Urban potential assessment method and system based on multi-index fusion

Abstract

The invention relates to the technical field of city management, and discloses a city potential assessment method and system based on multi-index fusion, and the method comprises the steps: deploying a plurality of data source collection points for a target potential assessment city, carrying out the preprocessing of initial environment index data, and determining target environment index data; extracting target environment index data of the same type, constructing a target environment index data sequence, and calculating an environment index data pointing coefficient; based on a preset environment index data directivity coefficient sequence, carrying out set division on the environment index data directivity coefficient, and calculating an initial potential evaluation factor; potential evaluation identifiers are generated for the data source collection points; counting a positive number and a negative number, setting a potential evaluation optimization coefficient, optimizing an initial potential evaluation factor to obtain a target potential evaluation factor, and evaluating the city potential according to a plurality of environmental index data to ensure the accuracy and comprehensiveness of city potential evaluation. And a basis is provided for comprehensive development of cities and key city selection of operators and enterprises.

Description

Technical Field

[0001] The present invention relates to the technical field of urban management, and in particular, to a method and system for evaluating urban potential based on multi-index fusion. Background Art

[0002] In the current era of globalization and rapid industrialization, the process of urbanization is accelerating continuously, leading to problems such as environmental pollution, traffic congestion, and high housing prices in big cities, and then there appears a short-term phenomenon of counter-urbanization, which restricts the sustainable development of cities. Environmental pollution not only affects the ecological environment quality of cities, but also is directly related to the comprehensive development of cities. Therefore, accurately evaluating the urban environment and identifying its potential environmental risks and development bottlenecks are of great significance for the comprehensive development of cities and promoting the sustainable development of cities.

[0003] Traditional urban evaluation methods often focus on a single environmental parameter, ignoring the comprehensive impact of various environmental parameters on the comprehensive potential of cities. Moreover, traditional urban evaluation methods mainly rely on expert review, which is not only time-consuming and laborious, but also easily affected by subjective factors. Different experts may have different cognitions of the same parameter, resulting in inconsistent evaluation results and unable to ensure the comprehensiveness and accuracy of urban potential evaluation. Summary of the Invention

[0004] In view of this, the present invention proposes a method and system for evaluating urban potential based on multi-index fusion, aiming to solve the problems in the current technology that the urban potential cannot be evaluated according to environmental indicators in multiple aspects and the accuracy and comprehensiveness of urban potential evaluation cannot be ensured.

[0005] The present invention proposes a method for evaluating urban potential based on multi-index fusion, including:

[0006] Deploying multiple data source collection points for the target potential evaluation city, obtaining the initial environmental indicator data corresponding to each data source collection point, preprocessing the initial environmental indicator data, and determining the target environmental indicator data based on the preprocessing result;

[0007] Extracting the same type of target environmental indicator data from each data source collection point, constructing a sequence of target environmental indicator data, processing the sequence of target environmental indicator data, and calculating the environmental indicator data pointing coefficient of the sequence of target environmental indicator data based on the processing result;

[0008] Performing set partitioning on all environmental indicator data pointing coefficients based on a preset sequence of preset environmental indicator data pointing coefficients, and calculating the initial potential evaluation factor of the target potential evaluation city based on the partitioned sets, where the preset sequence of preset environmental indicator data pointing coefficients includes a first preset environmental indicator data pointing coefficient and a second preset environmental indicator data pointing coefficient;

[0009] Generate potential evaluation identifiers for the corresponding data source collection points according to the target environmental index data, where the potential evaluation identifiers include positive potential evaluation identifiers, negative potential evaluation identifiers, and pending potential evaluation identifiers;

[0010] Count the positive quantity of the positive potential evaluation identifiers, count the negative quantity of the negative potential evaluation identifiers, and set the potential evaluation optimization coefficient of the target potential evaluation city according to the positive quantity and the negative quantity;

[0011] Optimize the initial potential evaluation factors based on the potential evaluation optimization coefficient to obtain the target potential evaluation factors of the target potential evaluation city, where the target potential evaluation factors are the product values of the potential evaluation optimization coefficient and the initial potential evaluation factors;

[0012] When processing the target environmental index data sequence and calculating the environmental index data pointing coefficient of the target environmental index data sequence based on the processing result, it includes:

[0013] Determine the same target environmental index data from the target environmental index data sequence and construct multiple sub-target environmental index data sequences;

[0014] Count the number of initial data sequences of the sub-target environmental index data sequences;

[0015] Extract one target environmental index data from each of all the sub-target environmental index data sequences and calculate the sum value of the initial environmental index data;

[0016] Obtain the pre-calculated calculation data value, delete all sub-target environmental index data sequences smaller than the calculation data value, and count the number of final data sequences of the remaining sub-target environmental index data sequences;

[0017] Extract one target environmental index data from each of the remaining sub-target environmental index data sequences and calculate the sum value of the final environmental index data;

[0018] Calculate the environmental index data pointing coefficient of the target environmental index data sequence according to the number of initial data sequences, the sum value of the initial environmental index data, the number of final data sequences, and the sum value of the final environmental index data.

[0019] Further, when calculating the environmental index data pointing coefficient of the target environmental index data sequence according to the number of initial data sequences, the sum value of the initial environmental index data, the number of final data sequences, and the sum value of the final environmental index data, it includes:

[0020] Calculate the environmental index data pointing coefficient of the target environmental index data sequence according to the following formula:

[0021]

[0022] Among them, a is the environmental index data pointing coefficient of the target environmental index data sequence, r1 is the number of initial data sequences, r2 is the number of final data sequences, q1 is the sum value of the initial environmental index data, and q2 is the sum value of the final environmental index data.

[0023] Further, when partitioning all environmental index data pointing coefficients based on a preset sequence of preset environmental index data pointing coefficients and calculating the initial potential evaluation factor of the target potential evaluation city based on the partitioned sets, it includes:

[0024] When the environmental index data pointing coefficient is less than or equal to the first preset environmental index data pointing coefficient, the corresponding environmental index data pointing coefficient is partitioned into the first pointing coefficient set;

[0025] When the environmental index data pointing coefficient is greater than the first preset environmental index data pointing coefficient and less than the second preset environmental index data pointing coefficient, the corresponding environmental index data pointing coefficient is partitioned into the second pointing coefficient set;

[0026] When the environmental index data pointing coefficient is greater than or equal to the second preset environmental index data pointing coefficient, the corresponding environmental index data pointing coefficient is partitioned into the third pointing coefficient set;

[0027] Calculate the initial potential evaluation factor of the target potential evaluation city according to the first pointing coefficient set, the second pointing coefficient set, and the third pointing coefficient set.

[0028] Further, when calculating the initial potential evaluation factor of the target potential evaluation city according to the first pointing coefficient set, the second pointing coefficient set, and the third pointing coefficient set, it includes:

[0029] Calculate the first pointing coefficient mean value and the first pointing coefficient standard deviation of the first pointing coefficient set;

[0030] Determine the first pointing coefficient range corresponding to the first pointing coefficient set according to the first pointing coefficient mean value and the first pointing coefficient standard deviation;

[0031] Calculate the second pointing coefficient mean value and the second pointing coefficient standard deviation of the second pointing coefficient set;

[0032] Determine the second pointing coefficient range corresponding to the second pointing coefficient set according to the second pointing coefficient mean value and the second pointing coefficient standard deviation;

[0033] Calculate the mean and standard deviation of the third pointing coefficients of the third set of pointing coefficients;

[0034] Determine the corresponding third pointing coefficient range of the third set of pointing coefficients according to the mean of the third pointing coefficients and the standard deviation of the third pointing coefficients;

[0035] Compare the environmental index data pointing coefficients in each set of pointing coefficients with the corresponding pointing coefficient ranges. If the environmental index data pointing coefficients are within the corresponding pointing coefficient ranges, generate extraction pending marks for the environmental index data pointing coefficients;

[0036] Calculate the initial potential evaluation factor of the target potential evaluation city according to all the extraction pending marks.

[0037] Further, when determining the corresponding first pointing coefficient range of the first set of pointing coefficients according to the mean of the first pointing coefficients and the standard deviation of the first pointing coefficients, it includes:

[0038] Determine the corresponding first pointing coefficient range of the first set of pointing coefficients according to the following formula:

[0039] f(f1, f2) = (e1 × g1) ± (e2 × g2);

[0040] where f(f1, f2) is the corresponding first pointing coefficient range of the first set of pointing coefficients, e1 is the first calculation coefficient, e2 is the second calculation coefficient, g1 is the mean of the first pointing coefficients, g2 is the standard deviation of the first pointing coefficients, and e1 + e2 = 1, e1 > e2.

[0041] Further, when calculating the initial potential evaluation factor of the target potential evaluation city according to all the extraction pending marks, it includes:

[0042] Configure a third calculation coefficient for the first set of pointing coefficients, a fourth calculation coefficient for the second set of pointing coefficients, and a fifth calculation coefficient for the third set of pointing coefficients;

[0043] Calculate the initial potential evaluation factor of the target potential evaluation city according to the following formula:

[0044] k = h1 × m1 + h2 × m2 + h3 × m3;

[0045] where k is the initial potential evaluation factor of the target potential evaluation city, h1 is the third calculation coefficient, h2 is the fourth calculation coefficient, h3 is the fifth calculation coefficient, m1 is the number of extraction pending marks in the first set of pointing coefficients, m2 is the number of extraction pending marks in the second set of pointing coefficients, m3 is the number of extraction pending marks in the third set of pointing coefficients, and h1 > h2 > h3, h1 > 0, h2 > 0, h3 > 0.

[0046] Further, when generating the potential evaluation identifier for the corresponding data source collection point according to the target environmental index data, it includes:

[0047] Determine the standard environmental index data corresponding to each data source collection point;

[0048] Compare the target environmental index data with the standard environmental index data. If the target environmental index data are all less than or equal to the standard environmental index data, generate the positive potential evaluation identifier for the corresponding data source collection point;

[0049] If the target environmental index data are all greater than the standard environmental index data, generate the negative potential evaluation identifier for the corresponding data source collection point;

[0050] If there is one or more of the target environmental index data greater than the standard environmental index data, and there is one or more of the target environmental index data less than or equal to the standard environmental index data, generate the pending potential evaluation identifier for the corresponding data source collection point.

[0051] Further, when setting the potential evaluation optimization coefficient of the target potential evaluation city according to the positive quantity and the negative quantity, it includes:

[0052] Determine the quantity ratio n of the positive quantity and the negative quantity;

[0053] Preset the first preset potential evaluation optimization coefficient, the second preset potential evaluation optimization coefficient, and the third preset potential evaluation optimization coefficient;

[0054] When n ≤ 1, use the first preset potential evaluation optimization coefficient as the potential evaluation optimization coefficient of the target potential evaluation city;

[0055] When 1 < n ≤ 1.3, use the second preset potential evaluation optimization coefficient as the potential evaluation optimization coefficient of the target potential evaluation city;

[0056] When 1.3 < n, use the third preset potential evaluation optimization coefficient as the potential evaluation optimization coefficient of the target potential evaluation city.

[0057] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0058] Deploy multiple data source collection points in the target potential assessment city, preprocess the initial environmental indicator data, and determine the target environmental indicator data; extract the target environmental indicator data of the same type, construct a target environmental indicator data sequence, and calculate the environmental indicator data pointing coefficient; based on the preset environmental indicator data pointing coefficient sequence, perform set partitioning on the environmental indicator data pointing coefficient, and calculate the initial potential assessment factor; generate a potential assessment identifier for the data source collection point; count the positive quantity and negative quantity, set the potential assessment optimization coefficient, optimize the initial potential assessment factor, obtain the target potential assessment factor, and evaluate the city potential based on multiple environmental indicator data to ensure the accuracy and comprehensiveness of the city potential assessment, providing a basis for urban comprehensive development and operator enterprises to select key cities.

[0059] On the other hand, the present application also provides a city potential assessment system based on multi-index fusion, including:

[0060] A data determination module, configured to deploy multiple data source collection points in the target potential assessment city, obtain the initial environmental indicator data corresponding to each data source collection point, preprocess the initial environmental indicator data, and determine the target environmental indicator data based on the preprocessing result;

[0061] A first calculation module, configured to extract the target environmental indicator data of the same type from each data source collection point, construct a target environmental indicator data sequence, process the target environmental indicator data sequence, and calculate the environmental indicator data pointing coefficient of the target environmental indicator data sequence based on the processing result;

[0062] A second calculation module, configured to perform set partitioning on all the environmental indicator data pointing coefficients based on a preset environmental indicator data pointing coefficient sequence, and calculate the initial potential assessment factor of the target potential assessment city based on the partitioned sets, where the preset environmental indicator data pointing coefficient sequence includes a first preset environmental indicator data pointing coefficient and a second preset environmental indicator data pointing coefficient;

[0063] An identifier generation module, configured to generate a potential assessment identifier for the corresponding data source collection point according to the target environmental indicator data, where the potential assessment identifier includes a positive potential assessment identifier, a negative potential assessment identifier, and a pending potential assessment identifier;

[0064] An optimization setting module, configured to count the positive quantity of the positive potential assessment identifiers, count the negative quantity of the negative potential assessment identifiers, and set the potential assessment optimization coefficient of the target potential assessment city according to the positive quantity and the negative quantity;

[0065] A potential evaluation module, configured to optimize the initial potential evaluation factors based on the potential evaluation optimization coefficient to obtain the target potential evaluation factors of the target potential evaluation city, where the target potential evaluation factors are the product values of the potential evaluation optimization coefficient and the initial potential evaluation factors.

[0066] It can be understood that the above-provided urban potential evaluation system and method based on multi-index fusion have the same beneficial effects, which will not be elaborated here. Description of the Drawings

[0067] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0068] Figure 1 It is a schematic flow chart of the urban potential evaluation method based on multi-index fusion provided by an embodiment of the present invention;

[0069] Figure 2 It is a schematic structural diagram of the urban potential evaluation system based on multi-index fusion provided by an embodiment of the present invention. Detailed Embodiments

[0070] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. Hereinafter, the present invention will be described in detail with reference to the drawings and in combination with the embodiments.

[0071] As Figure 1 shown, in some embodiments of the present application, this embodiment provides an urban potential evaluation method based on multi-index fusion, including:

[0072] S110: Deploy a plurality of data source collection points for the target potential evaluation city, obtain the initial environmental index data corresponding to each data source collection point, preprocess the initial environmental index data, and determine the target environmental index data based on the preprocessing result;

[0073] In this embodiment, the number of deployed data source collection points is preferably 30, and can be specifically adjusted according to actual needs. When deploying the data source collection points, uniform deployment can be satisfied.

[0074] In this embodiment, the initial environmental index data includes pollutants such as PM2.5, PM10, sulfur dioxide, and nitrogen oxides.

[0075] In this embodiment, the preprocessing includes removing duplicate data and error data.

[0076] The beneficial effects of the above technical solution are as follows: The present invention deploys multiple data source collection points in the target potential assessment city, obtains the initial environmental index data corresponding to each data source collection point, which can meet the diversity and comprehensiveness of the data. By preprocessing the initial environmental index data and determining the target environmental index data based on the preprocessing results, the accuracy of the target environmental index data can be guaranteed, and potential assessment errors can be avoided.

[0077] S120: Extract the same type of target environmental index data from each data source collection point, construct a target environmental index data sequence, process the target environmental index data sequence, and calculate the environmental index data pointing coefficient of the target environmental index data sequence based on the processing results;

[0078] In some embodiments of the present application, when processing the target environmental index data sequence and calculating the environmental index data pointing coefficient of the target environmental index data sequence based on the processing results, it includes:

[0079] Determine the same target environmental index data from the target environmental index data sequence and construct multiple sub-target environmental index data sequences;

[0080] Count the number of initial data sequences of the sub-target environmental index data sequences;

[0081] Extract one target environmental index data from each of all the sub-target environmental index data sequences and calculate the sum of the initial environmental index data;

[0082] Obtain the pre-calculated calculation data value, delete all sub-target environmental index data sequences smaller than the calculation data value, and count the number of final data sequences of the remaining sub-target environmental index data sequences;

[0083] Extract one target environmental index data from each of the remaining sub-target environmental index data sequences and calculate the sum of the final environmental index data;

[0084] Calculate the environmental index data pointing coefficient of the target environmental index data sequence according to the number of initial data sequences, the sum of the initial environmental index data, the number of final data sequences, and the sum of the final environmental index data.

[0085] In this embodiment, if a target environmental index data sequence is constructed based on sulfur dioxide corresponding to each data source collection point, the sulfur dioxide here is the target environmental index data of the same type. If a target environmental index data sequence is constructed based on PM2.5, the PM2.5 here is the target environmental index data of the same type.

[0086] In this embodiment, if the target environmental index data sequence is sulfur dioxide, the same sulfur dioxide is extracted to construct multiple sub-target environmental index data sequences, such as {80 mg / m³, 80 mg / m³}, {85 mg / m³, 85 mg / m³, 85 mg / m³}, {100 mg / m³, 100 mg / m³}. Then the number of initial data sequences is 3. One target environmental index data, namely 80 mg / m³, 85 mg / m³, and 100 mg / m³, is extracted respectively. The calculated data value is the mean of the target environmental index data sequence. If it is 84 mg / m³, then the remaining sub-target environmental index data sequences are {85 mg / m³, 85 mg / m³, 85 mg / m³}, {100 mg / m³, 100 mg / m³}. The number of final data sequences is 2. One target environmental index data, namely 85 mg / m³ and 100 mg / m³, is extracted respectively. The above is shown by way of example, and it shall be subject to the actual situation.

[0087] The beneficial effects of the above technical solution are as follows: The present invention calculates the environmental index data pointing coefficient of the target environmental index data sequence based on the number of initial data sequences, the sum value of the initial environmental index data, the number of final data sequences, and the sum value of the final environmental index data, which can ensure the calculation accuracy of the environmental index data pointing coefficient and lay a foundation for the calculation of the initial potential evaluation factor for evaluating the initial potential of the target potential city.

[0088] In some embodiments of the present application, when calculating the environmental index data pointing coefficient of the target environmental index data sequence based on the number of initial data sequences, the sum value of the initial environmental index data, the number of final data sequences, and the sum value of the final environmental index data, it includes:

[0089] Calculate the environmental index data pointing coefficient of the target environmental index data sequence according to the following formula:

[0090]

[0091] Among them, a is the environmental index data pointing coefficient of the target environmental index data sequence, r1 is the number of initial data sequences, r2 is the number of final data sequences, q1 is the sum value of the initial environmental index data, and q2 is the sum value of the final environmental index data.

[0092] S130: Partition all environmental index data pointing coefficients based on a preset sequence of environmental index data pointing coefficients, and calculate an initial potential evaluation factor for the target potential evaluation city based on the partitioned sets, where the preset sequence of environmental index data pointing coefficients includes a first preset environmental index data pointing coefficient and a second preset environmental index data pointing coefficient;

[0093] In this embodiment, the preset sequence of environmental index data pointing coefficients is preset. The first preset environmental index data pointing coefficient is preferably 4, and the second preset environmental index data pointing coefficient is preferably 7. Specifically, it can also be adaptively adjusted according to actual requirements.

[0094] In some embodiments of the present application, when partitioning all environmental index data pointing coefficients based on a preset sequence of environmental index data pointing coefficients and calculating an initial potential evaluation factor for the target potential evaluation city based on the partitioned sets, it includes:

[0095] When the environmental index data pointing coefficient is less than or equal to the first preset environmental index data pointing coefficient, the corresponding environmental index data pointing coefficient is partitioned into the first pointing coefficient set;

[0096] When the environmental index data pointing coefficient is greater than the first preset environmental index data pointing coefficient and less than the second preset environmental index data pointing coefficient, the corresponding environmental index data pointing coefficient is partitioned into the second pointing coefficient set;

[0097] When the environmental index data pointing coefficient is greater than or equal to the second preset environmental index data pointing coefficient, the corresponding environmental index data pointing coefficient is partitioned into the third pointing coefficient set;

[0098] Calculate the initial potential evaluation factor for the target potential evaluation city according to the first pointing coefficient set, the second pointing coefficient set, and the third pointing coefficient set.

[0099] The beneficial effects of the above technical solution are: The present invention partitions the first pointing coefficient set, the second pointing coefficient set, and the third pointing coefficient set according to the environmental index data pointing coefficient, the first preset environmental index data pointing coefficient, and the second preset environmental index data pointing coefficient, ensuring the accuracy of the set partitioning and providing a basis for calculating the initial potential evaluation factor of the target potential evaluation city.

[0100] In some embodiments of the present application, when calculating the initial potential evaluation factor for the target potential evaluation city according to the first pointing coefficient set, the second pointing coefficient set, and the third pointing coefficient set, it includes:

[0101] Calculate the mean value and standard deviation of the first pointing coefficients of the first set of pointing coefficients;

[0102] Determine the range of the first pointing coefficients corresponding to the first set of pointing coefficients according to the mean value and standard deviation of the first pointing coefficients;

[0103] Calculate the mean value and standard deviation of the second pointing coefficients of the second set of pointing coefficients;

[0104] Determine the range of the second pointing coefficients corresponding to the second set of pointing coefficients according to the mean value and standard deviation of the second pointing coefficients;

[0105] Calculate the mean value and standard deviation of the third pointing coefficients of the third set of pointing coefficients;

[0106] Determine the range of the third pointing coefficients corresponding to the third set of pointing coefficients according to the mean value and standard deviation of the third pointing coefficients;

[0107] Compare the environmental index data pointing coefficients in each set of pointing coefficients with the corresponding range of pointing coefficients. If the environmental index data pointing coefficient is within the corresponding range of pointing coefficients, generate a to-be-extracted mark for the environmental index data pointing coefficient;

[0108] Calculate the initial potential evaluation factor of the target potential evaluation city according to all the to-be-extracted marks.

[0109] In this embodiment, each set of pointing coefficients corresponds to a range of pointing coefficients. Each range of pointing coefficients includes a left boundary value and a right boundary value, and the left boundary value is less than the right boundary value. When the environmental index data pointing coefficient is greater than or equal to the left boundary value and less than or equal to the right boundary value, it is determined that the environmental index data pointing coefficient is within the corresponding range of pointing coefficients.

[0110] In some embodiments of the present application, when determining the range of the first pointing coefficients corresponding to the first set of pointing coefficients according to the mean value and standard deviation of the first pointing coefficients, it includes:

[0111] Determine the range of the first pointing coefficients corresponding to the first set of pointing coefficients according to the following formula:

[0112] f(f1, f2) = (e1 × g1) ± (e2 × g2);

[0113] Wherein, f(f1, f2) is the range of the first pointing coefficients corresponding to the first set of pointing coefficients, e1 is the first calculation coefficient, e2 is the second calculation coefficient, g1 is the mean value of the first pointing coefficients, g2 is the standard deviation of the first pointing coefficients, and e1 + e2 = 1, e1 > e2.

[0114] In this embodiment, f1 is the left boundary value and f2 is the right boundary value.

[0115] In this embodiment, the determination methods of the second pointing coefficient range and the third pointing coefficient range are the same as that of the first pointing coefficient range, and will not be repeated here.

[0116] In some embodiments of the present application, when calculating the initial potential evaluation factor of the target potential evaluation city according to all the to-be-extracted tags, it includes:

[0117] Configure a third calculation coefficient for the first pointing coefficient set, a fourth calculation coefficient for the second pointing coefficient set, and a fifth calculation coefficient for the third pointing coefficient set;

[0118] Calculate the initial potential evaluation factor of the target potential evaluation city according to the following formula:

[0119] k = h1×m1 + h2×m2 + h3×m3;

[0120] Wherein, k is the initial potential evaluation factor of the target potential evaluation city, h1 is the third calculation coefficient, h2 is the fourth calculation coefficient, h3 is the fifth calculation coefficient, m1 is the number of to-be-extracted tags in the first pointing coefficient set, m2 is the number of to-be-extracted tags in the second pointing coefficient set, m3 is the number of to-be-extracted tags in the third pointing coefficient set, and h1 > h2 > h3, h1 > 0, h2 > 0, h3 > 0.

[0121] The beneficial effects of the above technical solution are: The present invention calculates the initial potential evaluation factor of the target potential evaluation city according to all the to-be-extracted tags, ensuring the calculation accuracy of the initial potential evaluation factor, realizing the primary potential evaluation of the target potential evaluation city, without manual participation, and avoiding subjectivity and one-sidedness.

[0122] S140: Generate a potential evaluation identifier for the corresponding data source collection point according to the target environmental index data, wherein the potential evaluation identifier includes a positive potential evaluation identifier, a negative potential evaluation identifier, and a pending potential evaluation identifier;

[0123] In some embodiments of the present application, when generating a potential evaluation identifier for the corresponding data source collection point according to the target environmental index data, it includes:

[0124] Determine the standard environmental index data corresponding to each data source collection point;

[0125] Compare the target environmental index data with the standard environmental index data. If all of the target environmental index data are less than or equal to the standard environmental index data, generate the positive potential evaluation identifier for the corresponding data source collection point;

[0126] If all of the target environmental index data are greater than the standard environmental index data, generate the negative potential evaluation identifier for the corresponding data source collection point;

[0127] If there is one or more of the target environmental index data greater than the standard environmental index data and there is one or more of the target environmental index data less than or equal to the standard environmental index data, generate the pending potential evaluation identifier for the corresponding data source collection point.

[0128] In this embodiment, the standard environmental index data and the target environmental index data are in one-to-one correspondence, and the standard environmental index data can be specifically set according to the data source collection point.

[0129] S150: Count the positive quantity of the positive potential evaluation identifiers, count the negative quantity of the negative potential evaluation identifiers, and set the potential evaluation optimization coefficient of the target potential evaluation city according to the positive quantity and the negative quantity;

[0130] In some embodiments of the present application, when setting the potential evaluation optimization coefficient of the target potential evaluation city according to the positive quantity and the negative quantity, it includes:

[0131] Determine the quantity ratio n of the positive quantity and the negative quantity;

[0132] Preset a first preset potential evaluation optimization coefficient, a second preset potential evaluation optimization coefficient, and a third preset potential evaluation optimization coefficient;

[0133] When n ≤ 1, then use the first preset potential evaluation optimization coefficient as the potential evaluation optimization coefficient of the target potential evaluation city;

[0134] When 1 < n ≤ 1.3, then use the second preset potential evaluation optimization coefficient as the potential evaluation optimization coefficient of the target potential evaluation city;

[0135] When 1.3 < n, then use the third preset potential evaluation optimization coefficient as the potential evaluation optimization coefficient of the target potential evaluation city.

[0136] In this embodiment, the first preset potential evaluation optimization coefficient is preferably 0.9, the second preset potential evaluation optimization coefficient is preferably 1.1, and the third preset potential evaluation optimization coefficient is preferably 1.2.

[0137] The beneficial effects of the above technical solution are as follows: According to the quantity ratio, the present invention selects the corresponding preset potential evaluation optimization coefficient, thereby realizing the dynamic optimization of the initial potential evaluation factor, ensuring the optimization accuracy, and further ensuring the accuracy of urban potential evaluation.

[0138] S160: Optimize the initial potential evaluation factor based on the potential evaluation optimization coefficient to obtain the target potential evaluation factor of the target potential evaluation city, where the target potential evaluation factor is the product value of the potential evaluation optimization coefficient and the initial potential evaluation factor.

[0139] The beneficial effects of the above technical solution are as follows: By determining the target potential evaluation factor of the target potential evaluation city, the present invention ensures the accuracy and comprehensiveness of urban potential evaluation. When the target potential evaluation factor is larger, the potential of the target potential evaluation city is greater, providing a basis for urban comprehensive development and operator enterprises to select key cities.

[0140] As Figure 2 shown, in another preferred embodiment based on the above embodiment, the present embodiment provides an urban potential evaluation system based on multi-index fusion, including:

[0141] A data determination module, configured to deploy multiple data source collection points for the target potential evaluation city, obtain the initial environmental index data corresponding to each data source collection point, preprocess the initial environmental index data, and determine the target environmental index data based on the preprocessing result;

[0142] A first calculation module, configured to extract the same type of target environmental index data from each data source collection point, construct a target environmental index data sequence, process the target environmental index data sequence, and calculate the environmental index data pointing coefficient of the target environmental index data sequence based on the processing result;

[0143] A second calculation module, configured to perform set partitioning on all environmental index data pointing coefficients based on a preset preset environmental index data pointing coefficient sequence, and calculate the initial potential evaluation factor of the target potential evaluation city based on the partitioned sets, where the preset environmental index data pointing coefficient sequence includes a first preset environmental index data pointing coefficient and a second preset environmental index data pointing coefficient;

[0144] An identification generation module, configured to generate a potential evaluation identification for the corresponding data source collection point according to the target environmental index data, where the potential evaluation identification includes a positive potential evaluation identification, a negative potential evaluation identification, and a pending potential evaluation identification;

[0145] An optimization setting module, configured to count the positive quantity of the positive potential evaluation identifiers, count the negative quantity of the negative potential evaluation identifiers, and set a potential evaluation optimization coefficient for the target potential evaluation city according to the positive quantity and the negative quantity;

[0146] A potential evaluation module, configured to optimize the initial potential evaluation factors based on the potential evaluation optimization coefficient to obtain the target potential evaluation factors of the target potential evaluation city, where the target potential evaluation factors are the product values of the potential evaluation optimization coefficient and the initial potential evaluation factors.

[0147] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0148] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowcharts and / or block diagrams can also be implemented. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0149] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0150] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide means for implementing the functions specified in Figure 1 one process or multiple processes and / or blocksFigure 1 Steps of the functions specified in one or more boxes.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the specific implementation manners of the present invention can still be modified or equivalently replaced, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for evaluating urban potential based on multi-index fusion, characterized in that: include: Deploy multiple data source collection points for the target potential assessment city, obtain initial environmental indicator data corresponding to each data source collection point, pre-process the initial environmental indicator data, and determine the target environmental indicator data based on the pre-processing results; Extracting the same type of target environmental indicator data from each data source collection point, and constructing a target environmental indicator data series, processing the target environmental indicator data series, and calculating the environmental indicator data pointing coefficient of the target environmental indicator data series based on the processing result; Based on a preset environmental indicator data pointing coefficient sequence, all environmental indicator data pointing coefficients are divided into sets, and an initial potential assessment factor of the target potential assessment city is calculated based on the divided sets, wherein the preset environmental indicator data pointing coefficient sequence includes a first preset environmental indicator data pointing coefficient and a second preset environmental indicator data pointing coefficient; Generating a potential assessment identifier for the corresponding data source collection point according to the target environmental indicator data, wherein the potential assessment identifier includes a positive potential assessment identifier, a negative potential assessment identifier, and a pending potential assessment identifier; Counting the positive number of the positive potential assessment identifiers, counting the negative number of the negative potential assessment identifiers, and setting a potential assessment optimization coefficient of the target potential assessment city according to the positive number and the negative number; Optimizing the initial potential assessment factor based on the potential assessment optimization coefficient to obtain a target potential assessment factor of the target potential assessment city, wherein the target potential assessment factor is the product value of the potential assessment optimization coefficient and the initial potential assessment factor; When the target environmental indicator data series is processed and the environmental indicator data pointing coefficient of the target environmental indicator data series is calculated based on the processing result, it includes: Determine the same target environmental indicator data from the target environmental indicator data series, and construct a plurality of sub-target environmental indicator data series; Count the initial data series of the sub-goal environmental indicator data series; Extract one target environmental indicator data from all sub-target environmental indicator data series respectively, and calculate the initial environmental indicator data and value; Obtaining a pre-calculated calculation data value, deleting all sub-target environmental indicator data series that are less than the calculation data value, and counting the number of final data series of the remaining sub-target environmental indicator data series; Extract one target environmental indicator data from the remaining sub-target environmental indicator data series respectively, and calculate the final environmental indicator data and value; The environmental indicator data pointing coefficient of the target environmental indicator data series is calculated according to the number of the initial data series, the initial environmental indicator data and value, the number of the final data series and the final environmental indicator data and value.

2. The urban potential assessment method based on multi-index fusion according to claim 1 is characterized in that: When calculating the environmental indicator data pointing coefficient of the target environmental indicator data series according to the number of the initial data series, the initial environmental indicator data and value, the number of the final data series and the final environmental indicator data and value, it includes: The environmental indicator data pointing coefficient of the target environmental indicator data series is calculated according to the following formula: Among them, a is the environmental indicator data pointing coefficient of the target environmental indicator data series, r1 is the number of initial data series, r2 is the number of final data series, q1 is the initial environmental indicator data and value, and q2 is the final environmental indicator data and value.

3. The urban potential assessment method based on multi-index fusion according to claim 1 is characterized in that: When all environmental indicator data pointing coefficients are divided into sets based on a preset environmental indicator data pointing coefficient sequence, and an initial potential assessment factor of the target potential assessment city is calculated based on the divided sets, it includes: When the environmental indicator data pointing coefficient is less than or equal to the first preset environmental indicator data pointing coefficient, the corresponding environmental indicator data pointing coefficient is divided into a first pointing coefficient set; When the environmental indicator data pointing coefficient is greater than the first preset environmental indicator data pointing coefficient and less than the second preset environmental indicator data pointing coefficient, the corresponding environmental indicator data pointing coefficient is divided into a second pointing coefficient set; When the environmental indicator data pointing coefficient is greater than or equal to the second preset environmental indicator data pointing coefficient, the corresponding environmental indicator data pointing coefficient is divided into a third pointing coefficient set; An initial potential assessment factor of the target potential assessment city is calculated according to the first pointing coefficient set, the second pointing coefficient set and the third pointing coefficient set.

4. The urban potential assessment method based on multi-index fusion according to claim 3 is characterized in that: When calculating the initial potential assessment factor of the target potential assessment city according to the first pointing coefficient set, the second pointing coefficient set, and the third pointing coefficient set, it includes: Calculate a first directivity coefficient mean and a first directivity coefficient standard deviation of the first directivity coefficient set; Determine a first directivity coefficient range corresponding to the first directivity coefficient set according to the first directivity coefficient mean and the first directivity coefficient standard deviation; Calculate the second directivity coefficient mean and the second directivity coefficient standard deviation of the second directivity coefficient set; Determining a second directivity coefficient range corresponding to the second directivity coefficient set according to the second directivity coefficient mean and the second directivity coefficient standard deviation; Calculating a third directivity coefficient mean and a third directivity coefficient standard deviation of the third directivity coefficient set; Determining a third directivity coefficient range corresponding to the third directivity coefficient set according to the third directivity coefficient mean and the third directivity coefficient standard deviation; Compare the environmental indicator data pointing coefficient in each pointing coefficient set with the corresponding pointing coefficient range, and if the environmental indicator data pointing coefficient is within the corresponding pointing coefficient range, generate a to-be-extracted mark for the environmental indicator data pointing coefficient; The initial potential assessment factor of the target potential assessment city is calculated based on all the marks to be extracted.

5. The urban potential assessment method based on multi-index fusion according to claim 4 is characterized in that: When determining a first directivity coefficient range corresponding to the first directivity coefficient set according to the first directivity coefficient mean and the first directivity coefficient standard deviation, it includes: The first directivity coefficient range corresponding to the first directivity coefficient set is determined according to the following formula: f(f1, f2)=(e1×g1)±(e2×g2); Among them, f(f1, f2) is the first directivity coefficient range corresponding to the first directivity coefficient set, e1 is the first calculation coefficient, e2 is the second calculation coefficient, g1 is the first directivity coefficient mean, g2 is the first directivity coefficient standard deviation, and e1+e2=1, e1>e2.

6. The urban potential assessment method based on multi-index fusion according to claim 5 is characterized in that: When calculating the initial potential assessment factor of the target potential assessment city according to all the to-be-extracted marks, it includes: configuring a third calculation coefficient for the first directivity coefficient set, configuring a fourth calculation coefficient for the second directivity coefficient set, and configuring a fifth calculation coefficient for the third directivity coefficient set; The initial potential assessment factor of the target potential assessment city is calculated according to the following formula: k = h1×m1+h2×m2+h3×m3; Among them, k is the initial potential assessment factor of the target potential assessment city, h1 is the third calculation coefficient, h2 is the fourth calculation coefficient, h3 is the fifth calculation coefficient, m1 is the number of marks to be extracted in the first pointing coefficient set, m2 is the number of marks to be extracted in the second pointing coefficient set, m3 is the number of marks to be extracted in the third pointing coefficient set, and h1>h2>h3, h1>0, h2>0, h3>0.

7. The urban potential assessment method based on multi-index fusion according to claim 1 is characterized in that: When generating a potential assessment mark for a corresponding data source collection point according to the target environmental indicator data, it includes: Determine the standard environmental indicator data corresponding to each data source collection point; Comparing the target environmental index data with the standard environmental index data, if the target environmental index data are both less than or equal to the standard environmental index data, generating the positive potential assessment mark for the corresponding data source collection point; If the target environmental indicator data are all greater than the standard environmental indicator data, then generating the negative potential assessment mark for the corresponding data source collection point; If one or more of the target environmental indicator data are greater than the standard environmental indicator data, and one or more of the target environmental indicator data are less than or equal to the standard environmental indicator data, the pending potential assessment identifier is generated for the corresponding data source collection point.

8. The urban potential assessment method based on multi-index fusion according to claim 1 is characterized in that: When setting the potential assessment optimization coefficient of the target potential assessment city according to the positive quantity and the negative quantity, it includes: Determine a quantity ratio n of the positive quantity and the negative quantity; Presetting a first preset potential assessment optimization coefficient, a second preset potential assessment optimization coefficient, and a third preset potential assessment optimization coefficient; When n≤1, the first preset potential assessment optimization coefficient is used as the potential assessment optimization coefficient of the target potential assessment city; When 1<n≤1.3, the second preset potential assessment optimization coefficient is used as the potential assessment optimization coefficient of the target potential assessment city; When 1.3<n, the third preset potential assessment optimization coefficient is used as the potential assessment optimization coefficient of the target potential assessment city.

9. A city potential assessment system based on multi-index fusion, applied to the city potential assessment method based on multi-index fusion as claimed in any one of claims 1 to 8, characterized in that: include: A data determination module is used to deploy multiple data source collection points for the target potential assessment city, obtain initial environmental indicator data corresponding to each data source collection point, pre-process the initial environmental indicator data, and determine the target environmental indicator data based on the pre-processing result; A first calculation module is used to extract the same type of target environmental indicator data from each data source collection point, and construct a target environmental indicator data series, process the target environmental indicator data series, and calculate the environmental indicator data pointing coefficient of the target environmental indicator data series based on the processing result; A second calculation module is used to divide all environmental indicator data pointing coefficients into sets based on a preset environmental indicator data pointing coefficient sequence, and calculate the initial potential assessment factor of the target potential assessment city based on the divided sets, wherein the preset environmental indicator data pointing coefficient sequence includes a first preset environmental indicator data pointing coefficient and a second preset environmental indicator data pointing coefficient; An identification generation module, used to generate a potential assessment identification for the corresponding data source collection point according to the target environmental indicator data, wherein the potential assessment identification includes a positive potential assessment identification, a negative potential assessment identification and a pending potential assessment identification; An optimization setting module, used to count the positive number of the positive potential evaluation identifiers, count the negative number of the negative potential evaluation identifiers, and set the potential evaluation optimization coefficient of the target potential evaluation city according to the positive number and the negative number; A potential assessment module is used to optimize the initial potential assessment factor based on the potential assessment optimization coefficient to obtain a target potential assessment factor of the target potential assessment city, wherein the target potential assessment factor is the product value of the potential assessment optimization coefficient and the initial potential assessment factor.

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