow rate and other data to calculate the heat. Substituting into the solution, we can get: Based on this result, an objective function can be constructed. The objective function constructed is to optimize the control of the temperature change rate in the concrete within the preset controllable safety target range and take into account energy consumption. Optionally, the constructed objective function can be:

[0103]

[0104] Among them, T w , v are the inlet temperature and flow rate of the cooling water to be optimized, h and α are the corresponding heat exchange parameters of the cooling water under the current working conditions, s is the heat exchange area of ​​the cooling water pipe, ΔT target P(T W ) is the target cooling water temperature T that meets the current flow rate under the current working conditions w The energy consumption corresponding to the required consumption can be obtained by statistics or calculation based on the historical data of the equipment used, and λ and η are the corresponding adjustment coefficients. It should be noted that those skilled in the art can also conceive the corresponding objective function according to the actual situation based on this.

[0105] S133. Set corresponding optimization constraints according to temperature control requirements and safety:

[0106] in, The maximum temperature of the concrete currently collected, that is, the difference between the maximum temperature and the inlet water temperature is controlled between 10°-25° to prevent cracking, v min 、v max They are the minimum and maximum flow rates allowed under the current working conditions.

[0107] S134, continuously update T through a preset iterative algorithm w The values ​​of and v are iteratively calculated to minimize the value min(J) of the objective function J.

[0108] Specifically, during the solution process, optimization algorithms such as gradient descent, genetic algorithm, particle swarm optimization, etc. can be used to update the target temperature T w and the value of flow velocity v to reduce the objective function J, and multiple iterations are required until the optimal solution that satisfies the stopping criterion is found. Check whether the optimization process converges, that is, whether the value of the objective function J is small enough, or T w Whether the update amount of and v is less than a certain threshold, or the number of iterations reaches a preset number.

[0109] S135: Output the optimal water inlet temperature T that minimizes the objective function value. w and flow velocity v.

[0110] S14, rendering and displaying the three-dimensional finite element model data and temperature control data.

[0111] Specifically, in one embodiment, the present application renders and displays the three-dimensional finite element model data and temperature control data by setting a friendly user interaction and display interface, specifically including:

[0112] According to the temperature range, a color mapping table (palette) is established to map the temperature values ​​to specific colors. For example, cold colors such as blue and dark are used in low-temperature areas, and warm colors such as yellow and orange are used in high-temperature areas. A smooth color transition is generated within the temperature range through linear interpolation or nonlinear interpolation methods. Each temperature value is mapped to the corresponding color in the color mapping table, and the mapped color is filled into the corresponding spatial position using graphics rendering technology to form a color-coded temperature distribution map. The color-coded temperature distribution map is then displayed through visualization tools. Further settings are made to add auxiliary information such as legends and titles to help users better understand the temperature distribution.

[0113] The intelligent temperature control solution for large-volume concrete provided in the present application is based on three-dimensional finite element modeling, and effectively maps the data collected by the temperature sensor to the three-dimensional finite element model, and reconstructs the temperature field data, and then predicts the temperature change from the current collection time to the next collection time based on each temperature data, calculates and optimizes the corresponding cooling water control temperature and flow rate under the current conditions, and generates corresponding control instructions. It can effectively perform intelligent temperature control on large-volume concrete, and can realize real-time grasp of the development of concrete temperature, and dynamically adjust the water pipe flow rate and water temperature to ensure that the maximum internal temperature difference of the concrete is within a safe range, effectively preventing the generation of temperature cracks, thereby improving the durability of concrete and ensuring the quality of concrete.

[0114] Furthermore, the present application is also equipped with a cooling water intelligent circulation control system to perform intelligent automatic regulation and management of the flow and temperature of the cooling water pipe, which not only realizes the automatic regulation of the cooling water temperature, but also saves a lot of manpower, energy consumption, fresh water and other resources.

[0115] Furthermore, the method also includes: generating three-dimensional finite element model data of different resolutions and corresponding temperature curve display data; receiving access or query instructions from a remote target terminal, and sending corresponding resolution finite element model data and corresponding temperature curve data according to the type of the target terminal.

[0116] Specifically, in one embodiment of the present application, two resolutions of three-dimensional models and temperature curve data can be generated, and corresponding interface data can be sent to the target terminal according to the type of remote access terminal, such as a mobile terminal or a PC, to facilitate user viewing and monitoring. Figure 3 Shown is a three-dimensional model of a large volume of concrete and a temperature curve effect diagram corresponding to the mobile terminal resolution in an embodiment.

[0117] Furthermore, the method further comprises:

[0118] Receive the user's preset switching instructions and switch to display the corresponding on-site monitoring data. According to the user's switching instructions, the system can display the corresponding on-site monitoring video data, so that users can obtain temperature curve data in real time and understand the on-site conditions. Figure 4 The figure shows an embodiment in which the massive concrete system interface switches to a field monitoring effect diagram according to a user switching instruction.

[0119] like Figure 5 The present application provides a large volume concrete intelligent temperature control processing system, the system comprising:

[0120] 3D modeling unit, used to build 3D finite element model of large volume concrete;

[0121] A sensor control unit controls each temperature sensor to collect temperature data at a first preset frequency;

[0122] A temperature field reconstruction unit, used for mapping temperature data to a finite element model and reconstructing temperature field data;

[0123] A calculation control unit is used to predict the thermal change from the current acquisition moment to the next acquisition moment, calculate and optimize the corresponding cooling water control temperature and flow rate under the current conditions, and generate corresponding control instructions;

[0124] The rendering and display unit is used to render and display the three-dimensional finite element model data and temperature control data.

[0125] In one embodiment, the present application further provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor performs the following steps:

[0126] Establish a three-dimensional finite element model of mass concrete;

[0127] Controlling each temperature sensor to collect temperature data at a first preset frequency;

[0128] Mapping the temperature data to the three-dimensional finite element model and reconstructing temperature field data;

[0129] Predict the heat change from the current collection time to the next collection time based on each temperature data, calculate and optimize the corresponding cooling water control temperature and flow rate under the current conditions, and generate corresponding control instructions;

[0130] The three-dimensional finite element model data and the temperature control data are rendered and displayed.

[0131] In one embodiment, Figure 6The present application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the following steps:

[0132] Establish a three-dimensional finite element model of mass concrete;

[0133] Controlling each temperature sensor to collect temperature data at a first preset frequency;

[0134] Mapping the temperature data to the three-dimensional finite element model and reconstructing temperature field data;

[0135] Predict the heat change from the current collection time to the next collection time based on each temperature data, calculate and optimize the corresponding cooling water control temperature and flow rate under the current conditions, and generate corresponding control instructions;

[0136] The three-dimensional finite element model data and the temperature control data are rendered and displayed.

[0137] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0138] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0139] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for intelligent temperature control of large volume concrete, characterized in that: The method comprises: Establish a three-dimensional finite element model of large-volume concrete; Controlling each temperature sensor to collect temperature data at a first preset frequency; Mapping the temperature data to the three-dimensional finite element model and reconstructing temperature field data; Predict the thermal change from the current acquisition time to the next acquisition time based on each temperature data, calculate and optimize the corresponding cooling water control temperature and flow rate under the current conditions, and generate corresponding control instructions; The three-dimensional finite element model data and the temperature control data are rendered and displayed.

2. The method according to claim 1, characterized in that The method further comprises: Generate three-dimensional finite element model data of different resolutions and corresponding temperature curve display data; Receiving access or query instructions from a remote target terminal; According to the type of the target terminal, the corresponding resolution finite element model data and temperature curve data are sent.

3. The method according to claim 1, characterized in that The method further comprises: Monitoring the temperature trend of the mass concrete; If the temperature change trend meets the preset condition, each temperature sensor is controlled to collect temperature data at a second preset frequency.

4. The method according to claim 1, characterized in that: Mapping the temperature data to the three-dimensional finite element model and reconstructing the temperature field data includes: Establishing an association mapping relationship between each temperature sensor and the grid nodes of the three-dimensional finite element model; Mapping each temperature sensor data to a corresponding grid node according to the mapping relationship; According to the temperature data of each mapped grid node, the temperature field data of each node of the three-dimensional finite element is reconstructed: Among them, T p is the temperature data of any reconstructed grid node P(x,y,z), d ppi Map node P to i grid nodes p i The distance is , and k is the corresponding adjustment coefficient.

5. The method according to claim 4, characterized in that The establishing of the association mapping relationship between each temperature sensor and the grid node of the three-dimensional finite element model includes: Dividing the space of the three-dimensional finite element model into a plurality of sub-regions; Define a set of boundary coordinates for each sub-region, and record the boundary coordinates of each sub-region and the included grid node information; Traverse the location coordinate information of all temperature sensors to determine the sub-area to which each temperature sensor belongs; Calculate the distance between the sensor location and all grid nodes in the sub-area, and search for the grid node closest to the sensor location; The information of the successfully matched grid nodes is recorded, and the associated mapping relationship is established and stored.

6. The method according to claim 1, characterized in that The calculation and optimization of the cooling water control temperature and flow rate corresponding to the current condition include: According to the data collected by each temperature sensor, the change rate k of the heat of the mass concrete at the current collection time is predicted Q : Among them, C c 、m c , C w 、m w are the specific heat capacity and mass of the current concrete and water respectively, are the average values ​​of the temperature collected in the concrete at the current collection time and the previous collection time, are the cooling water inlet and outlet temperature differences at the current collection time and the previous collection time, respectively, and Δt is the current collection interval; Construct the calculation optimization objective function J: Among them, T w , v are the inlet temperature and flow rate of the cooling water to be optimized, h and α are the corresponding heat exchange parameters of the cooling water under the current working conditions, s is the heat exchange area of ​​the cooling water pipe, ΔT target P(T W ) is the target cooling water temperature T that meets the current flow rate under the current working conditions w The energy consumption required, λ and η are the corresponding adjustment coefficients; Set optimization constraints: in, is the highest temperature of the concrete currently collected, v min 、v max are the minimum and maximum flow rates allowed under the current working conditions respectively; Iterative update T w and v are optimized iteratively calculated to minimize the value min(J) of the objective function J; Output the optimal water inlet temperature T that minimizes the objective function value w and flow velocity v.

7. The method according to any one of claims 1 to 6, characterized in that: The method further comprises: Receive the user's preset switching instructions and switch to display the corresponding on-site monitoring data.

8. A large volume concrete intelligent temperature control treatment system, characterized in that: The system comprises: 3D modeling unit, used to build 3D finite element model of large volume concrete; A sensor control unit controls each temperature sensor to collect temperature data at a first preset frequency; A temperature field reconstruction unit, used for mapping temperature data to a finite element model and reconstructing temperature field data; A calculation control unit is used to predict the thermal change from the current acquisition moment to the next acquisition moment; and to calculate and optimize the corresponding cooling water control temperature and flow rate under the current conditions, and generate corresponding control instructions; The rendering and display unit is used to render and display the three-dimensional finite element model data and temperature control data.

9. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 7.

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