Water resource allocation methods, equipment and storage media considering multiple uncertainties

Abstract

The present invention discloses a water resource allocation method, equipment and storage medium considering multiple uncertainties. First, a water resource optimization model based on multiple uncertainty constraints is constructed; then, the water volume allocated by the water-using department is used as the position of the hiker and initialized, and then the first optimization is performed by setting multiple group sizes to obtain the first candidate position and the first candidate objective function; then, the hiking algorithm is used to update the hiker position, and all hiker positions that meet the constraints are calculated to obtain the second candidate position and the second candidate objective function, and the quality of the objective function is judged, and the better objective function and position are used for replacement, and the loop iteration reaches the maximum number of times, and the last first candidate position and the corresponding first candidate objective function are output as the result. The present invention takes into account multiple uncertainties, can simulate the actual situation more accurately, and can obtain the optimal solution more accurately in combination with the hiking algorithm, thereby improving the solution quality and efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of water resource management, and relates to a water resource allocation method, and specifically to a water resource allocation method, equipment and storage medium that consider multiple uncertainties. Background Art

[0002] Rational allocation of water resources refers to the scientific allocation of limited and different forms of water resources among water-using units in a specific river basin or region through engineering and non-engineering measures based on the principles of effectiveness, fairness and sustainability. Optimal allocation of water resources is the basis for the rational development and utilization of water resources and the fundamental guarantee for the sustainable utilization of water resources.

[0003] In the actual water use process, different water-using departments have different perceptions of water shortage. Water-using departments with a high degree of dependence on water resources are more likely to be dissatisfied with the water shortage problem. Therefore, in order to ensure the water satisfaction of each water-using department and each water-using unit, the existing method is to provide a high water resource allocation to each water-using unit of each water-using department to the maximum extent. However, it is difficult to achieve the best effect in practice. In addition, the water resource allocation in the existing technology has the following disadvantages:

[0004] 1. Inadequate uncertainty handling: Many traditional water resource optimization models ignore or simplify uncertainty factors (such as climate change and population growth), resulting in large errors in predictions and decisions. Some existing technologies only use a single uncertainty handling method, such as interval constraints or random constraints, which lacks comprehensive considerations, resulting in insufficient adaptability and flexibility of the model.

[0005] 2. The control of sewage discharge is not sophisticated enough:

[0006] Some traditional methods for controlling wastewater discharge only consider the basic pollutant removal rate, without considering complex emission constraints (such as COD, TP, TN, AN), making it difficult to meet increasingly stringent environmental protection standards.

[0007] Therefore, there is an urgent need for a water resources allocation method that takes into account multiple uncertainties to meet the needs of water resources allocation. Summary of the invention

[0008] The purpose of the present invention is to provide a water resources allocation method taking into account multiple uncertainties, comprehensively considering interval constraints, random constraints and fuzzy elastic constraints, comprehensively dealing with multiple uncertainties in water resources optimization, and using the hiking algorithm to find the optimal solution or approximate optimal solution, avoiding the shortcomings of traditional optimization methods and improving the solution quality and efficiency.

[0009] In order to solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0010] On the one hand, the present invention provides a water resource allocation method considering multiple uncertainties. First, taking the maximum water satisfaction as the optimization goal, a water resource optimization model based on multiple uncertainty constraints is constructed; then, the water resource optimization model is optimized and solved by a walking algorithm to obtain a water resource allocation plan, which specifically includes the following steps:

[0011] Initialization of hiking algorithm: the water volume allocated by the water use department is used as the location of the hiker, and the location of the hiker is initialized according to the upper and lower limits of the water volume allocated by the water use department. The location with the initialization value is used as the first candidate location, and the objective function of the optimization target when calculating the first candidate location is used as the first candidate objective function;

[0012] Hiker position update: initialize the hiker's speed, update and calculate the hiker's speed, obtain the hiker's current speed, use the current speed to update its position, substitute the hiker's new position into the water resource optimization model, determine whether the hiker's position meets the constraints in the water resource optimization model, if not, recalculate the hiker's speed and update its position until all hikers' positions meet the constraints in the water resource optimization model, take the positions of all hikers that meet the constraints as the second candidate positions, and calculate the corresponding objective function as the second candidate objective function;

[0013] Comparison of candidate objective functions: compare the first candidate objective function and the second candidate objective function: if the first candidate objective function is better, directly return to the hiker position update loop iteration until the set maximum number of iterations; if the second candidate objective function is better, use the second candidate objective function to update the first objective function, and the corresponding second candidate position updates the first candidate position, and return to the hiker position update loop iteration until the set maximum number of iterations;

[0014] The first candidate position after the iteration is completed is used as the water resources allocation plan.

[0015] Preferably, the water resource allocation method considering multiple uncertainties further includes optimizing the initialization value by adopting a multi-group strategy to obtain a first candidate position and a corresponding first objective function, as follows:

[0016] When initializing the value, multiple groups of hikers are set, the group with the best optimization target is selected as the best group, the position of the hikers in the best group is used as the first candidate position, and the corresponding objective function is calculated as the first candidate objective function.

[0017] Preferably, the method for initializing the hiker's position according to the upper and lower limits of the water volume allocated by the water use department is as follows:

[0018]

[0019] in , represents the serial number of the group, N is the total number of groups; represents the serial number of the hiker, dim represents the total number of hikers in the group; is the uth group v The location of each hiker represents the amount of water allocated by the water use department; Allocate upper and lower limits on water volumes for water users; is a random number between [0,1].

[0020] Preferably, the hiker's speed is calculated as follows:

[0021]

[0022] in, is the speed of the vth hiker, is the slope of the terrain faced by the vth hiker, calculated as:

[0023]

[0024] is the inclination angle of the terrain, A random number between .

[0025] Preferably, the hiker's location update method is as follows:

[0026]

[0027] is the position of the vth hiker, is the updated position of the vth hiker, is the current speed of the vth hiker.

[0028] Preferably, the method for updating the calculation of the hiker's speed is as follows:

[0029]

[0030] in, is a random number between [0, 1]; is the scanning factor, randomly selecting an integer between [1,3], is the speed of the vth hiker, is the current speed of the vth hiker after update, The first candidate position.

[0031] Preferably, the method for constructing a water resources optimization model based on multiple uncertainty constraints is as follows:

[0032] The objective function is constructed with maximum water satisfaction:

[0033]

[0034] in, for water satisfaction; is the water use satisfaction of the j-th water use department in the ith province or sub-district, I is the total number of provinces or sub-districts, and J is the total number of water use departments in each province or sub-district; the water use satisfaction of the j-th water use department in the ith province or sub-district is calculated as follows:

[0035]

[0036] in, is the water resource allocation of the kth water source in the jth water-using department in the ith province or sub-district, where K is the total number of water sources; , are the lower limit and upper limit of water demand of the j-th water-using department in the ith province, respectively; is the satisfaction weight of the j-th water-using department;

[0037] Build constraints:

[0038] The constraints include water demand constraints, water supply constraints, sewage discharge constraints and non-negative constraints, among which the water supply constraints include interval constraints, random constraints and fuzzy constraints.

[0039] Water demand constraints:

[0040]

[0041] in, , They are respectively the lower limit and upper limit of water demand of the j-th water-using department in the ith province.

[0042] The water supply constraints include interval constraints, random constraints and fuzzy constraints, which are used to apply different water sources. The specific constraints are as follows:

[0043] The first type of water supply constraint is the water supply constraint of the west line. The water supply of the west line is relatively stable, so an interval constraint is adopted. The water supply constraint of the west line is as follows:

[0044]

[0045] in, is the water resource allocation of the first water source (western water source) of the jth water-using department in the i-th province, It is the available water volume of the first water source, and in this embodiment, it is the available water volume of the west line.

[0046] The second water supply constraint is the surface water supply constraint. Since the annual surface water inflow in the Yellow River Basin is uncertain, the surface water supply constraint adopts the chance constraint programming in stochastic programming, that is, random constraint. The surface water supply constraint is as follows:

[0047]

[0048] in, is the water resource allocation of the second water source (surface water source) of the jth water-using department in the i-th province, is the total amount of surface water in the study area (Yellow River Basin), which is a random variable. When making decisions, this constraint is allowed to violate the constraint requirements to a certain extent, but the probability of the constraint should be higher than a certain confidence level, that is:

[0049]

[0050] Among them, p is the determined probability level, which represents the default risk level of the constraint condition; pr is the probability operator.

[0051] Assume that the cumulative probability distribution function of WP is , then:

[0052]

[0053] Therefore, the constraint can be expressed as:

[0054]

[0055] The third water supply constraint is the groundwater supply constraint. The groundwater supply in the Yellow River Basin is affected by climate change and other factors. The water supply in each province fluctuates slightly each year, and fuzzy elastic planning is used to deal with it.

[0056]

[0057] in, is the water resource allocation of the third water source (groundwater source) of the jth water-using department in the i-th province, is the groundwater supply of the ith province after fuzzy processing, which has the interval number of fuzzy lower bound and fuzzy upper bound.

[0058] Using fuzzy numbers Dealing with the elastic constraint in the model, the constraint can be expressed as:

[0059]

[0060] in, In this example, it is the groundwater supply in the i-th province, is the minimum satisfaction index of the elastic constraint, express i It can take any value between 1 and I. is a triangular fuzzy number , expressed as:

[0061]

[0062] are the three real parameters of triangular fuzzy numbers.

[0063] Wastewater discharge constraints include COD (Chemical Oxygen Demand) emission constraints, TP (Total Phosphorus) emission constraints, TN (Total Nitrogen) emission constraints, and AN (ammonia nitrogen) emission constraints:

[0064]

[0065]

[0066]

[0067]

[0068] in, is the sewage discharge coefficient of the kth water source of the jth water-using department in the ith province; , , , are the emission concentrations of COD, TP, TN, and AN in the kth water source of the jth water-using department in the ith province, respectively; , , , are the removal rates of COD, TP, TN, and AN in the k-th water source of the j-th water-using department in the ith province, respectively; , , , are the total discharge of COD, TP, TN and AN in all water sources in the i-th province respectively.

[0069] The non-negative constraint is that the water resource allocation of the kth water source in the jth water-using department in the ith province or region cannot be less than 0. The expression is as follows:

[0070]

[0071] Since the above water resource optimization model is a complex model and difficult to solve directly, the present invention converts it into two sub-models, the upper and lower sub-models, where the upper sub-model is:

[0072]

[0073]

[0074] in, The upper limit of water satisfaction; is the upper limit of water use satisfaction of the j-th water use department in the ith province or region, The upper limit of water resource allocation for the kth water source in the jth water-using sector in the ith province or sub-region; , The upper and lower limits of the water demand of the j-th water-using department in the ith province or sub-district; , The upper and lower limits of the water demand of the j-th water-using department in the ith province or sub-district; is the satisfaction weight of the j-th water-using department;

[0075] The constraints of the upper sub-model are:

[0076]

[0077]

[0078]

[0079]

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086] in, It is the upper limit of the water supply of the first water source. In this embodiment, it is the water supply of the west line.

[0087] The upper limit of water resource allocation for the first water source of the jth water-using sector in the ith province or sub-district;

[0088] The upper limit of the water resource allocation for the second water source of the jth water-using sector in the i-th province or sub-district;

[0089] The upper limit of the water resource allocation for the third water source of the jth water-using sector in the i-th province or sub-region;

[0090] is the total amount of surface water in the study area (Yellow River Basin) at the confidence level p;

[0091] is the upper limit of groundwater supply in province i;

[0092] is the lower limit of the sewage discharge coefficient of the kth water source of the jth water-using department in the ith province;

[0093] , , , are the lower limits of the discharge concentrations of COD, TP, TN, and AN in the k-th water source of the j-th water-using department in the ith province, respectively;

[0094] , , , are the upper limits of COD, TP, TN, and AN removal rates in the k-th water source of the j-th water-using department in the ith province, respectively;

[0095] , , , They are the upper limits of the total discharge of COD, TP, TN and AN in all water sources in the i-th province.

[0096] By solving, we get the upper optimal variable and corresponding water use satisfaction .

[0097] The lower sub-model is:

[0098]

[0099]

[0100] in, The lower limit of water satisfaction; is the lower limit of water use satisfaction of the j-th water use department in the ith province or region, The lower limit of water resource allocation for the kth water source in the jth water-using sector in the ith province or sub-region; , The upper and lower limits of the water demand of the j-th water-using department in the ith province or sub-district; , The upper and lower limits of the water demand of the j-th water-using department in the ith province or sub-district; is the satisfaction weight of the j-th water-using department;

[0101] The constraints of the lower sub-model are:

[0102]

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113] in, It is the lower limit of the water supply of the first water source, and in this embodiment, it is the water supply of the west line.

[0114] The lower limit of the water resource allocation for the first water source of the j-th water-using sector in the i-th province or sub-region;

[0115] The lower limit of the water resource allocation for the second water source of the jth water-using department in the i-th province or sub-district;

[0116] The lower limit of the water resource allocation for the third water source of the jth water-using sector in the i-th province or sub-region;

[0117] is the total amount of surface water in the study area (Yellow River Basin) at the confidence level p;

[0118] is the lower limit of groundwater supply in province i;

[0119] is the upper limit of the sewage discharge coefficient of the kth water source of the jth water-using department in the ith province;

[0120] , , , are the upper limits of the discharge concentrations of COD, TP, TN, and AN in the k-th water source of the j-th water-using department in the ith province, respectively;

[0121] , , , are the lower limits of COD, TP, TN, and AN removal rates in the k-th water source of the j-th water-using department in the ith province, respectively;

[0122] , , , They are the lower limits of the total discharge of COD, TP, TN and AN in all water sources in the i-th province.

[0123] By solving, we can get the optimal variables of the lower layer. and corresponding water use satisfaction .

[0124] Merge the upper and lower layer results to obtain the final result range and corresponding water use satisfaction .

[0125] On the other hand, the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the water resource allocation method considering multiple uncertainties when executing the program.

[0126] On the other hand, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the water resources allocation method considering multiple uncertainties.

[0127] On the other hand, the present invention provides a computer program product, comprising a computer program, which, when executed by a processor, implements the water resources allocation method considering multiple uncertainties.

[0128] Compared with the prior art, the present invention has the following beneficial effects:

[0129] 1. Comprehensively deal with multiple uncertainties:

[0130] The present invention comprehensively handles multiple uncertainties in water resource optimization by comprehensively considering interval constraints, random constraints and fuzzy elastic constraints. This method can simulate the actual situation more accurately, improve the adaptability of the model and the reliability of decision-making.

[0131] 2. Refine the wastewater discharge constraints:

[0132] By introducing four types of wastewater discharge constraints, namely COD, TP, TN and AN, the present invention can more finely control wastewater discharge, meet high standards of environmental protection, and ensure the sustainability of water resource utilization.

[0133] 3. Introducing hiking optimization algorithm:

[0134] The present invention adopts a walking optimization algorithm, which is a novel optimization method that can simulate actual terrain challenges and decision-making processes to find the optimal solution or approximate optimal solution, avoids the shortcomings of traditional optimization methods, and improves the solution quality and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0135] Figure 1 This is a logic flow chart of the water resource allocation method considering multiple uncertainties in an embodiment of the present invention.

[0136] Figure 2 The upper and lower limits of water volume are configured for different water-using departments in the Yellow River Basin in the embodiment of the present invention.

[0137] Figure 3 The upper and lower limits of water volume are configured for the nine provinces and regions along the Yellow River in the embodiment of the present invention.

[0138] Figure 4 This is a schematic structural diagram of an electronic device provided by the present invention.

[0139] Reference numerals: 810 - processor, 820 - communication interface, 830 - memory, 840 - communication bus. DETAILED DESCRIPTION

[0140] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0141] Taking the Yellow River Basin as an example, a water resources allocation system for the Yellow River Basin with multiple uncertainties is constructed. There are 9 provinces in the Yellow River Basin, namely I=9, namely Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia, Shaanxi, Shanxi, Henan and Shandong; each province contains four water-using departments, namely J=4, namely domestic water-using department, industrial water-using department, agricultural water-using department and ecological water-using department.

[0142] Regarding water resource allocation in the Yellow River Basin, Figure 1 As shown, the present invention provides a water resource allocation method considering multiple uncertainties, comprising the following steps:

[0143] S100, taking the maximum water satisfaction as the optimization goal, constructing a water resources optimization model based on multiple uncertainty constraints;

[0144] The present invention also provides a method for constructing a water resources optimization model, which is as follows:

[0145] S110, construct an objective function based on the maximum water satisfaction, the objective function is:

[0146]

[0147] in, for water satisfaction; is the water satisfaction of the j-th water use department in the ith province, I is the total number of provinces or regions, and J is the total number of water use departments in each province or region.

[0148] The calculation method of water use satisfaction of the j-th water use department in the i-th province or sub-district is:

[0149]

[0150] in, is the water resource allocation of the kth water source in the jth water-using department in the ith province; They are the upper and lower limits of water resource demand of the j-th water-using department in the ith province; is the satisfaction weight of the j-th water user department.

[0151] S120, constructing constraint conditions, wherein the constraint conditions include water demand constraint, water supply constraint, sewage discharge constraint and non-negative constraint, and the specific constraint conditions are as follows:

[0152] S121. Water demand constraints:

[0153]

[0154] in, , They are respectively the lower limit and upper limit of water demand of the j-th water-using department in the ith province.

[0155] S122, water supply constraints, including interval constraints, random constraints and fuzzy constraints, are used to apply different water sources. The specific constraints are as follows:

[0156] The first type of water supply constraint is the water supply constraint of the west line. The water supply of the west line is relatively stable, so an interval constraint is adopted. The water supply constraint of the west line is as follows:

[0157]

[0158] in, is the water resource allocation of the first water source (western water source) of the jth water-using department in the i-th province, It is the available water volume of the first water source, and in this embodiment, it is the available water volume of the west line.

[0159] The second water supply constraint is the surface water supply constraint. Since the annual surface water inflow in the Yellow River Basin is uncertain, the surface water supply constraint adopts the chance constraint programming in stochastic programming, that is, random constraint. The surface water supply constraint is as follows:

[0160]

[0161] in, is the water resource allocation of the second water source (surface water source) of the jth water-using department in the i-th province, is the total amount of surface water in the study area (Yellow River Basin), which is a random variable. When making decisions, this constraint is allowed to violate the constraint requirements to a certain extent, but the probability of the constraint should be higher than a certain confidence level, that is:

[0162]

[0163] Among them, p is the determined probability level (also the confidence level), which represents the default risk level of the constraint condition; pr is the probability operator.

[0164] Assume that the cumulative probability distribution function of WP is , then:

[0165]

[0166] Therefore, the constraint can be expressed as:

[0167]

[0168] The third water supply constraint is the groundwater supply constraint. The groundwater supply in the Yellow River Basin is affected by climate change and other factors. The water supply in each province fluctuates slightly each year, and fuzzy elastic planning is used to deal with it.

[0169]

[0170] in, is the water resource allocation of the third water source (groundwater source) of the jth water-using department in the i-th province, is the groundwater supply of the ith province after fuzzy processing, which has the interval number of fuzzy lower bound and fuzzy upper bound.

[0171] Using fuzzy numbers Dealing with the elastic constraint in the model, the constraint can be expressed as:

[0172]

[0173] in, In this example, it is the groundwater supply in the i-th province, is the minimum satisfaction index of the elastic constraint, express i It can take any value between 1 and I. is a triangular fuzzy number , expressed as:

[0174]

[0175] are the three real parameters of triangular fuzzy numbers.

[0176] S123, sewage discharge constraints, in this embodiment, include COD (Chemical Oxygen Demand) emission constraints, TP (Total Phosphorus) emission constraints, TN (Total Nitrogen) emission constraints, AN (ammonia nitrogen) emission constraints:

[0177]

[0178]

[0179]

[0180]

[0181] in, is the sewage discharge coefficient of the kth water source of the jth water-using department in the ith province; , , , are the emission concentrations of COD, TP, TN, and AN in the kth water source of the jth water-using department in the ith province, respectively; , , , are the removal rates of COD, TP, TN, and AN in the k-th water source of the j-th water-using department in the ith province, respectively; , , , are the total discharge of COD, TP, TN and AN in all water sources in the i-th province.

[0182] S124. Non-negative constraint, that is, the water resource allocation of the kth water source of the jth water-using department in the ith province or region cannot be less than 0, expressed as follows:

[0183]

[0184] Since the above water resource optimization model is a complex model and difficult to solve directly, the present invention converts it into two sub-models, the upper and lower sub-models, where the upper sub-model is:

[0185]

[0186]

[0187] in, The upper limit of water satisfaction; is the upper limit of water use satisfaction of the j-th water use department in the ith province or region, The upper limit of water resource allocation for the kth water source in the jth water-using sector in the ith province or sub-region; , The upper and lower limits of the water demand of the j-th water-using department in the ith province or sub-district; , The upper and lower limits of the water demand of the j-th water-using department in the ith province or sub-district; is the satisfaction weight of the j-th water-using department;

[0188] The constraints of the upper sub-model are:

[0189]

[0190]

[0191]

[0192]

[0193]

[0194]

[0195]

[0196]

[0197]

[0198]

[0199] in, It is the upper limit of the water supply of the first water source. In this embodiment, it is the water supply of the west line.

[0200] The upper limit of water resource allocation for the first water source of the jth water-using sector in the ith province or sub-district;

[0201] The upper limit of the water resource allocation for the second water source of the jth water-using sector in the i-th province or sub-district;

[0202] The upper limit of the water resource allocation for the third water source of the jth water-using sector in the i-th province or sub-region;

[0203] is the total amount of surface water in the study area (Yellow River Basin) at the confidence level p;

[0204] is the upper limit of groundwater supply in province i;

[0205] is the lower limit of the sewage discharge coefficient of the kth water source of the jth water-using department in the ith province;

[0206] , , , are the lower limits of the discharge concentrations of COD, TP, TN, and AN in the k-th water source of the j-th water-using department in the ith province, respectively;

[0207] , , , are the upper limits of COD, TP, TN, and AN removal rates in the k-th water source of the j-th water-using department in the ith province, respectively;

[0208] , , , They are the upper limits of the total discharge of COD, TP, TN and AN in all water sources in the i-th province.

[0209] By solving, we get the upper optimal variable and corresponding water use satisfaction .

[0210] The lower sub-model is:

[0211]

[0212]

[0213] in, The lower limit of water satisfaction; is the lower limit of water use satisfaction of the j-th water use department in the ith province or region, The lower limit of water resource allocation for the kth water source in the jth water-using sector in the ith province or sub-region; , The upper and lower limits of the water demand of the j-th water-using department in the ith province or sub-district; , The upper and lower limits of the water demand of the j-th water-using department in the ith province or sub-district; is the satisfaction weight of the j-th water-using department;

[0214] The constraints of the lower sub-model are:

[0215]

[0216]

[0217]

[0218]

[0219]

[0220]

[0221]

[0222]

[0223]

[0224]

[0225]

[0226] in, It is the lower limit of the water supply of the first water source, and in this embodiment, it is the water supply of the west line.

[0227] The lower limit of the water resource allocation for the first water source of the j-th water-using sector in the i-th province or sub-region;

[0228] The lower limit of the water resource allocation for the second water source of the jth water-using department in the i-th province or sub-district;

[0229] The lower limit of the water resource allocation for the third water source of the jth water-using sector in the i-th province or sub-region;

[0230] is the total amount of surface water in the study area (Yellow River Basin) at the confidence level p;

[0231] is the lower limit of groundwater supply in province i;

[0232] is the upper limit of the sewage discharge coefficient of the kth water source of the jth water-using department in the ith province;

[0233] , , , are the upper limits of the discharge concentrations of COD, TP, TN, and AN in the k-th water source of the j-th water-using department in the ith province, respectively;

[0234] , , , are the lower limits of COD, TP, TN, and AN removal rates in the k-th water source of the j-th water-using department in the ith province, respectively;

[0235] , , , They are the lower limits of the total discharge of COD, TP, TN and AN in all water sources in the i-th province.

[0236] By solving, we can get the optimal variables of the lower layer. and corresponding water use satisfaction .

[0237] Merge the upper and lower layer results to obtain the final result range and corresponding water use satisfaction .

[0238] S200, using the walking algorithm to optimize and solve the water resources optimization model to obtain a water resources allocation plan.

[0239] The hiking optimization algorithm is inspired by the experience that hikers face when climbing mountains, hills, or rocks. During hiking, travelers will consciously or unconsciously consider the steepness of the terrain and selectively avoid extremely steep terrain and narrow trails to maintain a higher walking speed. In other words, steep terrain and rugged trails will significantly slow down the pace, thereby extending the time of the entire hiking trip. By analyzing and estimating the terrain characteristics, travelers can roughly predict the time required to reach the top of the mountain. This process has certain similarities with the search for local optimal solutions or global optimal solutions in optimization problems. By simulating this process, the hiking optimization algorithm is committed to finding optimal solutions or approximate optimal solutions in optimization problems, reflecting an effective mapping of actual terrain challenges.

[0240] The specific steps include:

[0241] S210, hiking algorithm initialization: each water use part in each province corresponds to a hiker, and the water volume is allocated to the water use department Position as a hiker , according to the upper and lower limits of the water allocation of the water use department, the hiker's position is initialized and the position of the initial value is used as the first candidate position. The objective function of the optimization target when calculating the first candidate position is used as the first candidate objective function. The hiker's speed is initialized. ;

[0242] Directly initializing the value as the first candidate position will increase the computing power requirements in the subsequent iteration process, making the search for the optimal solution slower. Therefore, in this embodiment, a multi-group strategy is used to optimize the initialization value to obtain the first candidate position and the corresponding first objective function, as follows:

[0243] S211, parameter initialization: when initializing the values, set multiple groups of hikers, select the group with the best optimization target as the best group, use the position of the hikers in the best group as the first candidate position, and calculate the corresponding objective function as the first candidate objective function.

[0244] Initialization of hiker positions: When initializing the values, N groups of hikers can be set. The method for initializing the hikers' positions according to the upper and lower limits of the water volume allocated by the water use department is as follows:

[0245]

[0246] in , represents the serial number of the group, N is the total number of groups; represents the ordinal number of the hiker, dim represents the total number of hikers in the group, which is also equal to the total number of water-using sectors; is the uth groupv The location of each hiker represents the amount of water allocated by the water use department; Allocate upper and lower limits on water volumes for water users; is a random number between [0,1].

[0247] The values ​​are initialized according to the location of the hikers and substituted into the water resources optimization model to calculate the corresponding objective function, that is, the Gini coefficient in this scheme. Since there are N groups in total, N corresponding objective functions can be obtained .

[0248] From N objective functions Select the best one as the first candidate objective function , the corresponding hiker position is taken as the first candidate position .

[0249] S212, Hiker Location Update:

[0250] S212.1. Initialization of hiker speed:

[0251] The mathematical basis of the hiking optimization algorithm is Tobler's hiking function, which takes into account the steepness or slope of the terrain or path and determines the hiker's speed. The hiker's speed is calculated as:

[0252]

[0253] in, is the speed of the vth hiker, is the slope of the terrain faced by the vth hiker, calculated as:

[0254]

[0255] is the inclination angle of the terrain, A random number between .

[0256] S212.2. Update and calculate the speed of the hiker to obtain the current speed of the hiker. The specific formula is as follows:

[0257]

[0258] in, is a random number between [0, 1]; is the scanning factor, randomly selecting an integer between [1,3], is the speed of the vth hiker, is the current speed of the vth hiker after the update.

[0259] S212.3. Update its position using the current speed. The specific formula is as follows:

[0260]

[0261] is the position of the vth hiker, is the updated position of the vth hiker, is the current speed of the vth hiker.

[0262] S212.3. New location for hikers Substitute it into the water resources optimization model to determine the v The position of each hiker satisfies the constraints in the water resource optimization model. If not, the process returns to step S212.1 to recalculate the speed of the hiker and update its position until the positions of all hikers satisfy the constraints in the water resource optimization model. The positions of all hikers that satisfy the constraints are used as the second candidate positions. , calculate the corresponding objective function as the second candidate objective function ;

[0263] S212.4. Comparison of candidate objective functions: Compare the first candidate objective function and the second candidate objective function Advantages and disadvantages (e.g. ): If the first candidate objective function is better, then directly return to step S212.1 and iterate until the maximum number of iterations is set. ; If the second candidate objective function is better, then use the second candidate objective function to update the first objective function, , the corresponding second candidate position updates the first candidate position, , return to step S212.1 and iterate until the maximum number of iterations is reached. ;

[0264] S212.5, the first candidate position after the iteration (i.e. the allocated water volume for each water-using department) as a water resource allocation plan.

[0265] It should be noted that the group N determines the first candidate objective function obtained in the first optimization , the first candidate position The optimization degree is determined by the N value. Therefore, in theory, the larger the N, the better. However, considering the amount of matrix calculations and the current computer capabilities, it is generally more appropriate for N to be between 10 and 500, and the best value is around 100.

[0266] It should be noted that, in theory, the maximum number of iterations The larger the value, the more optimal the water resource allocation plan will be. Considering the actual computing power and accuracy requirements, the value is generally between 100-1000, and the optimal value is around 500.

[0267] According to the above, the model can be divided into two sub-models, the upper and lower sub-models, and the walking optimization algorithm is used to solve them respectively. The minimum satisfaction index of the elastic constraint is , confidence level The values ​​are 0.8 and 0.01 respectively, and the solution results are as follows:

[0268] from Figure 2 It can be found that the water demand in the Yellow River Basin varies significantly among the four sectors of life, industry, agriculture and ecology. The water consumption of life and industry is relatively stable, among which the fluctuation of life water is small, showing the regularity of its demand; the industrial water consumption is slightly higher, but the change is not large, indicating that industrial demand is also relatively stable. The agricultural water demand is the highest and the fluctuation range is large, reflecting its huge dependence on water resources and the influence of climate and seasonal changes. The ecological water consumption is stable, showing that the demand for ecological protection is highly fixed.

[0269] from Figure 3 It can be found that the upper and lower limits of the allocated water volume in Inner Mongolia range from 9.707 to 10.594 billion m3, and its allocated water volume is the highest among the nine provinces and regions; the upper and lower limits of the allocated water volume in Sichuan Province are 0.49 and 0.51 billion m3, and its allocated water volume is the lowest among the nine provinces and regions. The allocated water volume of each province has significant differences between different upper and lower limits. The upper and lower limits of the allocated water volume in Qinghai Province are 25.63 and 27.72, respectively, with a relatively small range, which indicates that the allocated water volume in Qinghai is relatively stable and the overall water demand is relatively low. The upper and lower limits of Ningxia range from 80.93 to 88.32, and its allocated water volume is slightly higher than that of Gansu, and remains relatively stable.

[0270] In general, the water allocation of each province in the Yellow River Basin varies within a certain range, mainly affected by the economic development, agricultural demand and natural conditions of each province. Further analysis of water demand and resource allocation of each province and formulation of reasonable water resources management policies will ensure the sustainable development of the Yellow River Basin and the rational use of water resources.

[0271] Figure 4 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 4As shown, the electronic device may include: a processor 810, a communication interface 120, a memory 830 and a communication bus 840, wherein the processor 810, the communication interface 820 and the memory 830 communicate with each other through the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute a water resource allocation method considering multiple uncertainties, the method comprising: firstly taking the maximum water satisfaction as the optimization goal, constructing a water resource optimization model based on multiple uncertainty constraints; then using the walking algorithm to optimize and solve the water resource optimization model, and obtaining a water resource allocation plan, specifically comprising the following steps:

[0272] Initialization of hiking algorithm: the water volume allocated by the water use department is used as the location of the hiker, and the location of the hiker is initialized according to the upper and lower limits of the water volume allocated by the water use department. The location with the initialization value is used as the first candidate location, and the objective function of the optimization target when calculating the first candidate location is used as the first candidate objective function;

[0273] Hiker position update: initialize the hiker's speed, update and calculate the hiker's speed, obtain the hiker's current speed, use the current speed to update its position, substitute the hiker's new position into the water resource optimization model, determine whether the hiker's position meets the constraints in the water resource optimization model, if not, recalculate the hiker's speed and update its position until all hikers' positions meet the constraints in the water resource optimization model, take the positions of all hikers that meet the constraints as the second candidate positions, and calculate the corresponding objective function as the second candidate objective function;

[0274] Comparison of candidate objective functions: compare the first candidate objective function and the second candidate objective function: if the first candidate objective function is better, directly return to the hiker position update loop iteration until the set maximum number of iterations; if the second candidate objective function is better, use the second candidate objective function to update the first objective function, and the corresponding second candidate position updates the first candidate position, and return to the hiker position update loop iteration until the set maximum number of iterations;

[0275] The first candidate position after the iteration is completed is used as the water resources allocation plan.

[0276] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0277] On the other hand, the present invention also provides a computer program product, the computer program product includes a computer program, the computer program can be stored on a non-transitory computer-readable storage medium, when the computer program is executed by a processor, the computer can execute the water resource allocation method considering multiple uncertainties provided by the above methods, the method includes: firstly, taking the maximum water satisfaction as the optimization goal, constructing a water resource optimization model based on multiple uncertainty constraints; then using the walking algorithm to optimize and solve the water resource optimization model, and obtaining a water resource allocation plan, specifically including the following steps:

[0278] Initialization of hiking algorithm: the water volume allocated by the water use department is used as the location of the hiker, and the location of the hiker is initialized according to the upper and lower limits of the water volume allocated by the water use department. The location with the initialization value is used as the first candidate location, and the objective function of the optimization target when calculating the first candidate location is used as the first candidate objective function;

[0279] Hiker position update: initialize the hiker's speed, update and calculate the hiker's speed, obtain the hiker's current speed, use the current speed to update its position, substitute the hiker's new position into the water resource optimization model, determine whether the hiker's position meets the constraints in the water resource optimization model, if not, recalculate the hiker's speed and update its position until all hikers' positions meet the constraints in the water resource optimization model, take the positions of all hikers that meet the constraints as the second candidate positions, and calculate the corresponding objective function as the second candidate objective function;

[0280] Comparison of candidate objective functions: compare the first candidate objective function and the second candidate objective function: if the first candidate objective function is better, directly return to the hiker position update loop iteration until the set maximum number of iterations; if the second candidate objective function is better, use the second candidate objective function to update the first objective function, the corresponding second candidate position updates the first candidate position, return to the hiker position update loop iteration until the set maximum number of iterations;

[0281] The first candidate position after the iteration is completed is used as the water resources allocation plan.

[0282] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the computer program is executed by a processor to execute the water resource allocation method considering multiple uncertainties provided by the above methods, the method comprising: firstly taking the maximum water satisfaction as the optimization goal, constructing a water resource optimization model based on multiple uncertainty constraints; then optimizing and solving the water resource optimization model using a walking algorithm to obtain a water resource allocation plan, specifically comprising the following steps:

[0283] Initialization of hiking algorithm: the water volume allocated by the water use department is used as the location of the hiker, and the location of the hiker is initialized according to the upper and lower limits of the water volume allocated by the water use department. The location with the initialization value is used as the first candidate location, and the objective function of the optimization target when calculating the first candidate location is used as the first candidate objective function;

[0284] Hiker position update: initialize the hiker's speed, update and calculate the hiker's speed, obtain the hiker's current speed, use the current speed to update its position, substitute the hiker's new position into the water resource optimization model, determine whether the hiker's position meets the constraints in the water resource optimization model, if not, recalculate the hiker's speed and update its position until all hikers' positions meet the constraints in the water resource optimization model, take the positions of all hikers that meet the constraints as the second candidate positions, and calculate the corresponding objective function as the second candidate objective function;

[0285] Comparison of candidate objective functions: compare the first candidate objective function and the second candidate objective function: if the first candidate objective function is better, directly return to the hiker position update loop iteration until the set maximum number of iterations; if the second candidate objective function is better, use the second candidate objective function to update the first objective function, the corresponding second candidate position updates the first candidate position, return to the hiker position update loop iteration until the set maximum number of iterations;

[0286] The first candidate position after the iteration is completed is used as the water resources allocation plan.

[0287] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0288] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0289] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

[0290] The above embodiments are only used to illustrate the present invention, but not to limit the present invention. Although the present invention is described in detail with reference to the embodiments, it should be understood by those skilled in the art that various combinations, modifications or equivalent substitutions of the technical solutions of the present invention do not depart from the spirit and scope of the technical solutions of the present invention, and should be included in the scope of the claims of the present invention.

Claims

1. A water resources allocation method considering multiple uncertainties, characterized in that: Firstly, taking the maximum water satisfaction as the optimization goal, a water resources optimization model based on multiple uncertainty constraints is constructed; then, the walking algorithm is used to optimize the water resources optimization model and obtain the water resources allocation plan, which specifically includes the following steps: Initialization of hiking algorithm: The water volume allocated by the water use department is used as the location of the hiker, and the location of the hiker is initialized according to the upper and lower limits of the water volume allocated by the water use department; The multi-group strategy is used to optimize the initialization value to obtain the first candidate position and the corresponding first objective function, as follows: When initializing the value, multiple groups of hikers are set, the group with the best optimization target is selected as the best group, the position of the hikers in the best group is used as the first candidate position, and the corresponding objective function is calculated as the first candidate objective function; Hiker position update: initialize the hiker's speed, update and calculate the hiker's speed, obtain the hiker's current speed, use the current speed to update its position, substitute the hiker's new position into the water resource optimization model, determine whether the hiker's position meets the constraints in the water resource optimization model, if not, recalculate the hiker's speed and update its position until all hikers' positions meet the constraints in the water resource optimization model, take the positions of all hikers that meet the constraints as the second candidate positions, and calculate the corresponding objective function as the second candidate objective function; Comparison of candidate objective functions: compare the first candidate objective function and the second candidate objective function: if the first candidate objective function is better, directly return to the hiker position update loop iteration until the set maximum number of iterations; if the second candidate objective function is better, use the second candidate objective function to update the first objective function, the corresponding second candidate position updates the first candidate position, return to the hiker position update loop iteration until the set maximum number of iterations; The first candidate position after the iteration is taken as the water resource allocation plan; The method for initializing the hiker's position according to the upper and lower limits of the water volume allocated by the water use department is as follows: in , represents the serial number of the group, N is the total number of groups; represents the serial number of the hiker, dim represents the total number of hikers in the group; is the uth group v The location of each hiker represents the amount of water allocated by the water use department; Allocate upper and lower limits on water volumes for water users; is a random number between [0,1].

2. The water resource allocation method considering multiple uncertainties according to claim 1, characterized in that: The hiker's speed is calculated as follows: in, is the speed of the vth hiker, is the slope of the terrain faced by the vth hiker, calculated as: is the inclination angle of the terrain, A random number between .

3. The water resource allocation method considering multiple uncertainties according to claim 2 is characterized in that: The method for updating the hiker's location is as follows: is the position of the vth hiker, is the updated position of the vth hiker, is the current speed of the vth hiker.

4. The water resource allocation method considering multiple uncertainties according to claim 1 is characterized in that: Update the method to calculate the hiker's speed as follows: in, is a random number between [0, 1]; is the scanning factor, randomly selecting an integer between [1,3], is the speed of the vth hiker, is the current speed of the vth hiker after update, The first candidate position.

5. The water resource allocation method considering multiple uncertainties according to claim 1, characterized in that: The method of constructing a water resources optimization model based on multiple uncertainty constraints is as follows: The objective function is constructed with maximum water satisfaction: in, for water satisfaction; is the water use satisfaction of the j-th water use department in the ith province or sub-district, I is the total number of provinces or sub-districts, and J is the total number of water use departments in each province or sub-district; the water use satisfaction of the j-th water use department in the ith province or sub-district is calculated as follows: in, is the water resource allocation of the kth water source in the jth water-using department in the ith province or sub-district, where K is the total number of water sources; , are the lower limit and upper limit of water demand of the jth water-using department in the ith province, respectively; is the satisfaction weight of the j-th water-using department; Build constraints: The constraints include water demand constraints, water supply constraints, sewage discharge constraints and non-negative constraints, among which the water supply constraints include interval constraints, random constraints and fuzzy constraints.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the water resource allocation method considering multiple uncertainties as described in any one of claims 1 to 5 is implemented.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the water resource allocation method considering multiple uncertainties as described in any one of claims 1 to 5 is implemented.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the water resource allocation method considering multiple uncertainties as described in any one of claims 1 to 5 is implemented.

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