Index prediction method and system based on water resource bearing capacity and storage medium

Abstract

The invention discloses an index prediction method and system based on water resource bearing capacity and a storage medium, and the method comprises the steps: obtaining an original value corresponding to each evaluation index of the water resource bearing capacity in m evaluation time periods, and carrying out the standardization processing, and obtaining a standard value; obtaining a comprehensive score of each evaluation time period and performing normalization processing to obtain a corresponding evaluation score; establishing a water resource bearing capacity curve through the evaluation score, and performing linear fitting to obtain a first fitting function; establishing a corresponding index change curve through the original value of any specified evaluation index, and performing linear fitting to obtain a second fitting function; and according to the intersection point of the two fitting functions and the health grade interval of the water resource bearing capacity, obtaining a prediction interval of the specified evaluation index in the planning period. According to the method, the prediction interval of the specified index can be quantitatively predicted according to the health level interval of the water resource bearing capacity, and a clear planning basis is provided for regional water resource management.

Description

Technical Field

[0001] The present invention relates to the technical field of resource planning, and particularly relates to an index prediction method, system and computer-readable storage medium based on water resource carrying capacity. Background Art

[0002] Water resources are the material basis for human survival and development, an important part of natural resources, and their carrying capacity is an important indicator for measuring the sustainable development of the economy and society. Urban water consumption mainly includes comprehensive domestic water consumption, industrial water consumption, ecological and environmental water consumption, and agricultural water consumption.

[0003] The existing technologies mainly predict urban water consumption based on the urban planning service population, but do not consider the limitations of the water resource carrying capacity on the population and economic scale in reverse, and the calculation method has the limitation of one-way conduction. Under the new policy background of "determining the city by water, determining the people by water, and determining the production by water" (that is, determining the city scale, population and industrial structure according to water resources), how to effectively plan the city according to the water resource carrying capacity is an urgent problem to be solved. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide an index prediction method, system and computer-readable storage medium based on water resource carrying capacity. By comprehensively considering the dynamic change relationship between water resource carrying capacity and a specified index, and taking the intersection point of the corresponding two fitting functions as the key reference point of the specified index in the future planning period, the prediction interval of the specified index can be quantitatively predicted, providing a clear planning basis for regional water resource management.

[0005] The first aspect of the embodiments of the present invention provides an index prediction method based on water resource carrying capacity, including:

[0006] Taking the water resource carrying capacity of a region as the evaluation target, and selecting a plurality of evaluation indexes from a candidate index set;

[0007] Obtaining the original values corresponding to each of the evaluation indexes in m evaluation periods, and performing standardization processing on the original values to obtain the corresponding standard values; where m≥1;

[0008] Using a preset principal component analysis algorithm for the standard values to obtain the comprehensive score of each evaluation period, and performing normalization processing to obtain the evaluation score of the water resource carrying capacity in each evaluation period;

[0009] Based on the evaluation scores, establishing a water resource carrying capacity curve, and performing linear fitting on the water resource carrying capacity curve to obtain a first fitting function;

[0010] Based on the original value of any specified evaluation index, establish a corresponding index change curve, and perform linear fitting on the index change curve to obtain a second fitting function;

[0011] Obtain the intersection point of the first fitting function and the second fitting function, and based on the health level interval of the water resource carrying capacity, obtain the prediction interval of the specified evaluation index during the planning period.

[0012] Optionally, taking the water resource carrying capacity of the region as the evaluation target, select multiple evaluation indexes from the candidate index set, including:

[0013] Taking the water resource carrying capacity as the evaluation target, establish an initial evaluation index system; wherein, the evaluation index system includes: a target layer, a criterion layer, and an index layer; the candidate indexes in the index layer are derived from the candidate index set;

[0014] Based on the analytic hierarchy process algorithm, obtain the weights of the candidate indexes;

[0015] Based on the weights, screen the candidate indexes to obtain multiple evaluation indexes.

[0016] Optionally, the evaluation indexes include: ecological environment water consumption, per capita comprehensive domestic water consumption, average mu water consumption for farmland irrigation, or industrial water consumption per unit output value.

[0017] Optionally, the establishing a corresponding index change curve based on the original value of any specified evaluation index includes:

[0018] Perform trend reverse processing on the original value of any specified evaluation index to obtain an index conversion value, and based on the index conversion value, establish a corresponding index change curve; wherein, the trend reverse processing enables the index change curve and the water resource carrying capacity curve to have a mirror image change trend.

[0019] Optionally, the trend reverse processing includes:

[0020] When the specified evaluation index is a positive correlation index, process the original value based on the negative index normalization formula to obtain the corresponding index conversion value;

[0021] When the specified evaluation index is a negative correlation index, process the original value based on the positive index normalization formula to obtain the corresponding index conversion value.

[0022] Optionally, the obtaining the intersection point of the first fitting function and the second fitting function, and based on the health level interval of the water resource carrying capacity, obtaining the prediction interval of the specified evaluation index during the planning period includes:

[0023] When the specified evaluation index is a positive correlation index, the first index value of the intersection point in the second fitting function is used as the minimum value of the prediction interval, and the corresponding second index value in the second fitting function when the first fitting function is the maximum value of the health level interval is used as the maximum value of the prediction interval;

[0024] When the specified evaluation index is a negative correlation index, the third index value of the intersection point in the second fitting function is used as the maximum value of the prediction interval, and the corresponding fourth index value in the second fitting function when the first fitting function is the minimum value of the health level interval is used as the minimum value of the prediction interval.

[0025] Optionally, the method further includes:

[0026] Based on the prediction interval of the specified evaluation index during the planning period, predict the carrying interval under the corresponding planning scenario; wherein, the planning scenario includes at least one of: planned population, agricultural land area, and industrial output value.

[0027] Optionally, when the planning scenario is the planned population, predicting the carrying interval of the planned population includes:

[0028] Obtain the upper limit value of the total water consumption control during the planning period, as well as the first average value of the comprehensive domestic water consumption, the second average value of the farmland irrigation water consumption, and the third average value of the industrial water consumption;

[0029] When the specified evaluation index is the ecological environment water consumption, based on the ecological environment water consumption change curve and the water resources carrying capacity curve, obtain the fourth average value of the ecological environment water consumption during the planning period;

[0030] Based on the first average value, the second average value, the third average value, and the fourth average value, obtain the water consumption ratio;

[0031] When the specified evaluation index is the per capita comprehensive domestic water consumption, based on the per capita comprehensive domestic water consumption change curve and the water resources carrying capacity curve, obtain the prediction interval of the per capita comprehensive domestic water consumption during the planning period;

[0032] Based on the upper limit value of the control, the water consumption ratio, and the prediction interval of the per capita comprehensive domestic water consumption, obtain the carrying interval of the planned population.

[0033] An embodiment of the second aspect of the present invention provides an index prediction system based on water resources carrying capacity, including:

[0034] An index selection module, configured to select multiple evaluation indexes from a candidate index set with the water resource carrying capacity of a region as an evaluation target;

[0035] A data acquisition module, configured to obtain the original values corresponding to each of the evaluation indexes within m evaluation periods, and perform standardization processing on the original values to obtain corresponding standard values; where m≥1;

[0036] An evaluation score module, configured to adopt a preset principal component analysis algorithm for the standard values, obtain the comprehensive score of each evaluation period, and perform normalization processing to obtain the evaluation score of the water resource carrying capacity within each evaluation period;

[0037] A first fitting module, configured to establish a water resource carrying capacity curve based on the evaluation score, and perform linear fitting on the water resource carrying capacity curve to obtain a first fitting function;

[0038] A second fitting module, configured to establish a corresponding index change curve based on the original value of any specified evaluation index, and perform linear fitting on the index change curve to obtain a second fitting function;

[0039] An index prediction module, configured to obtain the intersection point of the first fitting function and the second fitting function, and obtain the prediction interval of the specified evaluation index within the planning period based on the health level interval of the water resource carrying capacity.

[0040] An embodiment of the third aspect of the present invention provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program; where the computer program controls the device where the computer-readable storage medium is located to execute the index prediction method based on water resource carrying capacity according to any item of the first aspect when running.

[0041] Compared with the prior art, the embodiment of the present invention provides an index prediction method, system and computer-readable storage medium based on water resource carrying capacity. By comprehensively considering the dynamic change relationship between water resource carrying capacity and a specified index, and using the intersection point of the corresponding two fitting functions as the key reference point of the specified index within the future planning period, the prediction interval of the specified index can be quantitatively predicted, providing a clear planning basis for regional water resource management. Description of the Drawings

[0042] Figure 1 is a schematic flowchart of an embodiment of the index prediction method based on water resource carrying capacity provided by the present invention;

[0043] Figure 2 is an example diagram of an embodiment of obtaining the intersection point of the first fitting function and the second fitting function provided by the present invention;

[0044] Figure 3 It is a schematic structural diagram of an embodiment of an index prediction system based on water resource carrying capacity provided by the present invention. Specific implementation manners

[0045] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art in the technical field of the present invention without making creative efforts based on the embodiments of the present invention belong to the protection scope of the present invention.

[0046] See Figure 1 , which is a schematic flow diagram of an embodiment of an index prediction method based on water resource carrying capacity provided by the present invention.

[0047] The first aspect embodiment of the present invention provides an index prediction method based on water resource carrying capacity, including steps S1 to S6, specifically as follows:

[0048] Step S1: Taking the water resource carrying capacity of the region as the evaluation target, select multiple evaluation indexes from the candidate index set;

[0049] Step S2: Obtain the original values corresponding to each of the evaluation indexes in m evaluation periods, and perform standardization processing on the original values to obtain the corresponding standard values; where m≥1;

[0050] Step S3: Use a preset principal component analysis algorithm for the standard values, obtain the comprehensive scores of each of the evaluation periods, and perform normalization processing to obtain the evaluation scores of the water resource carrying capacity in each of the evaluation periods;

[0051] Step S4: Based on the evaluation scores, establish a water resource carrying capacity curve, and perform linear fitting on the water resource carrying capacity curve to obtain a first fitting function;

[0052] Step S5: Based on the original values of any specified evaluation index, establish a corresponding index change curve, and perform linear fitting on the index change curve to obtain a second fitting function;

[0053] Step S6: Obtain the intersection points of the first fitting function and the second fitting function, and based on the healthy level interval of the water resource carrying capacity, obtain the prediction interval of the specified evaluation index during the planning period.

[0054] It should be noted that the candidate index set in the embodiments of the present invention includes at least the following 15 indexes: the year-end resident population (A1), per capita disposable income (A2), GDP (A3), the proportion of the tertiary industry (A4), water consumption per 10,000 yuan of GDP (B1), per capita comprehensive domestic water consumption (B2), average water consumption per mu of farmland irrigation (B3), water consumption for ecological environment (B4), per capita water resources (C1), rainfall (C2), groundwater water consumption (C3), fixed asset investment (D1), water consumption (D2), sewage discharge (D3), industrial water consumption per unit output value (D4), a total of 15 indexes. In addition, in the specific implementation of the embodiments of the present invention, m usually takes an integer greater than 5, which is used to draw the water resources carrying capacity curve to further analyze the change trend of the water resources carrying capacity and multi-period evaluation, providing effective data support for water resources management and planning.

[0055] Draw the water resources carrying capacity curve according to the evaluation score. The water resources carrying capacity curve reflects the change of the carrying capacity within m evaluation periods (such as the recent 15 years), and the first fitting function is used to describe / speculate the change trend of the water resources carrying capacity over time. The index change curve reflects the historical change of the specified index within m evaluation periods, and the second fitting function is used to describe / speculate the change trend of the specified index over time.

[0056] The intersection point of the first fitting function and the second fitting function is the balance point between the specified index and the water resources carrying capacity, and also reflects the predicted critical threshold of the specified index. When the specified index is a positive index, the intersection point indicates that the influence of the positive index just meets the positive change of the water resources carrying capacity at this time. Therefore, the first index value of the positive index calculated based on the intersection point is the minimum value of its prediction interval; in actual planning, it is inclined to the better change of the water resources carrying capacity. Therefore, when the water resources carrying capacity takes the maximum value in the healthy level interval, the second index value of the positive index corresponding to the second fitting function is the maximum value of its prediction interval.

[0057] Similarly, when the specified index is a negative index, the intersection point indicates that the influence of the negative index just meets the negative change of the water resources carrying capacity at this time. Therefore, the third index value of the negative index calculated based on the intersection point is the maximum value of its prediction interval; in actual planning, it is inclined to reduce the influence of the negative change of the water resources carrying capacity. Therefore, when the water resources carrying capacity takes the minimum value in the healthy level interval, the fourth index value of the negative index corresponding to the second fitting function is the minimum value of its prediction interval.

[0058] As can be seen from the above, the embodiments of the present invention can quantitatively predict the prediction interval of the specified index by comprehensively considering the dynamic change relationship between the water resources carrying capacity and the specified index, and taking the intersection point of the corresponding two fitting functions as the key reference point of the specified index in the future planning period, providing a clear planning basis for regional water resources management.

[0059] In an optional embodiment, in step S1 described above, with the water resource carrying capacity of the region as the evaluation target, multiple evaluation indicators are selected from the candidate indicator set, including:

[0060] Taking the water resource carrying capacity as the evaluation target, an initial evaluation index system is established; wherein, the evaluation index system includes: a target layer, a criterion layer, and an index layer; the candidate indicators in the index layer are derived from the candidate indicator set;

[0061] Based on the analytic hierarchy process (AHP) algorithm, the weights of the candidate indicators are obtained;

[0062] Based on the weights, the candidate indicators are screened to obtain multiple evaluation indicators.

[0063] It should be noted that the embodiment of the present invention constructs an evaluation index system for water resource carrying capacity based on the analytic hierarchy process (AHP) algorithm, and the specific implementation process is as follows:

[0064] The first step: Establish an initial evaluation index system, including: a target layer, a criterion layer, and an index layer.

[0065] The target layer is the water resource carrying capacity (F); the criterion layer includes at least the following 4 first-level candidate indicators: socio-economic drive (A), urban water use efficiency (B), resource carrying status (C), and management policy response (D); the second-level candidate indicators in the index layer are derived from the candidate indicator set; the candidate indicator set includes at least the following 15 indicators: year-end resident population (A1), per capita disposable income (A2), GDP (A3), proportion of the tertiary industry (A4), water consumption per 10,000 yuan of GDP (B1), per capita comprehensive domestic water consumption (B2), mu water consumption for farmland irrigation (B3), ecological environment water consumption (B4), per capita water resource quantity (C1), rainfall (C2), groundwater water consumption (C3), fixed asset investment (D1), water consumption (D2), sewage discharge (D3), industrial water consumption per unit output value (D4).

[0066] The second step: Through index weighting, construct the final evaluation indicators.

[0067] ① Compare each element at the same level pairwise, determine the relative importance of each indicator under each criterion according to the selected scale, give a judgment in numerical form, and establish a judgment matrix A.

[0068] The expression of the judgment matrix A is as follows:

[0069] Wherein, aij is the importance scale of pairwise comparison between indicators Ui and Uj, and its selection rule is the assignment in Table 1.

[0070] Table 1. Assignment Scale Table

[0071] Assignment of aij Factor i compared to factor j Assignment of aij Factor i compared to factor j 1 Equally important 7 Significantly important 3 Slightly important 9 Extremely important 5 Quite important 2，4，6，8 Between the two

[0072] In addition, the judgment matrix A needs to satisfy the following conditions:

[0073]

[0074] ② Conduct single-level sorting and consistency test.

[0075] Calculate the maximum eigenvalue and the corresponding eigenvector of the judgment matrix A, that is

[0076] AW = λmaxW;

[0077] where λmax is the maximum eigenvalue of A, W is the corresponding eigenvector, and W = [w1, w2,..., wn] T The normalized component wi of

[0078] The indicators for calculating consistency are CI and CR, and the calculation formulas are as follows:

[0079]

[0080] where n is the number of indicators in this level, and RI is the average random consistency index, and its values are shown in Table 2.

[0081] Table 2. RI Value Table

[0082] n 2 3 4 5 6 7 8 9 10 11 12 13 14 15 RI 0 0.58 0.90 1.12 1.25 1.35 1.42 1.46 1.49 1.52 1.54 1.56 1.58 1.59

[0083] When CR = 0, the judgment matrix has perfect consistency. The larger CR is, the worse the consistency of the judgment matrix. If CR ≤ 0.1, that is, the consistency condition is satisfied and the weight distribution is reasonable. If not, the judgment matrix needs to be adjusted until the requirements are met.

[0084] ③ Hierarchical total sorting and consistency test.

[0085] When the consistency index of a certain factor in the target layer for wj single sorting is CIj, and the corresponding average random consistency index is RIj, then the CR of the hierarchical total sorting is:

[0086]

[0087] ④ Calculate the total weight of each indicator.

[0088] After calculating the weights of the criterion layer and the index layer respectively, multiplying them can obtain the final weights of each index [W1, W2, …, Wn]. Finally, by sorting the weights and eliminating the indexes with lower weights, it should be ensured that the total number of indexes ≥ 10 and the number of indexes in each criterion layer ≥ 2 to construct the final evaluation index system. The embodiment of the present invention can effectively reduce the subjectivity and randomness of human beings in the process of screening evaluation indexes.

[0089] In an alternative embodiment, multiple evaluation indexes selected from the candidate index set usually include: indexes such as ecological environment water consumption, per capita comprehensive domestic water consumption, average mu water consumption for farmland irrigation, and industrial water consumption per unit output value, which can be used for subsequent prediction of urban water consumption; among them, urban water consumption mainly includes: ecological environment water consumption, comprehensive domestic water consumption, industrial water consumption, and agricultural water consumption.

[0090] It should be noted that the specific implementation process of "using a preset principal component analysis algorithm for the standard values to obtain the comprehensive score of each evaluation period and performing normalization processing to obtain the evaluation score of the water resources carrying capacity in each evaluation period" in step S3 is as follows:

[0091] (1) Use Python tools to obtain the specific values (i.e., original values) of the evaluation index system in the research area for the past 15 years (i.e., m evaluation periods) through documents such as urban water resources bulletins and urban development yearbooks, and determine the upper and lower limits of the respective values of each index, designated as [min, max].

[0092] (2) Perform normalization standard processing on all values within the index value range, and the steps are as follows:

[0093] ① Determine the positive and negative correlations between each index and water resources. If the value of an index is larger, the higher the water resources carrying capacity, it is a positive correlation, and vice versa. In other words, different indexes may have different influence directions on the water resources carrying capacity. For positive indexes, the larger the index value, the better the water resources carrying capacity; on the contrary, for negative indexes, the smaller the index value, the better the water resources carrying capacity.

[0094] ② In order to keep the influence directions of all indexes consistent (usually transformed into "the larger the value, the better"), the embodiment of the present invention uses the linear transformation method for standardization processing. Assuming the index value is x and the corresponding index standard value is f, the original standardization processing is as follows:

[0095] Positive index normalization formula: f = (x - min) / (max - min);

[0096] Negative index normalization formula: f = (max - x) / (max - min).

[0097] (3) Construct a standardized matrix Z according to the obtained data.

[0098] Among them, m is the number of evaluation periods (number of sample years), and n is the number of indicators.

[0099] (4) Use MATLAB software to find the correlation coefficient matrix R of matrix Z.

[0100] (5) Find the eigenvalues and eigenvectors of matrix R.

[0101] The P eigenvalues λ of matrix R g (g = 1, 2,..., p), arranged in descending order of their absolute values as λ1 ≥ λ2 ≥... ≥ λ p ≥ 0, which is the variance of the principal component; each eigenvalue corresponds to an eigenvector L g (Lg = [lg1, lg2,..., lgp]).

[0102] (6) Determine the principal components, the number of principal components, and the contribution rate.

[0103] Select k principal components (k < p) to calculate the comprehensive score for each evaluation period / sample year. The value of k is determined by the variance contribution rate, that is

[0104] Among them, is the contribution rate of the first principal component. The larger this value is, the stronger the comprehensive information ability of the first principal component.

[0105] (7) Calculate the scores of each principal component and the comprehensive score.

[0106] Set F1 as the first principal component, F2 as the second principal component,..., F p as the pth principal component. Each principal component F g = l g1 Z1 + l g2 Z2 +... + l gp Z p ; here g is updated to g = 1, 2,..., k; l gi is the i-th component of the eigenvector corresponding to the g-th principal component, and Z i is the i-th column of the standardized matrix Z. The weighted sum of the variance contribution rates of each principal component can obtain the final expression of the principal component That is, the comprehensive score for each evaluation period / sample year can be obtained.

[0107] (8) Normalize the comprehensive score of each evaluation period to obtain the evaluation score of the water resource carrying capacity within each evaluation period.

[0108] (9) Hierarchical evaluation of water resource carrying capacity. It is divided into five levels according to the score of the evaluation. When the score is in the medium and good range, it is considered that the water resource carrying capacity is at a healthy level. The score values of each level are shown in Table 3.

[0109] Table 3. Score values of each level of water resource carrying capacity

[0110] Evaluation level Very poor Poor Medium Good Excellent Bearing capacity score ＜0.3 [0.3,0.5) [0.5,0.7) [0.7,0.9) ≥0.9

[0111] When the water resource carrying capacity level is excellent, it means that there is still a strong carrying capacity of water resources, there is still a high room for improvement in the water dependence of economic development, the utilization of water resources is still insufficient, and the driving force for urban development is weak; when the water resource carrying capacity level is poor or below, it means that the water resource carrying capacity is weak, the water resource utilization rate is poor or the water resources are overused, which is not conducive to the sustainable development of the city. Therefore, when the water resource carrying capacity is in the medium and good range, it can be considered to be at a healthy level, the utilization of water resources is relatively sufficient, and there is room for sustainable development. Therefore, the prediction of each evaluation index in the embodiment of the present invention is the corresponding prediction interval / healthy value interval under the condition that the water resource carrying capacity is at a healthy level.

[0112] In an alternative embodiment, establishing a corresponding index change curve based on the original value of any specified evaluation index includes:

[0113] Performing trend reverse processing on the original value of any specified evaluation index to obtain an index conversion value, and establishing a corresponding index change curve based on the index conversion value; wherein, the trend reverse processing enables the index change curve and the water resource carrying capacity curve to have a mirror image change trend.

[0114] Further, the trend reverse processing includes:

[0115] When the specified evaluation index is a positive correlation index, processing the original value based on the negative index normalization formula to obtain the corresponding index conversion value;

[0116] When the specified evaluation index is a negative correlation index, processing the original value based on the positive index normalization formula to obtain the corresponding index conversion value.

[0117] To better illustrate the technical solutions provided by the embodiments of the present invention, a specific implementation example will be given below:

[0118] With 15 years (i.e. m evaluation periods) as the horizontal axis and the annual evaluation score as the vertical axis, a curve of changes in water resource carrying capacity within the fifteen-year research scope can be drawn. According to the evaluation index corresponding to the maximum value of each element value lgi of the eigenvector corresponding to the first principal component, the significant impact index of water resource carrying capacity can be obtained to guide specific measures to improve water resource carrying capacity. If the significant impact index is used as the specified index, the solution process of the intersection of the first fitting function and the second fitting function is as follows:

[0119] According to the positive and negative correlation between the evaluation index system and the water resources carrying capacity, the prediction interval (healthy value interval) of the specified evaluation index under the healthy level of water resources carrying capacity is calculated. When the significant influencing index is positively correlated, the negative index normalization formula is used for processing, and the index change curve is drawn with time as the horizontal coordinate and the standard value as the vertical coordinate. The water resources carrying capacity curve is drawn on the same coordinate, and the vertical coordinate interval of these two curves is [0,1].

[0120] The change curve of the significant influencing index has a strong consistency with the direction and change trend of the water resource carrying capacity curve. Taking the positive correlation index as an example, if the positive correlation index is still processed by the normalization formula of the positive index, since the positive correlation index ↑, the water resource carrying capacity ↑, the two have a same-direction change relationship. If they are directly compared, "the phenomenon of two rising curves being difficult to observe the intersection" will appear, and it will be difficult to find the critical value of the index (such as when the water resource carrying capacity meets the standard, the minimum value of the positive correlation index should be how much). Therefore, the embodiment of the present invention processes the trend inversely, that is, the positive correlation index is processed by the normalization formula of the negative index to obtain the index conversion value. At this time, the index conversion value ↓, then the water resource carrying capacity ↑, the two have a mirror change trend, that is, a cross trend is formed, which is convenient for observing the intersection.

[0121] like Figure 2 , which is an example diagram of an embodiment of obtaining the intersection point of the first fitting function and the second fitting function provided by the present invention. Figure 2 In the above, the linear prediction interval x is determined according to the planning period, and the linear prediction lines of the two curves are generated, that is, the water resources carrying capacity curve is linearly fitted to obtain the first fitting function (such as y1 = mx1 + b1), and the indicator change curve (such as the significant impact indicator change curve) is linearly fitted to obtain the second fitting function (such as y2 = mx2 + b2), and the two equations are combined to solve the y value of the intersection. Due to the consistency of the curve and the extension of the linear prediction, the y value of the intersection will be ∈ [0.5, 0.9], that is, it falls in the water resources carrying capacity health level interval / health level interval. According to the y value, the specific value Y is calculated inversely, which is the health indicator value under the carrying capacity health level.

[0122] When the significant impact index is a negatively correlated index, the original value of the index is processed using the positive index normalization formula to obtain the corresponding index conversion value, and the remaining steps are the same as above.

[0123] It should be noted that when there is only one significant factor in the water resource carrying capacity curve, the change curves of the two are symmetric within the range of 0 - 1, that is, the intersection value is 0.5. However, in fact, there are multiple significant impact factors in the water resource carrying capacity curve. After the multiple weights are accumulated, although the two curves are not strictly symmetric, the overall shape and trend change little. After adding the linear prediction of several years to the abscissa, after multiple rounds of testing, the intersection values are all greater than 0.5, that is, they are all within the healthy level range.

[0124] In an alternative embodiment, obtaining the intersection of the first fitting function and the second fitting function, and based on the healthy level range of the water resource carrying capacity, obtaining the prediction range of the specified evaluation index during the planning period includes:

[0125] When the specified evaluation index is a positively correlated index, the first index value of the intersection in the second fitting function is used as the minimum value of the prediction range, and the corresponding second index value in the second fitting function when the first fitting function is the maximum value of the healthy level range is used as the maximum value of the prediction range;

[0126] When the specified evaluation index is a negatively correlated index, the third index value of the intersection in the second fitting function is used as the maximum value of the prediction range, and the corresponding fourth index value in the second fitting function when the first fitting function is the minimum value of the healthy level range is used as the minimum value of the prediction range.

[0127] It should be noted that in combination with the above embodiments, the solution process of the prediction range of the specified evaluation index is further described (taking the specified evaluation index as the significant impact index as an example), as follows:

[0128] When the significant impact index is positively correlated, the interval [Y, Y1] formed by the specific value Y (the first index value) obtained by back-calculation and the value Y1 (the second index value) corresponding to the water resource carrying score of 0.9 is the prediction range (healthy index range) of the significant index during the 15-year evaluation period and within the (x - 15)-year planning period, that is, from the current trend value to the theoretical demand value. Here, Y is the linear extrapolation value of the current development trend (equivalent to the prediction point without taking any measures), and Y1 is the theoretical demand value when the water resource carrying capacity reaches 0.9 points (excellent level).

[0129] When the significant impact index is negatively correlated, the interval [Y2, Y] formed by the specifically calculated value Y (the third index value) and the value Y2 (the fourth index value) corresponding to a water resources carrying capacity score of 0.5 is the prediction interval (health index interval) of the significant index during the 15-year evaluation period within the (x - 15)-year planning period, similar to withdrawing from the unhealthy verge to the current healthy area. At this time, Y2 is the maximum allowable value when the water resources carrying capacity remains at 0.5 points (the passing line).

[0130] It should be noted that as described in the background art, urban water consumption mainly includes comprehensive domestic water consumption, industrial water consumption, ecological and environmental water consumption, and agricultural water consumption. Common water consumption calculation methods include the comprehensive water consumption index method, the comprehensive domestic water consumption ratio correlation method, and the water consumption index method for different types of land use. The above three methods are basically calculated based on water use quotas formulated according to national standards and specifications such as the "Code for Urban Water Supply Engineering Planning" (GB50282-2016) and the "Standard for Outdoor Water Supply Design" (GB50013-2018), that is, the urban water consumption index can be calculated according to parameters such as the planned service population, water use quota, relevant coefficients for various types of water use, and land area. However, the water use quota in the calculation method of national standards and specifications mainly selects the numerical range based on the urban scale, and the numerical range of the quota is relatively wide, which fails to fully reflect the influence of factors such as the urban water resources situation, water-saving policies, environmental protection policies, and social and economic development status on urban water consumption. In addition, the calculation method of the existing technology has the limitation of one-way conduction. Under the new policy background of "determining the city, people, and production by water" (that is, determining the urban scale, population, and industrial structure according to water resources), it is impossible to effectively predict the evaluation index based on the water resources carrying capacity, and it is even more impossible to further predict the planned value range of the planned population, agricultural land area, and industrial output value / the carrying interval of water resources for them.

[0131] To solve the above technical problems, in an optional embodiment provided by the present invention, the method further includes:

[0132] Based on the prediction interval of the specified evaluation index within the planning period, predict the carrying interval under the corresponding planning scenario; wherein, the planning scenario includes at least one of the planned population, agricultural land area, and industrial output value.

[0133] Further, when the planning scenario is the planned population, predicting the carrying interval of the planned population includes:

[0134] Obtain the upper limit value of the control of the total water consumption within the planning period, as well as the first average value of the comprehensive domestic water consumption, the second average value of the farmland irrigation water consumption, and the third average value of the industrial water consumption.

[0135] When the specified evaluation index is the ecological environment water consumption, based on the ecological environment water consumption change curve and the water resources carrying capacity curve, obtain the fourth mean value of the ecological environment water consumption during the planning period;

[0136] Based on the first mean value, the second mean value, the third mean value and the fourth mean value, obtain the water consumption ratio;

[0137] When the specified evaluation index is the per capita comprehensive domestic water consumption, based on the per capita comprehensive domestic water consumption change curve and the water resources carrying capacity curve, obtain the prediction interval of the per capita comprehensive domestic water consumption during the planning period;

[0138] Based on the control upper limit value, the water consumption ratio and the prediction interval of the per capita comprehensive domestic water consumption, obtain the carrying interval of the planned population.

[0139] It should be noted that when the planning scenario is the planned population, the specific implementation process of predicting the carrying interval of the planned population at the healthy water resources carrying capacity level is as follows:

[0140] (1) Obtain the control upper limit value M of the total water consumption during the planning period in the upper-level planning study;

[0141] (2) According to the first mean value of the comprehensive domestic water consumption, the second mean value of the farmland irrigation water consumption, and the third mean value of the industrial water consumption in the obtained 15-year data, and determine the preliminary ratio a:b:c of the three;

[0142] (3) According to the ratio of the predicted values of various water consumption in the special plan, combined with the influence effects and measures of the foregoing water resources carrying capacity, such as adopting water-saving technologies or improving production levels to strictly control industrial water consumption, vigorously developing agriculture by improving farmland irrigation, etc., clarify the development directions of the three types of water consumption within the prediction interval, and appropriately adjust the preliminary ratio up and down to obtain the final ratio A:B:C;

[0143] (4) Calculate the predicted values of the comprehensive domestic water consumption, the farmland irrigation water consumption and the industrial water consumption according to the final ratio, that is, combine the ecological environment water consumption change curve and the water resources carrying capacity change curve in the foregoing embodiments, find the intersection value and calculate the specific value of the ecological environment water consumption by inverse calculation, and the fourth mean value of the ecological environment water consumption, and compare it with the average value of the foregoing three types of water volume to form the water consumption ratio A:B:C:D;

[0144] (5) Calculate the carrying interval [P1, P2] of the planned population:

[0145] P1 = M * [A / (A + B + C + D)] / the upper limit value of the healthy interval of the per capita comprehensive domestic water consumption index;

[0146] P2 = M * [A / (A + B + C + D)] / Lower limit value of the healthy range of per capita comprehensive domestic water consumption index;

[0147] The "healthy range of per capita comprehensive domestic water consumption index" in the above formula is the predicted range of per capita comprehensive domestic water consumption during the planning period obtained by coupling the change curve of per capita comprehensive domestic water consumption and the water resources carrying capacity curve in combination with the foregoing embodiments.

[0148] Referring to the above implementation process, the carrying ranges of agricultural land scale and industrial output value under the healthy water resources carrying capacity level can be predicted. For example, to calculate the carrying range [P3, P4] of agricultural land scale:

[0149] P3 = M * [B / (A + B + C + D)] / Upper limit value of the healthy range of water consumption per mu for farmland irrigation;

[0150] P4 = M * [B / (A + B + C + D)] / Lower limit value of the healthy range of water consumption per mu for farmland irrigation.

[0151] It should be noted that the embodiments of the present invention will also update the water resources carrying capacity curve in a rolling manner. After the updated data of each index in the latest year, replace the data of the earliest year in the fifteen years, update the water resources carrying capacity curve, and the implementation effect of the management measures in the latest year can be evaluated, and the change of water resources carrying capacity within the predicted range can be analyzed.

[0152] As can be seen from the above, on the one hand, the embodiments of the present invention can objectively reflect the influence of factors such as urban water resources status, water-saving policies, environmental protection policies, and social and economic development status on urban water consumption indicators, and guide administrative management decisions; on the other hand, based on the fact that the water resources carrying capacity is in a healthy state, the planned values such as the predicted population quantity, agricultural development land scale, and industrial output value target that can be carried can be predicted through urban water consumption indicators, breaking the traditional thinking of "determining water, production, and city based on people".

[0153] See Figure 3 , which is a schematic structural diagram of an embodiment of an index prediction system based on water resources carrying capacity provided by the present invention.

[0154] The second aspect of the embodiments of the present invention provides an index prediction system based on water resources carrying capacity for implementing the index prediction method based on water resources carrying capacity described in any one of the foregoing first aspects. The system includes:

[0155] An index selection module 11, configured to select a plurality of evaluation indexes from a candidate index set with the water resources carrying capacity of the region as the evaluation target;

[0156] A data acquisition module 12, configured to obtain the original values corresponding to each of the evaluation indicators within m evaluation periods, and perform normalization processing on the original values to obtain corresponding standard values; where m≥1;

[0157] An evaluation score module 13, configured to use a preset principal component analysis algorithm for the standard values to obtain the comprehensive scores of each evaluation period, and perform normalization processing to obtain the evaluation scores of the water resource carrying capacity within each evaluation period;

[0158] A first fitting module 14, configured to establish a water resource carrying capacity curve based on the evaluation scores, and perform linear fitting on the water resource carrying capacity curve to obtain a first fitting function;

[0159] A second fitting module 15, configured to establish a corresponding index change curve based on the original values of any specified evaluation indicator, and perform linear fitting on the index change curve to obtain a second fitting function;

[0160] An index prediction module 16, configured to obtain the intersection point of the first fitting function and the second fitting function, and obtain the prediction interval of the specified evaluation indicator within the planning period based on the healthy level interval of the water resource carrying capacity.

[0161] The system further includes:

[0162] A carrying interval prediction module, configured to predict the carrying interval under the corresponding planning scenario based on the prediction interval of the specified evaluation indicator within the planning period; where the planning scenario includes at least one of the planned population, the scale of agricultural land, and the industrial output value.

[0163] It should be noted that the index prediction system based on water resource carrying capacity provided in the second aspect embodiment of the present invention can implement all the processes of the index prediction method based on water resource carrying capacity described in any embodiment of the first aspect. The functions and the achieved technical effects of each module and unit in the system respectively correspond to the functions and the achieved technical effects of the index prediction method based on water resource carrying capacity described in any embodiment of the first aspect, and will not be elaborated here.

[0164] The third aspect embodiment of the present invention provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program; where the computer program controls the device where the computer-readable storage medium is located to execute the index prediction method based on water resource carrying capacity described in any embodiment of the first aspect when running.

[0165] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A water resources carrying capacity-based index prediction method, characterized in that: include: Taking the regional water resources carrying capacity as the evaluation target, multiple evaluation indicators are selected from the candidate indicator set; Obtaining the original value corresponding to each evaluation indicator in m evaluation periods, and performing standardization on the original value to obtain the corresponding standard value; wherein m≥1; A preset principal component analysis algorithm is used for the standard values ​​to obtain a comprehensive score for each evaluation period, and normalization is performed to obtain an evaluation score for the water resources carrying capacity in each evaluation period; Based on the evaluation score, a water resource carrying capacity curve is established, and a linear fitting is performed on the water resource carrying capacity curve to obtain a first fitting function; Based on the original value of any specified evaluation index, a corresponding index change curve is established, and a linear fitting is performed on the index change curve to obtain a second fitting function; The intersection point of the first fitting function and the second fitting function is obtained, and based on the health level interval of the water resources carrying capacity, the prediction interval of the specified evaluation index within the planning period is obtained.

2. The index prediction method based on water resources carrying capacity according to claim 1, characterized in that: The water resources carrying capacity of the region is taken as the evaluation target, and multiple evaluation indicators are selected from the candidate indicator set, including: Taking the water resources carrying capacity as the evaluation target, an initial evaluation index system is established; wherein the evaluation index system includes: a target layer, a criterion layer and an index layer; the candidate indicators in the index layer are derived from the candidate indicator set; Based on the hierarchical analysis algorithm, obtaining the weight of the candidate indicator; Based on the weights, the candidate indicators are screened to obtain a plurality of evaluation indicators.

3. The index prediction method based on water resources carrying capacity according to claim 1, characterized in that: The evaluation indicators include: water consumption for ecological environment, per capita comprehensive living water consumption, per mu water consumption for farmland irrigation or industrial water consumption per unit of output value.

4. The index prediction method based on water resources carrying capacity according to claim 1 is characterized in that: The method of establishing a corresponding indicator change curve based on the original value of any specified evaluation indicator includes: The original numerical value of any specified evaluation index is subjected to trend inversion processing to obtain an index conversion value, and a corresponding index change curve is established based on the index conversion value; wherein the trend inversion processing makes the index change curve and the water resources carrying capacity curve have a mirror-image change trend.

5. The index prediction method based on water resources carrying capacity according to claim 4 is characterized in that: The trend reversal process includes: When the designated evaluation index is a positive correlation index, the original value is processed based on a negative index normalization formula to obtain a corresponding index conversion value; When the designated evaluation index is a negative correlation index, the original value is processed based on a positive index normalization formula to obtain a corresponding index conversion value.

6. The index prediction method based on water resources carrying capacity according to claim 1, characterized in that: The obtaining of the intersection of the first fitting function and the second fitting function, and obtaining the prediction interval of the specified evaluation index within the planning period based on the health level interval of the water resources carrying capacity, includes: In the case where the designated evaluation index is a positive correlation index, the first index value of the intersection in the second fitting function is used as the minimum value of the prediction interval, and the second index value corresponding to the second fitting function when the first fitting function is the maximum value of the health level interval is used as the maximum value of the prediction interval; When the designated evaluation index is a negative correlation index, the third index value of the intersection in the second fitting function is taken as the maximum value of the prediction interval, and the fourth index value corresponding to the second fitting function when the first fitting function is the minimum value of the health level interval is taken as the minimum value of the prediction interval.

7. The index prediction method based on water resources carrying capacity according to claim 3 is characterized in that: The method further comprises: Based on the prediction interval of the specified evaluation index within the planning period, predict the carrying range under the corresponding planning scenario; wherein the planning scenario includes: at least one of the planned population, agricultural land scale, and industrial output value.

8. The index prediction method based on water resources carrying capacity according to claim 6 is characterized in that: When the planning scenario is a planned population, the carrying range of the planned population is predicted, including: Obtain the control upper limit of the total water consumption during the planning period, as well as the first mean of comprehensive domestic water consumption, the second mean of farmland irrigation water consumption, and the third mean of industrial water consumption; When the designated evaluation indicator is the ecological environment water consumption, based on the ecological environment water consumption change curve and the water resources carrying capacity curve, obtaining a fourth mean value of the ecological environment water consumption during the planning period; Based on the first mean value, the second mean value, the third mean value and the fourth mean value, obtaining a water consumption ratio; When the designated evaluation indicator is the per capita comprehensive domestic water consumption, based on the per capita comprehensive domestic water consumption change curve and the water resources carrying capacity curve, obtaining a prediction interval of the per capita comprehensive domestic water consumption during the planning period; Based on the predicted range of the control upper limit value, the water consumption ratio and the per capita comprehensive domestic water consumption, the carrying range of the planned population is obtained.

9. An indicator prediction system based on water resources carrying capacity, characterized in that: include: An indicator selection module is used to select multiple evaluation indicators from a candidate indicator set based on the regional water resources carrying capacity as the evaluation target; A data acquisition module is used to obtain the original value corresponding to each evaluation indicator in m evaluation time periods, and to perform standardization on the original value to obtain the corresponding standard value; wherein m≥1; An evaluation scoring module, used to use a preset principal component analysis algorithm for the standard value to obtain a comprehensive score for each evaluation period, and perform normalization processing to obtain an evaluation score for the water resources carrying capacity in each evaluation period; A first fitting module, used to establish a water resource carrying capacity curve based on the evaluation score, and perform linear fitting on the water resource carrying capacity curve to obtain a first fitting function; A second fitting module is used to establish a corresponding indicator change curve based on the original value of any specified evaluation indicator, and perform linear fitting on the indicator change curve to obtain a second fitting function; The indicator prediction module is used to obtain the intersection of the first fitting function and the second fitting function, and based on the health level interval of the water resources carrying capacity, obtain the prediction interval of the specified evaluation indicator within the planning period.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program; wherein, when the computer program is run, it controls the device where the computer-readable storage medium is located to execute the indicator prediction method based on water resource carrying capacity as described in any one of claims 1 to 8.

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