Temperature control method, device and system for large-volume fan foundation concrete

Abstract

The invention relates to the technical field of intelligent temperature control, in particular to a temperature control method, device and system for large-volume fan foundation concrete, and the method comprises the steps: obtaining the temperature data of each preset position under each preset depth in a fan foundation in real time; acquiring a linear feature vector at each acquisition moment; comprehensively analyzing the change trend of the temperature data of each preset position at each acquisition moment and all acquisition moments before the acquisition moments, and obtaining an accumulated trend check matrix of each acquisition moment; acquiring a layered monitoring difference coefficient at each acquisition moment; by analyzing the difference between the temperature data of each preset position at each collection moment and a preset standard temperature, and combining the distribution condition of layered monitoring difference coefficients of each collection moment and all previous collection moments, obtaining an error adjustment matrix of each collection moment; and the temperature of the fan foundation concrete is further controlled. The method aims at improving the accuracy of temperature control over the large-size draught fan foundation concrete.

Description

Technical Field

[0001] This application relates to the technical field of intelligent temperature control, and particularly to a temperature control method, device, and system for mass concrete of a wind turbine foundation. Background Art

[0002] Mass concrete of a wind turbine foundation refers to a large concrete foundation structure used to support a wind turbine tower in a wind power project. With the rapid development of wind power technology, the single-unit capacity of wind turbines has been continuously increasing, and the scale of wind turbine foundations has also been growing. The construction of mass concrete of a wind turbine foundation faces many challenges, among which temperature control is particularly crucial.

[0003] If the temperature control is improper, it will have a serious impact on the quality and performance of the wind turbine foundation, such as causing cracks in the concrete, reducing the durability of the concrete, etc., thereby affecting the safe and stable operation of the wind turbine, increasing the maintenance cost of the wind turbine foundation, and shortening its service life. Currently, in the process of temperature monitoring and control of mass concrete of a wind turbine foundation, temperature control is generally carried out based on the temperature data at the current moment, ignoring the long-term impact characteristics of the disordered temperature distribution, resulting in a large deviation in the temperature control of the mass concrete of the wind turbine foundation. Summary of the Invention

[0004] In view of the above, it is necessary to provide a temperature control method, device, and system for mass concrete of a wind turbine foundation, which improves the accuracy of temperature control for mass concrete of a wind turbine foundation compared with the traditional temperature control method for mass concrete of a wind turbine foundation: In a first aspect, an embodiment of the present application provides a temperature control method for mass concrete of a wind turbine foundation, and the method includes the following steps: Obtain the temperature data at each preset position at each preset depth inside the wind turbine foundation in real time; For each acquisition moment, by analyzing the collinearity between the temperature data at different preset depths, obtain the linear feature vector at each acquisition moment; Comprehensively analyze the change trends of the temperature data at each preset position at each acquisition moment and all previous acquisition moments, and obtain the cumulative trend test matrix at each acquisition moment; By analyzing the difference situation of the cumulative trend test matrix between each acquisition moment and all previous acquisition moments, and the similarity degree of the linear feature vectors between each acquisition moment and all previous acquisition moments, obtain the hierarchical monitoring difference coefficient at each acquisition moment; By analyzing the difference between the temperature data at each preset position at each acquisition moment and the preset standard temperature, and combining the distribution of the hierarchical monitoring difference coefficients at each acquisition moment and all previous acquisition moments, obtain the error adjustment matrix at each acquisition moment; Based on the error adjustment matrix, control the temperature of the concrete of the fan foundation.

[0005] In one embodiment, the process of obtaining the linear feature vector is as follows: For each acquisition moment, the temperature data at all the preset positions at each of the preset depths are combined to form the depth monitoring data sequences at each acquisition moment; wherein, the preset positions corresponding to the temperature data at the same position in all the depth monitoring sequences are on the same vertical line; Calculate the variance inflation factor of each depth monitoring data sequence at each acquisition moment; The linear feature vector is composed of all the variance inflation factors at each acquisition moment.

[0006] In one embodiment, the process of obtaining the cumulative trend test matrix is as follows: Take the temperature data of each preset position at each acquisition moment and all the acquisition moments before it as the input of the trend test algorithm, and output the trend statistic of each preset position at each acquisition moment; The cumulative trend test matrix is composed of the trend statistics of all the preset positions at each acquisition moment.

[0007] In one embodiment, the process of obtaining the hierarchical monitoring difference coefficient is as follows: Denote any acquisition moment as , calculate the difference degree of the cumulative trend test matrix between the th acquisition moment and the th acquisition moment before it, and the similarity degree of the linear feature vector between the th acquisition moment and the th acquisition moment before it, and calculate the sum value of the similarity degree and a preset value greater than 0; Calculate the ratio of the difference degree to the sum value, and calculate the ratio between the th acquisition moment and each acquisition moment before it; The hierarchical monitoring difference coefficient at the th acquisition moment is positively correlated with all the ratios corresponding to the th acquisition moment.

[0008] In one embodiment, the hierarchical monitoring difference coefficient at the th acquisition moment is the mean value of all the ratios corresponding to the th acquisition moment.

[0009] In one embodiment, the process of obtaining the error adjustment matrix is as follows: Calculate the proportion of the hierarchical monitoring difference coefficient at each acquisition moment in the hierarchical monitoring difference coefficients at all acquisition moments; Form an error matrix for each acquisition moment by taking the difference between the temperature data at all the preset positions at each acquisition moment and the preset standard temperature; Take the weighted fusion result of the error matrices at each acquisition moment and all the acquisition moments before it and the proportion as the error adjustment matrix for each acquisition moment.

[0010] In one embodiment, the calculation method of the error adjustment matrix is as follows: Calculate the product of the error matrix at each acquisition moment and the proportion, and the error adjustment matrix is the sum of the products at each acquisition moment and all the acquisition moments before it.

[0011] In one embodiment, the process of controlling the temperature of the fan foundation concrete is: based on the error adjustment matrix at the current acquisition moment, obtain a control instruction, and control the internal temperature of the concrete according to the control instruction.

[0012] In a second aspect, the embodiments of the present application also provide a temperature control device for mass concrete of a fan foundation, and there is a temperature monitoring device in the device; The temperature monitoring device includes a data acquisition terminal, a monitoring center server, and an actuator; Among them, the data acquisition terminal is used to obtain the temperature data at each preset position at each preset depth inside the fan foundation in real time; The monitoring center server is used to obtain the linear feature vector at each acquisition moment by analyzing the collinearity between the temperature data at different preset depths for each acquisition moment; Comprehensively analyze the change trend of the temperature data at each preset position at each acquisition moment and all the acquisition moments before it to obtain the cumulative trend test matrix at each acquisition moment; By analyzing the difference situation of the cumulative trend test matrix between each acquisition moment and all the acquisition moments before it, and the similarity degree of the linear feature vectors between each acquisition moment and all the acquisition moments before it, obtain the hierarchical monitoring difference coefficient at each acquisition moment; By analyzing the difference between the temperature data at each preset position at each acquisition moment and the preset standard temperature, and combining the distribution of the hierarchical monitoring difference coefficients at each acquisition moment and all the acquisition moments before it, obtain the error adjustment matrix at each acquisition moment; output a control instruction through the error adjustment matrix; The actuator is used to control the internal temperature of the concrete through the control instruction.

[0013] In a third aspect, an embodiment of the present application further provides a temperature control system for mass concrete of a large-volume fan foundation, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the temperature control method for mass concrete of a large-volume fan foundation described in any one of the above are implemented.

[0014] The present application has at least the following beneficial effects: In the prior art, there are deficiencies in the temperature monitoring of mass concrete of a large-volume fan foundation. Temperature control is only based on the temperature data at the current moment, and the long-term influence characteristics of the disordered temperature distribution are not fully considered, resulting in a large error in the temperature control of the fan foundation concrete. In the present application, by reasonably arranging the positions for collecting temperature data, data in different regions and at different depths inside the fan foundation concrete can be obtained. By analyzing the linear characteristics and cumulative trend characteristics of the internal temperature distribution of the fan foundation concrete, the dynamic change characteristics of the temperature inside the fan foundation concrete at different stages and different depths can be accurately captured. Furthermore, in the process of temperature monitoring and control of the fan foundation, by comprehensively analyzing the degree of deviation of the actual temperature at each position from the standard temperature at each collection moment, the linear characteristics and cumulative trend characteristics of the temperature distribution, an error adjustment matrix at the current collection moment can be obtained. Then, based on the error adjustment matrix, the temperature is controlled, which can avoid relying on the temperature data at the current moment and accurately adjust the control error caused by the long-term influence of temperature disorder, improving the accuracy of the temperature control of mass concrete of a large-volume fan foundation. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0016] Figure 1 It is a flowchart of the steps of the temperature control method for mass concrete of a large-volume fan foundation provided by an embodiment of the present application; Figure 2 It is a schematic diagram of the distribution of temperature collection points; Figure 3 It is a schematic diagram of the arrangement of temperature sensors at each temperature collection point; Figure 4 It is a schematic diagram of the acquisition process of the linear feature vector; Figure 5 It is a schematic diagram of the acquisition process of the error adjustment matrix. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] In the description of the embodiments of the present application, words such as "exemplary", "or", "for example", etc. are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary", "or", "for example" is intended to present relevant concepts in a specific manner.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs. The terms used in this application are only for the purpose of describing specific embodiments and are not intended to limit this application. It should be understood that unless otherwise specified in this application, " / " means "or".

[0019] In addition, it should be noted that the terms "first" and "second" in this application are used to distinguish similar objects and are not used to describe a specific order or sequence.

[0020] The following specifically describes the specific solutions of the temperature control method, device and system for mass concrete of a wind turbine foundation provided by this application with reference to the accompanying drawings.

[0021] Please refer to Figure 1 , which shows a flowchart of the steps of the temperature control method for mass concrete of a wind turbine foundation provided by an embodiment of this application. The method includes the following steps: Step S1: Real-time obtain the temperature data at each preset position at each depth inside the wind turbine foundation.

[0022] During the temperature control process of the mass concrete of the wind turbine foundation, in order to ensure the accuracy and reliability of the data, key parameters need to be accurately collected and processed. Specifically, temperature collection points are arranged on the wind turbine foundation to collect its monitoring data; among them, the distribution schematic diagram of the temperature collection points is as Figure 2 shown, Figure 2 where 1 to 16 all represent temperature collection points, and 0 represents the wind turbine foundation; Figure 2 It is a schematic diagram from a top view perspective. M temperature sensors at different depths are set at each temperature collection point. In this embodiment, the value of M is 3. The positions of the 3 temperature sensors are named the upper collection point, the middle collection point, and the lower collection point in ascending order of depth. The layout schematic diagram of the temperature sensors at each temperature collection point is as Figure 3 shown, Figure 3 where 1 represents the temperature collection point, represents a vertical line, , and respectively represent the temperature sensors at the upper collection point, the middle collection point, and the lower collection point, , and are located on the same vertical line where 101 represents the concrete slab of the fan foundation. All upper acquisition points are at the same depth, all middle acquisition points are at the same depth, and all lower acquisition points are also at the same depth. Among them, the value of M being 3 is only an embodiment of this application, and the implementer can set the value of M by himself / herself.

[0023] In this embodiment, the upper acquisition points are 30 cm away from the upper surface of the concrete slab of the fan foundation, the middle acquisition points are at the center of the depth of the fan foundation concrete, and the lower acquisition points are 30 cm away from the lower surface of the concrete slab of the fan foundation. Among them, the depths of the upper acquisition points, middle acquisition points, and lower acquisition points are preset manually, and the implementer can limit them according to the actual situation. This application does not make special restrictions.

[0024] During the concrete pouring and curing process, due to environmental factors and changes in the state of the instrument itself, noise interference may be introduced, affecting the data quality. To solve this problem, noise reduction processing is performed on the collected temperature data.

[0025] In this embodiment, a Wiener filter is used to perform noise reduction processing on the collected temperature data. As other implementation manners, on the basis of being able to perform noise reduction processing on the collected temperature data, the implementer can use other existing technologies, such as median filters, mean filters, etc. This application does not make special restrictions.

[0026] Step S2: Combine the linear characteristics and cumulative trend differences of the temperature distribution during the temperature monitoring and control process to obtain an error adjustment matrix.

[0027] In the temperature control of mass concrete for fan foundations, unstable temperature will cause many problems. Temperature fluctuations disrupt the internal stress distribution of the concrete, making the crack generation mechanism complex, affecting the guarantee of the durability and bearing performance of the concrete, changing the pore structure, and resulting in uncertainties in the evaluation of properties such as impermeability and erosion resistance. The stress instability induced by temperature fluctuations will cause the expansion of microcracks inside the concrete and the generation of new cracks, damaging the structural integrity, interfering with the migration of internal moisture and ions, and accelerating the performance degradation. When the internal temperature of the concrete rises, thermal expansion causes volume expansion and pore structure change, triggering stress redistribution and exacerbating stress instability; stress changes lead to local deformation of the structure, damaging the temperature uniformity and stability. Therefore, it is necessary to analyze the collected temperature data to control in a timely manner when the temperature deviates.

[0028] Step S2.1: For each acquisition moment, by analyzing the collinearity between the temperature data at different preset depths, obtain the linear feature vectors for each acquisition moment.

[0029] Since in the actual temperature control process, the temperatures at different temperature acquisition points change in an associated manner, and the temperatures at different depths also change in an associated manner. If the temperature changes at different positions exhibit chaotic characteristics, the temperature of the large-volume fan foundation concrete may be affected by environmental interference and have long-term temperature fluctuation effects.

[0030] Based on the above analysis, to effectively integrate the relationship of temperature state changes between different temperature acquisition points of the fan foundation concrete, and then accurately reflect the deviation of temperature monitoring and control of the fan foundation, for each acquisition moment, the temperature data of all upper acquisition points, all middle acquisition points, and all lower acquisition points are respectively composed into the depth monitoring data sequences of each acquisition moment. Among them, the temperature data at the same position in all depth monitoring sequences correspond to the same temperature acquisition point.

[0031] The depth monitoring data sequences of each acquisition moment are composed into the hierarchical monitoring data matrix of each acquisition moment. One row in the hierarchical monitoring data matrix is a depth monitoring data sequence.

[0032] Furthermore, the hierarchical monitoring data matrix of each acquisition moment reflects the temperature distribution at different positions at the same time during the temperature control monitoring process of the large-volume foundation. Therefore, considering that there is a linear consistency characteristic in the relationship of temperature state changes between different temperature acquisition points during the temperature monitoring and control process of the large-volume fan foundation, that is, under normal circumstances, if the temperature of one temperature acquisition point changes, the temperatures of other temperature acquisition points will also change accordingly. Therefore, taking the hierarchical monitoring data matrix of each acquisition moment as the input, the variance inflation factor of each row of data is obtained. The larger the variance inflation factor, the more significant the collinearity characteristic of each acquisition moment, that is, the more significant the consistency relationship of the temperature data at different positions at each acquisition moment. Among them, the calculation of the variance inflation factor is a well-known technology and will not be elaborated in this application.

[0033] Furthermore, all the variance inflation factors of each acquisition moment are composed into the linear feature vector of each acquisition moment. The schematic diagram of the acquisition process of the linear feature vector is as Figure 4 shown.

[0034] Step S2.2: Comprehensively analyze the change trends of the temperature data of each of the preset positions at each acquisition moment and all acquisition moments before it, and obtain the cumulative trend test matrix of each acquisition moment.

[0035] During the analysis of the temperature monitoring and control process of the fan foundation, the change trends of the temperature data at each position at each collection moment and all collection moments before it are analyzed. Specifically, all upper collection points, middle collection points, and lower collection points are collectively referred to as collection points. For any collection point, the temperature data of the any collection point at each collection moment and all collection moments before it are used as the input of the trend test algorithm, and the trend statistic of the any collection point at each collection moment is output. The trend statistics of all collection points at each collection moment form an accumulated trend test matrix, and the accumulated trend test matrix reflects the cumulative temperature change trend distribution up to each collection moment during the temperature monitoring and control process of the fan foundation; among them, one row in the accumulated trend test matrix is the trend statistic of the collection points at the same depth.

[0036] In this embodiment, the trend test algorithm is the Mann-Kendall trend test method, and the trend statistic is the statistic Z in the Mann-Kendall trend test method. As other implementation manners, on the basis of being able to measure the change trend of the temperature data, implementers can use other existing technologies for measurement, such as the slope method, the Cox-Stuart test, etc., and this application does not make special restrictions.

[0037] Step S2.3, by analyzing the difference situation of the accumulated trend test matrix between each collection moment and all collection moments before it, and the similarity degree between the linear eigenvectors between each collection moment and all collection moments before it, the hierarchical monitoring difference coefficient of each collection moment is obtained.

[0038] The expression of the hierarchical monitoring difference coefficient of each collection moment is: ; In the formula, represents the hierarchical monitoring difference coefficient of the th collection moment; and respectively represent the accumulated trend test matrices of the th and the th collection moments; represents the difference degree measurement function; and respectively represent the linear eigenvectors of the th and the th collection moments; represents the similarity degree measurement function; represents the total number of all collection moments before the th collection moment; represents a preset value greater than 0, and the purpose is to avoid the denominator being 0. The value of is preset manually, and implementers can set it by themselves. In this embodiment,

[0039] In this embodiment, the difference metric function is the SAD (Sum of Absolute Difference) value. As other implementation manners, on the basis of being able to measure the differences between elements at the same positions in the cumulative trend test matrix, implementers can use other existing technologies for measurement, such as Euclidean distance, mean square error, etc. This application does not have special restrictions.

[0040] In this embodiment, the similarity metric function can be cosine similarity. As other implementation manners, on the basis of being able to measure the similarity between linear feature vectors, implementers can use other calculation methods for measurement, such as the reciprocal of Hamming distance, the reciprocal of Euclidean distance, etc. This application does not have special restrictions.

[0041] It should be noted that: the larger the calculated hierarchical monitoring difference coefficient is, it indicates that based on the linear features and cumulative trend difference analysis in the temperature monitoring and control process of the comprehensive fan foundation, the greater the possibility of disorder in the temperature control distribution of the current fan foundation.

[0042] Step S2.4, by analyzing the differences between the temperature data at each of the preset positions at each acquisition moment and the preset standard temperature, and combining the distribution of the hierarchical monitoring difference coefficients at each acquisition moment and all the acquisition moments before it, obtain the error adjustment matrix at each acquisition moment.

[0043] Since in the temperature monitoring and control process of the fan foundation, the temperature changes at different moments are disorder characteristics caused by long-term effects, therefore, based on the analysis results of the temperature distribution disorder characteristics at each moment, analyze the distribution deviation of the temperature control of the fan foundation, and obtain the error adjustment matrix based on the analysis results.

[0044] Specifically, calculate the proportion of the hierarchical monitoring difference coefficient at each acquisition moment in the hierarchical monitoring difference coefficients at all acquisition moments. The purpose of calculating the proportion is to normalize the hierarchical monitoring difference coefficient, and based on the proportions at different acquisition moments, check and adjust the controlled temperature error. Calculate the difference between each element in each hierarchical monitoring data matrix and the preset standard temperature of its corresponding acquisition point, and form the error matrix at each acquisition moment with all the differences at each acquisition moment. Among them, the preset standard temperature of each acquisition point is determined by national standards, environmental conditions and construction requirements.

[0045] Furthermore, through the proportions and the error matrix at each acquisition moment and all the acquisition moments before it, obtain the error adjustment matrix at each acquisition moment, and the expression is: ; in the formula, represents the error adjustment matrix at the th acquisition moment; represents the the proportion at the v-th acquisition moment; denote the error matrix at the v-th acquisition moment; denote the total number of all acquisition moments before the v-th acquisition moment.

[0046] It should be noted that: the above cumulative calculation is a weighted fusion calculation between error matrices, that is, by integrating the linear features and cumulative trend differences at different moments, the distribution deviation of the temperature control at the v-th acquisition moment is adjusted, so as to achieve precise temperature control. The schematic diagram of the acquisition process of the error adjustment matrix is as shown. Figure 5 as

[0047] Step S3, based on the error adjustment matrix, control the temperature of the concrete of the fan foundation.

[0048] During the temperature monitoring and control process of the fan foundation, considering that when the temperature of the concrete of the fan foundation is disordered, there are temperature differences at different positions of the fan foundation, and combining the linear features and cumulative trend differences of the temperature distribution during the temperature monitoring and control process of the fan foundation to obtain the error adjustment matrix, reducing the dependence on the temperature data at the current moment, so as to avoid the continuous influence caused by control lag.

[0049] Furthermore, based on the error adjustment matrix at the current acquisition moment, the monitoring center server for monitoring the temperature of the concrete of the fan foundation outputs a control instruction, adjusts the water flow valve of the internal cooling water pipe of the fan foundation according to the control instruction, controls the cooling water volume and flow rate according to the water flow valve, and at the same time, for the thermal insulation and moisture preservation device for controlling the thermal insulation and moisture preservation state of the concrete surface, controls the control switch of the thermal insulation and moisture preservation device to regulate the thermal insulation and moisture preservation state of the concrete surface to control the internal temperature of the concrete.

[0050] Based on the same inventive concept as the above method, the embodiment of the present application also provides a temperature control device for mass concrete of a fan foundation, including: a cooling water pipe device, a temperature monitoring device, and a thermal insulation and moisture preservation device.

[0051] Cooling water pipe device: The cooling water pipe is made of high-strength and corrosion-resistant thin-walled steel pipes. Inside the steel bar skeleton of the fan foundation, the cooling water pipes are arranged according to precise design requirements, and positioning steel bars are used to fix the cooling water pipes at specific intervals (for example, every 1 meter), so as to ensure that the cooling water pipes always maintain a stable position state during the concrete pouring and subsequent use process, so as to ensure that the cooling function of the cooling water pipes can be effectively exerted.

[0052] Temperature monitoring device: including a data acquisition terminal, a monitoring center server, and an actuator.

[0053] The data acquisition terminal undertakes the key task of collecting the internal temperature data of concrete. It obtains temperature information through high-precision sensors distributed at different parts of the concrete (such as the monitoring points scientifically planned according to the shape and size of the fan foundation), and transmits this data to the monitoring center server; The monitoring center server is used to, for each acquisition moment, obtain the linear feature vector of each acquisition moment by analyzing the collinearity between the temperature data at different preset depths; comprehensively analyze the change trends of the temperature data of each preset position at each acquisition moment and all acquisition moments before it to obtain the cumulative trend test matrix of each acquisition moment; obtain the hierarchical monitoring difference coefficient of each acquisition moment by analyzing the difference situation of the cumulative trend test matrix between each acquisition moment and all acquisition moments before it, and the similarity degree of the linear feature vectors between each acquisition moment and all acquisition moments before it; obtain the error adjustment matrix of each acquisition moment by analyzing the difference between the temperature data of each preset position at each acquisition moment and the preset standard temperature, and combining the distribution of the hierarchical monitoring difference coefficients of each acquisition moment and all acquisition moments before it; output a control instruction through the error adjustment matrix; The actuator accurately adjusts the water flow valve of the cooling water pipe according to the received control instruction to control the water flow rate and velocity, and thus control the internal temperature of the concrete; at the same time, it controls the control switch of the heat and moisture preservation device to regulate the heat and moisture preservation state of the concrete surface to control the internal temperature of the concrete.

[0054] Heat and moisture preservation device: The heat preservation material designed according to the shape and size of the fan foundation is laid on the concrete surface in a uniform and non-omissive manner; and the heat and moisture preservation device adopts an advanced automatic spraying curing system to keep the concrete surface in a suitable moist state. The automatic spraying curing system real-time monitors the moist condition of the concrete surface through a humidity sensor, and sprays the water in the water storage tank onto the concrete surface in a uniform atomized form through a nozzle, so as to continuously keep the concrete surface in a suitable moist state, effectively preventing quality problems such as dry shrinkage cracks caused by surface water loss, and providing a strong guarantee for the quality stability of the large-volume fan foundation concrete.

[0055] Based on the same inventive concept as the above method, the embodiment of the present application also provides a temperature control system for large-volume fan foundation concrete, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above methods for the temperature control method of large-volume fan foundation concrete.

[0056] In summary, by reasonably arranging the positions for collecting temperature data, the present application obtains data at different regions and different depths inside the concrete of the fan foundation; by analyzing the linear characteristics and cumulative trend characteristics of the temperature distribution inside the concrete of the fan foundation, it can accurately capture the dynamic change characteristics of the temperature inside the concrete of the fan foundation at different stages and different depths. Furthermore, in the process of temperature monitoring and control of the fan foundation, by comprehensively analyzing the degree to which the actual temperature at each position deviates from the standard temperature, the linear characteristics and cumulative trend characteristics of the temperature distribution at each collection moment, an error adjustment matrix at the current collection moment is obtained, and then the temperature is controlled based on the error adjustment matrix. This can avoid relying on the temperature data at the current moment, accurately adjust the control error caused by the long-term influence of temperature disorder, and improve the accuracy of temperature control for the large-volume concrete of the fan foundation.

[0057] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the block may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description. Sometimes, there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. Each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0058] For those skilled in the art, it is obvious that the present application is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the basic characteristics of the present application. Therefore, from any point of view, the above embodiments of the present application should be regarded as exemplary and non-limiting.

Claims

1. Temperature control method for mass concrete of fan foundation, characterized in that The method includes the following steps: Obtain the temperature data at each preset position at each preset depth inside the fan foundation in real time; For each acquisition moment, by analyzing the collinearity between the temperature data at different preset depths, obtain the linear feature vector at each acquisition moment; Comprehensively analyze the change trends of the temperature data at each preset position at each acquisition moment and all acquisition moments before it, and obtain the cumulative trend test matrix at each acquisition moment; By analyzing the differences of the cumulative trend test matrix between each acquisition moment and all acquisition moments before it, and the similarity degree of the linear feature vectors between each acquisition moment and all acquisition moments before it, obtain the hierarchical monitoring difference coefficient at each acquisition moment; By analyzing the differences between the temperature data at each preset position at each acquisition moment and the preset standard temperature, and combining the distribution of the hierarchical monitoring difference coefficients at each acquisition moment and all acquisition moments before it, obtain the error adjustment matrix at each acquisition moment; Based on the error adjustment matrix, control the temperature of the fan foundation concrete.

2. The temperature control method for mass concrete of a large-volume fan foundation according to claim 1, characterized in that The process of obtaining the linear feature vector is as follows: For each acquisition moment, form the depth monitoring data sequence at each acquisition moment with the temperature data at all preset positions at each preset depth; among them, the temperature data at the same position in all depth monitoring sequences corresponds to the preset positions on the same vertical line; Calculate the variance inflation factor of the depth monitoring data sequence at each acquisition moment; The linear feature vector is composed of all the variance inflation factors at each acquisition moment.

3. The temperature control method for mass concrete of a large-volume fan foundation according to claim 1, characterized in that, The process of obtaining the cumulative trend test matrix is as follows: Take the temperature data at each preset position at each acquisition moment and all acquisition moments before it as the input of the trend test algorithm, and output the trend statistic at each preset position at each acquisition moment; The cumulative trend test matrix is composed of the trend statistics at each preset position at each acquisition moment.

4. The temperature control method for mass concrete of a large-volume fan foundation according to claim 1, characterized in that The process of obtaining the hierarchical monitoring difference coefficient is as follows: Denote any acquisition moment as , calculate the difference degree of the cumulative trend test matrix between the -th acquisition moment and the -th acquisition moment before it, and the similarity degree of the linear feature vector between the -th acquisition moment and the -th acquisition moment before it, and calculate the sum value of the similarity degree and a preset value greater than 0; Calculate the ratio of the difference degree to the sum value, and calculate the ratio between the th acquisition moment and the previous acquisition moments; The stratified monitoring difference coefficient at the th acquisition moment is positively correlated with all of the said ratios corresponding to the 5. The temperature control method for mass concrete of a large-volume fan foundation according to claim 4, characterized in that, The hierarchical monitoring difference coefficient at the th acquisition moment is the mean of all the ratios corresponding to the th acquisition moment.

6. The temperature control method for mass concrete of a large-volume fan foundation according to claim 1, characterized in that The process of obtaining the error adjustment matrix is as follows: Calculate the proportion of the hierarchical monitoring difference coefficient at each acquisition moment in the hierarchical monitoring difference coefficients at all acquisition moments; Form the error matrix at each acquisition moment with the difference between the temperature data at all preset positions at each acquisition moment and the preset standard temperature; Take the weighted fusion result of the error matrices at each acquisition moment and all acquisition moments before it and the proportion as the error adjustment matrix at each acquisition moment.

7. The temperature control method for mass concrete of a large-volume fan foundation according to claim 6, characterized in that, The calculation method of the error adjustment matrix is as follows: Calculate the product of the error matrix at each acquisition moment and the proportion, and the error adjustment matrix is the sum of the products at each acquisition moment and all acquisition moments before it.

8. The temperature control method for mass concrete of a large-volume fan foundation according to claim 1, characterized in that, The process of controlling the temperature of the fan foundation concrete is: based on the error adjustment matrix at the current acquisition moment, obtain a control instruction, and control the internal temperature of the concrete according to the control instruction.

9. Temperature control device for mass concrete of fan foundation, characterized in that, There is a temperature monitoring device in the device; The temperature monitoring device includes a data acquisition terminal, a monitoring center server and an actuator; Among them, the data acquisition terminal is used to obtain the temperature data at each preset position at each preset depth inside the fan foundation in real time; The monitoring center server is used to obtain the linear feature vectors at each acquisition moment by analyzing the collinearity between the temperature data at different preset depths for each acquisition moment; Comprehensively analyze the change trends of the temperature data at each preset position at each acquisition moment and all acquisition moments before it to obtain the cumulative trend test matrix at each acquisition moment; By analyzing the differences in the cumulative trend test matrix between each acquisition moment and all acquisition moments before it, and the similarity degree of the linear feature vectors between each acquisition moment and all acquisition moments before it, obtain the hierarchical monitoring difference coefficient at each acquisition moment; By analyzing the differences between the temperature data at each preset position at each acquisition moment and the preset standard temperature, and combining the distribution of the hierarchical monitoring difference coefficients at each acquisition moment and all acquisition moments before it, obtain the error adjustment matrix at each acquisition moment; output a control instruction through the error adjustment matrix; The actuator is used to control the internal temperature of the concrete through the control instruction.

10. Temperature control system for mass concrete of fan foundation, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the temperature control method for the mass concrete of the large-volume fan foundation as described in any one of claims 1-8.

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