Water quantity accounting system based on artificial intelligence

Abstract

The invention discloses a water quantity accounting system based on artificial intelligence, and relates to the technical field of water quantity accounting analysis, and the system comprises a water quantity accounting platform which is in communication connection with a data acquisition module, an environment feature analysis module, an environment trend module, a water quantity accounting module, an intelligent decision support module and a user interaction feedback module. Wherein the modules are connected through electric signals; the data acquisition module is used for collecting multi-dimensional environmental condition data from different sources, including meteorological data, natural condition data and historical water volume data. According to the invention, intelligent support of water resource management is realized by integrating the water quantity accounting model, the supply and demand conditions of water resources can be analyzed, the future change trend of water quantity can be determined, and the management scheme and decision suggestions are generated according to the accounting analysis result, so that the scientificity and accuracy of decision making are improved, and the management efficiency is also remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of water volume accounting and analysis, and particularly relates to a water volume accounting system based on artificial intelligence. Background Art

[0002] With the growth of the global population and the acceleration of the urbanization process, the problem of water resource shortage has become increasingly serious. Water quality safety is an important link in water resource management. With the rapid development of technologies such as artificial intelligence (AI), big data, Internet of Things (IoT), and cloud computing, the traditional water volume accounting mode is facing innovation. In terms of water volume accounting, through artificial intelligence technology, complex pattern recognition, anomaly detection, and trend prediction can be carried out to assist relevant technical personnel in accurately mastering the distribution and usage of water resources, providing a scientific basis for optimizing water resource allocation.

[0003] In the prior art, affected by different environmental conditions, the accuracy of water volume accounting may be insufficient, making it difficult to meet the corresponding water resource demand and supply demand. Therefore, how to integrate multi-source data in the process of water volume accounting, clarify the relationship between water volume accounting and environmental conditions, and improve the accuracy of water volume accounting is the problem we need to solve. For this purpose, a water volume accounting system based on artificial intelligence is proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide a water volume accounting system based on artificial intelligence to solve the problems raised in the above background art.

[0005] To solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A water volume accounting system based on artificial intelligence, including a water volume accounting platform, which is communicatively connected to a data acquisition module, an environmental feature analysis module, an environmental trend module, a water volume accounting module, an intelligent decision support module, and a user interaction feedback module. Among them, the modules are electrically connected to each other;

[0007] The data acquisition module is used to collect multi-dimensional environmental condition data from different sources, including meteorological data, natural condition data, and historical water volume data;

[0008] The environmental feature analysis module analyzes the multi-dimensional environmental condition data, clarifies the environmental features affecting water volume accounting, and establishes an environmental feature sequence;

[0009] The environmental trend module analyzes the meteorological data and natural condition data in combination with the environmental feature sequence to clarify the environmental trend;

[0010] The water volume accounting module constructs a water volume accounting model in combination with the environmental feature sequence and the environmental trend analysis result to perform water volume accounting;

[0011] The intelligent decision-making support module provides intelligent decision-making suggestions for water resource managers according to the water volume accounting results;

[0012] The user interaction feedback module provides a user interaction interface to display the water volume accounting results and decision-making suggestions to the user.

[0013] A further improvement of the technical solution of the present invention lies in that: the data acquisition module specifically includes:

[0014] Clarify the multi-dimensional environmental condition data types for water volume accounting, which are meteorological data, natural condition data, and historical water volume data respectively. Among them, the sources of multi-dimensional environmental condition data include meteorological stations, natural resource survey and monitoring institutions, and hydrological databases and historical records, etc., but are not limited to this;

[0015] Obtain the multi-dimensional environmental condition data of the target area from each data source, and sort out the obtained multi-dimensional environmental condition data, and perform operations of data standardization and preprocessing;

[0016] Integrate the preprocessed environmental condition data, store it in the data warehouse, and perform data management, recording the source, collection time, and geographical location information of each piece of data for easy traceability and verification.

[0017] A further improvement of the technical solution of the present invention lies in that: the environmental feature analysis module specifically includes:

[0018] Receive the preprocessed multi-dimensional environmental condition data from the data acquisition module, and perform feature analysis on the meteorological data and natural condition data to determine the environmental features that affect water volume accounting respectively;

[0019] For the meteorological data of the target area, its environmental features are temperature, precipitation, and evaporation, and determine the evaluation indicators for the temperature feature, precipitation feature, and evaporation feature in a fixed evaluation period;

[0020] For the natural condition data of the target area, its environmental features are soil humidity, vegetation coverage rate, and water flow, and determine the evaluation indicators for the soil humidity feature, vegetation coverage rate feature, and water flow feature in a fixed evaluation period;

[0021] Integrate the selected environmental features and their evaluation indicators, and combine them with the historical water volume data in a fixed evaluation period, and arrange them in chronological order to form an environmental feature sequence.

[0022] A further improvement of the technical solution of the present invention lies in that: the evaluation indicators possessed by the features of the meteorological data and natural condition data include:

[0023] For temperature characteristics, analyze the evaluation indicators of average temperature and temperature change value; for precipitation characteristics, analyze the evaluation indicators of total precipitation and precipitation frequency; for evaporation characteristics, analyze the evaluation indicators of average evaporation and actual evaporation. Among them, the average temperature evaluates the average temperature within a fixed evaluation period to understand the overall temperature situation; the temperature change value calculates the change in temperature within a fixed evaluation period to understand the temperature fluctuation; the total precipitation counts the total precipitation within a fixed evaluation period to reflect the humidity of the region; the precipitation frequency records the number of precipitation events to evaluate the frequency of precipitation; the average evaporation calculates the average evaporation within a fixed evaluation period to reflect the rate of water loss; the actual evaporation records the actually observed evaporation volume.

[0024] For soil moisture characteristics, analyze the evaluation indicators of average soil moisture and soil moisture variability rate; for vegetation coverage characteristics, analyze the evaluation indicators of average vegetation coverage, vegetation coverage change rate, and vegetation health index; for water flow characteristics, analyze the evaluation indicators of average water flow and water flow peak. Among them, the average soil moisture measures the average water content in the soil to evaluate the humidity of the soil; the soil moisture variability rate calculates the fluctuation of soil moisture to understand the stability of soil moisture; the average vegetation coverage counts the average vegetation coverage in the region to reflect the degree of ecological restoration or degradation; the vegetation coverage change rate calculates the increase or decrease of vegetation coverage to evaluate the growth or decline trend of vegetation; the vegetation health index evaluates the health status of vegetation; the average water flow measures the average water flow in the target area to understand the abundance of water resources; the water flow peak records the maximum value reached by the water flow to evaluate the flood risk or the water supply capacity during drought.

[0025] A further improvement of the technical solution of the present invention lies in that: the environmental tendency module specifically includes:

[0026] Based on the feature data integrated from the environmental feature sequence, analyze the feature data of temperature, precipitation, and evaporation in the meteorological data, and analyze the feature data of soil moisture, vegetation coverage, and water flow in the natural condition data.

[0027] For the meteorological data, divide the fixed evaluation period into multiple sub-evaluation periods of fixed length, analyze the change tendency of the evaluation indicators of each feature of the meteorological data, integrate and analyze to calculate the meteorological interference tendency value, and judge the overall change trend of the meteorological data in the target area.

[0028] For the natural condition data, divide the fixed evaluation period into multiple sub-evaluation periods of fixed length, analyze the change tendency of the evaluation indicators of each feature of the natural condition data, integrate and analyze to calculate the natural condition interference tendency value, and clarify the overall change trend of the natural conditions in the target area.

[0029] A further improvement of the technical solution of the present invention lies in that: the calculation process of the meteorological interference tendency value is as follows:

[0030] Define the evaluation indicators in meteorological data, including average temperature, temperature change value, total precipitation, precipitation frequency, average evaporation, and actual evaporation, and standardize each meteorological data evaluation indicator so that all data can be compared on the same scale;

[0031] For each selected evaluation indicator of meteorological data, analyze its change trend within a fixed evaluation period by means of linear regression analysis;

[0032] Assign a weight to each evaluation indicator of meteorological data, sum the weighted trend values of each meteorological data evaluation indicator to obtain the final meteorological interference tendency value, so as to analyze the overall change trend of meteorological data. If the meteorological interference tendency value is large, it indicates that the meteorological conditions tend to deteriorate. If the meteorological interference tendency value is small, it indicates that the meteorological conditions tend to improve;

[0033] The calculation process of the natural condition interference tendency value is as follows:

[0034] Define the evaluation indicators in natural condition data, including average soil humidity, soil humidity variation rate, average vegetation coverage rate, vegetation coverage rate change rate, vegetation health index, average water flow, and water flow peak value;

[0035] For each selected evaluation indicator of natural condition data, calculate its change amount within a fixed evaluation period respectively;

[0036] Adopt the comprehensive scoring method of the analytic hierarchy process to convert the change amounts of each single indicator into unified standard scores, effectively eliminating the problem of inconsistent units between different indicators and facilitating further integration;

[0037] Preset the weight of each natural condition data evaluation indicator, sum the weighted trend values of each natural condition data evaluation indicator to obtain the final natural condition interference tendency value, so as to analyze the overall change trend of natural condition data.

[0038] A further improvement of the technical solution of the present invention lies in that: the water volume accounting module specifically includes:

[0039] Based on the environmental characteristic sequence, analyze the relevant historical water volume data and environmental condition data therein, and combine the meteorological interference tendency value and the natural condition interference tendency value to clarify the environmental condition tendency analysis result, and divide the training set and the test set;

[0040] Taking historical water volume data as the target variable and data of meteorological interference trend values and natural condition interference trend values as input variables, using the training set data, inputting the historical water volume data, meteorological interference trend values and natural condition interference trend values data into a multiple regression model for training, constructing a water volume accounting model, and adjusting the model parameters to improve the model output accuracy;

[0041] Using the test set to verify the water volume accounting model, evaluating the accounting performance of the model, adjusting and optimizing the water volume accounting model according to the verification results, and then using the water volume accounting model to analyze future water volume changes.

[0042] A further improvement of the technical solution of the present invention lies in that: the analysis process of the water volume accounting includes:

[0043] Using the constructed water volume accounting model to account for historical water volume data, inputting historical meteorological interference trend values and natural condition interference trend values data, outputting the corresponding water volume accounting values, comparing the accounting values with the actual values, and evaluating the accuracy of the model;

[0044] According to the accounting results, judging whether the water volume shows an upward, downward or stable trend. If the accounting value gradually increases over time, it indicates that the water volume is on an upward trend. If the accounting value gradually decreases over time, it indicates that the water volume is on a downward trend, and draw a water volume change curve to visually display;

[0045] Inputting current and recent meteorological interference trend values and natural condition interference trend values data into the water volume accounting model, using the water volume accounting model for water volume accounting analysis, and outputting the water volume accounting results for a period of time in the future.

[0046] A further improvement of the technical solution of the present invention lies in that: the intelligent decision-making support module specifically includes:

[0047] Based on the output results of the water volume accounting model, analyzing the water volume accounting results, and then analyzing the supply and demand situation of water resources, identifying the water resources supply and demand balance state, judging whether there are potential problems, including potential factors leading to water resource shortage or water quality deterioration, such as overexploitation, pollution discharge, uneven water resource distribution, etc., and analyzing the impact degree of potential problems on the sustainable utilization of water resources;

[0048] For the identified potential problems, formulating targeted management plans, including water-saving measures, water resource allocation plans and water quality improvement plans, etc. Based on the management plans, providing specific decision-making suggestions for water resource managers, simulating the plans, evaluating their effects and impacts, and analyzing the contributions of the plans to water resources supply and demand balance, water quality improvement and economic benefits;

[0049] Compare the simulation results of different management schemes, consider the implementation difficulty and cost-benefit ratio of the schemes, recommend the optimal or sub-optimal management scheme for water resource managers to refer to and make decisions, and provide information on the specific steps, schedule, and resource requirements for scheme implementation.

[0050] A further improvement of the technical solution of the present invention lies in that: the user interaction feedback module specifically includes:

[0051] Design a user interaction interface, divide the interface into multiple modules, including a water volume accounting result display area and a decision-making suggestion display area, to improve the readability and organization of information;

[0052] Real-time display the water volume accounting results through the user interaction interface, display the targeted decision-making suggestions output according to the water volume accounting results, and at the same time provide detailed explanations and background information of the decision-making suggestions to help users understand the rationality and feasibility of the suggestions;

[0053] Through the real-time monitoring of the system operation status, analyze the system response time, accuracy rate, and stability indicators, and mark the system anomalies and fault phenomena during the system operation process for the management personnel to perform subsequent operations to ensure the normal operation of the system and the accuracy of data.

[0054] Due to the adoption of the above technical solution, the technical progress achieved by the present invention compared with the prior art is:

[0055] 1. The present invention provides an artificial intelligence-based water volume accounting system. By integrating a water volume accounting model, it realizes intelligent support for water resource management, and can further analyze the water resource supply and demand situation, clarify the future change trend of water volume, and generate management schemes and decision-making suggestions according to the accounting analysis results, which not only improves the scientificity and accuracy of decision-making, but also significantly improves management efficiency.

[0056] 2. The present invention provides an artificial intelligence-based water volume accounting system. By integrating an intelligent decision support module, it can automatically generate multiple feasible management schemes and decision-making suggestions according to the prediction results, help water resource managers quickly identify potential problems and make scientific decisions, significantly improve the response speed and decision-making efficiency of water resource management, and contribute to the optimal allocation and sustainable utilization of water resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings.

[0058] Figure 1Schematic diagram of the system function modules of the present invention;

[0059] Figure 2 Schematic diagram of the working process of the water volume accounting module of the present invention. Detailed implementation manners

[0060] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0061] Embodiment 1, as Figure 1 shown, the present invention provides a water volume accounting system based on artificial intelligence, including a water volume accounting platform, which is communicatively connected to a data acquisition module, an environmental feature analysis module, an environmental trend module, a water volume accounting module, an intelligent decision-making support module, and a user interaction feedback module. Among them, the modules are electrically connected to each other;

[0062] The data acquisition module is used to collect multi-dimensional environmental condition data from different sources, including meteorological data, natural condition data, and historical water volume data, and clarify the types of multi-dimensional environmental condition data for water volume accounting, namely meteorological data, natural condition data, and historical water volume data. Among them, the sources of multi-dimensional environmental condition data include weather stations, natural resource survey and monitoring institutions, and hydrological databases and historical records, etc., but are not limited thereto. The multi-dimensional environmental condition data of the target area is obtained from each data source, and the obtained multi-dimensional environmental condition data is sorted out, and operations of data standardization and preprocessing are implemented. Outliers are removed and missing values are filled through data cleaning, and data conversion is used to unify data in different formats into a standard format for subsequent analysis, ensuring that data from different sources has the same time resolution, matching the geospatial data with the target area to avoid deviations caused by inconsistent coordinate systems, integrating the preprocessed environmental condition data, storing it in a data warehouse, and performing data management, recording the source, collection time, and geographical location information of each piece of data for traceability and verification;

[0063] The environmental feature analysis module analyzes multi-dimensional environmental condition data, identifies the environmental features affecting water volume accounting, and establishes an environmental feature sequence. It receives the preprocessed multi-dimensional environmental condition data from the data acquisition module, and conducts feature analysis on meteorological data and natural condition data to determine the environmental features that affect water volume accounting for each. For the meteorological data of the target area, its environmental features are temperature, precipitation, and evaporation. Evaluation indicators for the temperature feature, precipitation feature, and evaporation feature within a fixed evaluation period are determined. For the temperature feature, evaluation indicators of average temperature and temperature change value are analyzed. For the precipitation feature, evaluation indicators of total precipitation and precipitation frequency are analyzed. For the evaporation feature, evaluation indicators of average evaporation and actual evaporation are analyzed. Among them, the average temperature evaluates the average temperature within a fixed evaluation period to understand the overall temperature situation. The temperature change value calculates the change in temperature within a fixed evaluation period to understand the temperature fluctuation. The total precipitation statistics the total precipitation within a fixed evaluation period to reflect the humidity of the area. The precipitation frequency records the occurrence times of precipitation events to evaluate the frequency of precipitation. The average evaporation calculates the average evaporation within a fixed evaluation period to reflect the rate of water loss. The actual evaporation records the actually observed evaporation;

[0064] For the natural condition data of the target area, its environmental features are soil humidity, vegetation coverage rate, and water flow. Evaluation indicators for the soil humidity feature, vegetation coverage rate feature, and water flow feature within a fixed evaluation period are determined. For the soil humidity feature, evaluation indicators of average soil humidity and soil humidity variation rate are analyzed. For the vegetation coverage rate feature, evaluation indicators of average vegetation coverage rate, vegetation coverage rate change rate, and vegetation health index are analyzed. For the water flow feature, evaluation indicators of average water flow and water flow peak are analyzed. Among them, the average soil humidity measures the average water content in the soil to evaluate the humidity of the soil. The soil humidity variation rate calculates the fluctuation of soil humidity to understand the stability of soil moisture. The average vegetation coverage rate statistics the average vegetation coverage in the area to reflect the degree of ecological restoration or degradation. The vegetation coverage rate change rate calculates the increase or decrease of the vegetation coverage rate to evaluate the growth or decline trend of vegetation. The vegetation health index evaluates the health status of vegetation. The average water flow measures the average water flow of the target area to understand the abundance of water resources. The water flow peak records the maximum value reached by the water flow to evaluate the flood risk or water supply capacity during drought periods. Integrate the selected environmental features and their evaluation indicators, and combine with the historical water volume data within a fixed evaluation period, and arrange them in chronological order to form an environmental feature sequence;

[0065] The environmental trend module analyzes meteorological data and natural condition data in combination with the environmental feature sequence to clarify the environmental trend. Based on the feature data integrated by the environmental feature sequence, it analyzes the feature data of temperature, precipitation, and evaporation in the meteorological data, and analyzes the feature data of soil humidity, vegetation coverage rate, and water flow in the natural condition data. For the meteorological data, the fixed evaluation period is divided into multiple sub-evaluation periods of fixed length, and the change trend of the evaluation indicators of each feature of the meteorological data is analyzed. The meteorological interference trend value is calculated through integrated analysis to judge the overall change trend of the meteorological data in the target area. For the natural condition data, the fixed evaluation period is divided into multiple sub-evaluation periods of fixed length, and the change trend of the evaluation indicators of each feature of the natural condition data is analyzed. The natural condition interference trend value is calculated through integrated analysis to clarify the overall change trend of the natural conditions in the target area;

[0066] The calculation process of the meteorological interference trend value is as follows:

[0067] Clarify the evaluation indicators in the meteorological data, including average temperature, temperature change value, total precipitation, precipitation frequency, average evaporation, and actual evaporation. Standardize each meteorological data evaluation indicator so that all data can be compared on the same scale. For each selected evaluation indicator of the meteorological data, analyze its change trend within the fixed evaluation period through linear regression analysis. Assign a weight to each meteorological data evaluation indicator, and sum the weighted trend values of each meteorological data evaluation indicator to obtain the final meteorological interference trend value to analyze the overall change trend of the meteorological data. If the meteorological interference trend value is large, it indicates that the meteorological conditions tend to deteriorate. If the meteorological interference trend value is small, it indicates that the meteorological conditions tend to improve;

[0068] The calculation process of the natural condition interference trend value is as follows:

[0069] Clarify the evaluation indicators in the natural condition data, including average soil humidity, soil humidity variation rate, average vegetation coverage rate, vegetation coverage rate change rate, vegetation health index, average water flow, and water flow peak. For each selected evaluation indicator of the natural condition data, calculate its change amount within the fixed evaluation period respectively. Adopt the comprehensive scoring method of the analytic hierarchy process to convert the change amounts of each single indicator into unified standard scores, effectively eliminating the problem of inconsistent units between different indicators and facilitating further integration. Preset the weight of each natural condition data evaluation indicator, and sum the weighted trend values of each natural condition data evaluation indicator to obtain the final natural condition interference trend value to analyze the overall change trend of the natural condition data;

[0070] The water volume accounting module constructs a water volume accounting model in combination with the environmental feature sequence and the results of environmental trend analysis to conduct water volume accounting;

[0071] The intelligent decision-making support module provides intelligent decision-making suggestions for water resource managers based on the water volume accounting results, optimizes the water resource management plan, improves the water resource utilization efficiency, and ensures the sustainable utilization of water resources;

[0072] The user interaction feedback module provides a user interaction interface, displays the water volume accounting results and decision-making suggestions to the user, enhances the usability and user experience of the system, improves the user's trust and satisfaction with the system, and monitors the running state of the system in real time to ensure that the system can respond to environmental changes in a timely manner.

[0073] Embodiment 2, as Figure 2 shown, based on Embodiment 1, the present invention provides a technical solution: Preferably, the water volume accounting module specifically includes:

[0074] Based on the environmental feature sequence, analyze the relevant historical water volume data and environmental condition data therein, and combine the meteorological interference trend value and the natural condition interference trend value to clarify the environmental condition trend analysis result, divide the training set and the test set, use the historical water volume data as the target variable, and the data of the meteorological interference trend value and the natural condition interference trend value as the input variables. Use the training set data, input the historical water volume data, the meteorological interference trend value and the natural condition interference trend value data into the multiple regression model for training, construct the water volume accounting model, adjust the model parameters to improve the model output accuracy, use the test set to verify the water volume accounting model, evaluate the accounting performance of the model, adjust and optimize the water volume accounting model according to the verification result, and then use the water volume accounting model to analyze the future water volume change. Use the constructed water volume accounting model to account for the historical water volume data, input the historical meteorological interference trend value and the natural condition interference trend value data, output the corresponding water volume accounting value, compare the accounting value with the actual value, evaluate the accuracy of the model, and judge whether the water volume shows an upward, downward or stable trend according to the accounting result. If the accounting value gradually increases over time, it indicates that the water volume is on an upward trend; if the accounting value gradually decreases over time, it indicates that the water volume is on a downward trend, and draw a water volume change curve to visually display. Input the current and recent meteorological interference trend value and natural condition interference trend value data into the water volume accounting model, use the water volume accounting model for water volume accounting analysis, and output the water volume accounting result for a future period of time;

[0075] The intelligent decision-making support module specifically includes: based on the output results of the water volume accounting model, analyzing the water volume accounting results, and then analyzing the supply and demand situation of water resources, identifying the balance state of water resources supply and demand, judging whether there are potential problems, including potential factors leading to water resource shortage or water quality deterioration, such as over-exploitation, pollution discharge, uneven water resource distribution, etc., and analyzing the impact degree of potential problems on the sustainable utilization of water resources. For the identified potential problems, formulate targeted management plans, including water-saving measures, water resource allocation plans, and water quality improvement plans, etc. Based on the management plan, provide specific decision-making suggestions for water resource managers, simulate the plan, evaluate its effects and impacts, analyze the contributions of the plan to the balance of water resources supply and demand, water quality improvement, and economic benefits, compare the simulation results of different management plans, consider the implementation difficulty and cost-benefit ratio factors of the plan, recommend the optimal or sub-optimal management plan for water resource managers to refer to and make decisions, and provide the specific steps, schedule, and resource requirement information for plan implementation;

[0076] The user interaction feedback module specifically includes: designing a user interaction interface, dividing the interface into multiple modules, including a water volume accounting result display area and a decision-making suggestion display area, to improve the readability and organization of information. Real-time display the water volume accounting results through the user interaction interface, and display the targeted decision-making suggestions output according to the water volume accounting results. At the same time, provide detailed explanations and background information of the decision-making suggestions to help users understand the rationality and feasibility of the suggestions. By real-time monitoring the operation status of the system, analyze the response time, accuracy rate, and stability indicators of the system, and mark the system anomalies and fault phenomena during the system operation process for management personnel to perform subsequent operations to ensure the normal operation of the system and the accuracy of data.

[0077] As described above, it is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application shall be subject to the protection scope of the claimed rights.

Claims

1. A water volume accounting system based on artificial intelligence, including a water volume accounting platform, characterized in that: The water volume accounting platform is communicatively connected with a data acquisition module, an environmental feature analysis module, an environmental trend module, a water volume accounting module, an intelligent decision support module and a user interaction feedback module, wherein the modules are electrically connected with each other; The data acquisition module is used to collect multi-dimensional environmental condition data from different sources, including meteorological data, natural condition data and historical water volume data; The environmental characteristics analysis module analyzes multi-dimensional environmental condition data, identifies environmental characteristics that affect water volume accounting, and establishes an environmental characteristics sequence; The environmental trend module analyzes the meteorological data and natural condition data in combination with the environmental feature sequence to clarify the environmental trend; The water volume accounting module constructs a water volume accounting model based on the environmental feature sequence and environmental trend analysis results to perform water volume accounting; The intelligent decision support module provides intelligent decision suggestions based on the water volume calculation results; The user interaction feedback module provides a user interaction interface to display water volume calculation results and decision-making suggestions to users.

2. The water volume calculation system based on artificial intelligence according to claim 1 is characterized in that: The data acquisition module specifically includes: The types of multi-dimensional environmental condition data for water accounting are clarified, namely meteorological data, natural condition data and historical water volume data. The sources of multi-dimensional environmental condition data include meteorological stations, natural resource survey and monitoring agencies, and hydrological databases and historical records. Acquire multi-dimensional environmental condition data of the target area from various data sources, organize the acquired multi-dimensional environmental condition data, and implement data standardization and preprocessing operations; Integrate the pre-processed environmental condition data, store it in the data warehouse, and manage the data, recording the source, collection time and geographical location information of each data.

3. The water volume calculation system based on artificial intelligence according to claim 2 is characterized in that: The environmental feature analysis module specifically includes: Receive pre-processed multi-dimensional environmental condition data from the data acquisition module, and perform feature analysis on meteorological data and natural condition data to determine the environmental characteristics that affect water volume accounting; For the meteorological data of the target area, the environmental characteristics are temperature, precipitation and evaporation, and the evaluation indicators of temperature characteristics, precipitation characteristics and evaporation characteristics of a fixed evaluation period are determined; For the natural condition data of the target area, the environmental characteristics are soil moisture, vegetation coverage and water flow, and the evaluation indicators of soil moisture characteristics, vegetation coverage characteristics and water flow characteristics in a fixed evaluation period are determined; The selected environmental characteristics and their evaluation indicators are integrated and combined with the historical water volume data within a fixed evaluation period, and they are arranged in chronological order to form an environmental characteristic sequence.

4. The water quantity calculation system based on artificial intelligence according to claim 3 is characterized by: The characteristics of the meteorological data and natural condition data include the following evaluation indicators: For temperature characteristics, the evaluation indicators of average temperature and temperature change value are analyzed; for precipitation characteristics, the evaluation indicators of total precipitation and precipitation frequency are analyzed; for evaporation characteristics, the evaluation indicators of average evaporation and actual evaporation are analyzed; For soil moisture characteristics, the evaluation indicators of average soil moisture and soil moisture variation rate are analyzed. For vegetation coverage characteristics, the evaluation indicators of average vegetation coverage, vegetation coverage change rate and vegetation health index are analyzed. For water flow characteristics, the evaluation indicators of average water flow and water flow peak are analyzed.

5. The water volume calculation system based on artificial intelligence according to claim 4 is characterized in that: The environmental trend module specifically includes: Based on the characteristic data integrated by the environmental characteristic sequence, the characteristic data of temperature, precipitation and evaporation in the meteorological data are analyzed, and the characteristic data of soil moisture, vegetation coverage and water flow in the natural condition data are analyzed; For meteorological data, the fixed evaluation period is divided into multiple sub-evaluation periods of fixed length, the changing trend of the evaluation indicators of each feature of the meteorological data is analyzed, the meteorological interference trend value is integrated and analyzed, and the overall changing trend of the meteorological data in the target area is determined; For natural condition data, the fixed evaluation period is divided into multiple sub-evaluation periods of fixed length, the changing trend of the evaluation indicators of each feature of the natural condition data is analyzed, the natural condition interference trend value is integrated and analyzed, and the overall changing trend of the natural conditions in the target area is clarified.

6. The water volume calculation system based on artificial intelligence according to claim 5 is characterized by: The calculation process of the meteorological interference trend value is: Clarify the evaluation indicators in meteorological data, including average temperature, temperature change value, total precipitation, precipitation frequency, average evaporation and actual evaporation, and standardize the evaluation indicators of meteorological data; For each selected meteorological data evaluation index, its changing trend within a fixed evaluation period is analyzed by linear regression analysis; Assign a weight to each meteorological data evaluation index, and sum the trend values ​​of each meteorological data evaluation index to obtain the final meteorological interference trend value, so as to analyze the overall change trend of meteorological data. If the meteorological interference trend value is large, it indicates that the meteorological conditions are tending to deteriorate, and if the meteorological interference trend value is small, it indicates that the meteorological conditions are tending to improve. The calculation process of the natural condition interference trend value is: Clarify the evaluation indicators in the natural condition data, including average soil moisture, soil moisture variation rate, average vegetation coverage, vegetation coverage change rate, vegetation health index, average water flow and water flow peak; For each selected evaluation index of natural condition data, calculate its change within a fixed evaluation period; The comprehensive scoring method of analytic hierarchy process is adopted to convert the change of each single indicator into a unified standard score; The weight of each natural condition data evaluation index is set in advance, and the trend values ​​of each natural condition data evaluation index are weighted and summed to obtain the final natural condition interference trend value to analyze the overall change trend of the natural condition data.

7. The water volume calculation system based on artificial intelligence according to claim 6 is characterized by: The water volume calculation module specifically includes: Based on the environmental feature sequence, the relevant historical water volume data and environmental condition data are analyzed, and the environmental condition trend analysis results are clarified by combining the meteorological interference trend value and the natural condition interference trend value, and the training set and the test set are divided; The historical water volume data is used as the target variable, the data of meteorological interference trend value and natural condition interference trend value are used as the input variable, and the training set data is used to input the historical water volume data, meteorological interference trend value and natural condition interference trend value data into the multivariate regression model for training to construct the water volume accounting model; The test set is used to verify the water accounting model, evaluate the model's accounting performance, adjust and optimize the water accounting model based on the verification results, and then use the water accounting model to analyze future water volume changes.

8. The water volume calculation system based on artificial intelligence according to claim 7 is characterized by: The analysis process of water volume accounting includes: Use the constructed water volume accounting model to calculate the historical water volume data, input the historical meteorological interference trend value and natural condition interference trend value data, output the corresponding water volume accounting value, compare the accounting value with the actual value, and evaluate the accuracy of the model; According to the calculation results, it is determined whether the water volume is showing an upward, downward or stable trend. If the calculated value gradually increases over time, it indicates that the water volume is on an upward trend. If the calculated value gradually decreases over time, it indicates that the water volume is on a downward trend. A water volume change curve is drawn for intuitive display; The current and recent meteorological interference trend values ​​and natural conditions interference trend value data are input into the water quantity accounting model, and the water quantity accounting model is used to perform water quantity accounting analysis to output the water quantity accounting results for a period of time in the future.

9. The water volume calculation system based on artificial intelligence according to claim 8 is characterized in that: The intelligent decision support module specifically includes: Based on the output of the water accounting model, analyze the water accounting results, and then analyze the supply and demand of water resources, identify the balance of water supply and demand, determine whether there are potential problems, including potential factors that lead to water shortages or water quality deterioration, and analyze the impact of potential problems on the sustainable use of water resources; Develop targeted management plans for the identified potential problems, including water conservation measures, water resource allocation plans and water quality improvement plans. Based on the management plans, provide specific decision-making recommendations to water resource managers, simulate the plans, evaluate their effects and impacts, and analyze the plans' contributions to water resource supply and demand balance, water quality improvement and economic benefits; By comparing the simulation results of different management plans, considering the difficulty of implementation and cost-effectiveness of the plans, the optimal or suboptimal management plan is recommended for reference and decision-making by water resource managers, and the specific steps, timetable and resource demand information for the implementation of the plan are provided.

10. The water volume calculation system based on artificial intelligence according to claim 9 is characterized in that: The user interaction feedback module specifically includes: Design the user interaction interface and divide it into multiple modules, including the water volume calculation result display area and the decision-making suggestion display area; The user interface displays water volume accounting results in real time, and displays targeted decision-making suggestions based on the water volume accounting results, while providing detailed explanations and background information of the decision-making suggestions; Through real-time monitoring of the system operation status, the system response time, accuracy and stability indicators are analyzed, and system anomalies and failure phenomena during system operation are marked for management personnel to perform subsequent operations.

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