Intelligent Regulation Method, Equipment and Platform for Water Supply and Demand under the Extreme Drought Situation

Abstract

The present invention provides a method, device and platform for intelligent regulation of water supply and demand under extremely severe drought conditions, relating to the technical field of water resource regulation. By traversing the target area, a water source supply and demand fluctuation data set is generated, the supply and demand elasticity coefficient is determined, recovery analysis is carried out based on the supply and demand elasticity coefficient, a multi-layer discrimination index system is constructed, the supply and demand elasticity coefficient is comprehensively evaluated, bilateral regulation is carried out according to multi-objective decision-making, bilateral regulation measures for water supply and demand are formulated, ecological effect analysis is carried out, hierarchical control is carried out according to multiple regulation influence values in combination with extremely severe drought scenario parameters, and a two-way limit allocation plan for water sources is formulated to carry out two-way limit regulation of water supply and demand. It solves the technical problem in the prior art that the regulation method is single, resulting in the inability to effectively achieve the balance of water resource supply and demand under extremely severe drought. It achieves the technical effects of dynamically adapting to supply and demand changes and optimizing water resource allocation, and realizes the technical effect of efficient regulation of water resource supply and demand balance under extremely severe drought conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to an intelligent regulation method, device and platform for water supply and demand under the scenario of extreme drought. Background Art

[0002] In the scenario of extreme drought, significant structural changes occur in the water resource supply and demand network, and the spatial and temporal distribution of water resources is extremely uneven, resulting in the break of the originally fragile water supply and demand balance. Traditional drought-resistant water source regulation measures usually rely on quantitative water source allocation and simple demand suppression, such as restricting water use, adjusting water supply frequency, increasing the call of emergency water sources, etc. However, most of these methods are based on static water supply and demand patterns and do not fully consider the dynamic changes of the water supply and demand network under extreme drought conditions, resulting in the inability to effectively achieve the water supply and demand balance under extreme drought.

[0003] The prior art has technical problems such as a single regulation method and lack of dynamic adaptability, resulting in the inability to effectively achieve the water supply and demand balance under extreme drought. Summary of the Invention

[0004] The present application provides an intelligent regulation method, device and platform for water supply and demand under the scenario of extreme drought, which is used to solve the technical problems in the prior art that the regulation method is single and lacks dynamic adaptability, resulting in the inability to effectively achieve the water supply and demand balance under extreme drought.

[0005] In view of the above problems, the present application provides an intelligent regulation method, device and platform for water supply and demand under the scenario of extreme drought.

[0006] In the first aspect of the present application, an intelligent regulation method for water supply and demand under the scenario of extreme drought is provided. The method includes: traversing the target area to conduct water source supply and demand analysis, generating a water source supply and demand fluctuation data set, calculating according to the water source supply and demand fluctuation data set to determine the supply and demand elasticity coefficient; based on the supply and demand elasticity coefficient, conducting recovery analysis, constructing a multi-layer discrimination index system, comprehensively evaluating the supply and demand elasticity coefficient through the multi-layer discrimination index system to obtain a water supply and demand balance coefficient, where the water supply and demand balance coefficient includes multiple discrimination indexes; according to the water supply and demand balance coefficient, conducting multi-objective analysis on the multiple discrimination indexes to generate a multi-objective decision, formulating a bilateral regulation measure for water supply and demand according to the multi-objective decision; implementing the bilateral regulation measure for water supply and demand to conduct ecological effect analysis, generating multiple regulation influence values; according to the multiple regulation influence values and combining with the extreme drought scenario parameters, conducting hierarchical control, formulating a two-way limit allocation plan for water sources to conduct two-way limit regulation on water supply and demand.

[0007] In the second aspect of the present application, a water supply and demand intelligent regulation device in an extremely severe drought scenario is provided. The device includes: a processor and a memory; the processor is connected to the memory through a communication bus: wherein, the processor is configured to call and execute a program stored in the memory; the memory is configured to store a program, and the program is at least used to execute the steps of the water supply and demand intelligent regulation method in the extremely severe drought scenario described in any one of the above first aspects.

[0008] In the third aspect of the present application, a water supply and demand intelligent regulation platform in an extremely severe drought scenario is provided. The platform includes: a data calculation module, which is configured to traverse the target area for water source supply and demand analysis, generate a water source supply and demand fluctuation data set, and calculate based on the water source supply and demand fluctuation data set to determine the supply and demand elasticity coefficient; a supply and demand water balance coefficient acquisition module, which is configured to perform a recovery analysis based on the supply and demand elasticity coefficient, construct a multi-level discrimination index system, and comprehensively evaluate the supply and demand elasticity coefficient through the multi-level discrimination index system to obtain a supply and demand water balance coefficient, and the supply and demand water balance coefficient includes multiple discrimination indexes; a multi-objective analysis module, which is configured to perform multi-objective analysis on the multiple discrimination indexes according to the supply and demand water balance coefficient to generate a multi-objective decision, and perform bilateral regulation according to the multi-objective decision to formulate water supply and demand bilateral regulation measures; an ecological effect analysis module, which is configured to perform ecological effect analysis by executing the water supply and demand bilateral regulation measures to generate multiple regulation impact values; a water supply and demand regulation module, which is configured to perform hierarchical control according to the multiple regulation impact values in combination with the extremely severe drought scenario parameters, and formulate a two-way limit allocation plan for water sources to perform two-way limit regulation on water supply and demand.

[0009] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0010] The method provided by the embodiment of the present application performs water source supply-demand analysis by traversing the target area, generates a water source supply-demand fluctuation data set, calculates according to the water source supply-demand fluctuation data set to determine the supply-demand elasticity coefficient; performs recovery analysis based on the supply-demand elasticity coefficient, constructs a multi-layer discrimination index system, and comprehensively evaluates the supply-demand elasticity coefficient through the multi-layer discrimination index system to obtain a supply-demand water balance coefficient, and the supply-demand water balance coefficient includes multiple discrimination indexes; according to the supply-demand water balance coefficient, performs multi-objective analysis on the multiple discrimination indexes to generate a multi-objective decision, and performs bilateral regulation according to the multi-objective decision to formulate supply-demand water bilateral regulation measures; executes the supply-demand water bilateral regulation measures to perform ecological effect analysis and generate multiple regulation influence values; performs hierarchical control according to the multiple regulation influence values in combination with the extreme drought scenario parameters, and formulates a two-way extreme configuration plan for the water source to perform two-way extreme regulation of the supply-demand water. It achieves the technical effects of dynamically adapting to supply-demand changes and optimizing water resource allocation, and realizes the technical effect of efficient water resource supply-demand balance regulation in the extreme drought situation. Description of the Drawings

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0012] Figure 1 Schematic flow chart of the supply-demand water intelligent regulation method in the extreme drought situation provided by the present application;

[0013] Figure 2 Schematic structural diagram of the supply-demand water intelligent regulation device in the extreme drought situation provided by the present application;

[0014] Figure 3 Schematic structural diagram of the supply-demand water intelligent regulation platform in the extreme drought situation provided by the present application.

[0015] Description of the reference numerals: Processor 101, Memory 102, Data calculation module 11, Supply-demand water balance coefficient acquisition module 12, Multi-objective analysis module 13, Ecological effect analysis module 14, Supply-demand water regulation module 15. Detailed Embodiments

[0016] The present application provides a method, device and platform for intelligent regulation of water supply and demand under extremely severe drought conditions, which are used to solve the technical problems existing in the prior art, such as single regulation method and lack of dynamic adaptability, resulting in the inability to effectively achieve the balance of water resource supply and demand under extremely severe drought. It achieves the technical effects of dynamically adapting to supply and demand changes and optimizing water resource allocation, and realizes the technical effect of efficient regulation of water resource supply and demand balance under extremely severe drought conditions.

[0017] Next, the technical solutions in the present invention will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited by the example embodiments described herein. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention. Additionally, it should be noted that for the sake of description, only the parts related to the present invention are shown in the accompanying drawings rather than all of them.

[0018] Embodiment 1, as Figure 1 shown, the present application provides a method for intelligent regulation of water supply and demand under extremely severe drought conditions, and the method includes:

[0019] Traverse the target area to conduct a water source supply and demand analysis, generate a water source supply and demand fluctuation data set, and calculate based on the water source supply and demand fluctuation data set to determine the supply and demand elasticity coefficient.

[0020] Specifically, comprehensively collect and analyze the water resource supply and demand situation of the entire target area to obtain the analysis result of the water source supply and demand relationship in the target area. Then, based on the analysis result of the supply and demand relationship, construct a water source supply and demand fluctuation data set containing time series data. The water source supply and demand fluctuation data set details the supply volume and water consumption of the water source in different time periods, as well as their fluctuations over time. Furthermore, calculate based on the water source supply and demand fluctuation data set to determine the supply and demand elasticity coefficient. The supply and demand elasticity coefficient is an important indicator used to describe the sensitivity of the water resource supply and demand relationship. By comprehensively analyzing the water source supply and demand in the target area, constructing a detailed water source supply and demand fluctuation data set, and calculating accurate supply and demand elasticity coefficients based on these data, it lays a data foundation for the bilateral regulation of water resource supply and demand under extremely severe drought conditions.

[0021] Further, traverse the target area for water supply and demand analysis, generate a water supply and demand fluctuation dataset, and calculate according to the water supply and demand fluctuation dataset to determine the supply-demand elasticity coefficient. The method includes: dividing the target area into multiple sub-areas according to historical water supply and demand characteristics, traversing the multiple sub-areas for data collection to obtain a water source monitoring dataset of the multiple sub-areas; performing fluctuation calculation on the water source monitoring dataset according to the collection time sequence to determine a water supply and demand fluctuation dataset, where the water supply and demand fluctuation dataset includes a water source fluctuation amplitude value; analyzing according to the water source fluctuation amplitude value according to the water source monitoring period to obtain a water source fluctuation period; extracting sudden fluctuation values according to the water source fluctuation period for water supply and demand analysis, and calculating according to the water supply and demand analysis results to determine the supply-demand elasticity coefficient.

[0022] Specifically, according to the historical water consumption demands, water source supplies, and other influencing factors in each area of the target region, such as population distribution, seasonal and environmental differences in water resources, etc., the target region is divided into multiple sub-regions according to the historical water resource supply and demand characteristics. In each of the divided sub-regions, water source monitoring devices are set up for data collection. The water source monitoring devices include flow meters, water consumption monitors, precipitation monitoring devices, evaporation meters, soil moisture sensors, etc. These monitoring devices are combined to form a comprehensive water source monitoring network, providing high-precision and real-time data support for the water supply and demand analysis of each sub-region. The collected data includes the current water supply (referring to the water volume that various water sources can provide), water demand (the summary of different water consumption demands such as residents, agriculture, and industry), and other environmental data affecting the supply and demand (such as precipitation, evaporation, soil moisture, etc.). Based on the current water supply, water demand, and other environmental data affecting the supply and demand, a water source monitoring data set for each sub-region is formed. The water source monitoring data set for each sub-region is subjected to fluctuation calculation according to the chronological order of data collection to calculate the water supply and demand fluctuation amplitude. The fluctuation amplitude = |current water supply - current water demand|. The fluctuation calculation can capture the dynamic changes in water supply and water demand, generating a water supply and demand fluctuation data set. The water supply and demand fluctuation data set contains water source fluctuation amplitude values, which can intuitively reflect the supply and demand changes of water resources at different times, that is, the degree of difference between water supply and water demand. According to the water source fluctuation amplitude values, analysis is carried out according to the monitoring period, such as daily, weekly, or monthly, to identify the water source fluctuation periods of each sub-region. The fluctuation period reflects the periodic changes between water supply and water demand, such as seasonal supply and demand fluctuation rules. On the basis of the fluctuation period analysis, the sudden fluctuation value is further extracted, that is, the abnormal fluctuation value in the water supply and demand, such as a sharp reduction in water supply or a sudden increase in water consumption demand in a certain sub-region. Further water supply and demand analysis is carried out for the abnormal fluctuation value to identify high-risk water supply and demand imbalance points. Finally, according to the results of the above water supply and demand analysis, the supply and demand elasticity coefficient is calculated. The elasticity coefficient is a quantitative indicator measuring the response degree of water supply to changes in water demand, which is the ratio of the changes in supply and demand quantities. Its calculation formula is: supply and demand elasticity coefficient = Δ water demand / Δ water supply, where Δ water supply is the change in water supply within a certain period, and Δ water demand is the change in water demand within the corresponding period. The higher the supply and demand elasticity coefficient, the more flexible the supply and demand relationship, and the stronger the ability of water supply regulation to adapt to demand changes. On the contrary, a lower elasticity coefficient reflects that the water supply regulation has a weak response to changes in water demand and insufficient flexibility in extremely dry situations. Calculating the supply and demand elasticity coefficient provides key data for subsequent two-way water resource regulation, ensuring the accuracy and rationality of subsequent regulation measures and optimizing the supply and demand relationship.

[0023] Based on the supply and demand elasticity coefficient, a recovery analysis is carried out to construct a multi-layer discrimination index system. Through the multi-layer discrimination index system, the supply and demand elasticity coefficient is comprehensively evaluated to obtain a supply and demand balance coefficient, and the supply and demand balance coefficient includes multiple discrimination indexes.

[0024] Further, based on the supply-demand elasticity coefficient, a recovery analysis is carried out to construct a multi-layer discrimination index system. The method includes: traversing and identifying the multiple sub-regions based on the supply-demand elasticity coefficient, sorting the identification results in ascending order according to the supply-demand elasticity coefficient to determine the water supply sequence to be restored; setting a recovery target, carrying out a recovery analysis according to the recovery target and the water supply sequence to be restored to determine the recovery ability information of the multiple sub-regions; carrying out a recovery analysis based on the recovery ability information combined with the supply-demand elasticity coefficient to determine the water supply recovery criterion and the water demand recovery criterion; making a balance judgment according to the water supply recovery criterion and the water demand recovery criterion to determine the balance criterion; associating and integrating the water supply recovery criterion, the water demand recovery criterion, and the balance criterion to construct a criterion layer; carrying out a recovery evaluation according to the water supply recovery criterion and the water demand recovery criterion according to the recovery ability information to formulate a water supply side recovery index and a water demand side recovery index; associating and integrating the water supply side recovery index and the water demand side recovery index to construct an index layer; constructing a discriminator based on a neural network, introducing a loss function into the discriminator to construct a data layer, wherein the output end of the criterion layer, the output end of the index layer, and the input end of the data layer are in communication connection; connecting the criterion layer, the index layer, and the data layer in communication to construct the multi-layer discrimination index system.

[0025] Specifically, based on the calculated supply-demand elasticity coefficients, each sub-region is traversed and identified. The supply-demand elasticity coefficients of each sub-region are analyzed one by one, and the identification results are sorted in ascending order of the supply-demand elasticity coefficients to generate a water supply sequence to be determined. Regions with low supply-demand elasticity coefficients often have weaker recovery capabilities and appear in the water supply sequence to be determined first to ensure the priority restoration of regions with relatively unbalanced supply and demand. In the context of an extreme drought, considering the actual demand and environmental conditions comprehensively and combining expert experience, a recovery goal is set. The recovery goal refers to the specific water supply-demand balance state or the standard of the restored water volume to be achieved through supply-demand regulation and recovery measures. According to the set recovery goal, combined with the water supply sequence to be determined, the recovery capabilities of each sub-region are analyzed, and the water resource system recovery capability indicators of each sub-region under a specific water supply sequence are evaluated, such as the recovery speed and recovery degree, to generate the recovery capability information of each sub-region, including the time and resources required for the region to restore the supply-demand balance. Furthermore, based on the recovery capability information and supply-demand elasticity coefficients of each sub-region, a water supply recovery criterion and a water demand recovery criterion are obtained. The water supply recovery criterion refers to the standard for restoring the water supply volume and is used to evaluate the recovery capability of water supply regulation under drought conditions. Based on the water supply elasticity coefficient, the possibility of restoring the water supply in the short term is judged. The water demand recovery criterion refers to the control standard for the water demand volume and is used to evaluate the demand adjustment and response capabilities on the water demand side to meet the basic living, agricultural, and industrial demands. By comparing the water supply recovery criterion and the water demand recovery criterion, a balance determination is made to determine the balance criterion for achieving the supply-demand balance. The balance criterion is a comprehensive standard for the recovery of both the supply and demand sides and is used to evaluate the supply-demand stability during the recovery process and determine whether additional regulation is required to maintain the supply-demand balance. Then, the water supply recovery criterion, the water demand recovery criterion, and the balance criterion are associated and integrated to form a criterion layer. The criterion layer provides the basic framework and standard for the overall recovery and provides guidance for the subsequent specific recovery indicators. Next, based on the water supply recovery criterion and the water demand recovery criterion, the recovery of each sub-region is evaluated according to the recovery capability information, and water supply side recovery indicators and water demand side recovery indicators are formulated. The water supply side recovery indicators and the water demand side recovery indicators refine the specific recovery requirements of each sub-region in terms of water supply and water demand. The water supply side recovery indicators, including the recovery time indicator, the water supply stability indicator, and the water source sustainability indicator, are used to evaluate the recovery speed, stability, and sustainability of the water supply system respectively. The water demand side recovery indicators, including the water demand adjustment response time, the water use priority indicator, and the water use efficiency indicator, are used to evaluate the adjustment speed and water use efficiency of the water demand system during the recovery process. And the water supply side recovery indicators are integrated with the water demand side recovery indicators to form an indicator layer. The indicator layer is a specific quantitative standard and is used to gradually restore the supply-demand relationship of the sub-regions to ensure that each region achieves the recovery goal under the framework of the criterion layer.Build a discriminator based on a neural network to evaluate the recovery effect of supply-demand balance. Optionally, first, use the neural network model to conduct discriminant analysis on the supply-demand relationship during the recovery process, and use the outputs of the criterion layer and the index layer as the inputs of the neural network to form the data layer. During this process, the water supply recovery criterion, water demand recovery criterion, balance criterion in the criterion layer, and the water supply side recovery index and water demand side recovery index in the index layer are used as the input data of the data layer to ensure that the recovery standards and recovery evaluation information can be accurately transmitted to the data layer. To improve the accuracy of the discriminator, a loss function is introduced into the neural network. The loss function is used to evaluate the deviation between the discriminant result of the neural network and the actual supply-demand balance state. By optimizing and iterating the deviation, the discriminant accuracy of the discriminator for the recovery effect is gradually improved. The loss function can include mean square error or cross-entropy loss, etc. Finally, the criterion layer, the index layer, and the data layer are connected for communication to form a complete multi-layer discriminant index system. In the multi-layer discriminant index system, the criterion layer provides the recovery standards, the index layer refines the specific indicators of supply-demand recovery, and the data layer evaluates the supply-demand balance state through the neural network discriminator and continuously optimizes the discriminant model to improve the system's comprehensive judgment ability for the recovery effect. The multi-layer discriminant index system supports real-time supply-demand balance analysis and dynamic regulation through multi-level communication and data feedback, provides an intelligent decision-making basis for water resource management, and realizes the reasonable regulation and optimal allocation of water resources.

[0026] Furthermore, comprehensively evaluate the supply-demand elasticity coefficient through the multi-layer discriminant index system to obtain the supply-demand water balance coefficient. The method includes: calculating the weights of the criterion layer and the index layer using the analytic hierarchy process to obtain the criterion weight score and the index weight score; inputting the criterion weight score and the index weight score into the data layer for discrimination to generate a recovery discrimination result; classifying the recovery of multiple sub-regions based on the recovery discrimination result to determine multiple recovery levels, where the multiple recovery levels include multiple discriminant indicators; establishing a fuzzy membership function according to the multiple recovery levels to conduct a fuzzy comprehensive evaluation of the supply-demand elasticity coefficient to obtain multiple fuzzy membership degrees; conducting a supply-demand water balance analysis based on the multiple fuzzy membership degrees in combination with the multiple discriminant indicators, constructing a fuzzy evaluation matrix for weighted summation, and setting a limit balance level; dynamically updating the multiple discriminant indicators according to the limit balance level to determine the supply-demand water balance coefficient.

[0027] Specifically, first, the analytic hierarchy process is used to calculate the weights of the indicators in the criterion layer and the index layer. Through the analytic hierarchy process, the weights of each water supply restoration criterion, water demand restoration criterion, and balance criterion in the criterion layer are determined, and the criterion weight scores of each item are calculated. Similarly, the weights of the water supply side restoration indicators and water demand side restoration indicators in the index layer are calculated to obtain the index weight scores of each indicator under different restoration scenarios. The criterion weight scores and index weight scores are input into the data layer, and a restoration discrimination result is generated through a neural network discriminator, thereby determining the restoration status of each sub-region. And according to the restoration discrimination result, each sub-region is classified for restoration, divided into multiple restoration levels, such as high restoration, medium restoration, and low restoration regions. The high restoration region has a strong water supply restoration ability, the medium restoration region has a moderate restoration ability, and the low restoration region has a weak restoration ability. The multiple restoration levels include multiple discrimination indicators for quantifying and evaluating the restoration characteristics and abilities of this level. Then, fuzzy membership functions are established according to the multiple restoration levels, that is, within each restoration level, a fuzzy membership function is established for each discrimination indicator (such as water supply restoration, water demand restoration, etc.) to determine the fuzzy membership degree of each discrimination indicator to reflect the fuzzy characteristics of each restoration level. Optionally, by setting the membership degree intervals of different restoration levels (for example, values between 0 and 1) to represent the adaptability of each discrimination indicator in these levels, the threshold ranges of each restoration level are defined to determine the membership degree of each sub-region at different restoration levels. For example, the high restoration region has a high membership degree in water supply restoration, while the extreme restoration region has a low membership degree. The fuzzy comprehensive evaluation of the supply-demand elasticity coefficients of each sub-region is carried out using the membership function to obtain the fuzzy membership degrees of each region in high restoration, medium restoration, and low restoration. The fuzzy membership degrees are used to characterize the membership relationship of each region at each restoration level. Based on the multiple fuzzy membership degrees and multiple discrimination indicators, a fuzzy evaluation matrix is constructed, and the supply-demand water balance state of each sub-region is quantitatively analyzed through weighted summation to ensure a reasonable match of water resource allocation at different restoration levels. According to the fuzzy evaluation result, an extreme balance level is set. The extreme balance level is a critical value used to measure whether the water resource restoration state of each sub-region reaches the expected balance effect. According to the requirements of the extreme balance level, the discrimination indicators are dynamically updated, that is, the weights of the restoration indicators are adjusted in real time according to the extreme balance level to achieve adaptive optimization under different supply-demand scenarios, thereby maintaining the overall balance. Finally, by synthesizing all the updated discrimination indicators, the supply-demand water balance coefficient is calculated and determined. The supply-demand water balance coefficient includes multiple discrimination indicators, including water supply side restoration indicators and water demand side restoration indicators. By determining the supply-demand water balance coefficient, the comprehensive evaluation result of the multi-layer discrimination index system is made more scientific and dynamically adaptable, and the accuracy and reliability of the intelligent regulation of water resource supply and demand in the extreme drought situation are improved.

[0028] Further, the multiple discrimination indexes are dynamically updated according to the limit balance level, and a water supply-demand balance coefficient is determined. The method includes: comparing the multiple discrimination indexes with the limit balance level to determine whether the multiple discrimination indexes are greater than the limit balance level; if the multiple discrimination indexes are greater than the limit balance level, extracting the discrimination indexes greater than the limit balance level for water supply-demand analysis to determine a water supply limit index and / or a water demand limit index; updating the weights according to the water supply limit index and / or the water demand limit index, and performing synchronous update calculation on the water supply limit index and / or the water demand limit index according to the update result to determine the water supply-demand balance coefficient.

[0029] Specifically, compare multiple judgment indexes with a preset limit balance level, check the multiple discrimination indexes one by one, and determine whether they are greater than the limit balance level. When there is a discrimination index greater than the limit balance level, it indicates that the index has reached or even exceeded the limit balance state in the restoration of water supply and demand. For the discrimination indexes exceeding the limit balance level, extract them for further water supply-demand analysis. By analyzing these indexes, determine the water supply limit index and / or the water demand limit index, that is, the key factors that may cause imbalance in the water supply-demand relationship. For example, if the water demand restoration index in a certain area is significantly higher than the limit balance level, the water demand limit index should be strengthened to ensure precise management of water demand. Based on the limit indexes of water supply and water demand, adjust the weights of each index. When the value of an index is close to or exceeds the limit balance level, it indicates that this index has higher importance for the current water resource supply-demand balance state, and accordingly increase the weight of this index in the model so that this factor can be given priority consideration in the supply-demand balance calculation. Through the weight update mechanism, it can be ensured that the influence of high-priority indexes on the water supply-demand balance coefficient is more significant. Finally, according to the weight update result, perform synchronous update calculation on the water supply limit index and / or the water demand limit index again, re-evaluate the balance state of water resource supply and demand, so as to dynamically adjust the water supply-demand balance coefficient and obtain a more accurate water supply-demand balance coefficient that reflects the current actual situation. As a key parameter for overall water resource allocation, the balance coefficient reflects the change of the recovery ability of the region through real-time update, supports more precise supply-demand regulation, and realizes the optimal management of water resources under the extremely arid situation.

[0030] According to the water supply-demand balance coefficient, perform multi-objective analysis on the multiple discrimination indexes to generate a multi-objective decision, and perform bilateral regulation according to the multi-objective decision to formulate water supply-demand bilateral regulation measures.

[0031] Further, according to the supply-demand water balance coefficient, multi-objective analysis is performed on the multiple discriminant indicators to generate a multi-objective decision. The method includes: using the supply-demand water balance coefficient as a reference value, traversing the multiple discriminant indicators for division, constructing multiple to-be-executed objectives according to the index division results, where the multiple to-be-executed objectives include a water supply objective, a water demand objective, and a balance objective; evaluating the multiple discriminant indicators according to the water supply objective in combination with the supply-demand water balance coefficient to generate a first index evaluation value; evaluating the multiple discriminant indicators according to the water demand objective in combination with the supply-demand water balance coefficient to generate a second index evaluation value; evaluating the multiple discriminant indicators according to the balance objective in combination with the supply-demand water balance coefficient to generate a third index evaluation value; based on the first index evaluation value, the second index evaluation value, and the third index evaluation value, performing priority sorting on the water supply objective, the water demand objective, and the balance objective to determine the multi-objective decision.

[0032] Specifically, first, taking the supply-demand water balance coefficient as the benchmark value, traverse and analyze multiple discriminant indicators one by one, and divide different to-be-executed targets according to the performance of these indicators, forming three major categories: water supply targets, water demand targets, and balance targets. The water supply target refers to the measures to restore or improve the regional water supply level. The water demand target is to meet and manage the actual water demand of the region. The balance target is to coordinate water supply and water demand to achieve overall supply-demand balance. After determining various to-be-executed targets, evaluate the discriminant indicators of each type of target in combination with the supply-demand water balance coefficient. For the water supply target, in combination with the supply-demand water balance coefficient, evaluate multiple discriminant indicators, such as evaluating the matching degree between water supply restoration and the supply-demand water balance coefficient, and generate the first indicator evaluation value. The first indicator evaluation value reflects the influence degree of each discriminant indicator on the water resource supply situation under the guidance of the water supply target. Then, for the water demand target, evaluate multiple discriminant indicators in combination with the supply-demand water balance coefficient to generate the second indicator evaluation value, which reflects the influence of each discriminant indicator on the regional water resource demand situation. Finally, according to the requirements of the balance target, conduct a balance evaluation on all water supply and water demand discriminant indicators to generate the third indicator evaluation value, which is used to measure the compliance degree of the overall supply-demand balance state. After obtaining these three indicator evaluation values, based on these three indicator evaluation values, rank the water supply target, water demand target, and balance target in terms of priority, so as to determine the priority execution order of multi-objective decision-making. The priority ranking is based on the relative magnitudes of the evaluation values and the influence degree on water resource balance. For example, in areas with severe water shortages, the water demand target may have the highest priority, while in areas with relatively balanced water resource supply and demand, the balance target may occupy a higher priority. Finally, according to the priority of multi-objective decision-making, implement bilateral regulation on the water resource system, that is, dynamically adjust from both the water supply and water demand aspects at the same time, formulate bilateral regulation measures for water supply and demand. The bilateral regulation measures for water demand include: increasing water source allocation or raising the reservoir water supply volume according to the water supply target, optimizing the allocation according to the water demand target, controlling water use demand, and on the basis of meeting water supply and water demand, regulating the balance target to make the supply-demand relationship reach balance dynamically. Through these bilateral regulation measures, ensure the reasonable allocation and continuous restoration of water resources in each region under the scenario of extreme drought, and then effectively respond to the water resource supply-demand problem, and promote the reasonable allocation and efficient utilization of water resources.

[0033] Execute the bilateral regulation measures for water supply and demand to conduct ecological effect analysis, and generate multiple regulation influence values.

[0034] Specifically, in the context of an extreme drought, after implementing the dual-sided regulation measures for water supply and demand, in order to evaluate their impact on the ecosystem, an ecological effect analysis is required. First, based on the emergy theory of ecological economics, the ecological effects of the regulation measures are analyzed. The emergy theory is a method for measuring the flow of energy, matter, and information in natural and human systems, which helps to quantify the production and transformation processes of different ecological-economic systems. Under the guidance of this theory, the impact of the dual-sided regulation measures on the structure of the water supply and demand system and ecological functions is analyzed, and the impact of the dynamic regulation of water supply and demand on the flow and transformation of energy and matter within the system under drought conditions is analyzed. Next, an energy system diagram is constructed. The energy system diagram is a diagram used to describe how energy, matter, and information flow and transform within an ecological-economic system, and it details the energy and matter flow processes of the water supply and demand system under different regulation measures. For example, how the water source allocation on the water supply side affects the water resource distribution, and how the demand suppression measures on the water demand side reduce the water resource consumption of the ecosystem. Based on this, the impact of the changes in the regulation measures on the system is analyzed and quantified to form multiple regulation impact values. The regulation impact values reflect the potential impact of different regulation strategies on the ecological environment, such as the ecological restoration impact value, the water resource utilization efficiency impact value, the drought mitigation effect impact value, etc. Among them, the larger the impact value, the more significant the improvement effect of the regulation measure on the water supply and demand balance, and the smaller the impact value, the more limited the impact of the regulation measure on the ecosystem. For example, a larger ecological restoration impact value indicates that the regulation measure effectively supports ecological restoration and contributes to the stability of biodiversity and the ecosystem, while a smaller ecological restoration impact value may indicate insufficient support of the regulation measure for the ecosystem, resulting in difficulty in restoring the ecological environment. Through the above analysis and quantification, the generated regulation impact values not only reflect the overall ecological-economic effect of the dual-sided regulation but also provide a scientific basis for subsequent optimization and adjustment of the regulation measures, ensuring the achievement of water supply and demand balance while maintaining ecological stability in the context of an extreme drought.

[0035] Based on the multiple regulation impact values and combined with the extreme drought scenario parameters, hierarchical control is carried out to formulate a two-way extreme configuration plan for water sources to conduct two-way extreme regulation of water supply and demand.

[0036] Furthermore, based on the multiple regulation impact values and combined with the extreme drought scenario parameters, hierarchical control is carried out. The method includes: conducting regulation analysis based on the extreme drought scenario parameters to obtain regulation requirements, matching the multiple regulation impact values with the supply and demand according to the regulation requirements, and setting multiple supply and demand regulation levels; activating the hierarchical control mechanism based on the multiple supply and demand regulation levels, conducting extreme configuration on the water supply side through the hierarchical control mechanism to generate a water supply configuration plan; conducting extreme configuration on the water demand side through the hierarchical control mechanism to generate a water demand configuration plan; coordinating the water supply configuration plan and the water demand configuration plan, and when the coordination coefficient is within the desired supply and demand balance threshold, generating the two-way extreme configuration plan for water sources.

[0037] Specifically, first, in-depth regulation analysis is carried out based on detailed drought scenario parameters to identify the degree of water resource shortage and ecological impacts brought about by drought and clarify the specific regulation requirements. Then, multiple regulation impact values are matched with the current regulation requirements in terms of supply and demand to clarify which regions and water use demands should be given priority. Through the matching results, multiple supply-demand regulation levels are set, such as high, medium, and low regulation intensities, to adapt to the resource pressures in different regions and demands. Furthermore, according to multiple supply-demand regulation levels, a hierarchical control mechanism is activated to perform extreme allocation of resources on both the water supply side and the water demand side. On the water supply side, according to the regulation level, targeted extreme water supply allocation is implemented to give priority to ensuring the water source supply in key regions and generate specific water supply allocation plans. For example, in regions with high regulation intensity, it may be necessary to increase water source allocation or improve the water supply rate, while in regions with low regulation, the water supply volume can be reduced to save resources. At the same time, through the hierarchical control mechanism, extreme allocation is carried out on the water demand side to formulate a water demand allocation plan, that is, hierarchical regulation of the water use demands in different regions and industries. In the extreme allocation of the water demand side, priority support is given to basic demands such as domestic water use and agricultural production with high priority, while non-critical industry regions or low-priority demands may be restricted to a certain extent. The water supply allocation plan and the water demand allocation plan are coordinated in terms of allocation, that is, the allocation of water supply and water demand is integrated and optimized. When the coordination coefficient between the two reaches the preset supply-demand balance threshold, it indicates that the allocation of water supply and water demand has reached the optimal balance state, thereby generating a two-way extreme water source allocation plan. According to this two-way extreme water source allocation plan, two-way extreme regulation is implemented on the supply-demand system to achieve dynamic intelligent allocation and regulation of water resources under extreme drought conditions, ensuring that water resources can be used most reasonably and efficiently under extremely severe drought conditions.

[0038] Example 2, as Figure 2 shown, the present application provides a supply-demand water intelligent regulation device under extremely severe drought conditions. The device includes:

[0039] A processor 101 and a memory 102; the processor 101 is connected to the memory 102 through a communication bus: wherein, the processor 101 is used to call and execute the program stored in the memory 102; the memory 102 is used to store the program, and the program is at least used to execute the steps of the supply-demand water intelligent regulation method under extremely severe drought conditions in Example 1.

[0040] Example 3, based on the same inventive concept as the supply-demand water intelligent regulation method under extremely severe drought conditions in the foregoing embodiments, as Figure 3 shown, the present application provides a supply-demand water intelligent regulation platform under extremely severe drought conditions. Among them, the platform includes:

[0041] A data calculation module 11, which is used to traverse the target area for water source supply-demand analysis, generate a water source supply-demand fluctuation data set, and calculate based on the water source supply-demand fluctuation data set to determine the supply-demand elasticity coefficient; a supply-demand water balance coefficient acquisition module 12, which is used to perform restoration analysis based on the supply-demand elasticity coefficient, construct a multi-level discrimination index system, and comprehensively evaluate the supply-demand elasticity coefficient through the multi-level discrimination index system to obtain the supply-demand water balance coefficient, and the supply-demand water balance coefficient includes multiple discrimination indexes; a multi-objective analysis module 13, which is used to perform multi-objective analysis on the multiple discrimination indexes according to the supply-demand water balance coefficient, generate a multi-objective decision, and perform bilateral regulation according to the multi-objective decision to formulate supply-demand water bilateral regulation measures; an ecological effect analysis module 14, which is used to perform ecological effect analysis by implementing the supply-demand water bilateral regulation measures to generate multiple regulation influence values; a supply-demand water regulation module 15, which is used to perform hierarchical control according to the multiple regulation influence values in combination with the extreme drought scenario parameters, and formulate a two-way extreme configuration plan for water sources to perform two-way extreme regulation of the supply-demand water.

[0042] Further, the data calculation module 11 is used to perform the following steps: divide the target area into multiple sub-areas according to the historical water supply-demand characteristics, traverse the multiple sub-areas for data collection to obtain the water source monitoring data sets of the multiple sub-areas; perform fluctuation calculation on the water source monitoring data sets according to the collection time sequence to determine the water source supply-demand fluctuation data set, and the water source supply-demand fluctuation data set includes water source fluctuation amplitude values; analyze based on the water source fluctuation amplitude values according to the water source monitoring period to obtain the water source fluctuation period; extract sudden fluctuation values according to the water source fluctuation period for water supply-demand analysis, and calculate according to the water supply-demand analysis results to determine the supply-demand elasticity coefficient.

[0043] Further, the supply-demand water balance coefficient obtaining module 12 is used to perform the following steps: traversing and identifying the multiple sub-regions based on the supply-demand elasticity coefficient, sorting the identification results in ascending order according to the supply-demand elasticity coefficient to determine the water supply sequence to be restored; setting a restoration target, performing restoration analysis according to the restoration target and the water supply sequence to be restored to determine the restoration capacity information of the multiple sub-regions; performing restoration analysis based on the restoration capacity information in combination with the supply-demand elasticity coefficient to determine the water supply restoration criterion and the water demand restoration criterion; performing balance determination according to the water supply restoration criterion and the water demand restoration criterion to determine the balance criterion; associating and integrating the water supply restoration criterion, the water demand restoration criterion, and the balance criterion to construct a criterion layer; performing restoration evaluation according to the water supply restoration criterion and the water demand restoration criterion according to the restoration capacity information to formulate water supply side restoration indicators and water demand side restoration indicators; associating and integrating the water supply side restoration indicators and the water demand side restoration indicators to construct an indicator layer; constructing a discriminator based on a neural network, introducing a loss function into the discriminator to construct a data layer, wherein the output end of the criterion layer, the output end of the indicator layer, and the input end of the data layer are in communication connection; connecting the criterion layer, the indicator layer, and the data layer in communication to construct the multi-layer discriminant index system.

[0044] Further, the supply-demand water balance coefficient obtaining module 12 is also used to perform the following steps: calculating the weights of the criterion layer and the indicator layer by using the analytic hierarchy process to obtain the criterion weight score and the indicator weight score; inputting the criterion weight score and the indicator weight score into the data layer for discrimination to generate a restoration discrimination result; performing restoration grading on the multiple sub-regions based on the restoration discrimination result to determine multiple restoration levels, where the multiple restoration levels include multiple discriminant indicators; establishing a fuzzy membership function according to the multiple restoration levels to perform fuzzy comprehensive evaluation on the supply-demand elasticity coefficient to obtain multiple fuzzy membership degrees; performing supply-demand water balance analysis based on the multiple fuzzy membership degrees in combination with the multiple discriminant indicators, constructing a fuzzy evaluation matrix for weighted summation, and setting a limit balance level; dynamically updating the multiple discriminant indicators according to the limit balance level to determine the supply-demand water balance coefficient.

[0045] Further, the supply-demand water balance coefficient obtaining module 12 is also used to perform the following steps: comparing the multiple discriminant indicators with the limit balance level to determine whether the multiple discriminant indicators are greater than the limit balance level; if the multiple discriminant indicators are greater than the limit balance level, extracting the discriminant indicators greater than the limit balance level for supply-demand water analysis to determine the water supply limit indicator and / or the water demand limit indicator; performing weight update according to the water supply limit indicator and / or the water demand limit indicator, and performing synchronous update calculation on the water supply limit indicator and / or the water demand limit indicator according to the update result to determine the supply-demand water balance coefficient.

[0046] Further, the multi-objective analysis module 13 is used to perform the following steps: taking the supply-demand water balance coefficient as a reference value, traversing the multiple discrimination indicators for classification, constructing multiple to-be-executed objectives according to the indicator classification results, where the multiple to-be-executed objectives include a water supply objective, a water demand objective, and a balance objective; evaluating the multiple discrimination indicators according to the water supply objective in combination with the supply-demand water balance coefficient to generate a first indicator evaluation value; evaluating the multiple discrimination indicators according to the water demand objective in combination with the supply-demand water balance coefficient to generate a second indicator evaluation value; evaluating the multiple discrimination indicators according to the balance objective in combination with the supply-demand water balance coefficient to generate a third indicator evaluation value; performing priority ranking on the water supply objective, the water demand objective, and the balance objective based on the first indicator evaluation value, the second indicator evaluation value, and the third indicator evaluation value to determine the multi-objective decision.

[0047] Further, the supply-demand water regulation module 15 is used to perform the following steps: performing regulation analysis based on the extreme drought scenario parameters to obtain a regulation demand, matching the multiple regulation influence values according to the regulation demand for supply-demand matching, and setting multiple supply-demand regulation levels; activating a hierarchical control mechanism based on the multiple supply-demand regulation levels, and performing extreme configuration on the water supply side through the hierarchical control mechanism to generate a water supply configuration plan; performing extreme configuration on the water demand side through the hierarchical control mechanism to generate a water demand configuration plan; performing configuration coordination on the water supply configuration plan and the water demand configuration plan, and when the coordination coefficient is within the expected supply-demand balance threshold, generating the two-way extreme water source configuration plan.

[0048] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0049] This specification and the drawings are only exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and deformations to the present application without departing from the scope of the present application. Thus, if these modifications and deformations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and deformations.

Claims

1. A method for intelligently regulating water supply and demand under extreme drought conditions, characterized in that: The method comprises: Traversing the target area to perform water supply and demand analysis, generating a water supply and demand fluctuation data set, and performing calculations based on the water supply and demand fluctuation data set to determine a supply and demand elasticity coefficient; Performing recovery analysis based on the supply and demand elasticity coefficient, constructing a multi-layer discrimination index system, and comprehensively evaluating the supply and demand elasticity coefficient through the multi-layer discrimination index system to obtain a supply and demand water balance coefficient, wherein the supply and demand water balance coefficient includes multiple discrimination indicators; According to the water supply and demand balance coefficient, the multiple discrimination indicators are subjected to multi-objective analysis to generate a multi-objective decision, and bilateral regulation is performed according to the multi-objective decision to formulate bilateral regulation measures for water supply and demand; Execute the water supply and demand dual-side control measures to conduct ecological effect analysis and generate multiple control impact values; According to the multiple control impact values ​​combined with the extreme drought scenario parameters, hierarchical control is performed to formulate a water source two-way limit configuration plan to perform two-way limit control on water supply and demand; Traversing the target area to perform water supply and demand analysis, generating a water supply and demand fluctuation data set, and performing calculations based on the water supply and demand fluctuation data set to determine the supply and demand elasticity coefficient, the method comprising: Divide the target area into multiple sub-areas according to historical water supply and demand characteristics, traverse the multiple sub-areas to collect data, and obtain water source monitoring data sets for the multiple sub-areas; Performing fluctuation calculation on the water source monitoring data set according to the collection time sequence to determine a water source supply and demand fluctuation data set, wherein the water source supply and demand fluctuation data set includes a water source fluctuation amplitude value; Analyze the water source fluctuation amplitude value according to the water source monitoring period to obtain the water source fluctuation period; Extracting sudden fluctuation values ​​according to the water source fluctuation cycle to perform water supply and demand analysis, and performing calculations based on the water supply and demand analysis results to determine the supply and demand elasticity coefficient; Based on the supply and demand elasticity coefficient, recovery analysis is performed to construct a multi-layer discrimination index system, and the method includes: Traversing and marking the multiple sub-areas based on the supply and demand elasticity coefficient, sorting the marking results in ascending order according to the supply and demand elasticity coefficient, and determining a sequence to be supplied with water; Setting a restoration target, performing restoration analysis according to the restoration target and the sequence to be supplied with water, and determining restoration capacity information of the multiple sub-areas; Performing a recovery analysis based on the recovery capacity information and the supply and demand elasticity coefficient to determine a water supply recovery criterion and a water demand recovery criterion; Performing a balance judgment based on the water supply restoration criterion and the water demand restoration criterion to determine the balance criterion; The water supply restoration criterion, the water demand restoration criterion, and the balance criterion are associated and integrated to construct a criterion layer; Based on the water supply restoration criteria and the water demand restoration criteria, a restoration assessment is performed according to the restoration capacity information, and a water supply side restoration index and a water demand side restoration index are formulated; Associating and integrating the water supply side recovery index with the water demand side recovery index to construct an index layer; A discriminator is constructed based on a neural network, and a loss function is introduced into the discriminator to construct a data layer, wherein an output end of the criterion layer, an output end of the indicator layer and an input end of the data layer are in communication connection; The criterion layer, the indicator layer, and the data layer are communicatively connected to construct the multi-layer discrimination indicator system.

2. The method for intelligently regulating water supply and demand under extreme drought conditions according to claim 1, characterized in that: The supply and demand elasticity coefficient is comprehensively evaluated by the multi-layer discrimination index system to obtain the supply and demand water balance coefficient, and the method includes: Using the analytic hierarchy process to calculate the weights of the criterion layer and the indicator layer to obtain a criterion weight score and an indicator weight score; Inputting the criterion weight score and the indicator weight score into the data layer for discrimination, and generating a recovery discrimination result; Based on the restoration discrimination result, the plurality of sub-areas are graded for restoration to determine a plurality of restoration levels, wherein the plurality of restoration levels include a plurality of discrimination indicators; Establishing a fuzzy membership function according to the multiple recovery levels, performing a fuzzy comprehensive evaluation on the supply and demand elasticity coefficient, and obtaining multiple fuzzy membership degrees; Based on the multiple fuzzy memberships and the multiple discrimination indicators, a water balance analysis of supply and demand is performed, a fuzzy evaluation matrix is ​​constructed for weighted summation, and a limit balance level is set; The plurality of identification indicators are dynamically updated according to the limit balance level to determine the water supply and demand balance coefficient.

3. The intelligent control method for water supply and demand in a severe drought situation according to claim 2, characterized in that: The multiple discrimination indicators are dynamically updated according to the limit balance level to determine the water supply and demand balance coefficient, and the method includes: Comparing the plurality of discrimination indicators with the limit balance level to determine whether the plurality of discrimination indicators are greater than the limit balance level; If the plurality of discrimination indicators are greater than the limit balance level, extracting the discrimination indicators greater than the limit balance level to perform water supply and demand analysis, and determining a water supply limit indicator and / or a water demand limit indicator; The weight is updated according to the water supply limit index and / or the water demand limit index, and the water supply limit index and / or the water demand limit index are synchronously updated and calculated according to the update result to determine the water supply and demand balance coefficient.

4. The method for intelligently regulating water supply and demand in a severe drought situation according to claim 1, characterized in that: According to the water supply and demand balance coefficient, the multiple discrimination indicators are subjected to multi-objective analysis to generate a multi-objective decision, the method comprising: Taking the water supply and demand balance coefficient as a reference value, traversing the multiple discrimination indicators for division, and constructing multiple targets to be executed according to the indicator division results, wherein the multiple targets to be executed include a water supply target, a water demand target, and a balance target; Evaluate the plurality of discrimination indicators according to the water supply target and the water supply and demand balance coefficient to generate a first indicator evaluation value; Evaluate the plurality of discrimination indicators according to the water demand target and the water supply and demand balance coefficient to generate a second indicator evaluation value; Evaluate the plurality of discrimination indicators according to the balance target and the water supply and demand balance coefficient to generate a third indicator evaluation value; The water supply target, the water demand target, and the balance target are prioritized based on the first indicator evaluation value, the second indicator evaluation value, and the third indicator evaluation value to determine the multi-objective decision.

5. The method for intelligently regulating water supply and demand in a severe drought situation according to claim 1, characterized in that: The method includes: performing hierarchical control according to the multiple control impact values ​​combined with the extreme drought scenario parameters: Performing regulation analysis based on the extreme drought scenario parameters to obtain regulation requirements, matching the multiple regulation impact values ​​with supply and demand according to the regulation requirements, and setting multiple supply and demand regulation levels; activating a hierarchical control mechanism based on the multiple supply and demand regulation levels, performing extreme configuration on the water supply side through the hierarchical control mechanism, and generating a water supply configuration plan; Perform limit configuration on the water demand side through the hierarchical control mechanism to generate a water demand configuration plan; The water supply configuration plan is coordinated with the water demand configuration plan, and when the coordination coefficient is at the expected supply and demand balance threshold, the water source two-way limit configuration plan is generated.

6. Intelligent water supply and demand control equipment under extreme drought conditions, characterized in that: include: Processor and memory; The processor and the memory are connected via a communication bus: Wherein, the processor is used to call and execute the program stored in the memory; The memory is used to store a program, and the program is used at least to execute the steps of the method for intelligent regulation of water supply and demand under extreme drought situations described in any one of claims 1 to 5.

7. Intelligent water supply and demand control platform under extreme drought conditions, characterized by: The steps for implementing the method for intelligently regulating water supply and demand in a severe drought situation as described in any one of claims 1 to 5 include: A data calculation module, which is used to traverse the target area to perform water supply and demand analysis, generate a water supply and demand fluctuation data set, and perform calculations based on the water supply and demand fluctuation data set to determine the supply and demand elasticity coefficient; A module for obtaining a water supply and demand balance coefficient, wherein the module is used to perform a recovery analysis based on the supply and demand elasticity coefficient, construct a multi-layer discrimination index system, and comprehensively evaluate the supply and demand elasticity coefficient through the multi-layer discrimination index system to obtain a water supply and demand balance coefficient, wherein the water supply and demand balance coefficient includes multiple discrimination indicators; A multi-objective analysis module, wherein the multi-objective analysis module is used to perform a multi-objective analysis on the multiple discrimination indicators according to the water supply and demand balance coefficient, generate a multi-objective decision, perform bilateral regulation according to the multi-objective decision, and formulate bilateral regulation measures for water supply and demand; An ecological effect analysis module, the ecological effect analysis module is used to perform ecological effect analysis on the water supply and demand dual-side control measures to generate multiple control impact values; The water supply and demand regulation module is used to perform hierarchical control according to the multiple regulation impact values ​​combined with the parameters of the extreme drought scenario, and formulate a water source two-way limit configuration plan to regulate the two-way limit of water supply and demand.

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