Method for generating running water curing strategy for rockfill dam face concrete

Abstract

The present invention belongs to the technical field of concrete construction, and relates to a method for generating a running water maintenance strategy for rockfill dam panel concrete, including: 1) determining multi-source monitoring data based on the rockfill dam panel concrete maintenance strategy; 2) real-time monitoring of the multi-source monitoring data determined in step 1); 3) transmitting the multi-source monitoring data obtained by real-time monitoring in step 2) to a cloud server; 4) integrating and analyzing the multi-source monitoring data on the cloud server, obtaining the integrated analysis results, and providing a dynamic maintenance strategy for panel concrete in real time and dynamically according to the integrated analysis results. The present invention provides a method for generating a running water maintenance strategy for rockfill dam panel concrete, which provides effective data support for temperature control measures in engineering construction, provides a dynamic maintenance strategy for panel concrete in real time and online, and ensures the quality and safety of construction.

Description

Technical Field

[0001] The present invention belongs to the technical field of concrete construction, and relates to a method for generating a running water curing strategy for rockfill dam panel concrete, and in particular to a method for generating a running water curing strategy for rockfill dam panel concrete based on multi-source measured data. Background Art

[0002] Under the action of hydration heat, concrete gradually hardens and increases its strength, and the hydration heat reaction of concrete requires certain temperature and humidity conditions. Therefore, when the external environment cannot meet the temperature and humidity conditions for the hydration heat reaction of concrete, human intervention is required. During the pouring process of concrete, due to the hydration heat reaction, the internal temperature of the pouring will also rise, resulting in excessive temperature difference between the inside and outside, causing temperature stress, and then cracks. The concrete panel rockfill dam is a rockfill dam with a rockfill body as the supporting structure and a concrete panel as the anti-seepage structure on its upstream surface. The quality of the concrete panel directly affects the safety of the entire dam. Based on this, the maintenance of the concrete panel is particularly important. Summary of the invention

[0003] In order to solve the above-mentioned technical problems existing in the background technology, the present invention provides a method for generating a flowing water maintenance strategy for rockfill dam panel concrete, which provides effective data support for temperature prevention and control measures in engineering construction, provides a real-time online dynamic maintenance strategy for panel concrete, and ensures the quality and safety of construction.

[0004] In order to solve the above technical problems, the technical solution of the present invention is:

[0005] A method for generating a running water curing strategy for rockfill dam face concrete, characterized in that the method comprises the following steps:

[0006] 1) Determine the multi-source monitoring data based on the maintenance strategy of rockfill dam face concrete;

[0007] 2) real-time monitoring of the multi-source monitoring data determined in step 1);

[0008] 3) transmitting the multi-source monitoring data obtained by real-time monitoring in step 2) to a cloud server;

[0009] 4) Integrate and analyze the multi-source monitoring data on the cloud server, obtain the integrated analysis results, and provide dynamic maintenance strategies for panel concrete in real time based on the integrated analysis results.

[0010] Preferably, the multi-source monitoring data in step 1) adopted in the present invention includes the internal temperature of the concrete panel, the surface temperature of the concrete panel, the ambient temperature of the environment in which the concrete panel is located, the ambient humidity of the environment in which the concrete panel is located, the solar radiation intensity of the environment in which the concrete panel is located, and the wind speed of the environment in which the concrete panel is located.

[0011] Preferably, in step 2) adopted in the present invention, the internal temperature of the concrete panel and the surface temperature of the concrete panel are both obtained by monitoring through distributed optical fiber sensors; the ambient temperature of the environment in which the concrete panel is located and the ambient humidity of the environment in which the concrete panel is located are both obtained by monitoring through temperature and humidity sensors; the solar radiation intensity of the environment in which the concrete panel is located and the wind speed of the environment in which the concrete panel is located are both obtained by monitoring through a meteorological station.

[0012] Preferably, the distributed optical fiber sensors used in the present invention include at least internal distributed optical fiber sensors and external distributed optical fiber sensors; the internal distributed optical fiber sensors are pre-tied and fixed to the steel mesh of the rockfill dam panel in layers before concrete pouring, and are buried as the concrete is poured; the external distributed optical fiber sensors are laid on the surface of the rockfill dam panel after initial setting.

[0013] Preferably, the transmission process in step 3) adopted in the present invention is based on the LoRA communication protocol, ModbusRTU protocol and / or MQTT protocol of the Internet of Things.

[0014] Preferably, the specific implementation method of step 4) adopted by the present invention is:

[0015] 4.1) Prioritize multi-source monitoring data;

[0016] 4.2) Calculate the weights of multi-source monitoring data at the same priority level;

[0017] 4.3) Generate panel concrete curing strategy based on the priority level.

[0018] Preferably, the division method in step 4.1) adopted in the present invention is the grey correlation method.

[0019] Preferably, the method of calculating the weight in step 4.2) adopted by the present invention is the entropy weight method.

[0020] Preferably, the priority levels in step 4.3) adopted in the present invention are carried out in sequence according to the rank of the priorities under different priorities; and in sequence according to the weight size under the same priority.

[0021] Preferably, the specific implementation method of the generation in step 4.3) adopted by the present invention is:

[0022] According to the influencing factors on the panel concrete, the priority of each influencing factor is given priority. If the influencing factors belong to different priorities, the priority order of each influencing factor is considered; if the influencing factors belong to the same priority, the weight order of each influencing factor is considered; if the influencing factors at different priorities are not unique, the priority order is given priority, and then the weight order of each influencing factor at the same priority is considered.

[0023] The advantages of the present invention are:

[0024] The present invention provides a method for generating a running water maintenance strategy for rockfill dam panel concrete, comprising the following steps: 1) determining multi-source monitoring data based on the management strategy for rockfill dam panel concrete; 2) real-time monitoring of the multi-source monitoring data determined in step 1); 3) transmitting the multi-source monitoring data obtained by real-time monitoring in step 2) to a cloud server; 4) integrating and analyzing the multi-source monitoring data on the cloud server, obtaining the integrated analysis results, and providing a dynamic management strategy for panel concrete in real time and dynamically according to the integrated analysis results. The present invention constructs a concrete panel management strategy analysis system to monitor the multi-source data of concrete in an all-round and multi-dimensional manner, so that the temperature change of concrete can be grasped in real time and dynamically, providing data support for subsequent temperature field analysis, temperature calculation of intelligent management, etc., which has important guiding significance for reducing destructive phenomena such as temperature cracks, thereby ensuring the quality and safety of dam projects. The method for generating a running water maintenance strategy for rockfill dam panel concrete based on multi-source measured data provided by the present invention monitors multi-source data in real time, analyzes and processes the collected data, generates a strategy according to the analysis results, and feeds back to the workbench, and realizes intelligent management of panel concrete by uniformly adjusting the opening of the solenoid valve. The present invention can effectively solve the problem that traditional panel concrete relies on manual watering for curing and the curing effect is not obvious. The intelligent curing system with high-frequency and high-density concrete temperature perception, intelligent evaluation of curing effect, and real-time control of curing measures can realize the intelligent regulation of the real temperature and humidity state of panel concrete and improve the refinement and intelligence level of on-site quality control. The present invention can provide effective data support for temperature control measures in engineering construction, can provide dynamic curing strategies for panel concrete in real time online, and can ensure the quality and safety of construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flow chart of data collection and data management adopted by the present invention;

[0026] Figure 2 It is a schematic diagram of multi-source monitoring data collection adopted by the present invention;

[0027] Figure 3 It is a flow chart of the Internet of Things technology adopted by the present invention;

[0028] Figure 4 It is a schematic diagram of the intelligent management and maintenance strategy generation process adopted by the present invention. DETAILED DESCRIPTION

[0029] The present invention provides a method for generating a flow water curing strategy for rockfill dam face concrete, the method comprising the following steps:

[0030] 1) Determine the multi-source monitoring data based on the maintenance strategy of rockfill dam face concrete;

[0031] Among them, the multi-source monitoring data include the internal temperature of the concrete panel, the surface temperature of the concrete panel, the ambient temperature of the environment where the concrete panel is located, the ambient humidity of the environment where the concrete panel is located, the solar radiation intensity of the environment where the concrete panel is located, and the wind speed of the environment where the concrete panel is located. These multi-source monitoring data play a decisive role in the hydration heat reaction of concrete, surface water evaporation, temperature stress, etc., and directly affect the quality, strength and whether cracks will occur in the concrete.

[0032] Multi-source monitoring data is determined based on the concrete maintenance strategy for the rockfill dam panel. On the one hand, it is to monitor the temperature changes inside and on the surface of the concrete panel in real time, accurately and dynamically during the pouring process, so as to take measures to reduce the temperature stress of the concrete and prevent the occurrence of cracks; on the other hand, it is to monitor the concrete temperature, ambient temperature, ambient humidity, solar radiation, wind speed and other indicators in real time, accurately and dynamically to prevent the evaporation of surface moisture and ensure the hydration thermal environment, thereby ensuring the strength and quality of the concrete.

[0033] 2) real-time monitoring of the multi-source monitoring data determined in step 1);

[0034] Among them, the internal temperature of the concrete panel and the surface temperature of the concrete panel are monitored by distributed fiber optic sensors (DTS); the ambient temperature of the environment where the concrete panel is located and the ambient humidity of the environment where the concrete panel is located are monitored by temperature and humidity sensors; the solar radiation intensity of the environment where the concrete panel is located and the wind speed of the environment where the concrete panel is located are monitored by the meteorological station (PC-6). Distributed fiber optic sensors include at least internal distributed fiber optic sensors and external distributed fiber optic sensors; the internal distributed fiber optic sensors are pre-tied and fixed to the steel mesh of the rockfill dam panel in layers before concrete pouring, and buried as the concrete is poured; the external distributed fiber optic sensors are laid on the surface of the rockfill dam panel after initial setting, thereby realizing hierarchical multi-point monitoring;

[0035] 3) transmitting the multi-source monitoring data obtained by monitoring in step 2) to a cloud server and storing it;

[0036] The transmission is carried out through the LoRA communication protocol, ModbusRTU protocol and / or MQTT protocol of the Internet of Things. In a space with a wide range and a long length of concrete panels, and in an environment where construction is very likely to affect data communication, the present invention utilizes the LoRA communication protocol of the Internet of Things to realize long-distance, large-scale wireless data collection and transmission, and aggregates the data into the Internet of Things gateway of the monitoring room. The Internet of Things technology is used to collect, transmit and manage the multi-source data required for the intelligent maintenance of concrete panels. Taking full advantage of the economic value, information exchange value, application value, etc. of the Internet of Things, based on the LoRA communication protocol, ModbusRTU protocol, and MQTT protocol of the Internet of Things, the data is collected and transmitted, and uploaded to the cloud server for storage and management. The acquisition card of the present invention is connected to the Internet of Things gateway through the 232 protocol / 485 protocol to realize communication, and the Internet of Things gateway is connected to the Internet. By configuring the ModbusRTU protocol and MQTT protocol of the IoT gateway on the PC host, creating device access information, creating products, adding product attributes on the cloud platform, and then adding IoT gateway product devices and IoT gateway sub-devices, the IoT gateway and the cloud platform can be connected and communicated, and finally the collected data can be uploaded to the cloud for storage and management.

[0037] 4) Construct a concrete panel maintenance strategy analysis system that integrates data perception, data management, data analysis, and decision-making feedback control, integrate and analyze the measured multi-source monitoring data, and formulate a maintenance plan based on the analysis results to dynamically maintain the panel concrete. The overall architecture is as follows: Figure 1 shown.

[0038] The construction method of the concrete panel maintenance strategy analysis system in step 4) is: first, based on the collected multi-source monitoring data, the indicator data is prioritized using the grey correlation method, and then the entropy weight method is used to calculate the weight of the multivariate indicators under the same priority. The indicator priority is given priority under different priorities, and then the weight of each indicator under the same priority is considered. Finally, the panel concrete maintenance strategy is generated and fed back to the opening of the solenoid valve.

[0039] Next, we will formulate corresponding strategies based on the established priority weight system: when considering the situation where the influencing factors are at different priorities and the same priority, different strategies and methods need to be adopted. For influencing factors of different priorities, give priority to and arrange factors with higher priorities. For influencing factors of the same priority, their mutual relationship and role need to be considered to avoid conflicts and contradictions. By calculating the weights corresponding to the same priority, the size is compared, and the order of consideration is carried out in descending order of weight. However, it should be noted that the factors under the same priority are in an "or" relationship. If one of the indicators does not meet the maintenance requirements, a strategy needs to be implemented. If multiple indicators do not meet the maintenance requirements at the same time, they must be considered and the order of strategy implementation should be considered according to the size of the allocated weights. When implementing the strategy, attention should be paid to changes in other indicators. At the same time, it is also necessary to consider the actual situation and resource constraints to determine the appropriate solution. In short, under the influence of multiple factors, panel concrete gives priority to the priority of each factor according to the degree of influence of each indicator on the panel. If multiple indicators belong to different priorities, the priority order of each indicator is considered; if multiple indicators belong to the same priority, the weight order of each indicator is considered; if both exist, the priority is given priority, and then the weight of the indicator is considered.

[0040] In order to meet the requirements of the concrete maintenance strategy for rockfill dam panels, the present invention requires uniform and real-time multi-point monitoring of the temperature, ambient temperature, ambient humidity, solar radiation, wind speed and other data of the concrete panels, and centralized sorting and analysis of the data. The concrete panels cover a wide range and a large length, and the monitoring locations of multi-source data are scattered. The collection and aggregation of data are difficult. At the same time, the construction of the project has a great impact on the collection of data. Therefore, it is difficult to ensure the accuracy and real-time nature of the data using traditional methods. Therefore, the Internet of Things technology is used to realize large-scale, multi-span data collection and transmission methods, which also greatly reduces the impact of construction on data collection and transmission.

[0041] The technical solution provided by the present invention is described in detail below with reference to the accompanying drawings:

[0042] Based on the collection, aggregation and organization of multi-source measured data, a concrete panel maintenance strategy analysis system is constructed to extract and analyze multi-source measured data in real time, accurately grasp the changes in multi-source data, and thus provide dynamic maintenance strategies for panel concrete in a timely, dynamic, fast and accurate manner.

[0043] The present invention provides a method for generating a flow water curing strategy for rockfill dam face concrete, which specifically comprises the following steps:

[0044] Step 1: According to the requirements of the maintenance strategy for rockfill dam slab concrete and the construction site conditions, determine the multi-source data required for the maintenance strategy for rockfill dam slab concrete; including the internal and external temperature of concrete, ambient temperature, ambient humidity, solar radiation, wind speed, etc.

[0045] Step 2: Determine the monitoring plan for various indicators based on the on-site conditions of concrete panel construction, including monitoring of the internal and surface temperature of the concrete panel, ambient temperature monitoring, ambient humidity monitoring, solar radiation monitoring, wind speed monitoring, etc. Figure 2 shown.

[0046] Step 3: According to the monitoring plan, make preparations before data perception monitoring, including the configuration of the on-site monitoring room, the debugging of the distributed optical fiber temperature measurement system, the laying of optical fibers, the debugging of temperature and humidity sensors, and the debugging of high-precision weather stations. Configure the monitoring room on site, and move the distributed optical fiber temperature measurement host, PC host, acquisition card, IoT gateway and other equipment to the monitoring room, and ensure the normal communication between the equipment and the Internet. Before laying the optical fiber, it is necessary to weld the optical fiber and the pigtail, and calibrate and debug the temperature measuring instrument;

[0047] Step 4: According to the monitoring plan, the distributed optical fiber is buried, and the optical fiber and the pigtail are fused, and the temperature measuring instrument is calibrated and debugged. At the same time, the temperature and humidity sensor and high-precision weather station are also debugged and deployed in the early stage.

[0048] Step 5: Connect the PC to the data acquisition card, and connect each instrument to the IoT gateway according to the 232 / 485 protocol, while ensuring that all devices can access the Internet. Then, create a product in the cloud and add corresponding services to the product. Then register the IoT gateway device in the cloud and add the corresponding sensing sub-device. After completion, configure the corresponding cloud platform connection information in the IoT gateway, including the cloud platform access address, MQTT center address, access ClientID, UserName, PassWord, and set the attribute information corresponding to the cloud platform (configuration process as shown in the figure). Figure 3 As shown). After completion, the purpose of data collection and uploading to cloud management is achieved. The instrument sensing data is first collected, transmitted, and aggregated to the acquisition card through the LoRA protocol, and then the ModbusRTU protocol of the IoT gateway, the configuration of the MQTT protocol, and the setting of the cloud platform docking properties are used to achieve normal communication between the gateway device and the cloud platform. At the same time, the collected data is uploaded to the cloud server for storage and management;

[0049] Step 6: Based on the multi-source measured data collected through monitoring, a concrete panel maintenance strategy analysis system is constructed to provide real-time online dynamic maintenance strategies for panel concrete. A concrete panel maintenance strategy analysis system is constructed to extract, integrate and analyze the measured multi-source data, and in combination with the requirements of concrete panel maintenance specifications and the actual conditions of the construction site, real-time and dynamic feedback on the concrete panel maintenance strategy is provided to ensure the quality and safety of the construction.

[0050] In Step 6, according to the requirements of the dynamic maintenance strategy for rockfill dam slab concrete and the construction site conditions, the multi-source data required for the maintenance strategy for rockfill dam slab concrete are determined, including the temperature difference between the inside and outside of the concrete, ambient temperature, ambient humidity, solar radiation, wind speed, etc. To formulate a strategy based on the priority of the task, first of all, it is necessary to clarify the priority of the factors, and then, according to the tasks at each priority level, the corresponding strategy can be formulated.

[0051] First, the factors affecting the temperature of the concrete panel are analyzed: based on multi-source measured data, a comprehensive analysis is conducted on the possible damage to the concrete panel of the rockfill dam. The factors affecting the formation of cracks in the concrete panel include the maximum temperature difference between the inside and outside of the surface, the maximum temperature of the concrete surface, the temperature difference between the core and the surface of the concrete, the surface humidity of the concrete, the surface temperature of the concrete, the ambient temperature, the instantaneous value of the total radiation, and the wind speed.

[0052] Secondly, formulate a priority system: Combine the factors that affect the cracking of panel concrete and use the grey correlation method and the panel reliability influencing mechanism to conduct a priority analysis of the influencing factors based on measured data, historical experience and expert judgment. When it is necessary to determine the priority of the strategy, the grey correlation between different factors or objects is calculated to determine the strength of the correlation between them, so as to determine their priority order. First, it is necessary to clarify the evaluation indicators, which should be able to comprehensively and systematically reflect the characteristics and influence of each factor or object. Then, since the original data may have different dimensions and orders of magnitude, direct comparison may be misleading. Therefore, it is necessary to non-dimensionalize the data and convert it into a data sequence that can be directly compared. Next, determine the reference sequence, which is usually a data sequence that can reflect the behavioral characteristics of the system, and it serves as a comparison benchmark. According to the actual situation of the problem and the characteristics of the data, different weights can be given to different indicators to reflect their importance to the system. The correlation coefficient is used to measure the degree of correlation between the comparison sequence and the reference sequence at each moment. It is usually calculated based on the difference between the comparison sequence and the reference sequence. The smaller the difference, the larger the correlation coefficient, indicating a higher degree of correlation. After determining the correlation coefficient, the final correlation degree can be obtained by weighted averaging the correlation coefficient. The weighted correlation degree comprehensively considers the degree of correlation of each factor at different times and takes into account the weight of each factor. When calculating the grey correlation degree, the grey correlation formula can be used for calculation based on the sample data of each factor or object. The higher the calculated correlation degree, the stronger the correlation between the factor or object and the target, so it should be ranked higher in the priority sorting. Finally, according to the size of the grey correlation degree, the priority order of each factor or object can be determined. This sorting result helps decision makers better understand the relative importance of each factor or object, so as to formulate a more scientific and reasonable decision-making plan. Exemplarily, the calculation steps for priority division using the grey correlation method are as follows:

[0053] (1) Determine the subsequence and parent sequence: There are i samples and j indicators, where i represents the number of priorities and j represents the number of influencing factors in each priority. i (j) (i=1, 2, 3, ..., m; j=1, 2, 3, ..., n) represents the jth indicator value in the i-th year, and y0(j) is the optimal value of each indicator.

[0054] The subsequences are:

[0055]

[0056] The parent sequence is:

[0057] y0(j)=(y0(1),…y0(n))

[0058] (2) Dimensionless processing of each indicator data:

[0059] Nondimensionalize a subsequence:

[0060]

[0061] The dimensionless mother sequence is:

[0062]

[0063] (3) Calculate the absolute value, maximum value, and minimum value of the difference between each element in the comparison sequence and the corresponding element in the reference sequence:

[0064]

[0065] (4) Determine the weight value of each indicator and obtain the comprehensive weight of each indicator from both objective and subjective perspectives.

[0066] (5) Calculate the grey correlation coefficient:

[0067]

[0068] Where ζ is the resolution coefficient, which is usually taken as 0.5;

[0069]

[0070] (6) Calculate the correlation:

[0071] The product of the grey correlation coefficient and the comprehensive weight of each early warning indicator is used to represent the measurement, and the obtained early warning measurement value is transformed into the range of (0, 10]. The calculation formula and conversion formula are as follows.

[0072]

[0073]

[0074] The final priority system is as follows: the first priority factor is the concrete surface humidity; the second priority factor is the maximum temperature difference between the inside and outside of the surface, the maximum temperature of the concrete surface, the temperature difference between the concrete core and the surface, and the concrete surface temperature; the third priority factor is the ambient temperature, the instantaneous value of total radiation, and the wind speed. However, it should be noted that although the gray correlation method has a certain objectivity in determining the priority, it still needs to be comprehensively considered in combination with the actual situation.

[0075] Then evaluate the weight of each factor: Since the temperature of the concrete panel will be affected by multiple factors, the decision criteria are numerous and difficult to quantify, and the influencing factors under the same priority are not single, the entropy weight method is used to assign weights to the factors under the same priority, quantify the decision criteria, and determine the complex problems according to multiple criteria. For factors at the same priority, a judgment matrix is ​​formed based on experimental data, historical experience, and relevant expert judgments, so as to determine the weights corresponding to each factor of the same priority. First, the original data needs to be preprocessed, including data cleaning, missing value processing, and outlier processing. Standardize or normalize the data and convert the data of different indicators into dimensionless values ​​for comparison and calculation. Next, according to the definition and calculation formula of entropy, calculate the entropy value of each indicator. The entropy value reflects the degree of dispersion of the indicator. The smaller the entropy value, the greater the degree of dispersion of the indicator, and the greater the influence of the indicator on the comprehensive evaluation (i.e., weight). Calculate the proportion P of the indicator value of the i-th project under the j-th indicator ij , and then calculate the entropy value of the j-th indicator. If the information entropy of an indicator is smaller, it means that the degree of variation of the indicator value is greater, the amount of information provided is more, the role it can play in the comprehensive evaluation is greater, and its weight is greater. On the contrary, if the information entropy of an indicator is larger, it means that the degree of variation of the indicator value is smaller, the amount of information provided is less, and the role it plays in the comprehensive evaluation is smaller. According to the entropy value of each indicator, the weight of each indicator is further calculated. The weight of an indicator can be obtained by the ratio of the entropy value of the indicator to the sum of the entropy values ​​of all indicators. Exemplarily, the steps for determining the objective weight calculation by the entropy weight method are as follows:

[0076] (1) Normalize the collected data. The purpose is to unify the measurement, convert the absolute value of the indicator into the relative value of the indicator, and let x ij =|x ij |.

[0077] The conversion formula of positive indicators:

[0078]

[0079] Conversion formula of negative indicator:

[0080]

[0081] (2) Calculate the variability of the indicator. Calculate the proportion P of the index value of the i-th item under the j-th indicator. ij :

[0082]

[0083] (3) Calculate information entropy. Calculate the information entropy value e of the jth indicator j (e>0):

[0084]

[0085] (4) Calculate the redundancy of information entropy. Calculate the information redundancy g of the jth indicator j :

[0086] g j =1-e j

[0087] (5) Calculate the weight of each indicator. Calculate the weight w of the jth indicator j :

[0088]

[0089] Next, we will formulate corresponding strategies based on the established priority weight system: different strategies and methods need to be adopted when considering factors with different priorities and the same priority. For factors with different priorities, give priority to and arrange factors with higher priorities. For factors with the same priority, their mutual relationship and role need to be considered to avoid conflicts and contradictions. The weights corresponding to the same priority are calculated for comparison, and the order of consideration is from large to small. However, it should be noted that the factors under the same priority are in an "or" relationship. If one of the indicators does not meet the maintenance requirements, a strategy needs to be implemented. If multiple indicators do not meet the maintenance requirements at the same time, they must be considered and the order of strategy implementation should be considered according to the size of the allocated weights. When implementing the strategy, attention should be paid to changes in other indicators. At the same time, it is also necessary to consider the actual situation and resource constraints to determine a suitable solution.

[0090] In short, under the influence of multiple factors, the panel concrete should give priority to the priority of each factor according to the degree of influence of each indicator on the panel. If multiple indicators belong to different priorities, the priority order of each indicator should be considered; if multiple indicators belong to the same priority, the weight order of each indicator should be considered; if both exist, the priority should be given first, and then the weight of the indicator should be considered. At the same time, in the process of implementing the priority strategy, feedback and data are continuously collected to continuously improve and optimize the strategy, and the priority strategy is constantly reviewed and updated to ensure that it always reflects the current environment and needs, such as Figure 4 shown.

[0091] For example, in order to make the formulated priority system meet the requirements of the concrete maintenance strategy, the formulated strategy is reflected in the opening size of the solenoid valve. The rockfill dam panel concrete adopts an intelligent maintenance system during the maintenance process. By intelligently adjusting the opening of the solenoid valve, it ensures that the maintenance requirements of the panel concrete are met, and realizes automated maintenance according to the priority index. According to the requirements of the rockfill dam panel concrete maintenance strategy, the humidity of channel 2, which is the first priority factor, is given priority. When the humidity of channel 2 is ≤95.0%, the opening of the solenoid valve is set to 16, and when the humidity of channel 2 is 95.0% to 100%, the opening of the solenoid valve is set to 8. Secondly, the factors at the second priority are considered. Since the influencing factors at the second priority are not unique, in order to avoid contradictions when considering factors to formulate strategies, they can be considered in descending order according to the weight distribution under the same priority. When the temperature difference between the inside and outside of the surface is ≥15℃, the opening of the solenoid valve is set to 16; when the temperature difference between the inside and outside of the surface is between 10 and 15℃, the opening of the solenoid valve is set to 14; when the temperature difference between the inside and outside of the surface is ≤10℃, the opening of the solenoid valve is set to 8; when the maximum temperature of the concrete surface is ≥25.00℃, the opening of the solenoid valve is set to 16; when the maximum temperature of the concrete surface is between 20 and 25℃, the opening of the solenoid valve is set to 14; when the maximum temperature of the concrete surface is ≤20℃, the opening of the solenoid valve is set to 12; When the temperature difference between the concrete core and the surface is ≥20.00℃, the opening of the solenoid valve is set to 16, when the temperature difference between the concrete core and the surface is between 15 and 20℃, the opening of the solenoid valve is set to 14, when the temperature difference between the concrete core and the surface is ≤15℃, the opening of the solenoid valve is set to 12; when the concrete surface temperature is ≥25.0℃, the opening of the solenoid valve is set to 16, when the concrete surface temperature is between 20 and 25℃, the opening of the solenoid valve is set to 14, when the concrete surface temperature is ≤20℃, the opening of the solenoid valve is set to 12. Finally, consider the factors at the third priority. Since the influencing factors at the third priority are not unique, in order to avoid contradictions when considering factors to formulate strategies, they can be considered in descending order according to the weight distribution under the same priority. When the ambient temperature is ≥20.0℃, the opening of the solenoid valve is set to 16; when the ambient temperature is between 15 and 20℃, the opening of the solenoid valve is set to 14; when the ambient temperature is ≤15℃, the opening of the solenoid valve is set to 12; when the instantaneous value of total radiation is ≥25W / ㎡, the opening of the solenoid valve is set to 16; when the instantaneous value of total radiation is between 15 and 25W / ㎡, the opening of the solenoid valve is set to 14; when the instantaneous value of total radiation is ≤15W / ㎡, the opening of the solenoid valve is set to 8; when the wind speed is ≥8m / s (about level 5), the opening of the solenoid valve is set to 16; when the wind speed is between 5 and 8m / s, the opening of the solenoid valve is set to 14; when the wind speed is ≤5m / s, the opening of the solenoid valve is set to 8.In the process of implementing the priority strategy, continuously collect feedback and data to continuously improve and optimize the strategy, and constantly review and update the priority strategy to ensure that it always reflects the current environment and needs.

[0092] The method for generating a flow curing strategy for rockfill dam panel concrete provided by the present invention analyzes the factors affecting the panel concrete temperature based on real-time multi-source monitoring data, and finally determines that the factors affecting the panel concrete temperature and causing cracks include the maximum temperature difference between the inside and outside of the surface, the maximum temperature of the concrete surface, the temperature difference between the concrete core and the surface, the concrete surface humidity, the concrete surface temperature, the ambient temperature, the instantaneous value of total radiation, the wind speed, etc.; on the basis of determining the influencing indicators, the gray correlation method and the panel reliability influencing mechanism are combined to perform a priority analysis on the influencing factors, and the gray correlation between different factors or objects is calculated to judge the strength of the correlation between them, thereby determining their priority order; because the concrete panel temperature will be affected by multiple factors, under the priority system formulated above, there are many decision criteria and they are not easy to quantify, and the factors affecting the same priority level are The factors are not single, and the entropy weight method is used to weight the factors under the same priority, quantify the decision criteria, and determine the complex problems according to multiple criteria. For factors at the same priority, a judgment matrix is ​​formed based on experimental data, historical experience and relevant expert judgment, so as to determine the weights corresponding to the factors at the same priority; formulate corresponding strategies according to the established priority weight system: when generating strategies, the influence of different indicators first considers the priority order under different priorities, gives priority to and arranges the factors with higher priorities, and then considers the influence of each indicator under the same priority on the basis of the priority order. Since the influencing factors may not be unique, it is necessary to consider their mutual relationship and role when formulating strategies to avoid contradictions and conflicts. According to the weight distribution under the same priority, the weight can be considered in descending order. The present invention is based on real-time multi-source monitoring data, and the intelligent analysis system will automatically generate a reasonable maintenance strategy according to the established priority weight system, and reflect the formulated strategy on the opening size of the solenoid valve. The system ensures that the maintenance requirements of the panel concrete are met by intelligently adjusting the opening of the solenoid valve, and realizes the intelligent management and maintenance of the panel concrete. During the implementation of the priority strategy, feedback and data are continuously collected to continuously improve and optimize the strategy. The priority strategy is constantly reviewed and updated to ensure that it always reflects the current environment and meets the maintenance needs of panel concrete.

Claims

1. A method for generating a running water curing strategy for rockfill dam face concrete, characterized in that: The method for generating a running water curing strategy for rockfill dam face concrete comprises the following steps: 1) Determine the multi-source monitoring data based on the maintenance strategy of rockfill dam face concrete; The multi-source monitoring data in step 1) includes the internal temperature of the concrete panel, the surface temperature of the concrete panel, the ambient temperature of the environment in which the concrete panel is located, the ambient humidity of the environment in which the concrete panel is located, the solar radiation intensity of the environment in which the concrete panel is located, and the wind speed of the environment in which the concrete panel is located; 2) Real-time monitoring of the multi-source monitoring data determined in step 1); In step 2), the internal temperature of the concrete panel and the surface temperature of the concrete panel are both monitored by distributed optical fiber sensors; the ambient temperature of the environment in which the concrete panel is located and the ambient humidity of the environment in which the concrete panel is located are both monitored by temperature and humidity sensors; the solar radiation intensity of the environment in which the concrete panel is located and the wind speed of the environment in which the concrete panel is located are both monitored by a meteorological station; 3) transmitting the multi-source monitoring data obtained from the real-time monitoring in step 2) to the cloud server; 4) Integrate and analyze the multi-source monitoring data on the cloud server, obtain the integrated analysis results, and provide dynamic maintenance strategies for panel concrete in real time based on the integrated analysis results. Specifically: 4.1) Prioritize the multi-source monitoring data; the priority level is determined by using the grey correlation method; 4.2) Calculate the weights of multi-source monitoring data at the same priority level; the weights are calculated using an entropy weight method; 4.3) Generate a panel concrete curing strategy according to the priority level; the priority levels are carried out in sequence according to the priority ranking under different priorities; under the same priority level, they are carried out in sequence according to the weight size.

2. The method for generating a running water curing strategy for rockfill dam face concrete according to claim 1, characterized in that: The distributed optical fiber sensor includes at least an internal distributed optical fiber sensor and an external distributed optical fiber sensor; the internal distributed optical fiber sensor is pre-tied and fixed on the steel mesh of the rockfill dam panel in layers before concrete pouring, and is buried as the concrete is poured; the external distributed optical fiber sensor is laid on the surface of the rockfill dam panel after initial setting.

3. The method for generating a running water curing strategy for rockfill dam face concrete according to claim 2, characterized in that: The transmission process in step 3) is based on the LoRA communication protocol, ModbusRTU protocol and / or MQTT protocol of the Internet of Things.

4. The method for generating a running water curing strategy for rockfill dam face concrete according to any one of claims 1 to 3, characterized in that: The specific implementation method generated in step 4.3) is: According to the influencing factors on the panel concrete, the priority of each influencing factor is given priority. If the influencing factors belong to different priorities, the priority order of each influencing factor is considered; if the influencing factors belong to the same priority, the weight order of each influencing factor is considered; if the influencing factors at different priorities are not unique, the priority order is given priority, and then the weight order of each influencing factor at the same priority is considered.

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