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

Abstract

The present application discloses a water conservancy and hydropower project monitoring and management method and system based on big data analysis, which belongs to the field of project monitoring and management. 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 construction process risk assessment for the next time period, and conducts a task warning 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 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 the risk warning for water conservancy tasks.

Description

Technical Field

[0001] This application belongs to the field of engineering monitoring and management, specifically a water conservancy and hydropower engineering monitoring and management method and system 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 returns. When carrying out 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 cause This can result in the early warning system being unable to provide comprehensive warnings in complex actual situations. Some early warning systems rely on regular manual monitoring or fixed data collection frequencies, which may not reflect the real-time status of water conservancy facilities in real time, thereby affecting the timeliness of early warnings. Due to the complexity and uncertainty of environmental factors, existing early warning systems may have certain limitations in accurately predicting the time, location and scale of disasters. To overcome these shortcomings, future water hazard early warning systems need to adopt more advanced technologies, such as the Internet of Things, big data analysis, and artificial intelligence, to improve the comprehensiveness, accuracy and timeliness of early warnings, and enhance the system's compatibility, scalability and user experience.

[0003] In order to solve the problems raised by this background technology, this application designs a water conservancy and hydropower project monitoring and management method and system based on big data analysis. Summary of the Invention

[0004] In order to solve the deficiencies in the existing technology 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 construction process data to conduct a construction process risk assessment for the next time period, and conducts a task warning for the next time period based on the construction process risk assessment results for the next time period. Through the prediction and analysis of the weather and hydrological data for the next stage, the environmental anomalies in the construction process are accurately assessed, and the risk 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 the risk warning for water conservancy tasks.

[0005] To achieve the above objectives, the present application provides the following technical solutions: First, 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:

[0006] S1. Obtaining predicted weather data, predicted hydrological data, and construction process data during the construction of water conservancy and hydropower projects;

[0007] S2. Obtaining forecasted weather data and hydrological data during the construction of water conservancy and hydropower projects to conduct environmental anomaly analysis;

[0008] S3. Obtain environmental anomaly analysis results and construction process data to conduct construction process hazard assessment for the next time period;

[0009] 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.

[0010] As a preferred technical solution for the water conservancy and hydropower project monitoring and management method based on big data analysis, the weather data in S1 includes weather data such as precipitation, temperature and wind force in the local weather forecast that affect the construction process; the hydrological data in S1 includes hydrological data such as water level and flow that affect the construction process; the construction process data in S1 includes construction subject data and construction equipment operation data during 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 that the hydrological data such as water level and flow that affect the construction process are obtained through prediction;

[0011] In this corresponding step, a deep learning neural network is constructed through historical hydrological and weather data to accurately predict hydrological information.

[0012] 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 in S2 for environmental anomaly analysis includes the following specific steps:

[0013] 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. The weather anomaly analysis formula may 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 influence 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 integration constant;

[0014] S22. Obtain hydrological data during the construction of water conservancy and hydropower projects, 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;

[0015] S23. Obtain weather anomaly analysis results and hydrological risk assessment results, weight them separately, and then sum them to obtain environmental anomaly analysis results.

[0016] As a preferred technical solution for the water conservancy and hydropower project monitoring and management method based on big data analysis, 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:

[0017] S31. Obtain the weight of the main construction body, the connection strength and height data of the connection parts during the construction process of the next time period, and perform a construction risk assessment based on the weight of the main construction body, the connection strength and height data of the connection parts during the construction process of the next time period. The construction risk assessment formula is: , where hs is the increased height data of the construction body during the construction process of 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 increased weight of the construction body during the construction process of the next time period, g is the acceleration of gravity, which is a constant, and Fs is the average connection strength of the connection parts. In this way, the danger assessment of the construction process is carried out based on the characteristics of the construction tasks and the construction body during the construction process of the next time period;

[0018] 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.

[0019] 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 results for the next time period includes the following specific contents:

[0020] Compare the construction process risk assessment result of the next time period with the set construction process risk assessment threshold. If the construction process risk assessment result of 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 of the next time period is less than the set construction process risk assessment threshold, no task warning for the next time period is issued.

[0021] 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 process of water conservancy and hydropower projects; an environmental anomaly analysis module, which obtains predicted weather data and hydrological data during the construction process 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 for construction process hazard assessment for the next time period; a task warning module, which performs task warning for the next time period based on the construction process hazard assessment results for the next time period.

[0022] 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;

[0023] 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.

[0024] 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.

[0025] Compared with the existing technology, the beneficial effects of the present application are: 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 construction process risk assessment for the next time period, and conducts a task warning for the next time period based on the construction process risk assessment results for the next time period. Through the forecast analysis of the weather and hydrological data for the next stage, the environmental anomalies in the construction process are accurately assessed, and the risk 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 the risk warning for water conservancy tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] 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;

[0027] Figure 1 A schematic diagram of a flow chart of an embodiment of the method of this application;

[0028] Figure 2 This is a schematic diagram of step S1 of an embodiment of the method of this application;

[0029] Figure 3 This is a schematic diagram of step S2 of an embodiment of the system of the present application;

[0030] Figure 4 This is a schematic diagram of the overall framework of the system embodiment of this application. DETAILED DESCRIPTION

[0031] 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 merely descriptions of exemplary embodiments of the present application and are not intended to 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.

[0032] In the accompanying drawings, the size, dimensions, and shapes of elements have been slightly adjusted for ease of illustration. The drawings are for illustration only and are not drawn strictly to scale. As used herein, the terms "substantially," "approximately," and similar terms are intended to indicate approximation, not degree, and are intended to illustrate the inherent variations in measured or calculated values that would be recognized by one of ordinary skill in the art. Furthermore, in this application, the order in which the various steps are described does not necessarily represent the order in which these steps would occur in actual operation, unless otherwise specified or inferred from the context. It should also be understood that expressions such as "comprises," "including," "having," "includes," and / or "comprising" are open-ended, not closed-ended, expressions in this specification, indicating 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. Furthermore, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire list of features, not just the individual elements in the list. Furthermore, when describing embodiments of the present application, the use of "may" means "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 commonly understood by those skilled in the art to which this application belongs. It should also be understood that, unless otherwise specified 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.

[0033] Example 1

[0034] In order to solve the technical problems raised in the background technology, this 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:

[0035] S1. Obtaining predicted weather data, predicted hydrological data, and construction process data during the construction of water conservancy and hydropower projects;

[0036] In this embodiment, the weather data in S1 includes the precipitation, temperature, and wind speed of the local weather forecast, which affect the construction process. The hydrological data in S1 includes the water level and flow rate of the water body, which affect the construction process. The construction process data in S1 includes the construction subject data and construction equipment operation data during the construction process. Among them, 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 that the hydrological data such as the water level and flow rate of the water body in this embodiment that affect the construction process are obtained through prediction, and the prediction method is:

[0037] S11. Collect water level and flow data for corresponding locations in the same historical period, and simultaneously obtain weather data for 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 for corresponding locations in the period;

[0038] S12. Divide the acquired water level and flow data of corresponding locations in the same historical period and the weather data of the same historical period into a 70% training set and a 30% validation set, and use the 70% training set to input the deep learning neural network model for training to obtain an initial deep learning neural network model; use the 30% parameter test set to test the initial deep learning neural network model, and output the initial deep learning neural network model that meets the maximum accuracy of the water level and flow data at the corresponding location 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 neuron i in the nth layer and p-item neurons in the n+1th layer, Yip is the output of neuron i in the nth layer, and cp(n+1) is the bias between neuron i in the nth layer and p-item neurons in the n+1th layer;

[0039] S13. Obtain 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;

[0040] 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;

[0041] S2. Obtaining forecasted weather data and hydrological data during the construction of water conservancy and hydropower projects to conduct environmental anomaly analysis;

[0042] In this embodiment, S2 includes the following specific steps:

[0043] 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. The weather anomaly analysis formula may 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 influence 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 integration 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 risks of water conservancy and hydropower project construction caused by weather;

[0044] S22. Obtain hydrological data during the construction of water conservancy and hydropower projects, 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 of the hazards in the construction process of water conservancy and hydropower projects is carried out by analyzing the hydrological information;

[0045] S23. Obtain weather anomaly analysis results and hydrological risk assessment results, weight them separately, and then sum them to obtain environmental anomaly analysis results. In this step, a comprehensive analysis of environmental anomalies is conducted based on the impact of weather and hydrology on the construction process of water conservancy and hydropower projects;

[0046] S3. Obtain environmental anomaly analysis results and construction process data to conduct construction process hazard assessment for the next time period;

[0047] In this embodiment, S3 includes the following specific steps:

[0048] S31. Obtain the weight of the main construction body, the connection strength and height data of the connection parts during the construction process of the next time period, and perform a construction risk assessment based on the weight of the main construction body, the connection strength and height data of the connection parts during the construction process of the next time period. The construction risk assessment formula is: , where hs is the increased height data of the construction body during the construction process of 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 increased weight of the construction body during the construction process of the next time period, g is the acceleration of gravity, which is a constant, and Fs is the average connection strength of the connection parts. In this way, the danger assessment of the construction process is carried out based on the characteristics of the construction tasks and the construction body during the construction process of the next time period;

[0049] S32. Obtain the environmental anomaly analysis results and the construction risk assessment results obtained from the analysis. Based on the environmental anomaly analysis results and the construction risk assessment results for the next time period, the construction process risk is assessed. 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 a final construction process risk assessment result.

[0050] It should be particularly pointed out in this embodiment that the setting parameters in this embodiment are obtained by fitting historical building construction data. The specific acquisition method can be: obtaining weather data, hydrological data and construction process data during the historical water conservancy and hydropower project construction process, performing construction process risk assessment result analysis through the above steps of this embodiment, and obtaining whether a dangerous situation occurred during the historical water conservancy and hydropower project construction process, importing the analyzed construction process risk assessment result and the judgment result of whether a dangerous situation occurred into the fitting software, performing continuous fitting iterations, and outputting the value of the setting parameter that meets the maximum judgment result accuracy rate;

[0051] S4. Based on the construction process risk assessment results for the next time period, a task warning for the next time period is issued;

[0052] 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.

[0053] What needs to be explained in this example is that this embodiment has the following advantages over the existing technology: obtaining predicted weather data and hydrological data during the construction process 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. Through the forecast analysis of the weather and hydrological data for the next stage, the environmental anomalies in the construction process are accurately assessed, and the risk 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 risk warnings for water conservancy tasks.

[0054] Example 2

[0055] like Figure 4 As 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, which specifically includes: a data acquisition module for acquiring predicted weather data, predicted hydrological data and construction process data during the construction process of water conservancy and hydropower projects; an environmental anomaly analysis module for acquiring predicted weather data and hydrological data during the construction process of water conservancy and hydropower projects to perform environmental anomaly analysis; a construction process risk assessment module for acquiring environmental anomaly analysis results and construction process data to perform construction process risk assessment for the next time period; a task warning module for performing task warning for the next time period based on the construction process risk 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 risk 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.

[0056] Example 3

[0057] This embodiment provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0058] 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.

[0059] The electronic device may vary significantly due to different configurations or performance, and may include one or more processors and one or more memories, wherein the memories store at least one computer program, which 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 implementing the device functions. For example, the electronic device may also have components such as wired or wireless network interfaces and input and output interfaces to facilitate data input and output. This embodiment will not be described in detail here.

[0060] Example 4

[0061] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0062] 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.

[0063] 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.

[0064] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are fully or partially generated. 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 transferred from one computer-readable storage medium to another. For example, computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. A computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0065] 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 comprises a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0066] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of application of this application is not limited to technical solutions formed by a specific combination of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned application concept. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A water conservancy and hydropower project monitoring and management method based on big data analysis is characterized by: It includes the following specific steps: S1. Obtaining predicted weather data, predicted hydrological data, and construction process data during the construction of water conservancy and hydropower projects; S2. Obtaining forecasted weather data and hydrological data during the construction of water conservancy and hydropower projects to conduct environmental anomaly analysis; The specific steps include: 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 of the weather data for the current period with the safety weather data for the construction process; S22. Obtain hydrological data during the construction of water conservancy and hydropower projects, and conduct a hydrological risk assessment of 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 then summing them to obtain environmental anomaly analysis results; S3. Obtain environmental anomaly analysis results and construction process data to conduct construction process hazard assessment for the next time period; S4. Based on the construction process risk assessment results for 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 for the next time period includes the following specific steps: S31. Obtain the weight of the main construction body, the connection strength and height data of the connection parts during the construction process of the next time period, and perform a construction risk assessment based on the weight of the main construction body, the connection strength and height data of the connection parts during the construction process of the next time period. The construction risk assessment formula is: , where hs is the increased height data of the construction body during the construction process of 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 increased weight of the construction body during the construction process of the next time period, g is the acceleration of gravity, which is a constant, and Fs is the average connection strength of the connection parts; 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 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: Compare the construction process risk assessment result of the next time period with the set construction process risk assessment threshold. If the construction process risk assessment result of 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 of the next time period is less than the set construction process risk assessment threshold, no task warning for the next time period is issued.

3. The water conservancy and hydropower project monitoring and management method based on big data analysis according to claim 2 is characterized in that: The hydrological data are obtained through prediction, and the prediction method is: Collect water level and flow data at corresponding locations in the same historical period, and simultaneously obtain weather data for 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 at corresponding locations in the period; The water level and flow data of corresponding locations in the same historical period and the weather data of the same historical period are divided into a 70% training set and a 30% validation set. 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 maximum accuracy of the water level and flow data at the corresponding location is output as the deep learning neural network model. Obtain weather data for this period and import the water level and flow data of the corresponding location in the output period into the deep learning neural network model.

4. The water conservancy and hydropower project monitoring and management method based on big data analysis according to claim 3 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 influence 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 integration constant.

5. 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 4, characterized in that: It specifically includes: a data acquisition module for acquiring predicted weather data, predicted hydrological data, and construction process data during the construction of water conservancy and hydropower projects; an environmental anomaly analysis module for acquiring 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 for acquiring environmental anomaly analysis results and construction process data to conduct construction process hazard assessment for 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.

6. 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 4 by calling the computer program stored in the memory.

7. A computer-readable storage medium, characterized in that Instructions are stored, and when the instructions are run on a computer, the computer is caused 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 4.

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