Watershed water circulation reverse deduction method and system based on artificial intelligence

Abstract

The invention discloses a watershed water circulation reverse deduction method and system based on artificial intelligence. The method comprises the steps of data acquisition, watershed sensing range expansion, probability analysis model construction, space-time accurate matching and result evaluation and verification. The invention belongs to the technical field of intelligent water conservancy, and particularly relates to a watershed water circulation reverse deduction method and system based on artificial intelligence. According to the scheme, the watershed sensing range is expanded, specifically, a sensing network basic framework is constructed, an information transmission path and node connection are defined, and the effective coverage probability of sensor nodes and target points is calculated; the sensor node distribution is dynamically adjusted according to the calculation result to optimize the sensing network; spatial and temporal precise matching is adopted, specifically, a spatial data fusion framework is expanded to a time-space domain, constant additive deviation is adopted to correct system errors, the dependency of space and time reflected by a latent process is calculated, finally, a visual display plate is established to present fused spatial and temporal data, and dynamic updating is carried out.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent water conservancy technology, and specifically is a basin water cycle reverse deduction method and system based on artificial intelligence. Background Art

[0002] The reverse deduction method of water cycle in a river basin is a method that uses artificial intelligence, conditional probability analysis and reverse probability analysis to simulate and deduce the process of water and drought disasters in the river basin and reversely deduce the influencing factors. At the same time, it combines the big data platform to perform logical deduction and judgment of artificial condition settings.

[0003] However, the existing methods for reverse deduction of river basin water cycle lack large-scale river basin perception data, which leads to inaccurate and incomplete deduction results, making it difficult to truly reflect the actual situation of river basin water cycle, and affecting the scientific planning of water resources management and water environment protection. There are technical problems such as difficulty in matching time and space dimensions, and inability to accurately reflect the characteristics and changes of river basin water cycle at different times and spaces, which limits the construction of digital twin river basins. Summary of the invention

[0004] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a method and system for reverse deduction of water cycle in a river basin based on artificial intelligence. In view of the lack of large-scale watershed perception data, which leads to inaccurate and incomplete deduction results, it is difficult to truly reflect the actual situation of water cycle in the river basin, and affects the scientific planning of water resources management and water environment protection. The method of expanding the perception range of the river basin is adopted, specifically, to build a basic architecture of the perception network, clarify the information transmission path and node connection, calculate the effective coverage probability of sensor nodes and target points, and dynamically adjust the distribution of sensor nodes according to the calculation results to optimize the perception network; in view of the technical problems that it is difficult to match in time and space dimensions, it is impossible to accurately reflect the characteristics and changes of water cycle in the river basin at different time and space, and limit the construction of digital twin river basins, precise time and space matching is adopted, specifically, the spatial data fusion framework is extended to the time and space domain, a constant additive deviation is used to correct the system error, and the latent process is calculated to reflect the dependence of space and time. Finally, a visual display panel is established to present the fused time and space data, and it is dynamically updated.

[0005] The technical solution adopted by the present invention is as follows: The artificial intelligence-based water cycle reverse deduction method provided by the present invention comprises the following steps:

[0006] Step S1: data collection, specifically collecting data and performing data preprocessing to obtain a watershed water cycle data set;

[0007] Step S2: Expand the sensing range of the watershed, specifically by building the basic architecture of the sensing network, clarifying the information transmission path and node connections, calculating the effective coverage probability of sensor nodes and target points, and dynamically adjusting the distribution of sensor nodes according to the calculation results to optimize the sensing network;

[0008] Step S3: construct a probability analysis model, specifically setting initial conditions, performing forward calculations, and using reverse probability analysis to infer influencing factors when floods and droughts occur;

[0009] Step S4: accurate time-space matching, specifically, expanding the spatial data fusion framework to the time-space domain, using a constant additive bias to correct the system error, calculating the latent process to reflect the spatial and temporal dependencies, and finally establishing a visual display panel to present the fused time-space data and dynamically update it;

[0010] Step S5: Result evaluation and verification, specifically, comparing the simulation results with the actual basin water cycle related data in detail, analyzing the error distribution and verifying the results.

[0011] Furthermore, in step S1, the data collection includes the following steps:

[0012] Step S11: Collect meteorological data, obtain precipitation, temperature, wind speed, and humidity information through meteorological stations and satellites, and use them to analyze precipitation input and evaporation processes in the water cycle;

[0013] Step S12: Collect hydrological data, obtain flow, water level, and water quality data from river monitoring stations and lake monitoring points to understand the flow and changes of water in the basin;

[0014] Step S13: Collect topographic data, and use topographic mapping and remote sensing technology to obtain the topographic relief, slope and altitude of the basin, which have an important impact on the flow path and potential energy distribution of water;

[0015] Step S14: collecting soil data, including soil texture, moisture content, and permeability, to reflect the soil's ability to retain and transmit moisture;

[0016] Step S15: Collecting human water use activity data, including domestic water use, industrial water use, drainage data, and water project operation data;

[0017] Step S16: Summarize meteorological data, hydrological data, topographic data, soil data and human water use activity data, and perform data preprocessing to obtain a watershed water cycle data set.

[0018] Further, in step S2, the expansion of the watershed sensing range includes the following steps:

[0019] Step S21: Construct the basic architecture of the perception network to clarify the information transmission path and the connection between nodes, laying the foundation for the perception range. The cluster is composed of sensor nodes. Each individual sensor node has the ability to communicate directly with the corresponding cluster head. The spatial interval between the sensor node and the target point is quantified. The formula used is as follows:

[0020] ;

[0021] In the formula, represents the spatial separation distance between the sensor node and the target point, represents the i-th sensor node, represents the jth target point, represents the horizontal coordinate of the i-th sensor node, represents the horizontal coordinate of the jth target point, represents the ordinate of the i-th sensor node, Represents the ordinate of the jth target point;

[0022] Step S22: Calculate the probability that a single sensor covers the target point. The formula used is as follows:

[0023] ;

[0024] In the formula, represents the probability of the sensor node covering the target point, γ represents the induction attenuation coefficient, r i represents the maximum ideal sensing radius of the sensor node, which means that the node has a greater probability of sensing coverage within this radius. e represents the sensing error inherent in a given sensor node;

[0025] Step S23: Calculate the effective coverage probability of the target point. Based on the probability of a single sensor node covering the target point, the probability of the target point being effectively covered is obtained by calculating the complement of the product of the coverage probabilities of all sensor nodes to the target point. The formula used is as follows:

[0026] ;

[0027] In the formula, represents the probability that the target point is effectively covered, and n represents the total number of sensor nodes;

[0028] Step S24: Optimize the perception network, dynamically adjust the distribution of sensor nodes according to the calculated effective coverage efficiency of the target points, optimize the coverage effect and accuracy of the perception network, continuously monitor and evaluate the performance of the perception network, and further improve the perception network based on feedback information.

[0029] Furthermore, in step S3, constructing the probability analysis model includes the following steps:

[0030] Step S31: Influencing factor analysis, establishing a mathematical model that can analyze the probability of flood and drought disasters and related influencing factors, and comprehensively considering various factors that may affect the occurrence of flood and drought disasters, including rainfall, temperature, soil moisture and river flow;

[0031] Step S32: setting initial conditions, clarifying the parameter values ​​and states when the model starts calculating, including the initial rainfall value and temperature range within the specified time period;

[0032] Step S33: forward calculation is performed to calculate the probability of flood and drought disasters under the initial conditions and the specific circumstances that may occur based on the set initial conditions and the rules of the model. Through forward calculation, the probability of disasters occurring under a specific combination of conditions and the possible consequences can be understood;

[0033] Step S34: Use reverse probability analysis to infer the influencing factors. When flood and drought disasters have occurred, the probability model is used to infer the influencing factors that caused the disaster. For example, if a severe drought occurs, reverse analysis can be used to find out whether it was caused by too little rainfall in the early stage, too high temperature, and too fast evaporation of soil moisture. Reverse analysis helps us better understand the causes and mechanisms of disasters, thereby providing a basis for formulating response strategies and preventive measures.

[0034] Furthermore, in step S4, the time-space precise matching includes the following steps:

[0035] Step S41: Expand the spatial data fusion framework to the spatiotemporal domain so that the model can process information in both spatial and temporal dimensions simultaneously, thereby analyzing and fusing data more comprehensively and accurately. The format used is as follows:

[0036] ;

[0037] In the formula, It represents the spatiotemporal data result after fusion at spatial position c and time t. c represents the index in the spatial dimension, which is used to distinguish different spatial positions. t represents the specific moment in the time dimension. a represents the deviation term that remains unchanged in space and time, which is understood as a systematic and fixed deviation factor. It is a hidden space-time process that changes with space and time, that is, a latent process, which contains space-time dynamic information and laws. It represents the measurement error of remote sensing and is used to reflect the uncertainty of measurement;

[0038] Step S42: A constant additive deviation is used to correct the systematic error in the satellite image and improve the data accuracy. The formula used is as follows:

[0039] ;

[0040] In the formula, It represents the fused spatiotemporal data result after correction at the spatial position c1 and time t. It represents the latent process at the spatial position c1 and at time t after correction when the error is not considered. represents the measurement error of the original data, that is, the constant additive bias;

[0041] Step S43: Calculate the latent process to reflect the dependency between space and time. The formula used is as follows:

[0042] ;

[0043] In the formula, represents the latent process, taking into account the spatial and temporal dependencies. represents variables related to time and space, represents the spatiotemporal variable factor, Represents spatially dependent variables that follow a Gaussian distribution;

[0044] Step S44: Visual display, establish a visualization display section for spatiotemporal data, present the fused spatiotemporal data in an intuitive form, dynamically update and calibrate the spatiotemporal data in real time, and ensure the timeliness and accuracy of the data.

[0045] Furthermore, in step S5, the result evaluation and verification comprises the following steps:

[0046] Step S51: Compare the actual observation results, and compare the deduced results with the actual basin water cycle related data in detail to check the consistency and deviation degree;

[0047] The water cycle-related data of the basin include water level, flow, and water quality;

[0048] Step S52: Analyze the error distribution, determine the distribution of the error of the deduction result in different time, space and situations, and understand the stability of the model;

[0049] Step S53: Result verification, cross-validation of data from different time periods and different regions to ensure the adaptability of the model under different conditions, and backtesting the model using historical data to determine whether it can accurately reproduce past water cycle conditions.

[0050] The artificial intelligence-based water cycle reverse deduction system provided by the present invention includes a data acquisition module, a watershed perception range expansion module, a probability analysis model construction module, a time-space precise matching module and a result evaluation and verification module;

[0051] The data acquisition module specifically collects data and performs data preprocessing to obtain a watershed water cycle data set;

[0052] The module for expanding the watershed perception range specifically builds the basic architecture of the perception network, clarifies the information transmission path and node connections, calculates the effective coverage probability of sensor nodes and target points, and dynamically adjusts the distribution of sensor nodes according to the calculation results to optimize the perception network;

[0053] The construction of the probability analysis model module specifically sets initial conditions, performs forward calculations, and uses reverse probability analysis to infer the impact factors when flood and drought disasters occur;

[0054] The spatiotemporal precise matching module specifically extends the spatial data fusion framework to the spatiotemporal domain, uses a constant additive bias to correct the system error, calculates the latent process to reflect the spatial and temporal dependencies, and finally establishes a visual display panel to present the fused spatiotemporal data and dynamically updates it;

[0055] The result evaluation and verification module specifically compares the deduction results with the actual basin water cycle related data in detail, analyzes the error distribution and verifies the results.

[0056] The beneficial results achieved by the present invention using the above scheme are as follows:

[0057] (1) In order to solve the technical problem that the lack of large-scale watershed sensing data has resulted in inaccurate and incomplete simulation results, making it difficult to truly reflect the actual situation of water cycle in the watershed and affecting the scientific planning of water resources management and water environment protection, the sensing range of the watershed is expanded. Specifically, the basic architecture of the sensing network is constructed, the information transmission path and node connection are clarified, the effective coverage probability of sensor nodes and target points is calculated, and the distribution of sensor nodes is dynamically adjusted according to the calculation results to optimize the sensing network;

[0058] (2) In order to solve the technical problems that are difficult to match in time and space dimensions and cannot accurately reflect the characteristics and changes of the water cycle in the basin at different times and spaces, which limits the construction of digital twin basins, precise time and space matching is adopted. Specifically, the spatial data fusion framework is extended to the time and space domain, and the system error is corrected by using a constant additive bias. The latent process is calculated to reflect the dependence of space and time. Finally, a visual display panel is established to present the fused time and space data and update it dynamically. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A schematic diagram of the flow chart of the watershed water cycle reverse deduction method based on artificial intelligence provided by the present invention;

[0060] Figure 2A schematic diagram of a watershed water cycle reverse deduction system based on artificial intelligence provided by the present invention;

[0061] Figure 3 is a schematic flow chart of step S2;

[0062] Figure 4 is a schematic flow chart of step S3;

[0063] Figure 5 It is a schematic diagram of the process of step S4.

[0064] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0065] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0066] In the description of the present invention, it should be understood that terms such as “upper”, “lower”, “front”, “back”, “left”, “right”, “top”, “bottom”, “inside” and “outside” indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as limiting the present invention.

[0067] Example 1, see Figure 1 The present invention provides a method for reverse deduction of water cycle in a watershed based on artificial intelligence, the method comprising the following steps:

[0068] Step S1: data collection, specifically collecting data and performing data preprocessing to obtain a watershed water cycle data set;

[0069] Step S2: Expand the sensing range of the watershed, specifically by building the basic architecture of the sensing network, clarifying the information transmission path and node connections, calculating the effective coverage probability of sensor nodes and target points, and dynamically adjusting the distribution of sensor nodes according to the calculation results to optimize the sensing network;

[0070] Step S3: construct a probability analysis model, specifically setting initial conditions, performing forward calculations, and using reverse probability analysis to infer influencing factors when floods and droughts occur;

[0071] Step S4: accurate time-space matching, specifically, expanding the spatial data fusion framework to the time-space domain, using a constant additive bias to correct the system error, calculating the latent process to reflect the spatial and temporal dependencies, and finally establishing a visual display panel to present the fused time-space data and dynamically update it;

[0072] Step S5: Result evaluation and verification, specifically, comparing the simulation results with the actual basin water cycle related data in detail, analyzing the error distribution and verifying the results.

[0073] Example 2, see Figure 1 This embodiment is based on the above embodiment. In step S1, the data collection includes the following steps:

[0074] Step S11: Collect meteorological data, obtain precipitation, temperature, wind speed, and humidity information through meteorological stations and satellites, and use them to analyze precipitation input and evaporation processes in the water cycle;

[0075] Step S12: Collect hydrological data, obtain flow, water level, and water quality data from river monitoring stations and lake monitoring points to understand the flow and changes of water in the basin;

[0076] Step S13: Collect topographic data, and use topographic mapping and remote sensing technology to obtain the topographic relief, slope and altitude of the basin, which have an important impact on the flow path and potential energy distribution of water;

[0077] Step S14: collecting soil data, including soil texture, moisture content, and permeability, to reflect the soil's ability to retain and transmit moisture;

[0078] Step S15: Collecting human water use activity data, including domestic water use, industrial water use, drainage data, and water project operation data;

[0079] Step S16: Summarize meteorological data, hydrological data, topographic data, soil data and human water use activity data, and perform data preprocessing to obtain a watershed water cycle data set.

[0080] Example 3, see Figure 1 and Figure 3 This embodiment is based on the above embodiment. In step S2, the expansion of the watershed sensing range includes the following steps:

[0081] Step S21: Construct the basic architecture of the perception network to clarify the information transmission path and the connection between nodes, laying the foundation for the perception range. The cluster is composed of sensor nodes. Each individual sensor node has the ability to communicate directly with the corresponding cluster head. The spatial interval between the sensor node i and the target point j is quantified. The formula used is as follows:

[0082] ;

[0083] In the formula, represents the spatial separation distance between the sensor node and the target point, represents the i-th sensor node, represents the jth target point, represents the horizontal coordinate of the i-th sensor node, represents the horizontal coordinate of the jth target point, represents the ordinate of the i-th sensor node, Represents the ordinate of the jth target point;

[0084] Step S22: Calculate the probability that a single sensor covers the target point. The formula used is as follows:

[0085] ;

[0086] In the formula, represents the probability of the sensor node covering the target point, γ represents the induction attenuation coefficient, r i represents the maximum ideal sensing radius of the sensor node, which means that the node has a greater probability of sensing coverage within this radius. e represents the sensing error inherent in a given sensor node;

[0087] Step S23: Calculate the effective coverage probability of the target point. Based on the probability of a single sensor node covering the target point, the probability of the target point being effectively covered is obtained by calculating the complement of the product of the coverage probabilities of all sensor nodes to the target point. The formula used is as follows:

[0088] ;

[0089] In the formula, represents the probability that the target point is effectively covered, and n represents the total number of sensor nodes;

[0090] Step S24: Optimize the perception network, dynamically adjust the distribution of sensor nodes according to the calculated effective coverage efficiency of the target points, optimize the coverage effect and accuracy of the perception network, continuously monitor and evaluate the performance of the perception network, and further improve the perception network based on feedback information.

[0091] By executing the above operations, the basin perception range is expanded. Specifically, the basic architecture of the perception network is constructed, the information transmission path and node connections are clarified, the effective coverage probability of sensor nodes and target points is calculated, and the sensor node distribution is dynamically adjusted according to the calculation results to optimize the perception network. The technical problem of lack of large-scale basin perception data, resulting in inaccurate and incomplete deduction results, making it difficult to truly reflect the actual situation of the basin water cycle, and affecting the scientific planning of water resources management and water environment protection is solved.

[0092] Example 4, see Figure 1 and Figure 4 This embodiment is based on the above embodiment. In step S3, constructing a probability analysis model includes the following steps:

[0093] Step S31: Influencing factor analysis, establishing a mathematical model that can analyze the probability of flood and drought disasters and related influencing factors, and comprehensively considering various factors that may affect the occurrence of flood and drought disasters, including rainfall, temperature, soil moisture and river flow;

[0094] Step S32: setting initial conditions, clarifying the parameter values ​​and states when the model starts calculating, including the initial rainfall value and temperature range within the specified time period;

[0095] Step S33: forward calculation is performed to calculate the probability of flood and drought disasters under the initial conditions and the specific circumstances that may occur based on the set initial conditions and the rules of the model. Through forward calculation, the probability of disasters occurring under a specific combination of conditions and the possible consequences can be understood;

[0096] Step S34: Use reverse probability analysis to infer the influencing factors. When flood and drought disasters have occurred, the probability model is used to infer the influencing factors that caused the disaster. For example, if a severe drought occurs, reverse analysis can be used to find out whether it was caused by too little rainfall in the early stage, too high temperature, and too fast evaporation of soil moisture. Reverse analysis helps us better understand the causes and mechanisms of disasters, thereby providing a basis for formulating response strategies and preventive measures.

[0097] Example 5, see Figure 1 and Figure 5 This embodiment is based on the above embodiment. In step S4, the time-space precise matching includes the following steps:

[0098] Step S41: Expand the spatial data fusion framework to the spatiotemporal domain so that the model can process information in both spatial and temporal dimensions simultaneously, thereby analyzing and fusing data more comprehensively and accurately. The format used is as follows:

[0099] ;

[0100] In the formula, It represents the spatiotemporal data result after fusion at spatial position c and time t. c represents the index in the spatial dimension, which is used to distinguish different spatial positions. t represents the specific moment in the time dimension. a represents the deviation term that remains unchanged in space and time, which is understood as a systematic and fixed deviation factor. It is a hidden space-time process that changes with space and time, that is, a latent process, which contains space-time dynamic information and laws. It represents the measurement error of remote sensing and is used to reflect the uncertainty of measurement;

[0101] Step S42: A constant additive deviation is used to correct the systematic error in the satellite image and improve the data accuracy. The formula used is as follows:

[0102] ;

[0103] In the formula, It represents the fused spatiotemporal data result after correction at the spatial position c1 and time t. It represents the latent process at the spatial position c1 and at time t after correction when the error is not considered. represents the measurement error of the original data, that is, the constant additive bias;

[0104] Step S43: Calculate the latent process to reflect the dependency between space and time. The formula used is as follows:

[0105] ;

[0106] In the formula, represents the latent process, taking into account the spatial and temporal dependencies. represents variables related to time and space, represents the spatiotemporal variable factor, Represents spatially dependent variables that follow a Gaussian distribution;

[0107] Step S44: Visual display, establish a visualization display section for spatiotemporal data, present the fused spatiotemporal data in an intuitive form, dynamically update and calibrate the spatiotemporal data in real time, and ensure the timeliness and accuracy of the data.

[0108] By executing the above operations, precise time and space matching is adopted, specifically, the spatial data fusion framework is expanded to the time and space domain, a constant additive bias is used to correct the system error, the latent process is calculated to reflect the dependence of space and time, and finally a visual display panel is established to present the fused time and space data, and it is dynamically updated, which solves the technical problems of difficulty in matching time and space dimensions, inability to accurately reflect the characteristics and changes of the water cycle in the basin at different time and space, and limiting the construction of digital twin basins.

[0109] Example 6, see Figure 1 This embodiment is based on the above embodiment. In step S5, the result evaluation and verification includes the following steps:

[0110] Step S51: Compare the actual observation results, and compare the deduced results with the actual basin water cycle related data in detail to check the consistency and deviation degree;

[0111] The water cycle-related data of the basin include water level, flow, and water quality;

[0112] Step S52: Analyze the error distribution, determine the distribution of the error of the deduction result in different time, space and situations, and understand the stability of the model;

[0113] Step S53: Result verification, cross-validation of data from different time periods and different regions to ensure the adaptability of the model under different conditions, and backtesting the model using historical data to determine whether it can accurately reproduce past water cycle conditions.

[0114] Embodiment 7, see Figure 2 , this embodiment is based on the above embodiment, and the watershed water cycle reverse deduction system based on artificial intelligence provided by the present invention includes a data acquisition module, a watershed perception range expansion module, a probability analysis model construction module, a time-space precise matching module and a result evaluation and verification module;

[0115] The data acquisition module specifically collects data and performs data preprocessing to obtain a watershed water cycle data set;

[0116] The module for expanding the watershed perception range specifically builds the basic architecture of the perception network, clarifies the information transmission path and node connections, calculates the effective coverage probability of sensor nodes and target points, and dynamically adjusts the distribution of sensor nodes according to the calculation results to optimize the perception network;

[0117] The construction of the probability analysis model module specifically sets initial conditions, performs forward calculations, and uses reverse probability analysis to infer the impact factors when flood and drought disasters occur;

[0118] The spatiotemporal precise matching module specifically extends the spatial data fusion framework to the spatiotemporal domain, uses a constant additive bias to correct the system error, calculates the latent process to reflect the spatial and temporal dependencies, and finally establishes a visual display panel to present the fused spatiotemporal data and dynamically updates it;

[0119] The result evaluation and verification module specifically compares the deduction results with the actual basin water cycle related data in detail, analyzes the error distribution and verifies the results.

[0120] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0121] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

[0122] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. A basin water cycle reverse deduction method based on artificial intelligence, characterized by: The method comprises the following steps: Step S1: data collection, specifically collecting data and performing data preprocessing to obtain a watershed water cycle data set; Step S2: Expand the sensing range of the watershed, specifically by building the basic architecture of the sensing network, clarifying the information transmission path and node connections, calculating the effective coverage probability of sensor nodes and target points, and dynamically adjusting the distribution of sensor nodes according to the calculation results to optimize the sensing network; Step S3: construct a probability analysis model, specifically setting initial conditions, performing forward calculations, and using reverse probability analysis to infer influencing factors when floods and droughts occur; Step S4: accurate time-space matching, specifically, expanding the spatial data fusion framework to the time-space domain, using a constant additive bias to correct the system error, calculating the latent process to reflect the spatial and temporal dependencies, and finally establishing a visual display panel to present the fused time-space data and dynamically update it; Step S5: Result evaluation and verification, specifically, comparing the simulation results with the actual basin water cycle related data in detail, analyzing the error distribution and verifying the results.

2. The method for reverse deduction of water cycle in a watershed based on artificial intelligence according to claim 1 is characterized in that: In step S4, the time-space precise matching includes the following steps: Step S41: Expand the spatial data fusion framework to the spatiotemporal domain so that the model can process information in both spatial and temporal dimensions simultaneously. The format used is as follows: ； In the formula, It represents the spatiotemporal data result after fusion at spatial position c and time t. c represents the index in the spatial dimension, which is used to distinguish different spatial positions. t represents the specific moment in the time dimension. a represents the deviation term that remains unchanged in space and time, which is understood as a systematic and fixed deviation factor. It is a hidden space-time process that changes with space and time, that is, a latent process, which contains space-time dynamic information and laws. It represents the measurement error of remote sensing and is used to reflect the uncertainty of measurement; Step S42: A constant additive deviation is used to correct the systematic error in the satellite image. The formula used is as follows: ； In the formula, It represents the fused spatiotemporal data result after correction at the spatial position c1 and time t. It represents the latent process at the spatial position c1 and at time t after correction when the error is not considered. represents the measurement error of the original data, that is, the constant additive bias; Step S43: Calculate the latent process to reflect the dependency between space and time. The formula used is as follows: ； In the formula, represents the latent process, taking into account the spatial and temporal dependencies. represents the variables related to time and space, β represents the time and space variable factor, Represents spatially dependent variables that follow a Gaussian distribution; Step S44: Visual display, establish a visualization display section for spatiotemporal data, present the fused spatiotemporal data in an intuitive form, and dynamically update and calibrate the spatiotemporal data in real time.

3. The method for reverse deduction of water cycle in a river basin based on artificial intelligence according to claim 1 is characterized in that: In step S2, the expansion of the watershed sensing range includes the following steps: Step S21: Construct the basic architecture of the perception network to clarify the information transmission path and the connection between nodes, laying the foundation for the perception range. The cluster is composed of sensor nodes. Each individual sensor node has the ability to communicate directly with the corresponding cluster head. The spatial interval between the sensor node and the target point is quantified. The formula used is as follows: ； In the formula, represents the spatial separation distance between the sensor node and the target point, represents the i-th sensor node, represents the jth target point, represents the horizontal coordinate of the i-th sensor node, represents the horizontal coordinate of the jth target point, represents the ordinate of the i-th sensor node, Represents the ordinate of the jth target point; Step S22: Calculate the probability that a single sensor covers the target point. The formula used is as follows: ； In the formula, represents the probability of the sensor node covering the target point, γ represents the induction attenuation coefficient, r i represents the maximum ideal sensing radius of the sensor node, r e represents the sensing error inherent in a given sensor node; Step S23: Calculate the effective coverage probability of the target point. Based on the probability of a single sensor node covering the target point, the probability of the target point being effectively covered is obtained by calculating the complement of the product of the coverage probabilities of all sensor nodes to the target point. The formula used is as follows: ； In the formula, represents the probability that the target point is effectively covered, and n represents the total number of sensor nodes; Step S24: Optimize the perception network, dynamically adjust the distribution of sensor nodes according to the calculated effective coverage efficiency of the target points, optimize the coverage effect and accuracy of the perception network, continuously monitor and evaluate the performance of the perception network, and improve the perception network according to the feedback information.

4. The method for reverse deduction of water cycle in a river basin based on artificial intelligence according to claim 1 is characterized in that: In step S3, the construction of the probability analysis model includes the following steps: Step S31: Analyze influencing factors, establish a mathematical probability model to analyze the probability of flood and drought disasters and related influencing factors, and comprehensively consider various factors that affect the occurrence of flood and drought disasters; Step S32: setting initial conditions, clarifying the parameter values ​​and states when the mathematical probability model starts calculating, including the initial rainfall value and temperature range within a specified time period; Step S33: forward calculation is performed to understand the possibility of disaster occurrence and the consequences thereof based on the set initial conditions and the rules of the model; Step S34: Use reverse probability analysis to infer the influencing factors. When flood and drought disasters have occurred, use mathematical probability models to infer the influencing factors that led to the disaster. This helps us better understand the causes and mechanisms of the disaster, thereby providing a basis for formulating response strategies and preventive measures.

5. The method for reverse deduction of water cycle in a river basin based on artificial intelligence according to claim 1 is characterized in that: In step S1, the data collection includes the following steps: Step S11: Collect meteorological data, obtain precipitation, temperature, wind speed, and humidity information through meteorological stations and satellites, and use them to analyze precipitation input and evaporation processes in the water cycle; Step S12: Collect hydrological data, obtain flow, water level, and water quality data from river monitoring stations and lake monitoring points to understand the flow and changes of water in the basin; Step S13: Collect topographic data, and use topographic mapping and remote sensing technology to obtain the topographic relief, slope and altitude of the basin, which have an important impact on the flow path and potential energy distribution of water; Step S14: collecting soil data, including soil texture, moisture content, and permeability, to reflect the soil's ability to retain and transmit moisture; Step S15: Collecting human water use activity data, including domestic water use, industrial water use, drainage data, and water project operation data; Step S16: Summarize meteorological data, hydrological data, topographic data, soil data and human water use activity data, and perform data preprocessing to obtain a watershed water cycle data set.

6. The method for reverse deduction of water cycle in a river basin based on artificial intelligence according to claim 1 is characterized in that: In step S5, the result evaluation and verification includes the following steps: Step S51: Compare the actual observation results, and compare the deduced results with the actual basin water cycle related data in detail to check the consistency and deviation degree; The water cycle-related data of the basin include water level, flow, and water quality; Step S52: Analyze the error distribution, determine the distribution of the error of the deduction result in different time, space and situations, and understand the stability of the model; Step S53: Result verification, cross-validation of data from different time periods and different regions to ensure the adaptability of the model under different conditions, and backtesting the model using historical data to determine whether it can accurately reproduce past water cycle conditions.

7. A watershed water cycle reverse deduction system based on artificial intelligence, used to implement the watershed water cycle reverse deduction method based on artificial intelligence as described in any one of claims 1 to 6, characterized in that: It includes data collection module, watershed perception range expansion module, probability analysis model construction module, time and space precise matching module and result evaluation and verification module.

8. The artificial intelligence-based water cycle reverse deduction system of claim 7 is characterized by: The data acquisition module specifically collects data and performs data preprocessing to obtain a watershed water cycle data set; The module for expanding the watershed perception range specifically builds the basic architecture of the perception network, clarifies the information transmission path and node connections, calculates the effective coverage probability of sensor nodes and target points, and dynamically adjusts the distribution of sensor nodes according to the calculation results to optimize the perception network; The construction of the probability analysis model module specifically sets initial conditions, performs forward calculations, and uses reverse probability analysis to infer the impact factors when flood and drought disasters occur; The spatiotemporal precise matching module specifically extends the spatial data fusion framework to the spatiotemporal domain, uses a constant additive bias to correct the system error, calculates the latent process to reflect the spatial and temporal dependencies, and finally establishes a visual display panel to present the fused spatiotemporal data and dynamically updates it; The result evaluation and verification module specifically compares the deduction results with the actual basin water cycle related data in detail, analyzes the error distribution and verifies the results.

Images