Water conservancy and hydropower engineering monitoring management method and system based on big data analysis

Abstract

The invention discloses a water conservancy and hydropower engineering monitoring management method and system based on big data analysis, and belongs to the field of engineering monitoring management. The method comprises the following steps: acquiring an environment anomaly analysis result and construction process data to perform construction process risk assessment in the next time period, performing task early warning in the next time period based on the construction process risk assessment result in the next time period, and accurately assessing the environment anomaly in the construction process by predicting and analyzing weather and hydrological data in the next stage. And the risk assessment of the construction process is performed by integrating the situation abnormity of the building main body in the construction process, so that the risk early warning accuracy of the water conservancy task is improved.

Description

Technical Field

[0001] The present application belongs to the field of engineering monitoring and management, and specifically to a method and system for monitoring and management of water conservancy and hydropower projects based on big data analysis. Background Art

[0002] Water conservancy and hydropower project monitoring and management refers to the activities of systematic supervision, inspection, control and coordination of project quality, progress, safety, cost and other aspects during the entire water conservancy and hydropower project construction process. Through effective monitoring and management, water conservancy and hydropower projects can better achieve project goals, ensure project quality and safety, and improve investment benefits. When conducting water conservancy and hydropower project monitoring and management, a water conservancy and hydropower project monitoring and management system is required; in the process of project monitoring, many existing water conservancy hazard warning systems usually only focus on a single water level or rainfall indicator, ignoring other factors that may affect water conservancy safety, such as geological conditions, water temperature changes, water quality conditions, etc. This may It may lead to the early warning system being unable to provide comprehensive early warning in complex actual situations. Some early warning systems rely on regular manual monitoring or fixed data collection frequencies, which may not be able to reflect the real-time status of water conservancy facilities in real time, thus affecting the timeliness of early warning. Due to the complexity and uncertainty of environmental factors, the existing early warning system may have certain limitations in accurately predicting the time, location and scale of disasters. In order to overcome these defects, the future water conservancy hazard early warning system needs to adopt more advanced technologies, such as the Internet of Things, big data analysis, artificial intelligence, etc., to improve the comprehensiveness, accuracy and timeliness of early warnings, and enhance the system's compatibility, scalability and user experience. In order to solve the problems raised by this background technology, the present application designs a water conservancy and hydropower project monitoring and management method and system based on big data analysis. Summary of the invention

[0003] In order to address the deficiencies in the prior art mentioned in the background technology, the present application proposes a water conservancy and hydropower project monitoring and management method and system based on big data analysis. The present application obtains the predicted weather data and hydrological data during the construction process of the water conservancy and hydropower project to conduct environmental anomaly analysis, obtains the environmental anomaly analysis results and the construction process data to conduct a hazard assessment of the construction process for the next time period, and conducts task warnings for the next time period based on the hazard assessment results of the construction process for the next time period. Through the predictive analysis of the weather and hydrological data for the next stage, the environmental anomalies in the construction process are accurately assessed, and the hazard assessment of the construction process is conducted based on the abnormal conditions of the main building during the construction process, thereby improving the accuracy of hazard warnings for water conservancy tasks.

[0004] To achieve the above objectives, the present application provides the following technical solutions: In the first aspect, the present application provides a water conservancy and hydropower project monitoring and management method based on big data analysis, which includes the following specific steps: S1. Obtain the predicted weather data, predicted hydrological data and construction process data during the construction of water conservancy and hydropower projects; S2. Obtain the predicted weather data and hydrological data during the construction of water conservancy and hydropower projects to conduct environmental anomaly analysis; S3. Obtain the environmental anomaly analysis results and construction process data to conduct a construction process hazard assessment for the next time period; S4. Based on the risk assessment results of the construction process in the next time period, a task warning for the next time period is issued.

[0005] As a preferred technical solution of the monitoring and management method of water conservancy and hydropower projects based on big data analysis, the weather data in S1 includes the weather data such as precipitation, temperature and wind force in the weather forecast of the region that affect the construction process, the hydrological data in S1 includes the hydrological data such as water level and flow that affect the construction process, and the construction process data in S1 includes the construction subject data and construction equipment operation data in the construction process, wherein the construction subject data is the construction process data of the construction subject, including the weight, connection strength and height data of the construction subject. It should be noted here that the hydrological data such as water level and flow that affect the construction process are obtained through prediction; In this corresponding step, a deep learning neural network is constructed through historical hydrological and weather data to accurately predict hydrological information.

[0006] As a preferred technical solution for the water conservancy and hydropower project monitoring and management method based on big data analysis, the acquisition of predicted weather data and hydrological data during the construction of the water conservancy and hydropower project for environmental anomaly analysis in S2 includes the following specific steps: S21. Obtain the weather data of the current period during the construction of the water conservancy and hydropower project, and perform weather anomaly analysis based on the comparison results of the weather data of the current period and the safety weather data of the construction process, wherein the weather anomaly analysis formula can be: , where T is the construction duration of this cycle, N is the type of weather data that affects the construction process, aj is the impact weight of the j-th weather data type, kjt is the value of the j-th weather data at time t, kjm is the median of the safety range of the j-th weather data, and dt is the time integral constant; S22. Obtain the hydrological data during the construction of the water conservancy and hydropower project, and conduct a hydrological hazard assessment during the construction process based on the hydrological data of this period. The hydrological hazard calculation formula is: , where vt is the water level data at time t, vm is the safe water level data during the construction of water conservancy and hydropower projects, Rt is the flow data at time t, and Rm is the safe flow data during the construction of water conservancy and hydropower projects. is the weight of the water level; S23, obtaining weather anomaly analysis results and hydrological risk assessment results, weighting them respectively and summing them up to obtain environmental anomaly analysis results.

[0007] As a preferred technical solution for the monitoring and management method of water conservancy and hydropower projects based on big data analysis, the acquisition of environmental anomaly analysis results and construction process data for the next time period construction process risk assessment includes the following specific steps: S31, obtaining the weight of the construction body, the connection strength and height data of the connection parts during the construction process of the next time period, and performing construction risk assessment based on the weight of the construction body, the connection strength and height data of the connection parts during the construction process of the next time period, wherein the construction risk assessment formula is: , where hs is the height increase data of the construction body in the next time period, hz is the set height standard value, exp() is the power of the natural constant e, mz is the current mass of the construction body, mc is the weight increase of the construction body in the next time period, g is the acceleration of gravity, which is a constant, and Fs is the average connection strength of the connection part. In this way, the danger assessment of the construction process is carried out through the characteristics of the construction tasks and the construction body in the next time period; S32. Obtain the environmental anomaly analysis results and construction hazard assessment results obtained through analysis, and conduct a construction process hazard assessment based on the environmental anomaly analysis results and construction hazard assessment results of the next time period. The construction process hazard is proportional to the environmental anomaly analysis results, and the construction process hazard is also proportional to the construction hazard assessment results. Therefore, the construction process hazard assessment can be calculated by weighted summation of the environmental anomaly analysis results and the construction hazard assessment results to obtain the final construction process hazard assessment result.

[0008] As a preferred technical solution for the water conservancy and hydropower project monitoring and management method based on big data analysis, the task warning for the next time period based on the construction process risk assessment result for the next time period includes the following specific contents: The hazard assessment result of the construction process in the next time period is compared with the set hazard assessment threshold of the construction process. If the hazard assessment result of the construction process in the next time period is greater than or equal to the set hazard assessment threshold of the construction process, it means that the construction process is prone to dangerous situations, and a task warning for the next time period is issued to remind the task of delaying the next time period. If the hazard assessment result of the construction process in the next time period is less than the set hazard assessment threshold of the construction process, no task warning for the next time period is issued.

[0009] On the second aspect, the present application provides a water conservancy and hydropower project monitoring and management system based on big data analysis, which is implemented based on the above-mentioned water conservancy and hydropower project monitoring and management method based on big data analysis, and specifically includes: a data acquisition module, used to obtain predicted weather data, predicted hydrological data and construction process data during the construction of water conservancy and hydropower projects; an environmental anomaly analysis module, which obtains predicted weather data and hydrological data during the construction of water conservancy and hydropower projects for environmental anomaly analysis; a construction process hazard assessment module, which obtains environmental anomaly analysis results and construction process data to conduct construction process hazard assessment for the next time period; a task warning module, which conducts task warning for the next time period based on the construction process hazard assessment results for the next time period.

[0010] In a third aspect, the present application provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned water conservancy and hydropower project monitoring and management method based on big data analysis by calling the computer program stored in the memory.

[0011] In a fourth aspect, the present application provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute the water conservancy and hydropower project monitoring and management method based on big data analysis as described above.

[0012] Compared with the prior art, the beneficial effects of the present application are as follows: the present application obtains the predicted weather data and hydrological data during the construction process of water conservancy and hydropower projects to conduct environmental anomaly analysis, obtains the environmental anomaly analysis results and construction process data to conduct a hazard assessment of the construction process for the next time period, and conducts a task warning for the next time period based on the hazard assessment results of the construction process for the next time period. By predicting and analyzing the weather and hydrological data for the next stage, the environmental anomalies in the construction process are accurately assessed, and the hazard assessment of the construction process is conducted based on the abnormal conditions of the main building during the construction process, thereby improving the accuracy of hazard warnings for water conservancy tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings; Figure 1 A schematic diagram of a flow chart of an embodiment of the method of the present application; Figure 2 This is a schematic diagram of step S1 of an embodiment of the method of the present application; Figure 3 This is a schematic diagram of step S2 of the system embodiment of the present application; Figure 4 It is a schematic diagram of the overall framework of an embodiment of the system of the present application. DETAILED DESCRIPTION

[0014] In order to better understand the present application, various aspects of the present application will be described in more detail with reference to the accompanying drawings. It should be understood that these detailed descriptions are only descriptions of exemplary embodiments of the present application and do not limit the scope of the present application in any way. Throughout the specification, the same reference numerals refer to the same elements. The expression "and / or" includes any and all combinations of one or more of the associated listed items.

[0015] In the accompanying drawings, the size, dimensions and shape of the elements have been slightly adjusted for ease of explanation. The accompanying drawings are only examples and are not strictly drawn to scale. As used herein, the terms "substantially", "approximately" and similar terms are used as terms to indicate approximation, not as terms to indicate degree, and are intended to illustrate the inherent deviations in measured or calculated values ​​that will be recognized by those of ordinary skill in the art. In addition, in the present application, the order in which the steps are processed does not necessarily represent the order in which these processes occur in actual operation, unless otherwise clearly defined or can be derived from the context. It should also be understood that expressions such as "including", "including", "having", "including" and / or "including" are open rather than closed expressions in this specification, which indicate the presence of the stated features, elements and / or components, but do not exclude the presence of one or more other features, elements, components and / or combinations thereof. In addition, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire list of features, rather than just modifying the individual elements in the list. In addition, when describing the embodiments of the present application, "may" is used to represent "one or more embodiments of the present application". Furthermore, the term "exemplary" is intended to refer to an example or illustration. Unless otherwise specified, all words used herein (including engineering terms and scientific and technological terms) have the same meaning as those commonly understood by those of ordinary skill in the art to which this application belongs. It should also be understood that, unless otherwise clearly stated in this application, words defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an idealized or overly formal sense.

[0016] Example 1

[0017] In order to solve the technical problems raised in the background technology, the present application provides a preferred embodiment: Figure 1-Figure 3 As shown, the water conservancy and hydropower project monitoring and management method based on big data analysis includes the following specific steps: S1. Obtain the predicted weather data, predicted hydrological data and construction process data during the construction of water conservancy and hydropower projects; In this embodiment, the weather data in S1 includes the weather data such as precipitation, temperature and wind force in the weather forecast of the region that affect the construction process. The hydrological data in S1 includes the hydrological data such as water level and flow that affect the construction process. The construction process data in S1 includes the construction subject data and construction equipment operation data in the construction process, wherein the construction subject data is the construction process data of the construction subject, including the weight, connection strength and height data of the construction subject. It should be noted here that the hydrological data such as water level and flow that affect the construction process in this embodiment are obtained by prediction, and the prediction method is: S11, collecting water level and flow data of corresponding positions in the same historical period, and obtaining weather data of the same historical period, and constructing a deep learning neural network model whose input is the weather data of the period and whose output is the water level and flow data of corresponding positions in the period; S12, the water level and flow data of the corresponding positions of the same historical period and the weather data of the same historical period are divided into 70% training sets and 30% validation sets, and the 70% training sets are used to input the deep learning neural network model for training to obtain an initial deep learning neural network model; the initial deep learning neural network model is tested with the 30% parameter test set, and the initial deep learning neural network model that satisfies the accuracy of the water level and flow data of the maximum corresponding position is output as the deep learning neural network model, wherein the output formula of the corresponding neuron of the deep learning neural network model is: , where m is the number of neurons in the nth layer of the neural network model, Yp(n+1) is the output of the pth neuron in the n+1th layer, sig() is the activation function, wipn is the connection weight between the nth layer neuron i and the n+1th layer p-item neuron, Yip is the output of the nth layer neuron i, and cp(n+1) is the bias between the nth layer neuron i and the n+1th layer p-item neuron; S13, obtaining weather data for this period, and importing water level and flow data of corresponding positions in the output period into the deep learning neural network model; It should be noted here that in the corresponding steps, a deep learning neural network is constructed through historical hydrological and weather data to accurately predict hydrological information; S2. Obtain the predicted weather data and hydrological data during the construction of water conservancy and hydropower projects to conduct environmental anomaly analysis; In this embodiment, S2 includes the following specific steps: S21. Obtain the weather data of the current period during the construction of the water conservancy and hydropower project, and perform weather anomaly analysis based on the comparison results of the weather data of the current period and the safety weather data of the construction process, wherein the weather anomaly analysis formula can be: , where T is the construction duration of this cycle, N is the type of weather data that affects the construction process, aj is the impact weight of the jth weather data type, kjt is the value of the jth weather data at time t, kjm is the median of the safety range of the jth weather data, and dt is the time integral constant; in this step, the weather data of this cycle is comprehensively compared with the safety weather data of the construction process to evaluate the dangers of water conservancy and hydropower project construction caused by weather; S22. Obtain the hydrological data during the construction of the water conservancy and hydropower project, and conduct a hydrological hazard assessment during the construction process based on the hydrological data of this period. The hydrological hazard calculation formula is: , where vt is the water level data at time t, vm is the safe water level data during the construction of water conservancy and hydropower projects, Rt is the flow data at time t, and Rm is the safe flow data during the construction of water conservancy and hydropower projects. is the weight of the water level. It should be noted that in this step, the hydrological risk assessment is performed on the hazards of the water conservancy and hydropower project construction process by analyzing the hydrological information; S23, obtaining weather anomaly analysis results and hydrological risk assessment results, weighting them respectively and summing them to obtain environmental anomaly analysis results. In this step, a comprehensive analysis of environmental anomalies is performed based on the impact of weather and hydrology on the construction process of water conservancy and hydropower projects; S3. Obtain the environmental anomaly analysis results and construction process data to conduct a construction process hazard assessment for the next time period; In this embodiment, S3 includes the following specific steps: S31, obtaining the weight of the construction body, the connection strength and height data of the connection parts during the construction process of the next time period, and performing construction risk assessment based on the weight of the construction body, the connection strength and height data of the connection parts during the construction process of the next time period, wherein the construction risk assessment formula is: , where hs is the height increase data of the construction body in the next time period, hz is the set height standard value, exp() is the power of the natural constant e, mz is the current mass of the construction body, mc is the weight increase of the construction body in the next time period, g is the acceleration of gravity, which is a constant, and Fs is the average connection strength of the connection part. In this way, the danger assessment of the construction process is carried out through the characteristics of the construction tasks and the construction body in the next time period; S32, obtaining the environmental anomaly analysis results and the construction risk assessment results obtained by analysis, and conducting a construction process risk assessment based on the environmental anomaly analysis results and the construction risk assessment results of the next time period. The construction process risk is proportional to the environmental anomaly analysis results, and the construction process risk is also proportional to the construction risk assessment results. Therefore, the construction process risk assessment can be calculated by weighted summing the environmental anomaly analysis results and the construction risk assessment results to obtain the final construction process risk assessment result; It should be particularly pointed out in this embodiment that the setting parameters in this embodiment are obtained by fitting the historical building construction data. The specific acquisition method can be: obtaining weather data, hydrological data and construction process data in the historical water conservancy and hydropower project construction process, analyzing the construction process risk assessment results through the above steps of this embodiment, and obtaining whether a dangerous situation occurred in the historical water conservancy and hydropower project construction process, importing the analyzed construction process risk assessment results and the judgment results of whether a dangerous situation occurred into the fitting software, performing continuous fitting iterations, and outputting the value of the setting parameters that meets the maximum judgment result accuracy rate; S4. Based on the risk assessment results of the construction process in the next time period, a task warning for the next time period is issued; In this embodiment, S4 includes the following specific steps: obtaining the construction process risk assessment result for the next time period, comparing the construction process risk assessment result for the next time period with the set construction process risk assessment threshold; if the construction process risk assessment result for the next time period is greater than or equal to the set construction process risk assessment threshold, it means that the construction process is prone to dangerous situations, and a task warning for the next time period is issued to remind the task of delaying the next time period; if the construction process risk assessment result for the next time period is less than the set construction process risk assessment threshold, no task warning for the next time period is issued.

[0018] What needs to be explained in this example is that the present embodiment has the following advantages over the prior art: obtaining predicted weather data and hydrological data during the construction of water conservancy and hydropower projects to conduct environmental anomaly analysis, obtaining environmental anomaly analysis results and construction process data to conduct construction process risk assessment for the next time period, and conducting task warnings for the next time period based on the construction process risk assessment results for the next time period. By predicting and analyzing the weather and hydrological data for the next stage, the environmental anomalies in the construction process are accurately assessed, and the risk of the construction process is assessed based on the abnormal conditions of the main building during the construction process, thereby improving the accuracy of risk warnings for water conservancy tasks.

[0019] Example 2

[0020] like Figure 4As shown, a water conservancy and hydropower project monitoring and management system based on big data analysis is implemented based on the above-mentioned water conservancy and hydropower project monitoring and management method based on big data analysis, and specifically includes: a data acquisition module, used to obtain predicted weather data, predicted hydrological data and construction process data during the construction of water conservancy and hydropower projects; an environmental anomaly analysis module, which obtains predicted weather data and hydrological data during the construction of water conservancy and hydropower projects to conduct environmental anomaly analysis; a construction process hazard assessment module, which obtains environmental anomaly analysis results and construction process data to conduct construction process hazard assessment for the next time period; a task warning module, which conducts task warning for the next time period based on the construction process hazard assessment results for the next time period. At the same time, this embodiment may also include a control module, which controls the operation of the corresponding data acquisition module, environmental anomaly analysis module, construction process hazard assessment module and task warning module by issuing control instructions; at the same time, the data transmission direction of each module in this embodiment is as follows Figure 4 As shown by the arrow direction in the figure, the specific steps of each module in this embodiment have been described in detail in the above method embodiment and will not be repeated here.

[0021] Example 3

[0022] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned water conservancy and hydropower project monitoring and management method based on big data analysis by calling the computer program stored in the memory.

[0023] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors and one or more memories, wherein the memory stores at least one computer program, and the computer program is loaded and executed by the processor to implement the water conservancy and hydropower project monitoring and management method based on big data analysis provided by the above method embodiment. The electronic device may also include other components for realizing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface to input and output data. This embodiment will not be described in detail here.

[0024] Example 4

[0025] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon; When the computer program runs on a computer device, the computer device executes the above-mentioned water conservancy and hydropower project monitoring and management method based on big data analysis.

[0026] For example, the computer readable storage medium can be a read-only memory, a random access memory, a read-only CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0027] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0028] The terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article, or apparatus.

[0029] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of application involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned application concept. For example, the above features are replaced with (but not limited to) technical features with similar functions applied in the present application.

Claims

1. A water conservancy and hydropower project monitoring and management method based on big data analysis, characterized in that: It includes the following specific steps: S1. Obtain the predicted weather data, predicted hydrological data and construction process data during the construction of water conservancy and hydropower projects; S2. Obtain the predicted weather data and hydrological data during the construction of water conservancy and hydropower projects to conduct environmental anomaly analysis; S3. Obtain the environmental anomaly analysis results and construction process data to conduct a construction process hazard assessment for the next time period; S4. Based on the risk assessment results of the construction process in the next time period, a task warning for the next time period is issued; The acquisition of environmental anomaly analysis results and construction process data for construction process risk assessment in the next time period includes the following specific steps: S31, obtaining the weight of the construction body, the connection strength and height data of the connection parts during the construction process of the next time period, and performing a construction risk assessment based on the weight of the construction body, the connection strength and height data of the connection parts during the construction process of the next time period; S32. Obtain the environmental anomaly analysis results and construction risk assessment results obtained by analysis, and conduct a risk assessment of the construction process based on the environmental anomaly analysis results and construction risk assessment results for the next time period.

2. The water conservancy and hydropower project monitoring and management method based on big data analysis according to claim 1 is characterized in that: The acquisition of predicted weather data and hydrological data during the construction of water conservancy and hydropower projects for environmental anomaly analysis includes the following specific steps: S21. Obtain weather data for the current period during the construction of the water conservancy and hydropower project, and perform weather anomaly analysis based on the comparison results between the weather data for the current period and the safety weather data during the construction process; S22. Obtain hydrological data during the construction of water conservancy and hydropower projects, and conduct hydrological hazard assessment during the construction process based on the hydrological data of this period; S23, obtaining weather anomaly analysis results and hydrological risk assessment results, weighting them respectively and summing them up to obtain environmental anomaly analysis results.

3. The water conservancy and hydropower project monitoring and management method based on big data analysis as claimed in claim 2 is characterized in that: The task warning for the next time period based on the construction process risk assessment result for the next time period includes the following specific contents: The hazard assessment result of the construction process in the next time period is compared with the set hazard assessment threshold of the construction process. If the hazard assessment result of the construction process in the next time period is greater than or equal to the set hazard assessment threshold of the construction process, it means that the construction process is prone to dangerous situations, and a task warning for the next time period is issued to remind the task of delaying the next time period. If the hazard assessment result of the construction process in the next time period is less than the set hazard assessment threshold of the construction process, no task warning for the next time period is issued.

4. The water conservancy and hydropower project monitoring and management method based on big data analysis as claimed in claim 3 is characterized in that: The hydrological data is obtained by prediction, and the prediction method is: Collect water level and flow data of corresponding locations in the same historical period, and simultaneously obtain weather data of the same historical period, and construct a deep learning neural network model whose input is the weather data of the period and whose output is the water level and flow data of corresponding locations in the period; The water level and flow data of the corresponding positions in the same historical period and the weather data of the same historical period obtained are divided into a 70% training set and a 30% validation set, and the 70% training set is used to input the deep learning neural network model for training to obtain an initial deep learning neural network model; the initial deep learning neural network model is tested using the 30% parameter test set, and the initial deep learning neural network model that meets the accuracy of the water level and flow data at the maximum corresponding position is output as the deep learning neural network model; Obtain the weather data for this period and import the water level and flow data of the corresponding position in the output period into the deep learning neural network model.

5. The water conservancy and hydropower project monitoring and management method based on big data analysis as claimed in claim 4 is characterized in that: The weather anomaly analysis formula is: , where T is the construction duration of this cycle, N is the type of weather data that affects the construction process, aj is the impact weight of the j-th weather data type, kjt is the value of the j-th weather data at time t, kjm is the median of the safety range of the j-th weather data, and dt is the time integral constant.

6. A water conservancy and hydropower project monitoring and management system based on big data analysis, which is implemented based on the water conservancy and hydropower project monitoring and management method based on big data analysis as claimed in any one of claims 1 to 5, characterized in that: It specifically includes: a data acquisition module, which is used to obtain the predicted weather data, predicted hydrological data and construction process data during the construction of water conservancy and hydropower projects; an environmental anomaly analysis module, which obtains the predicted weather data and hydrological data during the construction of water conservancy and hydropower projects for environmental anomaly analysis; a construction process hazard assessment module, which obtains the environmental anomaly analysis results and construction process data for construction process hazard assessment in the next time period; The task warning module provides task warning for the next time period based on the risk assessment results of the construction process in the next time period.

7. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the water conservancy and hydropower project monitoring and management method based on big data analysis as described in any one of claims 1 to 5 by calling the computer program stored in the memory.

8. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer is enabled to execute the water conservancy and hydropower project monitoring and management method based on big data analysis as described in any one of claims 1 to 5.

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