Construction project planning site selection and land pre-auditing evaluation method based on multi-factor analysis

Abstract

The invention discloses a construction project planning site selection and land use pre-examination evaluation method based on multi-factor analysis, and particularly relates to the technical field of construction project planning, and the method comprises the following steps: firstly, collecting basic factor, environmental factor and social factor data related to a project; then independently analyzing the basic factor data, calculating deviation values and combining the deviation values to form a deviation vector; carrying out predictive quantitative evaluation on the environmental factor data, and calculating an environmental influence index; performing qualitative analysis on the social factor data to generate a social influence index; and finally, inputting the basic factor deviation vector, the environmental influence index and the social influence index into a pre-trained multi-factor decision model, classifying layer by layer, and outputting an evaluation result to provide a scientific basis for site selection. According to the method, the limitation of a traditional single-factor decision-making method is overcome, the suitability of the site selection area is comprehensively evaluated, the result is more persuasive due to the interpretability and transparency of the decision-making process, and project approval and communication are facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of construction project planning, and more specifically, to a method for evaluating the site selection and pre - examination of land use for construction projects based on multi - factor analysis. Background Art

[0002] Currently, with the rapid development of social economy, the planning and implementation of various construction projects have a more significant impact on regional development. However, in the process of project site selection and pre - examination of land use, traditional decision - making methods usually only focus on single factors, such as economic benefits or infrastructure conditions, while ignoring comprehensive factors such as environmental protection and social adaptability.

[0003] Traditional site - selection methods often make decisions based on a single dimension (such as land cost, traffic convenience), and cannot comprehensively evaluate the suitability of project site selection. This way may lead to problems such as environmental damage and social conflicts in the later operation of the project, thus increasing economic and management costs. Existing environmental assessment methods are usually limited to static analysis of current data and lack predictive consideration of future environmental change trends. In site - selection decisions, factors such as social acceptance and cultural adaptability are often difficult to quantify and are not systematically incorporated into the decision - making model. The lack of comprehensive consideration of dimensions such as community response, employment contribution, and social fairness may lead to public doubts or even resistance in the later stage of project site selection. Therefore, the present invention proposes a method for evaluating the site selection and pre - examination of land use for construction projects based on multi - factor analysis to solve the above problems. Summary of the Invention

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A method for evaluating the site selection and pre - examination of land use for construction projects based on multi - factor analysis, comprising the following steps:

[0006] Within a preset evaluation time window, collect preset basic factor data, environmental factor data, and social factor data related to the construction project;

[0007] Independently analyze the basic factor data to obtain the deviation values of each basic factor, and combine the deviation values to form a basic factor deviation vector;

[0008] Conduct predictive quantitative evaluation on the environmental factor data, comprehensively form an environmental impact index based on each environmental impact factor, and at the same time conduct qualitative analysis on the social factor data to generate a social impact index;

[0009] Input the basic factor deviation vector, environmental impact index, and social impact index into a pre - trained multi - factor decision - making model, output the evaluation result of multi - factor decision - making, and obtain the suitability level of the proposed site - selection area.

[0010] In a preferred embodiment, the pre-trained multi-factor decision-making model refers to a decision tree model.

[0011] In a preferred embodiment, the independent analysis of the basic factor data to obtain the deviation values of each basic factor means:

[0012] Number each basic factor in the basic factor data as i, with the total number being n, and obtain the difference value ΔX between the actual data of the basic factor corresponding to the number i and the preset ideal data i :

[0013] X ideal，i represents the ideal data of the basic factor corresponding to the number i, and X act，i represents the actual data of the basic factor corresponding to the number i, and ∈ is a preset constant value to prevent the denominator from being zero;

[0014] Then calculate the weight coefficient of the difference value ΔX i :

[0015] α is a regulation factor, with a value range of 0 to 1, and w i represents the weight coefficient of the basic factor corresponding to the number i;

[0016] Then multiply the difference value ΔX i corresponding to the basic factor i by the weight coefficient w i to obtain the deviation value of the basic factor i.

[0017] In a preferred embodiment, combining the deviation values to form a basic factor deviation vector means:

[0018] First, perform an amplification process on the deviation value of the basic factor i, and then summarize the results after the amplification process in the order of the number i to obtain the basic factor deviation vector. The amplification process steps are:

[0019] k represents a preset linear coefficient for gentle amplification, m represents a preset non-linear coefficient for exponential amplification, Threshold i represents the deviation threshold corresponding to the basic factor i, PC i represents the deviation value of the basic factor i, and f(PC i ) represents the deviation value amplification processing function.

[0020] In a preferred embodiment, the predictive quantitative evaluation of the environmental factor data and the comprehensive formation of an environmental impact index based on each environmental impact factor means:

[0021] First, obtain the predicted data YC of R environmental impact factors through a preset prediction model r, r represents the number of environmental impact factors, and the impact coefficient W obtained from expert evaluation is acquired r , first calculate the comprehensive environmental impact value:

[0022] E represents the comprehensive environmental impact value, p represents a preset non-linear adjustment parameter, which is used to control the non-linearity degree of the impact coefficient;

[0023] Then introduce a penalty mechanism to calculate the total penalty value:

[0024] P r represents the monomer penalty value corresponding to the environmental impact factor r, CF represents the total penalty value, P r =max(Y r , YC r ); Y r represents the limit value corresponding to the environmental impact factor r;

[0025] The calculation formula of the environmental impact index is:

[0026] HI=E·(1-β·CF); β represents a preset penalty intensity coefficient, and its value ranges from [0,1], HI represents the environmental impact index.

[0027] In a preferred embodiment, performing qualitative analysis on social factor data to generate a social impact index refers to:

[0028] Obtain the qualitative survey results of each social factor data, then summarize them into a qualitative survey vector, and calculate the Euclidean distance between the qualitative survey vector and a preset qualitative standard vector. The value of the social impact index is the same as the value of the Euclidean distance.

[0029] In a preferred embodiment, the evaluation result of multi-factor decision-making is calculated through a decision tree model and classified layer by layer according to a preset index threshold to output an optimal decision.

[0030] In a preferred embodiment, the evaluation result includes one of the following levels: high suitability, medium suitability, and low suitability.

[0031] Advantages of the present invention:

[0032] The method for evaluating the planning site selection and pre - examination of land use for construction projects based on multi - factor analysis proposed by the present invention comprehensively considers basic factors, environmental factors, and social factors, and conducts comprehensive analysis in combination with the decision - tree model, which can significantly improve the scientificity, reliability, and operability of the site - selection decision. By incorporating the basic - factor deviation vector, environmental - impact index, and social - impact index into the comprehensive analysis, the present invention overcomes the limitations of traditional single - factor decision - making methods and comprehensively evaluates the suitability of the site - selection area. It realizes the integrated analysis from the suitability of basic conditions, the acceptability of environmental impacts to social adaptability, ensuring that the site - selection decision is more scientific and reasonable.

[0033] The present invention can identify the environmental risks and potential future ecological changes in the site - selection area by introducing a prediction model to dynamically and quantitatively evaluate environmental factors. The non - linear adjustment parameter and penalty mechanism further highlight the impact of factors exceeding the environmental limit value on the evaluation result, thus effectively supporting the balance between environmental protection and project development. It supports the precise development of ecologically sensitive areas and promotes the rational utilization of resources and the sustainable development of the environment. By transforming qualitative analysis into a social - impact index, the present invention converts difficult - to - quantify social factors (such as community acceptance, cultural adaptability, employment contribution, etc.) into comparable numerical values, solving the problem of difficult quantification of social impacts in traditional methods. The social - impact index measures the difference between the actual and ideal states through the Euclidean distance, providing a clear optimization direction for decision - makers and reducing the risk of social contradictions and public opposition.

[0034] The nodes and thresholds of the decision - making model can be adjusted according to the actual needs of different projects, ensuring that the method has strong generality and flexibility. The present invention outputs clear evaluation results such as high suitability, medium suitability, and low suitability through the decision - tree model, providing an intuitive and clear reference for the site - selection decision. The interpretability and transparency of the decision - making process make the results more persuasive and facilitate project approval and communication. Through methods such as data standardization, deviation - vector generation, and index calculation, the present invention quickly converts multi - dimensional data into quantifiable indicators, significantly improving the evaluation efficiency. By focusing on environmental over - limit values and social risks, potential site - selection problems are avoided in advance, reducing the costs of later adjustment and error correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] For the convenience of those skilled in the art to understand, the present invention will be further described below in conjunction with the drawings;

[0036] Figure 1 It is the schematic diagram of the method for evaluating the planning site selection and pre - examination of land use for construction projects based on multi - factor analysis in the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0037] 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 based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0038] Referring to Figure 1 the following embodiments are obtained:

[0039] Embodiment 1: Traditional site selection methods often rely on a single factor (such as economy or land conditions) for evaluation, ignoring the complex impacts of the environment and society, which may lead to site selection failure. The site selection of construction projects involves multiple factors (foundation, environment, society), and these factors interact with each other and are difficult to directly quantify. This method conducts a quantitative comprehensive analysis of factors in different dimensions through deviation vectors, environmental impact indices, and social impact indices, providing a systematic solution for complex problems. With the increasing importance of ecological protection and social stability, the site selection of construction projects must consider environmental and social impacts. Traditional methods often ignore these soft factors, resulting in obstacles in the later implementation of the project. The present invention helps to avoid environmental and social risks by predicting environmental impacts and qualitatively analyzing social factors.

[0040] The decision tree model can clearly display the decision-making logic through layer-by-layer classification, making the evaluation results more transparent and interpretable, and facilitating decision-makers to understand and apply. The construction of modern projects needs to meet the requirements of sustainable development. Through comprehensive multi-factor analysis, the present invention can effectively balance economic, environmental, and social goals, ensuring that project planning complies with the principles of sustainable development.

[0041] The present invention proposes a method for evaluating the planning and site selection of construction projects and pre-examination of land use based on multi-factor analysis, including the following steps:

[0042] Within a preset evaluation time window, collect the preset basic factor data, environmental factor data, and social factor data related to the construction project; data collection is the basis of the entire evaluation process, ensuring that various factors affecting project site selection and land use suitability can be comprehensively and accurately incorporated into the analysis scope. The basic factor data provides the hard conditions for project site selection, such as land quality, traffic convenience, etc. The environmental factor data reflects the impact of the selected site on the ecosystem, natural resources, and environmental protection. The social factor data reveals the potential impact of the site selection on society, such as cultural conflicts, community acceptance, etc. Project site selection is a multi-dimensional issue, and simply considering a single factor will lead to decision-making errors. Comprehensively collecting basic, environmental, and social factor data can provide reliable data support for subsequent multi-factor analysis. Collecting data within the preset time window ensures the timeliness and consistency of the data, avoiding the impact of data lag or inconsistent data at different time points on the evaluation results.

[0043] Conduct an independent analysis of the basic factor data to obtain the deviation values of each basic factor, and combine the deviation values to form a basic factor deviation vector; the independent analysis of the basic factors enables each basic factor to be individually evaluated for its difference from the ideal state (deviation value), thereby identifying whether the basic conditions for project site selection meet the standards. Combining the deviation values into a deviation vector can quantify the comprehensive deviation of multiple basic factors, forming a clear input for subsequent decision-making model analysis. Different basic factors have different impacts on the project. By independently analyzing the deviation values, key deficiencies can be accurately identified and used as a basis for decision-making. The way of combining deviation values with weights and amplification processing can highlight the influence degree of important basic factors, ensuring that the decision-making results are more reasonable and scientific.

[0044] Conduct a predictive quantitative assessment of the environmental factor data, form an environmental impact index based on the comprehensive environmental impact factors, and at the same time conduct a qualitative analysis of the social factor data to generate a social impact index; Predictive quantitative assessment of environmental factor data: By quantitatively evaluating environmental impact factors (such as air quality, water pollution, noise level, etc.), a comprehensive environmental impact index is obtained to quantify environmental risks and the environmental suitability of the site selection. Predictive assessment can identify potential environmental problems that may occur in the future, avoiding project failure due to environmental unsustainability. Qualitative analysis of social factor data: Social factors (such as community reaction, cultural conflict, etc.) are usually difficult to directly quantify numerically. By generating a social impact index through qualitative analysis, the social acceptance and suitability of project site selection can be more intuitively evaluated. Environmental assessment ensures that project site selection meets requirements such as ecological protection and rational utilization of resources, conforms to sustainable development goals, and avoids policy and legal risks. Social assessment is because site selection decisions not only need to meet hard conditions but also need the support of society and the public. Considering social factors can improve the feasibility and success rate of project implementation.

[0045] Input the basic factor deviation vector, environmental impact index, and social impact index into the pre-trained multi-factor decision-making model, output the evaluation results of multi-factor decision-making, and obtain the suitability level of the proposed site area. By comprehensively inputting basic, environmental, and social factors into a multi-factor decision-making model (such as a decision tree model), multi-dimensional comprehensive analysis can be achieved. The decision tree model classifies the input data layer by layer, and based on the weights and index thresholds of different factors, finally outputs a clear site selection suitability level (such as high suitability, medium suitability, low suitability). The multi-factor decision-making model can quickly give reasonable and scientific evaluation results based on complex and multi-dimensional data through pre-trained rules and weight allocation. Comprehensive evaluation can avoid single-factor dominated decision-making, improve the objectivity and accuracy of site selection decisions, and at the same time meet the specific requirements of different projects for basic, environmental, and social factors.

[0046] Independent analysis of the basic factor data to obtain the deviation values of each basic factor refers to:

[0047] Number each basic factor in the basic factor data as i, with a total quantity of n, and obtain the difference value ΔX between the actual data of the basic factor corresponding to the number i and the preset ideal data i :

[0048] X ideal，i represents the ideal data of the basic factor corresponding to the number i, X act，i represents the actual data of the basic factor corresponding to the number i, ∈ is a preset constant value to prevent the denominator from being zero, ensuring the stability of the formula, and this letter appears throughout the text with the same function; the difference value ΔX i is used to quantify the difference between the actual value of the current basic condition and the ideal state. By normalizing the difference value with the denominator X ideal，i +∈, the problem of inconsistent dimensions of different basic factors is solved (for example, the value of traffic conditions may be expressed in kilometers, while soil quality may be a score). The normalization process makes the deviation value reflect relative changes rather than absolute value differences, ensuring the comparability of evaluation results. Adding ∈ prevents the denominator from being zero and ensures the robustness of the calculation formula.

[0049] Then calculate the weight coefficient of the difference value ΔX i :

[0050] α is a regulation factor, with a value range from 0 to 1, used to control the proportion of the deviation value and the actual value in the weight calculation. When the value is 1, the weight is completely determined by the deviation value. When the value is 0, the weight is completely determined by the actual value distribution, w i represents the weight coefficient of the basic factor corresponding to the number i; represents the normalized proportion of the deviation value of the basic factor corresponding to the number i in all basic factor deviation values, It indicates the normalized ratio of the actual value of the basic factor corresponding to number i to the actual values ​​of all basic factors. The weight coefficient adjusts the influence of the deviation value and the actual value through the adjustment factor, and can flexibly adapt to different scenarios. For example, when focusing on basic conditions with large differences, a higher adjustment factor can be set; when the actual importance of the basic conditions is more critical, a lower adjustment factor can be set. The normalization of the deviation value and the actual value enables the weight to be adjusted dynamically, and the importance of different factors is reasonably reflected in the weight distribution. The deviation value and the actual value jointly determine the weight, which can more comprehensively reflect the actual situation, rather than relying on a single indicator.

[0051] Then the difference value ΔX corresponding to the basic factor i is i With the weight coefficient w i By multiplying them together, we get the deviation value of basic factor i. By combining the deviation value with the weight coefficient, the deviation of the basic conditions that have a greater impact on the overall project can be magnified in the final result. The normalized weight distribution ensures that all basic conditions can play a role in the final result and prevents distortion of the evaluation result due to the extreme value of a certain condition.

[0052] Combining the deviation values ​​to form the basic factor deviation vector means:

[0053] The deviation value of the basic factor i is first amplified, and then the amplified results are summarized in the order of i to obtain the basic factor deviation vector. The amplification steps are:

[0054] k represents the preset linear coefficient of mild amplification, which is used to moderately adjust the small deviation value. m represents the preset nonlinear coefficient of exponential amplification, which is used to significantly amplify the deviation value exceeding the threshold. i Indicates the deviation threshold corresponding to the basic factor i, which is used to distinguish between "small deviation" and "large deviation". PC i represents the deviation value of basic factor i, f(PC i)Represents the deviation value amplification processing function. When the deviation value of the basic factor is small, only gentle amplification is performed to maintain its limited impact on the comprehensive deviation. Using linear amplification can simply adjust small deviation values and avoid unreasonable evaluation results caused by excessive amplification. When the deviation value of the basic factor exceeds the threshold, it indicates that this factor has a significant negative impact on the suitability of the site selection, and its importance must be highlighted. Through exponential amplification, the impact of large deviation values is significantly amplified, thus attracting the key attention of decision-makers in subsequent comprehensive evaluations. Nonlinear amplification of large deviation values enables those basic factors with particularly large deviations to have greater weights in comprehensive evaluations. This method can ensure that the evaluation results can reflect the key deficiencies of the site selection and avoid major problems being covered up by minor deviations. Only using linear amplification for small deviation values can balance the sensitivity of the model, avoid small deviation values having too much impact on the evaluation results, and ensure the stability of the evaluation results. Using exponential amplification for large deviation values can enhance the sensitivity of the model and make the evaluation results better reflect the actual risks and challenges.

[0055] Actual application scenario description:

[0056] Traffic conditions in project site selection: Small deviation: If the traffic accessibility has only a slight gap with the ideal state (such as a very small difference in road network density), linear amplification is used to ensure its small impact on the result. Large deviation: If the traffic conditions have a large gap with the ideal state (such as a complete lack of expressways), then exponential amplification is used to significantly enhance its impact, prompting decision-makers that there are major problems with this site selection.

[0057] Land suitability: Small deviation: The soil quality is slightly lower than the standard, and linear amplification is used to indicate a slight deficiency. Large deviation: The soil is severely polluted, and exponential amplification is used to significantly increase its impact weight, prompting that this site is not suitable for construction.

[0058] Infrastructure conditions: Small deviation: The water and electricity supply is slightly lower than the standard, and linear amplification is used to show a slight deficiency. Large deviation: The infrastructure is completely lacking, and exponential amplification shows it as a key problem.

[0059] The core design of the amplification processing lies in distinguishing the degree of deviation and adopting different amplification strategies to highlight the impact of key issues while controlling the interference of minor deviations on the overall evaluation results. This flexible amplification processing method can provide scientific, reasonable, and comprehensive support for the site selection evaluation of construction projects, improving the accuracy and operability of the evaluation results.

[0060] Performing predictive quantitative evaluation on environmental factor data and forming an environmental impact index based on the comprehensive environmental impact factors refers to:

[0061] First, obtain the predicted data YC of R environmental impact factors through a preset prediction model r, r represents the number of environmental impact factors, and the impact coefficient W obtained through expert evaluation is acquired. r , first calculate the comprehensive environmental impact value:

[0062] E represents the comprehensive environmental impact value, p represents a preset non - linear adjustment parameter used to control the non - linear degree of the impact coefficient. When the value is greater than 1, it emphasizes the dominant role of large - value environmental factors (such as major pollution) in the comprehensive impact. When the value is less than 1, it reduces the impact of extreme values and is suitable for equilibrium analysis. When the value is equal to 1, it degenerates into a weighted average, representing a linear combination. The impact coefficient reflects the weight of each environmental factor on the comprehensive environmental impact. For example, water quality may have a greater impact on the site selection of agricultural projects, while air pollution may have a more significant impact on the site selection of industrial projects, providing a clear comprehensive environmental impact indicator and a quantitative basis for the evaluation of site selection suitability.

[0063] The preset prediction model belongs to the prior art. In the prior art, predicting based on current data usually relies on the following principles: Time - series analysis: Basic principle: Based on time - series data (such as the historical change trend of environmental pollutants), a model is constructed to predict future values. Common methods include: autoregressive moving average model, exponential smoothing method, long - short - term memory network, etc. Application: Predict dynamic environmental data such as air quality and water pollution trends, and finally obtain the predicted data YC of R environmental impact factors. r , which will not be elaborated in the prior art.

[0064] Then introduce a penalty mechanism and calculate the total penalty value: P r represents the single - body penalty value corresponding to the environmental impact factor r, CF represents the total penalty value, Pr = max(Y r , YC r ); Y r represents the limit value corresponding to the environmental impact factor r; CF represents the total penalty value of the comprehensive environmental factors, which is a total quantitative measure of the degree to which each single - body environmental factor P r exceeds the limit value. When the environmental impact factor exceeds the limit value, the penalty mechanism will be triggered, increasing the penalty value of the corresponding factor. By introducing the penalty mechanism, it clearly gives additional weight penalties to project areas where the environmental impact exceeds the limit value, warning of the risks in environmental suitability in these areas. Combining with the impact weights, it can highlight the environmental factors that have a greater impact on the project.

[0065] The formula for calculating the environmental impact index is:

[0066] HI = E·(1 - β·CF); β represents a preset penalty intensity coefficient, with a value in the range of [0, 1]. HI represents the environmental impact index, which synthesizes the overall impact value E of environmental factors and the over-limit penalty value CF. The penalty intensity coefficient controls the adjustment effect of the penalty value on the environmental impact index. When the value is 0, there is no penalty mechanism, and the environmental impact index is completely determined by the comprehensive value E. When the value is 1, the impact of the penalty mechanism is maximized, highlighting the significant risk of the over-limit value to environmental suitability. The value of (1 - β·CF) is within a certain range and is used to proportionally adjust the comprehensive impact value E, unifying the comprehensive environmental impact value and the over-limit penalty value into a comprehensive index, directly reflecting the environmental suitability of the selected site area. The level of the environmental impact index can be used as a decision-making basis to guide the assessment and optimization of environmental impacts during the project site selection process.

[0067] Conducting qualitative analysis on social factor data to generate the social impact index means:

[0068] Obtain the qualitative survey results of each social factor data, then summarize them into a qualitative survey vector, and calculate the Euclidean distance between the qualitative survey vector and the preset qualitative standard vector. The value of the social impact index is the same as the Euclidean distance value. The social impact index is an indicator that quantifies the suitability degree of the selected site area for social factors. By calculating the difference between the qualitative survey results and the preset social standards, a value is obtained to represent the acceptability or adaptability of the selected site in the social dimension. The qualitative survey vector represents the actual data of social factors in the selected site area (such as community acceptance, employment contribution, cultural adaptability, etc.). The qualitative standard vector represents the ideal state of social factors (such as policy goals, social planning standards). The Euclidean distance is used to measure the difference between the actual and ideal values, quantifying social factors that are difficult to directly measure: Many social factors (such as community acceptance, cultural conflicts, etc.) are difficult to directly quantify. Through qualitative analysis and distance calculation, they can be transformed into quantifiable indicators, clarifying the adaptability degree of social factors: The social impact index reflects the deviation degree between the actual social conditions and the ideal state, helps to evaluate whether the selected site can be socially accepted, reduces potential social risks, supports multi-dimensional comprehensive decision-making. As an important input, the social impact index can be comprehensively considered together with the basic factor deviation and the environmental impact index to support more comprehensive multi-factor decision-making analysis.

[0069] For example: Suppose a construction project site selection needs to evaluate the following social factors: community acceptance (scored from 0 to 10, the higher the better), employment contribution (the number of new jobs created per year), and cultural adaptability (scored from 0 to 10, the higher the better). Obtain the actual social factor data of the site selection area from a qualitative survey, where the community acceptance is 7 points, the employment contribution is 150 people / year, and the cultural adaptability is 6 points. The preset ideal social standard vector is: the ideal value of community acceptance is 10 points, the ideal value of employment contribution is 200 people / year, and the ideal value of cultural adaptability is 8 points. The social impact index is 50.13. A higher index value indicates a large gap between the actual social conditions and the ideal state, and there may be certain social risks in the site selection area. For example: The community acceptance is lower than the ideal value, which may lead to the risk of public opposition to the project. The employment contribution is less than expected, which may reduce the positive impact of the project on the local economy. The lower cultural adaptability may trigger cultural conflicts.

[0070] Convert qualitative social factors into quantitative indicators (Euclidean distance) for unified comparison and comprehensive analysis with other factors (such as environment and basic conditions). Provide a basis for optimized decision-making: By analyzing the index results, decision-makers can identify and focus on the weak links in the social dimension and propose targeted improvement measures. Reduce social risks: Evaluating the social impact index helps to discover potential social acceptability problems of the project and avoid project failure caused by public opposition or cultural conflicts in the later stage of site selection. Support comprehensive suitability evaluation: The social impact index, together with the environmental impact index and the basic factor deviation, constitutes a multi-dimensional system for site selection evaluation, making the project site selection more scientific and comprehensive. Example summary: In the above example, the social impact index of 50.13 indicates that there is a large room for improvement in the social dimension of the current site selection area. By calculating the Euclidean distance between the qualitative survey and the qualitative standard, the gap of social factors is quantitatively evaluated, providing a clear optimization direction for decision-makers (such as improving community acceptance and increasing employment contribution), thus supporting a more scientific site selection decision.

[0071] The pre-trained multi-factor decision-making model refers to a decision tree model. The evaluation results of multi-factor decision-making are calculated by the decision tree model and classified layer by layer according to the preset index thresholds to output the optimal decision. The evaluation results include one of the following levels: high suitability, medium suitability, and low suitability.

[0072] In the multi-factor decision-making of the present invention, the decision tree model realizes the comprehensive analysis of the basic factor deviation vector, the environmental impact index, and the social impact index through the following steps, and finally outputs the evaluation result.

[0073] In the first step, data input is performed. The input data includes: the basic factor deviation vector, which is composed of the results of magnifying the deviation values of multiple basic factors; the environmental impact index, which comprehensively reflects the environmental impact; and the social impact index, which reflects the social adaptability and acceptance.

[0074] In the second step, hierarchical classification is carried out. Root node classification: The decision tree starts from the first condition (for example, the environmental impact index) and determines whether it is lower than a certain preset threshold. If the environmental impact index is less than the threshold, it enters the "environmentally suitable" branch; otherwise, it enters the "environmentally unsuitable" branch.

[0075] Intermediate node classification: According to the branch path of the previous node, continue to analyze the next condition (for example, the social impact index). If the social impact index is close to the ideal value, it enters the "socially suitable" branch; otherwise, it enters the "socially unsuitable" branch.

[0076] Leaf node output: When the decision tree reaches the leaf node, according to the comprehensive judgment of all conditions, an evaluation level (such as high suitability, medium suitability, low suitability) is output.

[0077] In the third step, for the comprehensive result, the decision tree decomposes the complex multi-factor decision-making problem into multiple simple judgment conditions through hierarchical classification. Finally, the model outputs the suitability level of the selected site area, providing a clear decision-making basis for the decision maker.

[0078] The above formulas are all dimensionless and take their numerical values for calculation. The formula is obtained by collecting a large amount of data and performing software simulation to get a formula closest to the real situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0079] It should be understood that in various embodiments of the present application, the magnitudes of the serial numbers of the above processes do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0080] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professionals can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present application.

[0081] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0082] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claimed rights.

Claims

1. A construction project planning site selection and land use pre-evaluation method based on multi-factor analysis, characterized by: The following steps are involved: Collect preset basic factor data, environmental factor data and social factor data related to the construction project within the preset evaluation time window; Independently analyze the basic factor data to obtain the deviation value of each basic factor, and combine the deviation values ​​to form a basic factor deviation vector; Conduct a predictive quantitative assessment of environmental factor data to form an environmental impact index based on the comprehensive environmental impact factors, and conduct a qualitative analysis of social factor data to generate a social impact index; The basic factor deviation vector, environmental impact index and social impact index are input into the pre-trained multi-factor decision-making model, and the evaluation results of the multi-factor decision-making are output to obtain the suitability level of the proposed site selection area.

2. The method for construction project planning site selection and land use pre-evaluation based on multi-factor analysis according to claim 1 is characterized in that: The pre-trained multi-factor decision model refers to the decision tree model.

3. The method for construction project planning site selection and land use pre-evaluation based on multi-factor analysis according to claim 2 is characterized in that: Independently analyzing the basic factor data to obtain the deviation value of each basic factor means: Number each basic factor in the basic factor data as i, the total number is n, and obtain the difference value ΔX between the actual data of the basic factor corresponding to number i and the preset ideal data i : X ideal，i represents the ideal data of the basic factor corresponding to number i, X act，i represents the actual data of the basic factor corresponding to number i, and ∈ is a preset constant value to prevent the denominator from being zero; Then calculate the difference ΔX i The weight coefficient of: α is the adjustment factor, ranging from 0 to 1, w i Indicates the weight coefficient of the basic factor corresponding to number i; Then the difference value ΔX corresponding to the basic factor i is i With the weight coefficient w i Multiply them together to get the deviation value of basic factor i.

4. The method for construction project planning site selection and land use preliminary evaluation based on multi-factor analysis according to claim 3 is characterized in that: Combining the deviation values ​​to form the basic factor deviation vector means: The deviation value of the basic factor i is first amplified, and then the amplified results are summarized in the order of i to obtain the basic factor deviation vector. The amplification steps are: k represents the preset linear coefficient of mild amplification, m represents the preset nonlinear coefficient of exponential amplification, Threshold i represents the deviation threshold corresponding to the basic factor i, PC i represents the deviation value of basic factor i, f(PC i ) represents the deviation value amplification processing function.

5. The method for construction project planning site selection and land use pre-evaluation based on multi-factor analysis according to claim 4 is characterized in that: The environmental impact index is formed by conducting a predictive quantitative assessment of environmental factor data based on the comprehensive environmental impact factors: First, obtain the prediction data YC of R environmental influencing factors through the preset prediction model r , r represents the number of the environmental impact factor, and obtains the impact coefficient W obtained by expert evaluation r , first calculate the comprehensive environmental impact value: E represents the comprehensive environmental impact value, and p represents the preset nonlinear adjustment parameter, which is used to control the nonlinear degree of the impact coefficient; Then introduce the penalty mechanism and calculate the total penalty value: P r represents the single penalty value corresponding to the environmental impact factor r, CF represents the total penalty value, P r =max(Y r , Y.C. r );Y r Indicates the limit value corresponding to the environmental impact factor r; The environmental impact index calculation formula is: HI = E·(1-β·CF); β represents the preset penalty intensity coefficient, which is in the range of [0,1]; HI represents the environmental impact index.

6. The method for construction project planning site selection and land use pre-evaluation based on multi-factor analysis according to claim 5 is characterized in that: Qualitative analysis of social factor data to generate a social impact index refers to: The qualitative survey results of each social factor data are obtained and then summarized into a qualitative survey vector. The Euclidean distance between the qualitative survey vector and the preset qualitative standard vector is calculated. The social impact index value is the same as the Euclidean distance value.

7. The method for construction project planning site selection and land use pre-evaluation based on multi-factor analysis according to claim 6 is characterized in that: The evaluation results of multi-factor decision-making are calculated through the decision tree model and classified layer by layer according to the preset indicator thresholds to output the optimal decision.

8. The method for construction project planning site selection and land use pre-evaluation based on multi-factor analysis according to claim 7 is characterized in that: The evaluation results include one of the following levels: high suitability, medium suitability, and low suitability.

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