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

Abstract

The present application relates to the field of intelligent temperature control technology, specifically to a method, device, and system for controlling the temperature of large-volume fan foundation concrete. The method comprises: acquiring temperature data at each preset location at each preset depth within the fan foundation in real time; acquiring a linear eigenvector at each acquisition moment; comprehensively analyzing the changing trends of the temperature data at each preset location at each acquisition moment and all previous acquisition moments to acquire a cumulative trend test matrix at each acquisition moment; acquiring a stratified monitoring difference coefficient at each acquisition moment; and acquiring an error adjustment matrix at each acquisition moment by analyzing the difference between the temperature data at each preset location at each acquisition moment and a preset standard temperature, and combining the distribution of the stratified monitoring difference coefficients at each acquisition moment and all previous acquisition moments; thereby controlling the temperature of the fan foundation concrete. The present application aims to improve the accuracy of temperature control of large-volume fan foundation concrete.

Description

Technical Field

[0001] The present application relates to the field of intelligent temperature control technology, and in particular to a method, device and system for controlling the temperature of large-volume fan foundation concrete. Background Art

[0002] Large-volume wind turbine foundation concrete refers to the large concrete foundation structure used to support the wind turbine tower in wind power generation projects. With the rapid development of wind power generation technology, the capacity of wind turbines has continued to increase, and the scale of wind turbine foundations has also become increasingly large. The construction of large-volume wind turbine foundation concrete faces many challenges, among which temperature control is particularly critical.

[0003] Improper temperature control can severely impact the quality and performance of fan foundations, such as causing cracks in the concrete and reducing its durability. This, in turn, can affect the safe and stable operation of the fan, increase maintenance costs, and shorten its service life. Currently, temperature monitoring and control of large-volume fan foundation concrete is typically performed based on current temperature data, ignoring the long-term impact of temperature disturbances. This results in significant deviations in temperature control of the fan foundation concrete. Summary of the Invention

[0004] In view of the above, it is necessary to provide a temperature control method, device and system for large-volume fan foundation concrete, which improves the accuracy of temperature control of large-volume fan foundation concrete compared with traditional temperature control methods for large-volume fan foundation concrete:

[0005] In a first aspect, an embodiment of the present application provides a method for controlling the temperature of a large-volume wind turbine foundation concrete, the method comprising the following steps:

[0006] Real-time acquisition of temperature data at each preset location at each preset depth within the fan foundation;

[0007] For each acquisition moment, by analyzing the collinearity between the temperature data at different preset depths, a linear feature vector at each acquisition moment is obtained;

[0008] Comprehensively analyzing the change trend of the temperature data of each of the preset locations at each collection moment and all previous collection moments to obtain a cumulative trend test matrix at each collection moment;

[0009] By analyzing the difference between the cumulative trend test matrix of each collection moment and all previous collection moments, and the similarity between the linear feature vectors of each collection moment and all previous collection moments, the hierarchical monitoring difference coefficient of each collection moment is obtained;

[0010] By analyzing the difference between the temperature data of each preset position at each collection moment and the preset standard temperature, and combining the distribution of the layered monitoring difference coefficient at each collection moment and all previous collection moments, an error adjustment matrix at each collection moment is obtained;

[0011] Based on the error adjustment matrix, the temperature of the fan foundation concrete is controlled.

[0012] In one embodiment, the linear feature vector is obtained as follows:

[0013] For each collection moment, the temperature data of all the preset positions at each preset depth are combined to form a depth monitoring data sequence at each collection moment; wherein the preset positions corresponding to the temperature data at the same position in all depth monitoring sequences are on the same vertical line;

[0014] Calculate the variance inflation factor of each depth monitoring data series at each acquisition moment;

[0015] The linear feature vector is composed of all the variance inflation factors at each acquisition moment.

[0016] In one embodiment, the process of obtaining the cumulative trend test matrix is ​​as follows:

[0017] Using the temperature data of each preset location at each sampling moment and all previous sampling moments as input to a trend detection algorithm, and outputting trend statistics of each preset location at each sampling moment;

[0018] The cumulative trend test matrix is ​​composed of trend statistics of all the preset positions at each collection moment.

[0019] In one embodiment, the process of obtaining the hierarchical monitoring difference coefficient is as follows:

[0020] Any collection time is recorded as , calculate the The collection moment and the The difference of the cumulative trend test matrix between the collection moments, and the The collection moment and the similarity of the linear feature vectors between the acquisition moments, and calculating the sum of the similarity and a preset value greater than 0;

[0021] Calculate the ratio of the difference to the sum, and calculate the The ratio between a collection moment and its previous collection moments;

[0022] No. The difference coefficient of stratified monitoring at the first collection moment is the same as that at the All the ratios corresponding to the acquisition moments are positively correlated.

[0023] In one embodiment, the The coefficient of difference of stratified monitoring at the collection moment is The mean of all the ratios corresponding to the acquisition moments.

[0024] In one embodiment, the error adjustment matrix is ​​obtained as follows:

[0025] Calculate the proportion of the stratified monitoring difference coefficient at each collection moment in the stratified monitoring difference coefficients of all collection moments;

[0026] The difference between the temperature data of all the preset positions at each collection moment and the preset standard temperature is used to form an error matrix at each collection moment;

[0027] The weighted fusion result of the error matrix of each acquisition moment and all previous acquisition moments and the proportion is used as the error adjustment matrix of each acquisition moment.

[0028] In one embodiment, the error adjustment matrix is ​​calculated as follows:

[0029] The product of the error matrix at each acquisition moment and the proportion is calculated, and the error adjustment matrix is ​​the sum of the products at each acquisition moment and all previous acquisition moments.

[0030] In one embodiment, the process of controlling the temperature of the fan foundation concrete is: obtaining a control instruction based on the error adjustment matrix at the current acquisition moment, and controlling the internal temperature of the concrete according to the control instruction.

[0031] In a second aspect, an embodiment of the present application further provides a temperature control device for large-volume wind turbine foundation concrete, wherein the device includes a temperature monitoring device;

[0032] The temperature monitoring device includes a data acquisition terminal, a monitoring center server and an actuator;

[0033] The data acquisition terminal is used to obtain temperature data of each preset position at each preset depth in the fan foundation in real time;

[0034] The monitoring center server is used to obtain a linear feature vector at each collection moment by analyzing the collinearity between the temperature data at different preset depths;

[0035] Comprehensively analyzing the change trend of the temperature data of each of the preset locations at each collection moment and all previous collection moments to obtain a cumulative trend test matrix at each collection moment;

[0036] By analyzing the difference between the cumulative trend test matrix of each collection moment and all previous collection moments, and the similarity between the linear feature vectors of each collection moment and all previous collection moments, the hierarchical monitoring difference coefficient of each collection moment is obtained;

[0037] By analyzing the difference between the temperature data of each preset position at each collection moment and the preset standard temperature, and combining the distribution of the layered monitoring difference coefficient at each collection moment and all previous collection moments, an error adjustment matrix for each collection moment is obtained; and a control instruction is outputted through the error adjustment matrix;

[0038] The actuator is used to control the internal temperature of concrete through control instructions.

[0039] In a third aspect, an embodiment of the present application also provides a temperature control system for large-volume fan foundation concrete, comprising 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 any one of the above-mentioned methods for controlling the temperature of large-volume fan foundation concrete are implemented.

[0040] This application has at least the following beneficial effects:

[0041] In the prior art, there are deficiencies in the temperature monitoring of large-volume fan foundation concrete. Temperature control is performed only based on the temperature data at the current moment, and the long-term impact characteristics of temperature disorder distribution are not fully considered, resulting in large errors in the temperature control of the fan foundation concrete. The present application obtains data from different areas and depths inside the fan foundation concrete by reasonably arranging the locations for collecting temperature data; analyzes the linear characteristics and cumulative trend characteristics of the temperature distribution inside the fan foundation concrete, and can accurately capture the dynamic change characteristics of the temperature inside the fan foundation concrete at different stages and different depths. Then, in the temperature monitoring and control process of the fan foundation, the degree to which the actual temperature of each position at each collection moment deviates from the standard temperature, the linear characteristics and cumulative trend characteristics of the temperature distribution are comprehensively analyzed to obtain the error adjustment matrix at the current collection moment, and then the temperature is controlled based on the error adjustment matrix. This can avoid dependence on the temperature data at the current moment, accurately adjust the control error caused by the long-term impact of temperature disorder, and improve the accuracy of temperature control of large-volume fan foundation concrete. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0043] Figure 1 A flowchart of the steps of a temperature control method for a large-volume fan foundation concrete according to one embodiment of the present application;

[0044] Figure 2 This is a schematic diagram of the distribution of temperature collection points;

[0045] Figure 3 It is a schematic diagram of the arrangement of temperature sensors at each temperature collection point;

[0046] Figure 4 Schematic diagram of the process of obtaining linear feature vectors;

[0047] Figure 5 Schematic diagram of the process of obtaining the error adjustment matrix. DETAILED DESCRIPTION

[0048] In the description of the embodiments of this application, words such as "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "or," and "for example" is intended to present the relevant concepts in a concrete manner.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application relates. The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. It should be understood that, unless otherwise indicated, " / " represents or.

[0050] It should also be noted that the terms "first" and "second" in this application are used to distinguish similar objects, rather than to describe a specific order or sequence.

[0051] The specific scheme of the temperature control method, device and system for large-volume fan foundation concrete provided by this application is described in detail below with reference to the accompanying drawings.

[0052] See also Figure 1 , which shows a flow chart of the steps of a temperature control method for a large-volume fan foundation concrete provided by an embodiment of the present application, the method comprising the following steps:

[0053] Step S1 : acquiring temperature data of each preset position at each depth within the fan foundation in real time.

[0054] In the temperature control process of large-volume fan foundation concrete, 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 fan foundation to collect its monitoring data; the distribution diagram of the temperature collection points is as follows Figure 2 As shown, Figure 2 1 to 16 represent temperature collection points, and 0 represents the fan foundation; Figure 2 The schematic diagram is a top-down view. 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 three temperature sensors are named upper collection point, middle collection point and lower collection point in descending order of depth. The layout diagram of the temperature sensors at each temperature collection point is shown in FIG. Figure 3 As shown, Figure 3 1 in the middle represents the temperature collection point, represents a vertical line, 、 and Respectively represent the temperature sensors at the upper, middle and lower collection points, 、 and On the same vertical line Above, 101 represents the concrete slab of the wind turbine foundation. All upper collection points are at the same depth, all middle collection points are at the same depth, and all lower collection points are also at the same depth. The value of M being 3 is merely an example of this application; implementers may set the value of M at their discretion.

[0055] In this embodiment, the upper collection point is 30 cm away from the upper surface of the fan foundation concrete slab, the middle collection point is the center of the fan foundation concrete depth, and the lower collection point is 30 cm away from the lower surface of the fan foundation concrete slab. The depths of the upper collection point, the middle collection point and the lower collection point are preset manually, and the implementer can limit them according to actual conditions. This application does not impose any special restrictions.

[0056] During the concrete pouring and curing process, environmental factors and changes in the instrument's own state may introduce noise interference, affecting data quality. To address this issue, noise reduction is performed on the collected temperature data.

[0057] In this embodiment, a Wiener filter is used to perform noise reduction processing on the collected temperature data. As other implementation methods, on the basis of being able to perform noise reduction processing on the collected temperature data, the implementer may adopt other existing technologies, such as a median filter, a mean filter, etc., and this application does not impose any special restrictions.

[0058] Step S2: Acquire an error adjustment matrix based on the linear characteristics and cumulative trend differences of the temperature distribution during the temperature monitoring and control process.

[0059] Temperature instability can cause numerous problems in the temperature control of large-volume wind turbine foundation concrete. Temperature fluctuations disrupt the stress distribution within the concrete, complicating the crack generation mechanism, affecting the durability and bearing capacity of the concrete, and altering the pore structure, leading to uncertainty in the evaluation of properties such as impermeability and corrosion resistance. Stress instability induced by temperature fluctuations can cause microcracks within the concrete to expand and new cracks to form, undermining structural integrity, interfering with internal moisture and ion migration, and accelerating performance degradation. When the temperature within the concrete rises, thermal expansion causes volume expansion and changes in the pore structure, triggering stress redistribution and exacerbating stress instability. Stress changes lead to local deformation of the structure, disrupting temperature uniformity and stability. Therefore, it is necessary to analyze the collected temperature data to implement timely control when temperature deviations occur.

[0060] Step S2.1: For each acquisition moment, the linear feature vector of each acquisition moment is obtained by analyzing the collinearity between the temperature data at different preset depths.

[0061] Since in the actual temperature control process, the temperatures at different temperature collection points change in a related manner, and the temperatures at different depths also change in a related manner, if the temperature changes at different locations show chaotic characteristics, the temperature of the large-volume fan foundation concrete may be affected by long-term temperature fluctuations due to environmental interference.

[0062] Based on the above analysis, in order to effectively integrate the relationship between the temperature state changes between different temperature collection points of the fan foundation concrete, and thus accurately reflect the deviation of the temperature monitoring and control of the fan foundation, for each collection moment, the temperature data of all upper collection points, all middle collection points, and all lower collection points are respectively composed into each depth monitoring data sequence at each collection moment, among which the temperature data at the same position in all depth monitoring sequences correspond to the same temperature collection point.

[0063] All depth monitoring data sequences at each acquisition moment are grouped into a layered monitoring data matrix at each acquisition moment, and one row in the layered monitoring data matrix is ​​a depth monitoring data sequence.

[0064] Furthermore, the layered monitoring data matrix at 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 the temperature monitoring and control process of the large-volume fan foundation, the temperature state change relationship between different temperature acquisition points has a linear consistency feature, that is, under normal circumstances, if the temperature of one temperature acquisition point changes, the temperature of other temperature acquisition points will also change accordingly; therefore, the layered monitoring data matrix at each acquisition moment is used as input to obtain the variance expansion factor of each row of data. The larger the variance expansion factor, the more significant the collinearity feature 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 expansion factor is a well-known technology and will not be repeated in this application.

[0065] Furthermore, all the variance inflation factors at each acquisition moment are combined to form a linear feature vector at each acquisition moment. The schematic diagram of the linear feature vector acquisition process is as follows: Figure 4 shown.

[0066] Step S2.2, comprehensively analyzing the change trend of the temperature data of each of the preset positions at each collection moment and all previous collection moments, and obtaining a cumulative trend test matrix at each collection moment.

[0067] The change trend of the temperature data of each location at each collection moment and all previous collection moments during the temperature monitoring and control process of the fan foundation is 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 previous collection moments is used as the input of the trend verification algorithm, and the trend statistics of the any collection point at each collection moment are output. The trend statistics of all collection points at each collection moment are composed into a cumulative trend verification matrix. The trend verification 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, a row in the cumulative trend verification matrix is ​​the trend statistics of the collection points at the same depth.

[0068] 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 methods, on the basis of being able to measure the changing trend of temperature data, the implementer can use other existing technologies for measurement, such as the slope method, Cox-Stuart test, etc., and this application does not impose any special restrictions.

[0069] Step S2.3, by analyzing the difference between the cumulative trend test matrix of each collection moment and all previous collection moments, and the similarity between the linear feature vectors of each collection moment and all previous collection moments, the hierarchical monitoring difference coefficient of each collection moment is obtained.

[0070] The expression of the stratified monitoring difference coefficient at each collection time is:

[0071] Where, Indicates the The coefficient of difference of stratified monitoring at each collection moment; and Respectively represent and The cumulative trend test matrix of each collection moment; represents the difference measurement function; and Respectively represent and The linear feature vector of each acquisition moment; represents the similarity measurement function; Indicates the The total number of all collection moments before the collection moment; Indicates that the value is preset to be greater than 0, in order to avoid the denominator being 0. The value of is preset by humans and can be set by the implementer. The value of is 0.01.

[0072] In this embodiment, the difference measurement function is the SAD (Sum of Absolute Difference) value. As other implementation methods, on the basis of being able to measure the differences between elements at the same position in the cumulative trend test matrix, the implementer can use other existing technologies for measurement, such as Euclidean distance, mean square error, etc., and this application does not impose any special restrictions.

[0073] In this embodiment, the similarity measurement function can be cosine similarity. As other implementation methods, on the basis of being able to measure the similarity between linear feature vectors, the implementer can use other calculation methods for measurement, such as the reciprocal of the Hamming distance, the reciprocal of the Euclidean distance, etc., and this application does not impose any special restrictions.

[0074] It should be noted that the larger the calculated hierarchical monitoring difference coefficient is, the greater the possibility of disorder in the temperature control distribution difference of the current fan foundation in the linear characteristics and cumulative trend difference analysis of the temperature monitoring and control process of the comprehensive fan foundation.

[0075] Step S2.4, by analyzing the difference between the temperature data of each preset position at each collection moment and the preset standard temperature, and combining the distribution of the layered monitoring difference coefficient at each collection moment and all previous collection moments, the error adjustment matrix of each collection moment is obtained.

[0076] Since the temperature changes at different moments in the temperature monitoring and control process of the fan foundation are disordered characteristics caused by long-term influences, the distribution deviation of the temperature control of the fan foundation is analyzed based on the analysis results of the disordered temperature distribution characteristics at each moment, and the error adjustment matrix is ​​obtained based on the analysis results.

[0077] Specifically, the ratio of the stratified monitoring difference coefficient at each collection moment to the stratified monitoring difference coefficient at all collection moments is calculated. The purpose of calculating this ratio is to normalize the stratified monitoring difference coefficient and to verify and adjust the controlled temperature error based on the ratio at different collection moments. The difference between each element in each stratified monitoring data matrix and the preset standard temperature of its corresponding collection point is calculated, and all these differences at each collection moment are combined to form an error matrix for each collection moment. The preset standard temperature for each collection point is determined by national standards, environmental conditions, and construction requirements.

[0078] Furthermore, the error adjustment matrix of each acquisition moment is obtained by using the proportion of each acquisition moment and all previous acquisition moments and the error matrix, and the expression is:

[0079] Where, Indicates the The error adjustment matrix at each acquisition moment; Indicates the The proportion of the collection time; represents the error matrix at the vth acquisition moment; Indicates the The total number of all collection moments before the collection moment.

[0080] It should be noted that the above cumulative calculation is a weighted fusion calculation between error matrices, that is, the linear characteristics and cumulative trend differences at different times are combined to calculate the first The distribution deviation of temperature control at each acquisition moment is adjusted to achieve accurate temperature control. The flowchart of obtaining the error adjustment matrix is ​​as follows: Figure 5 shown.

[0081] Step S3: controlling the temperature of the fan foundation concrete based on the error adjustment matrix.

[0082] In the temperature monitoring and control process of the fan foundation, when the temperature of the fan foundation concrete is disturbed, temperature differences occur at different positions of the fan foundation. The error adjustment matrix is ​​obtained by combining the linear characteristics of the temperature distribution and the cumulative trend difference in the temperature monitoring and control process of the fan foundation to reduce the dependence on the temperature data at the current moment, thereby avoiding the continuous impact caused by control lag.

[0083] Furthermore, based on the error adjustment matrix at the current acquisition moment, a monitoring center server for monitoring the temperature of the fan foundation concrete is used to output a control instruction, and the water flow valve of the cooling water pipe inside the fan foundation is adjusted according to the control instruction. The cooling water volume and flow rate are controlled according to the water flow valve. At the same time, for the thermal insulation and moisturizing device used to control the thermal insulation and moisturizing state of the concrete surface, the control switch of the thermal insulation and moisturizing device is controlled to adjust the thermal insulation and moisturizing state of the concrete surface to control the internal temperature of the concrete.

[0084] Based on the same inventive concept as the above method, an embodiment of the present application also provides a temperature control device for large-volume fan foundation concrete, including: a cooling water pipe device, a temperature monitoring device and a heat preservation and moisture retention device.

[0085] Cooling water pipe installation: The cooling water pipe is made of high-strength and corrosion-resistant thin-walled steel pipe. Inside the fan foundation steel frame, the cooling water pipe is laid out according to precise design requirements. Positioning steel bars are used to fix the cooling water pipe at specific intervals (for example, every 1 meter). This ensures that the cooling water pipe always maintains a stable position during concrete pouring and subsequent use, ensuring that the cooling function of the cooling water pipe can be effectively exerted.

[0086] Temperature monitoring device: includes data acquisition terminal, monitoring center server and actuator.

[0087] The data acquisition terminal is responsible for collecting temperature data inside the concrete. It obtains temperature information through high-precision sensors distributed at different locations within the concrete (e.g., monitoring points scientifically planned according to the shape and size of the fan foundation) and transmits this data to the monitoring center server.

[0088] The monitoring center server is used to obtain, for each collection moment, a linear feature vector at each collection moment by analyzing the collinearity between the temperature data at different preset depths; comprehensively analyze the change trend of the temperature data at each preset position at each collection moment and all previous collection moments to obtain a cumulative trend test matrix at each collection moment; obtain a stratified monitoring difference coefficient at each collection moment by analyzing the difference between the cumulative trend test matrix at each collection moment and all previous collection moments, and the similarity of the linear feature vectors between each collection moment and all previous collection moments; obtain an error adjustment matrix at each collection moment by analyzing the difference between the temperature data at each preset position at each collection moment and the preset standard temperature, and combining the distribution of the stratified monitoring difference coefficients at each collection moment and all previous collection moments; and output a control instruction through the error adjustment matrix;

[0089] The actuator accurately adjusts the water flow valve of the cooling water pipe according to the control instructions received to control the water flow and flow rate, thereby controlling the internal temperature of the concrete; at the same time, it controls the control switch of the thermal insulation and moisturizing device to regulate the thermal insulation and moisturizing state of the concrete surface to control the internal temperature of the concrete.

[0090] Thermal Insulation and Moisture Retention Device: Insulation materials designed based on the shape and size of the fan foundation are evenly and thoroughly applied to the concrete surface. Furthermore, the thermal insulation and moisture retention device utilizes an advanced automatic spray curing system to maintain the concrete surface in an optimally moist state. This system uses humidity sensors to monitor the concrete surface's moisture level in real time. Sprinklers then spray water from a storage tank onto the concrete surface in a uniform atomized form, maintaining a constant moisture level. This effectively prevents shrinkage cracks and other quality issues caused by surface water loss, providing a strong guarantee for the quality stability of large-volume fan foundation concrete.

[0091] Based on the same inventive concept as the above-mentioned method, an 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, the steps of any one of the above-mentioned temperature control methods for large-volume fan foundation concrete are implemented.

[0092] To sum up, this application obtains data from different areas and depths inside the fan foundation concrete by reasonably arranging the locations for collecting temperature data; analyzing the linear characteristics and cumulative trend characteristics of the temperature distribution inside the fan foundation concrete, it can accurately capture the dynamic change characteristics of the temperature inside the fan foundation concrete at different stages and depths, and then in the temperature monitoring and control process of the fan foundation, comprehensively analyze the degree to which the actual temperature of each position at each collection moment deviates from the standard temperature, the linear characteristics and cumulative trend characteristics of the temperature distribution, obtain the error adjustment matrix at the current collection moment, and then control the temperature based on the error adjustment matrix, which can avoid dependence on the temperature data at the current moment, accurately adjust the control error caused by the long-term impact of temperature disturbances, and improve the accuracy of temperature control of large-volume fan foundation concrete.

[0093] The flowcharts and block diagrams in the accompanying drawings show the possible implementation architectures, functions and operations of the systems, methods and computer program products according to the embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of the code, and the module, program segment or part of the code contains one or more executable instructions for implementing the specified logical functions. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, which can depend 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 boxes can also occur in an order different from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified function or action, or may be implemented by a combination of dedicated hardware and computer instructions.

[0094] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the basic characteristics of the present application. Therefore, from all perspectives, the above embodiments of the present application should be regarded as exemplary and non-restrictive.

Claims

1. A temperature control method for large-volume fan foundation concrete, characterized in that: The method comprises the following steps: Real-time acquisition of temperature data at each preset location at each preset depth within the fan foundation; For each acquisition moment, by analyzing the collinearity between the temperature data at different preset depths, a linear feature vector at each acquisition moment is obtained; Comprehensively analyzing the change trend of the temperature data of each of the preset locations at each collection moment and all previous collection moments to obtain a cumulative trend test matrix at each collection moment; By analyzing the difference between the cumulative trend test matrix of each collection moment and all previous collection moments, and the similarity between the linear feature vectors of each collection moment and all previous collection moments, the hierarchical monitoring difference coefficient of each collection moment is obtained; By analyzing the difference between the temperature data of each preset position at each collection moment and the preset standard temperature, and combining the distribution of the layered monitoring difference coefficient at each collection moment and all previous collection moments, an error adjustment matrix at each collection moment is obtained; Based on the error adjustment matrix, the temperature of the fan foundation concrete is controlled.

2. The temperature control method for large-volume fan foundation concrete according to claim 1, characterized in that: The process of obtaining the linear feature vector is as follows: For each collection moment, the temperature data of all the preset positions at each preset depth are combined to form a depth monitoring data sequence at each collection moment; wherein the preset positions corresponding to the temperature data at the same position in all depth monitoring sequences are on the same vertical line; Calculate the variance inflation factor of each depth monitoring data series 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 large-volume fan foundation concrete according to claim 1, characterized in that: The acquisition process of the cumulative trend test matrix is: Using the temperature data of each preset location at each sampling moment and all previous sampling moments as input to a trend detection algorithm, and outputting trend statistics of each preset location at each sampling moment; The cumulative trend test matrix is ​​composed of trend statistics of all the preset positions at each collection moment.

4. The temperature control method for large-volume fan foundation concrete according to claim 1, characterized in that: The process of obtaining the hierarchical monitoring difference coefficient is as follows: Any collection time is recorded as , calculate the The collection moment and the The difference of the cumulative trend test matrix between the collection moments, and the The collection moment and the similarity of the linear feature vectors between the acquisition moments, and calculating the sum of the similarity and a preset value greater than 0; Calculate the ratio of the difference to the sum, and calculate the The ratio between a collection moment and its previous collection moments; No. The difference coefficient of stratified monitoring at the first collection moment is the same as that at the All the ratios corresponding to the acquisition moments are positively correlated.

5. The temperature control method for large-volume fan foundation concrete according to claim 4, characterized in that: The said The coefficient of difference of stratified monitoring at the collection moment is The mean of all the ratios corresponding to the acquisition moments.

6. The temperature control method for large-volume fan foundation concrete according to claim 1, characterized in that: The process of obtaining the error adjustment matrix is ​​as follows: Calculate the proportion of the stratified monitoring difference coefficient at each collection moment in the stratified monitoring difference coefficients of all collection moments; The difference between the temperature data of all the preset positions at each collection moment and the preset standard temperature is used to form an error matrix at each collection moment; The weighted fusion result of the error matrix of each acquisition moment and all previous acquisition moments and the proportion is used as the error adjustment matrix of each acquisition moment.

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

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

9. Temperature control device for large-volume fan foundation concrete, characterized in that: The device includes a temperature monitoring device; The temperature monitoring device includes a data acquisition terminal, a monitoring center server and an actuator; The data acquisition terminal is used to obtain temperature data of each preset position at each preset depth in the fan foundation in real time; The monitoring center server is used to obtain a linear feature vector at each collection moment by analyzing the collinearity between the temperature data at different preset depths; Comprehensively analyzing the change trend of the temperature data of each of the preset locations at each collection moment and all previous collection moments to obtain a cumulative trend test matrix at each collection moment; By analyzing the difference between the cumulative trend test matrix of each collection moment and all previous collection moments, and the similarity between the linear feature vectors of each collection moment and all previous collection moments, the hierarchical monitoring difference coefficient of each collection moment is obtained; By analyzing the difference between the temperature data of each preset position at each collection moment and the preset standard temperature, and combining the distribution of the layered monitoring difference coefficient at each collection moment and all previous collection moments, an error adjustment matrix for each collection moment is obtained; and a control instruction is outputted through the error adjustment matrix; The actuator is used to control the internal temperature of concrete through control instructions.

10. A temperature control system for a large-volume wind turbine foundation concrete, 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, the steps of the temperature control method for large-volume wind turbine foundation concrete as described in any one of claims 1 to 8 are implemented.

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