A forest health industry competitiveness evaluation and path planning system

Abstract

The application discloses a kind of forest health care industry competitiveness evaluation and path planning system, it is related to forest health care industry planning and evaluation field, including: acquisition module, for gathering forest health care industry correlation data, execute data cleaning, normalization conversion and spatial coordinate registration process, to output standardization dataset;Setting module is used to receive standardization dataset, set the multi-level index item of forest health care industry competitiveness evaluation, complete the parameterization setting of the quantitative calculation rule, value range and attribute type of each index item;The application is built by multi-level evaluation index system and is combined with spatial heterogeneity correction to complete scientific empowerment and consistency check, so that the index weight result is more in line with actual development situation, can accurately calculate the comprehensive score of industry competitiveness, clearly analyze the spatial distribution, kernel density and spatial differentiation characteristics of industry elements, accurately identify core constraint index and quantitatively divide obstacle type, provide reliable basis for industry development diagnosis.

Description

Technical Field

[0001] This invention relates to the field of forest health and wellness industry planning and evaluation technology, specifically a forest health and wellness industry competitiveness evaluation and path planning system. Background Technology

[0002] With the deep development of the global health industry and the integration and upgrading of ecological and cultural tourism, forest-based health and wellness, as a core industry for the synergistic development of ecological value transformation and health services, is experiencing continuous expansion in global scale. The number of various forest-based health and wellness bases and unique experience projects is rapidly increasing, and the industry has shifted from a stage of large-scale expansion to a stage of high-quality and refined development. In the process of industrial development, the demand for professional and technical expertise in quantitatively assessing the comprehensive competitiveness of regional forest-based health and wellness industries and planning development paths tailored to local resources is continuously increasing. The construction of related technical systems has become an important technical direction supporting the sustainable development of the forest-based health and wellness industry.

[0003] Patent application number 202511719382.8 discloses a tourism industry management and planning method for climate-based health and wellness. This application aims to address the problem that "existing tourism management and planning methods typically remain at the macro-level assessment of overall regional climate comfort, failing to establish a precise matching relationship between individual tourists' health characteristics and specific climate condition parameters. In terms of planning models, most existing technologies adopt static planning schemes based on historical data, lacking the ability to dynamically respond to real-time climate changes and sudden weather events. When encountering sudden weather changes or significant fluctuations in climate comfort, existing static planning struggles to achieve timely adjustments and optimizations to health and wellness activity arrangements. Furthermore, due to the lack of differentiated consideration of individual tourists' health conditions and climate adaptability, existing methods often fail to provide truly personalized health and wellness experiences."

[0004] However, existing technologies mostly use general-purpose cultural tourism project evaluation models and have not built a multi-dimensional quantitative evaluation algorithm specifically for the forest health and wellness industry. Their evaluation and planning processes lack data linkage mechanisms, making it impossible to accurately generate feasible quantitative planning solutions based on the evaluation results.

[0005] To this end, we propose a system for evaluating the competitiveness and planning the path of the forest-based health and wellness industry. Summary of the Invention

[0006] In view of the above-mentioned shortcomings of the existing technology, the present invention provides a forest health and wellness industry competitiveness assessment and path planning system, which can effectively solve the problems of the existing technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions; This invention discloses a system for evaluating the competitiveness and planning the path of the forest-based health and wellness industry, comprising: The data acquisition module collects data related to the forest health and wellness industry, performs data cleaning, normalization transformation, and spatial coordinate registration to output a standardized dataset. The setting module receives the standardized dataset, sets multi-level indicators for evaluating the competitiveness of the forest health and wellness industry, and parameterizes the quantitative calculation rules, value ranges, and attribute types for each indicator. The verification module constructs a judgment matrix based on the set indicators, performs quantitative calculations of the weights of each indicator, verifies the consistency of the calculation results, and outputs valid weights that meet the preset verification threshold. The parsing module calculates the comprehensive competitiveness score based on the standardized dataset and valid weight values, and simultaneously executes the forest health and wellness industry evaluation. The system includes: a spatial feature dataset for determining the spatial distribution of health and wellness industry elements, calculating kernel density, and quantitatively analyzing spatial differentiation characteristics; a diagnostic module for performing quantitative calculations of indicator factor contribution, deviation, and obstacle degree based on effective weight values, standardized datasets, and spatial feature datasets, identifying core constraint indicators, completing the quantitative classification of obstacle types, and outputting an obstacle factor dataset; and an output module for receiving the spatial feature dataset and obstacle factor dataset, matching them with a pre-defined planning parameter library that corresponds one-to-one with forest health and wellness industry indicators and obstacle types, performing parameterized matching calculations corresponding to core constraint indicators, and generating industry upgrading path entries and sub-item implementation parameter sets. The acquisition module is interconnected with a setting module via a wireless network. The setting module is interconnected with a verification module and a parsing module via a wireless network. The parsing module is interconnected with a diagnostic module and an output module via a wireless network. Among them, the data related to the forest health and wellness industry includes geospatial, socio-economic, thematic, policy text, and market entity data.

[0008] Furthermore, the acquisition module uses a preset geographic grid unit as the smallest anchoring unit to match and bind the acquired non-spatial forest health and wellness industry-related data with geospatial data to generate a full-scale associated dataset with spatial attributes. Then, outlier removal, missing value completion, and dimensional unification are performed on the full-scale associated dataset. Outlier removal is based on a preset quantile threshold (1%~99th percentile) to identify and remove outliers from the full associated dataset. Missing value completion uses a strong correlation index fitting interpolation method (linear fitting interpolation method) with the same spatial unit as the missing data to fill in the missing data.

[0009] Furthermore, the dimensional unification process follows: For indicator data with positive attributes: ; For indicator data with negative attributes: ; In the formula: This is the standardized output value of the j-th index for the i-th spatial unit; This represents the original matching value of the j-th index in the i-th spatial unit; This represents the original maximum value of the j-th index in the full spatial unit; It represents the original minimum value of the j-th index in the full spatial unit.

[0010] Furthermore, the multi-level indicator items constructed by the setting module include four progressive levels: target layer, criterion layer, element layer, and indicator layer. Among them, the target layer is the overall competitiveness level of the forest health and wellness industry, the criteria layer includes five parallel dimensions: resource base, industry operation, service support, market benefits and spatial adaptation, the element layer is the set of subdivided industry elements corresponding to each criteria layer, and the indicator layer is the smallest quantifiable indicator unit corresponding to each element layer. During the operation of the setting module, the attribute types of each indicator item in the indicator layer are simultaneously determined and classified. The attribute types include positive, negative, and moderate. Among them, the moderate indicator is a non-unidirectional attribute indicator whose optimal value falls within the indicator value range. obey: < < For the indicator items of the moderate attribute, the dimensional unification is completed by calculating using the improved cosine ridge model; in, This represents the original maximum value of the j-th index in the full spatial unit; It represents the original minimum value of the j-th index in the full spatial unit.

[0011] Furthermore, for multi-level indicator items, the verification module constructs corresponding judgment matrices for the criterion layer, element layer, and indicator layer from top to bottom, and performs weight calculation and consistency verification on the judgment matrix of each level. The weight calculation follows the following: ; In the formula: A is the judgment matrix of the current level; To determine the largest eigenvalue of matrix A; To determine the initial weight vector corresponding to the matrix; This represents the final effective weight value of the k-th indicator at the current level. is the initial adjusted weight value for the k-th indicator; n is the number of indicator dimensions in the current level judgment matrix; The scale value for setting the relative importance between the k-th and t-th indicators at the current level, which is set as a parameter for the module. is the spatial heterogeneity correction coefficient for the k-th index.

[0012] Furthermore, the consistency check obeys: ; In the formula: CI is the consistency index; RI is the preset average random consistency index corresponding to the dimension n of the judgment matrix; CR is the consistency ratio; Specifically, the weight calculation result of the current level is determined to be a valid weight only if CR is less than the preset verification threshold.

[0013] Furthermore, the parsing module calculates the comprehensive score of the forest health and wellness industry competitiveness of each spatial unit based on the standardized dataset and effective weight values: In the formula: The comprehensive score of the competitiveness of the forest health and wellness industry in the i-th spatial unit; The multi-criteria layer coupling degree of the i-th spatial unit; The multi-criteria layer comprehensive development index for the i-th spatial unit; The total number of dimensions in the criteria layer; This represents the development level value of the m-th criterion layer in the i-th spatial unit; The total number of indicators in the indicator layer corresponding to the m-th criterion layer; For the m-th criterion layer, the first... Effective weight values ​​for each indicator; For the i-th spatial unit, the m-th criterion layer, the... The standardized value of the indicator; The effective weight values ​​for the m-th criterion layer; The spatial feature dataset includes the results of global and local distribution type determination, the results of global and local spatial correlation index calculation, the results of continuous kernel density measurement of the entire geographical range, the results of spatial heterogeneity of global and regional areas, the results of spatial heterogeneity contribution of sub-indicators, and the spatial coordinate binding datasets corresponding to all results.

[0014] Furthermore, the diagnostic module performs quantitative calculations of the contribution, deviation, and obstacle degree of the performance indicators: ; In the formula: For the j-th criterion layer, the first... The overall contribution of each indicator to the entire region; N is the total number of spatial units; The effective weight value of the j-th criterion layer; For the j-th criterion layer, the first... Effective weight values ​​for each indicator; For the i-th spatial unit, the j-th criterion layer, the... The standardized value of the indicator; For the i-th spatial unit, the j-th criterion layer, the... Deviation of the indicator; For the j-th criterion layer in the entire region, the first... The standardized average of the indicators; For the i-th spatial unit, the j-th criterion layer, the... The degree of obstruction of the indicator; The diagnostic module uses preset judgment rules to comprehensively compare the calculated contribution, deviation, and obstacle values ​​of each indicator. It then selects indicators whose obstacle value is higher than a preset obstacle threshold, whose contribution value reaches a preset contribution threshold, and whose deviation exceeds a preset deviation range. These indicators are then used as the core constraint indicators for the entire region and each spatial unit. Based on the criterion layer dimension, indicator attribute characteristics, and obstacle causes of the core constraint indicators, the module quantifies and classifies obstacle types into resource-shortage, operationally inefficient, insufficiently supported, market-weak, and spatially mismatched types, and outputs the obstacle factor dataset for each spatial unit.

[0015] Furthermore, the output module stores a set of industry upgrading strategies, implementation parameter thresholds, and spatial adaptation rules that correspond one-to-one with each indicator item and each obstacle type in its preset planning parameter library; During the operation phase, the output module, based on the obstacle factor dataset and spatial feature dataset, completes the parameterized matching of core constraint indicators with the preset planning parameter library using the following formula: ; In the formula: The degree of matching between the core constraint index of the i-th spatial unit and the k-th group of planning parameters in the preset planning parameter library; The comprehensive obstacle degree is the core constraint index of the i-th spatial unit; This represents the total number of core constraint indicators for this spatial unit. The obstacle degree is the z-th core constraint indicator of the i-th spatial unit; This represents the effective weight value corresponding to the z-th core constraint indicator; This represents the overall competitiveness score of the i-th spatial unit. Let be the obstacle fit coefficient for the k-th group of planning parameters; Improve the fit coefficient for the competitiveness of the k-th group of planning parameters; For maintenance items; The output module sorts the planning parameters in the preset planning parameter library from high to low according to their matching degree, and selects the top N sets of planning parameters with a matching degree greater than a preset matching threshold as candidate parameter sets. Then, combined with the spatial clustering characteristics, gradient differentiation characteristics, and hot and cold spot distribution characteristics in the spatial feature dataset, the candidate parameter sets are subjected to spatial constraint screening according to preset spatial compatibility rules, and planning parameters that are incompatible with the spatial characteristics of the spatial unit are eliminated. Then, the screening candidate parameter sets are subjected to implementation feasibility verification according to preset feasibility verification dimensions, and planning parameters that do not meet the preset implementation conditions are eliminated, resulting in the final adapted parameter set. Subsequently, according to preset targeted matching rules, a one-to-one correspondence is established between the final adapted parameter set and the core constraint indicators of the spatial unit, generating industrial upgrading path entries that can directly solve the shortcomings of the corresponding core constraint indicators. Finally, based on the obstacle degree and effective weight value of the core constraint indicators, a corresponding quantitative control threshold is matched for each industrial upgrading path entry, generating a sub-item implementation parameter set.

[0016] Compared with the known prior art, the technical solution provided by this invention has the following beneficial effects: This invention comprehensively integrates various related data on the forest health and wellness industry, including geospatial, socio-economic, policy documents, and market entities. It cleans, normalizes, and spatially registers the data to form a unified data foundation, ensuring the accuracy and completeness of the assessment. By constructing a multi-level evaluation index system and combining spatial heterogeneity correction, it achieves scientific weighting and consistency verification, making the index weights more aligned with actual development. This allows for accurate calculation of the industry's overall competitiveness score, clear analysis of the spatial distribution, kernel density, and spatial differentiation characteristics of industry elements, precise identification of core constraints, and quantification of obstacle types, providing a reliable basis for industry development diagnosis. Simultaneously, it can match exclusive planning parameters based on obstacle factors and spatial characteristics to generate differentiated and feasible industrial upgrading paths and quantitative implementation parameters, taking into account both overall planning and local adaptation, effectively improving the scientific nature of the competitiveness assessment of the forest health and wellness industry and the targeting of path planning. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0018] Figure 1 This is a schematic diagram of a system for evaluating the competitiveness and planning the path of the forest health and wellness industry. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] The present invention will be further described below with reference to embodiments.

[0021] Example: This embodiment presents a forest-based health and wellness industry competitiveness assessment and pathway planning system, such as... Figure 1 As shown, it includes: The data acquisition module is used to collect data related to the forest health and wellness industry, perform data cleaning, normalization transformation and spatial coordinate registration to output a standardized dataset; The data acquisition module uses a preset geographic grid unit as the smallest anchoring unit to match and bind the collected non-spatial forest health and wellness industry-related data with geospatial data to generate a full-scale associated dataset with spatial attributes. Then, it performs outlier removal, missing value completion, and unit unification processing on the full-scale associated dataset. Among them, outlier removal is based on the identification and removal of outliers in the full associated dataset based on the preset quantile threshold, and missing value completion is completed by fitting interpolation method of strong correlation index in the same spatial unit as the missing data. Dimensional unification processing follows: For indicator data with positive attributes: ; For indicator data with negative attributes: ; In the formula: This is the standardized output value of the j-th index for the i-th spatial unit; This represents the original matching value of the j-th index in the i-th spatial unit; This represents the original maximum value of the j-th index in the full spatial unit; This represents the original minimum value of the j-th index in the full spatial unit; The above formula is based on logarithmic calculation to compress data extrema and unify the dimensions of indicators, so that industry indicators of different magnitudes have direct comparability, in order to adapt to the numerical distribution characteristics of various indicators in the forest health and wellness industry, thereby improving the adaptability and rationality of data standardization processing. The configuration module is used to receive standardized datasets, set multi-level indicators for evaluating the competitiveness of the forest health and wellness industry, and complete the parameterization settings of the quantitative calculation rules, value ranges, and attribute types for each indicator. The multi-level indicator items constructed by the setting module include four progressive levels: target layer, criterion layer, element layer, and indicator layer. Among them, the target layer is the overall competitiveness level of the forest health and wellness industry, the criteria layer includes five parallel dimensions: resource base, industry operation, service support, market benefits and spatial adaptation, the element layer is the set of subdivided industry elements corresponding to each criteria layer, and the indicator layer is the smallest quantifiable indicator unit corresponding to each element layer. During the module's operation phase, the attribute types of each indicator item in the indicator layer are simultaneously determined and classified. Attribute types include positive, negative, and moderate. Among them, moderate indicators are non-unidirectional attribute indicators whose optimal value falls within the indicator's value range, and their optimal moderate value is... obey: < < For the indicator items of the moderate attribute, the dimensional unification is achieved by calculating using the improved cosine ridge model: ; In the formula: This is the standardized output value of the j-th suitability index for the i-th spatial unit; This is the original matching value of the j-th index of the i-th spatial unit after outlier removal; This represents the optimal appropriate value for the j-th indicator; This represents the original maximum value of the j-th index in the full spatial unit; This represents the original minimum value of the j-th index in the full spatial unit; The above formula calculates the standardized results in segments according to the relative position of the indicator value and the optimal appropriate value, accurately quantifies the standard value of non-unidirectional optimal attribute indicators, and the indicator quantification is more in line with the actual industry situation. The verification module is used to construct a judgment matrix based on the set indicators, perform quantitative calculation of the weight of each indicator, perform consistency verification on the calculation results, and output the effective weights that meet the preset verification threshold. For multi-level indicators, the verification module constructs corresponding judgment matrices for the criterion layer, element layer, and indicator layer from top to bottom, and performs weight calculation and consistency verification on the judgment matrix of each level. Weight calculation follows: ; In the formula: A is the judgment matrix of the current level; To determine the largest eigenvalue of matrix A; To determine the initial weight vector corresponding to the matrix; This represents the final effective weight value of the k-th indicator at the current level. is the initial adjusted weight value for the k-th indicator; n is the number of indicator dimensions in the current level judgment matrix; The scale value for setting the relative importance between the k-th and t-th indicators at the current level, which is set as a parameter for the module. The spatial heterogeneity correction coefficient for the k-th index; The above formula is based on the design of the weight calculation formula of the largest eigenvalue of the judgment matrix and incorporates the spatial heterogeneity correction coefficient. It adjusts the weight value in combination with the regional variation characteristics of the indicator. It not only follows the scientific logic of the hierarchical analysis, but also makes up for the shortcomings of traditional methods that ignore spatial differences, so that the indicator weights are more in line with the actual spatial distribution of the industry. in, ∈[-0.25,0.25], and its absolute value is proportional to the coefficient of variation of the original data of each spatial unit in the entire study area; Consistency checks obey: ; In the formula: CI is the consistency index; RI is the preset average random consistency index corresponding to the dimension n of the judgment matrix; CR is the consistency ratio; The above formula verifies the weight calculation results through the consistency ratio formula, and judges the validity of the weight by the ratio of the consistency index to the average random consistency index, ensuring the credibility of the weights of each level of indicators, thereby improving the accuracy of the evaluation results from the source. Among them, the weight calculation result of the current level is determined to be a valid weight if and only if CR is less than the preset verification threshold; The parsing module is used to calculate the comprehensive competitiveness score based on the standardized dataset and effective weight values, and simultaneously perform the determination of the spatial distribution type of forest health and wellness industry elements, kernel density calculation and quantitative analysis of spatial differentiation characteristics, so as to output a spatial feature dataset. The parsing module calculates the comprehensive score of the forest health and wellness industry competitiveness of each spatial unit based on the standardized dataset and effective weight values: In the formula: The comprehensive score of the competitiveness of the forest health and wellness industry in the i-th spatial unit; The multi-criteria layer coupling degree of the i-th spatial unit; The multi-criteria layer comprehensive development index for the i-th spatial unit; The total number of dimensions in the criteria layer; This represents the development level value of the m-th criterion layer in the i-th spatial unit; The total number of indicators in the indicator layer corresponding to the m-th criterion layer; For the m-th criterion layer, the first... Effective weight values ​​for each indicator; For the i-th spatial unit, the m-th criterion layer, the... The standardized value of the indicator; The effective weight values ​​for the m-th criterion layer; The above formula uses a combination of coupling degree and comprehensive development index to calculate the comprehensive competitiveness score. At the same time, it is combined with formulas for spatial correlation, kernel density and spatial differentiation to carry out analysis. It can reflect the synergy level of multi-dimensional industrial elements and accurately depict the spatial distribution and differentiation pattern of industries, and finally achieve a deep integration of competitiveness assessment and spatial characteristic analysis. The spatial feature dataset includes the results of global and local distribution type determination, the results of global and local spatial correlation index calculation, the results of continuous kernel density measurement of the entire geographic range, the results of global and regional spatial heterogeneity, the results of spatial heterogeneity contribution of sub-indicators, and the spatial coordinate binding datasets corresponding to all results; The analysis module, based on the comprehensive competitiveness score of each spatial unit, the standardized dataset, and the spatial coordinate registration results, sequentially performs the following steps: determining the spatial distribution type of forest health and wellness industry elements, calculating kernel density, and quantitatively analyzing spatial differentiation characteristics. Spatial distribution type determination: ; ; In the formula: It is a global spatial correlation index; Let be the local spatial correlation index of the i-th spatial unit; This represents the total number of spatial units. The preset spatial weight matrix values ​​are for the i-th spatial unit and the j-th spatial unit; , These are the comprehensive scores of the competitiveness of the forest health and wellness industry for the i-th and j-th spatial units, respectively. This represents the average of the overall competitiveness scores of all spatial units in the region. Based on the calculation results, when When the correlation is greater than a preset positive correlation threshold, the entire region is determined to be a clustered distribution; when it is within a preset random interval, it is determined to be a random distribution; and when it is less than a preset negative correlation threshold, it is determined to be a dispersed distribution. Simultaneously, based on... The calculation results are used to classify the local distribution subtypes of each spatial unit. The local distribution subtypes include high-value clustering, high-value discrete, low-value clustering, and low-value discrete. Nuclear density calculation: In the formula: Geographic coordinates Core density values ​​of forest health and wellness industry elements at the location; Preset smooth bandwidth; This is the preset kernel function; The coordinates of the geographic center of the k-th spatial unit; This represents the development level value of the m-th criterion layer in the k-th spatial unit. Quantitative analysis of spatial differentiation features: ; The spatial differentiation of the competitiveness of the forest health and wellness industry across the entire region; Let q be the local heterogeneity of the q-th preset geographic region; The number of spatial units within the q-th preset geographic partition; This represents the average comprehensive competitiveness score of spatial units within the q-th preset geographical region; For the j-th criterion layer, the first... The contribution of each indicator to spatial differentiation; This represents the variance of the overall contribution of this indicator across the entire region. The variance of the overall competitiveness score for the entire region; For the j-th criterion layer, the first... The overall contribution of each indicator to the entire region; The diagnostic module is used to perform quantitative calculations of the contribution, deviation and obstacle degree of indicator factors based on effective weight values, standardized datasets and spatial feature datasets, identify core constraint indicator items, complete the quantitative classification of obstacle types, and output obstacle factor datasets. The diagnostic module performs quantitative calculations of the contribution, deviation, and obstacle degree of the performance indicators: ; In the formula: For the j-th criterion layer, the first... The overall contribution of each indicator to the entire region; N is the total number of spatial units; The effective weight value of the j-th criterion layer; For the j-th criterion layer, the first... Effective weight values ​​for each indicator; For the i-th spatial unit, the j-th criterion layer, the... The standardized value of the indicator; For the i-th spatial unit, the j-th criterion layer, the... Deviation of the indicator; For the j-th criterion layer in the entire region, the first... The standardized average of the indicators; For the i-th spatial unit, the j-th criterion layer, the... The degree of obstruction of the indicator; By designing a three-dimensional quantitative formula for contribution, deviation, and obstacle, the contribution, numerical deviation, and degree of constraint of the indicators to industrial development are comprehensively measured. The core constraint indicators are then screened through the formula calculation results to provide support for the diagnosis of industrial obstacles. The diagnostic module uses preset judgment rules to comprehensively compare the calculated contribution, deviation, and obstacle values ​​of each indicator. It then selects indicators whose obstacle value is higher than a preset obstacle threshold, whose contribution value reaches a preset contribution threshold, and whose deviation exceeds a preset deviation range. These indicators are designated as core constraint indicators for the entire region and each spatial unit. Based on the criterion layer dimension, indicator attribute characteristics, and obstacle causes of the core constraint indicators, the module quantifies and classifies obstacle types into resource deficiency type (obstacles caused by core constraint indicators of the resource baseline criterion layer), operational inefficiency type (obstacles caused by core constraint indicators of the industry operation criterion layer), insufficient support type (obstacles caused by core constraint indicators of the service support criterion layer), weak market type (obstacles caused by core constraint indicators of the market efficiency criterion layer), and spatial mismatch type (obstacles caused by core constraint indicators of the spatial adaptation criterion layer). The module outputs obstacle factor datasets for each spatial unit. These obstacle factor datasets include at least the name of the core constraint indicator, the contribution value, deviation value, obstacle value, obstacle type, and obstacle cause analysis for each core constraint indicator. The output module receives spatial feature datasets and obstacle factor datasets, matches them with a pre-defined planning parameter library that corresponds one-to-one with the forest health and wellness industry indicators and obstacle types, performs parameterized matching calculations for the core constraint indicators, and generates industry upgrading path entries and sub-item implementation parameter sets. The output module contains a pre-set planning parameter library that stores a set of industry upgrading strategies, implementation parameter thresholds, and spatial adaptation rules that correspond one-to-one with each indicator item and each type of obstacle. During the output module's runtime phase, based on the obstacle factor dataset and spatial feature dataset, the core constraint indicators are matched with the preset planning parameter library using the following formula: ; In the formula: The degree of matching between the core constraint index of the i-th spatial unit and the k-th group of planning parameters in the preset planning parameter library; The comprehensive obstacle degree is the core constraint index of the i-th spatial unit; This represents the total number of core constraint indicators for this spatial unit. The obstacle degree is the z-th core constraint indicator of the i-th spatial unit; This represents the effective weight value corresponding to the z-th core constraint indicator; This represents the overall competitiveness score of the i-th spatial unit. Let be the obstacle fit coefficient for the k-th group of planning parameters; Improve the fit coefficient for the competitiveness of the k-th group of planning parameters; For maintenance items; This formula combines indicator weights and adaptation coefficients to complete the planning parameter matching calculation. Through mathematical calculation, it enables the precise connection between obstacle factors and improvement paths, ensuring the targeting and feasibility of industrial improvement paths. The output module sorts the planning parameters in the preset planning parameter library from high to low matching degree, and selects the top N sets of planning parameters with a matching degree greater than the preset matching threshold as candidate parameter sets. Then, combined with the spatial clustering characteristics, gradient differentiation characteristics, and hot and cold spot distribution characteristics in the spatial feature dataset, the candidate parameter sets are subjected to spatial constraint screening according to the preset spatial compatibility rules, and planning parameters that are incompatible with the spatial characteristics of the spatial unit are eliminated. Then, the screening candidate parameter sets are subjected to implementation feasibility verification according to the preset feasibility verification dimensions, and planning parameters that do not meet the preset implementation conditions are eliminated, resulting in the final adapted parameter set. Subsequently, according to the preset targeted matching rules, a one-to-one correspondence is established between the final adapted parameter set and the core constraint indicators of the spatial unit, generating industrial upgrading path entries that can directly solve the shortcomings of the corresponding core constraint indicators. Finally, based on the obstacle degree and effective weight value of the core constraint indicators, a corresponding quantitative control threshold is matched for each industrial upgrading path entry, generating a sub-item implementation parameter set. The industrial upgrading path entries include the overall regional coordinated upgrading path and the spatial unit differentiated upgrading path. The sub-item implementation parameter set includes the implementing entity, implementation links, resource allocation requirements, stage progress nodes, and quantitative standards for effect acceptance of the corresponding upgrading path. in, and All values ​​are preset fixed values, and their range is [0,1]. The value of is directly proportional to the number of core constraint indicators that the set of planning parameters can cover, the comprehensive obstacle degree of the corresponding indicators, and the matching degree of obstacle types. The value of is directly proportional to the potential of this set of planning parameters to enhance the competitiveness of the forest health and wellness industry in the target spatial unit, the adaptability of the spatial pattern, and the degree of matching with the industrial development foundation. When the value is 1, Take the preset minimum value. When the value of is not 1, The value is 0; Preset spatial compatibility rules include, for example, using the hot and cold hot attributes, agglomeration level and gradient location of spatial units as the basis for judgment. Hot areas are only matched with planning parameters for high-density service supply, while cold areas are only matched with planning parameters for resource cultivation and infrastructure construction. Parameters that do not match the agglomeration type and planning function are prohibited from entering the candidate set. The feasibility verification dimensions are preset, for example: judging each item from four dimensions: land use compliance, existing infrastructure carrying capacity, market entity participation conditions, and policy support matching degree. If any dimension fails to meet the preset conditions, it is judged as infeasible and eliminated. Pre-defined targeted matching rules include: prioritizing core constraint indicators with planning parameters one-to-one according to the degree of obstacle from high to low, and prioritizing matching planning parameters with the strongest improvement capabilities for high obstacle indicators, so as to ensure that the improvement path directly points to the most critical constraint factors. Among them, the data related to the forest health and wellness industry includes geospatial, socio-economic, thematic, policy text, and market entity data; The acquisition module is connected to the setting module via a wireless network. The setting module is connected to the verification module and the parsing module via a wireless network. The parsing module is connected to the diagnostic module and the output module via a wireless network.

[0022] In this embodiment, the data acquisition module collects data related to the forest health and wellness industry, performs data cleaning, normalization transformation, and spatial coordinate registration to output a standardized dataset. The configuration module simultaneously receives the standardized dataset, sets multi-level indicators for evaluating the competitiveness of the forest health and wellness industry, and completes the parameterization of the quantitative calculation rules, value ranges, and attribute types for each indicator. The verification module subsequently constructs a judgment matrix based on the set indicators, performs quantitative calculations of the weights of each indicator, verifies the consistency of the calculation results, and outputs valid weights that meet the preset verification threshold. The analysis module further calculates the comprehensive competitiveness score based on the standardized dataset and the valid weight values, simultaneously executing... The system determines the spatial distribution type of forest health and wellness industry elements, calculates kernel density, and quantifies spatial differentiation characteristics to output a spatial feature dataset. Then, the diagnostic module, based on effective weight values, standardized datasets, and spatial feature datasets, performs quantitative calculations of indicator factor contribution, deviation, and obstacle degree to identify core constraint indicators, complete the quantitative classification of obstacle types, and output an obstacle factor dataset. Finally, the output module receives the spatial feature dataset and obstacle factor dataset, matches them with a pre-set planning parameter library that corresponds one-to-one with forest health and wellness industry indicators and obstacle types, performs parameterized matching calculations corresponding to core constraint indicators, and generates industry upgrading path items and sub-item implementation parameter sets.

[0023] In the above embodiments, the system can integrate multi-dimensional related data of the forest health and wellness industry and complete standardized processing to build a scientific and comprehensive multi-level evaluation system. Through weight calculation and consistency verification, the system can ensure the accuracy and reliability of the evaluation results, clearly analyze the spatial distribution and differentiation characteristics of the industry, accurately identify development shortcomings and classify obstacle types, match and adapt planning parameters with regional spatial characteristics, generate differentiated and implementable industry upgrading paths and quantitative implementation standards, and help the industry to implement precise policies and steadily improve quality.

[0024] In summary, this embodiment establishes a multi-level evaluation index system and combines spatial heterogeneity correction to achieve scientific weighting and consistency verification. This makes the index weight results more in line with the actual development situation, accurately calculates the comprehensive score of industrial competitiveness, clearly analyzes the spatial distribution, kernel density, and spatial differentiation characteristics of industrial elements, accurately identifies core constraint indicators, and quantifies the types of obstacles. This provides a reliable basis for industrial development diagnosis. At the same time, it can match exclusive planning parameters based on obstacle factors and spatial characteristics to generate differentiated and feasible industrial upgrading paths and quantitative implementation parameters, taking into account both overall planning and local adaptation. This effectively improves the scientific nature of the forest health and wellness industry competitiveness assessment and the targeting of path planning.

[0025] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A system for evaluating the competitiveness and planning the path of the forest-based health and wellness industry, characterized in that, include: The data acquisition module is used to collect data related to the forest health and wellness industry, perform data cleaning, normalization transformation and spatial coordinate registration to output a standardized dataset; The configuration module is used to receive standardized datasets, set multi-level indicators for evaluating the competitiveness of the forest health and wellness industry, and complete the parameterization settings of the quantitative calculation rules, value ranges, and attribute types for each indicator. The verification module is used to construct a judgment matrix based on the set indicators, perform quantitative calculation of the weight of each indicator, perform consistency verification on the calculation results, and output the effective weights that meet the preset verification threshold. The parsing module is used to calculate the comprehensive competitiveness score based on the standardized dataset and effective weight values, and simultaneously perform the determination of the spatial distribution type of forest health and wellness industry elements, kernel density calculation and quantitative analysis of spatial differentiation characteristics, so as to output a spatial feature dataset. The diagnostic module is used to perform quantitative calculations of the contribution, deviation and obstacle degree of indicator factors based on effective weight values, standardized datasets and spatial feature datasets, identify core constraint indicator items, complete the quantitative classification of obstacle types, and output obstacle factor datasets. The output module receives spatial feature datasets and obstacle factor datasets, matches them with a pre-defined planning parameter library that corresponds one-to-one with the forest health and wellness industry indicators and obstacle types, performs parameterized matching calculations for the core constraint indicators, and generates industry upgrading path entries and sub-item implementation parameter sets. Among them, the data related to the forest health and wellness industry includes geospatial, socio-economic, thematic, policy text, and market entity data.

2. The forest health and wellness industry competitiveness assessment and path planning system according to claim 1, characterized in that, The data acquisition module uses a preset geographic grid unit as the smallest anchoring unit to match and bind the collected non-spatial forest health and wellness industry-related data with geospatial data to generate a full-scale related dataset with spatial attributes. Then, it performs outlier removal, missing value completion, and unit unification processing on the full-scale related dataset. Among them, outlier removal is based on the identification and removal of outliers in the full associated dataset based on the preset quantile threshold, and missing value completion is completed by fitting interpolation method of strong correlation index in the same spatial unit as the missing data.

3. The forest health and wellness industry competitiveness assessment and path planning system according to claim 2, characterized in that, The dimensional unification process follows: For indicator data with positive attributes: ; For indicator data with negative attributes: ; In the formula: This is the standardized output value of the j-th index for the i-th spatial unit; This represents the original matching value of the j-th index in the i-th spatial unit; This represents the original maximum value of the j-th index in the full spatial unit; It represents the original minimum value of the j-th index in the full spatial unit.

4. The forest-based health and wellness industry competitiveness assessment and pathway planning system according to claim 1, characterized in that, The multi-level indicator items constructed by the setting module include four progressive levels: target layer, criterion layer, element layer, and indicator layer. Among them, the target layer is the overall competitiveness level of the forest health and wellness industry, the criteria layer includes five parallel dimensions: resource base, industry operation, service support, market benefits and spatial adaptation, the element layer is the set of subdivided industry elements corresponding to each criteria layer, and the indicator layer is the smallest quantifiable indicator unit corresponding to each element layer. During the operation of the setting module, the attribute types of each indicator item in the indicator layer are simultaneously determined and classified. The attribute types include positive, negative, and moderate. Among them, the moderate indicator is a non-unidirectional attribute indicator whose optimal value falls within the indicator value range. obey: < < For the indicator items of the moderate attribute, the dimensional unification is completed by calculating using the improved cosine ridge model; in, This represents the original maximum value of the j-th index in the full spatial unit; It represents the original minimum value of the j-th index in the full spatial unit.

5. The forest health and wellness industry competitiveness assessment and path planning system according to claim 1, characterized in that, For multi-level indicators, the verification module constructs corresponding judgment matrices for the criterion layer, element layer, and indicator layer from top to bottom, and performs weight calculation and consistency verification on the judgment matrix of each level. The weight calculation follows the following: ； In the formula: A is the judgment matrix of the current level; To determine the largest eigenvalue of matrix A; To determine the initial weight vector corresponding to the matrix; This represents the final effective weight value of the k-th indicator at the current level. This is the initial adjusted weight value for the k-th indicator; n is the number of indicator dimensions in the current level judgment matrix; The scale value for setting the relative importance between the k-th and t-th indicators at the current level, which is set as a parameter for the module. is the spatial heterogeneity correction coefficient for the k-th index.

6. The forest health and wellness industry competitiveness assessment and path planning system according to claim 5, characterized in that, The consistency check obeys: ； In the formula: CI is the consistency index; RI is the preset average random consistency index corresponding to the dimension n of the judgment matrix; CR is the consistency ratio; Specifically, the weight calculation result of the current level is determined to be a valid weight only if CR is less than the preset verification threshold.

7. The forest health and wellness industry competitiveness assessment and path planning system according to claim 1, characterized in that, The analysis module calculates the comprehensive score of the forest health and wellness industry competitiveness of each spatial unit based on the standardized dataset and effective weight values: In the formula: The comprehensive score of the competitiveness of the forest health and wellness industry in the i-th spatial unit; The multi-criteria layer coupling degree of the i-th spatial unit; The multi-criteria layer comprehensive development index for the i-th spatial unit; The total number of dimensions in the criteria layer; This represents the development level value of the m-th criterion layer in the i-th spatial unit; The total number of indicators in the indicator layer corresponding to the m-th criterion layer; For the m-th criterion layer, the first... Effective weight values ​​for each indicator; For the i-th spatial unit, the m-th criterion layer, the... The standardized value of the indicator; The effective weight values ​​for the m-th criterion layer; The spatial feature dataset includes the results of global and local distribution type determination, the results of global and local spatial correlation index calculation, the results of continuous kernel density measurement of the entire geographical range, the results of spatial heterogeneity of global and regional areas, the results of spatial heterogeneity contribution of sub-indicators, and the spatial coordinate binding datasets corresponding to all results.

8. The forest health and wellness industry competitiveness assessment and path planning system according to claim 1, characterized in that, The diagnostic module performs quantitative calculations of the contribution, deviation, and obstacle degree of the performance indicators: ； In the formula: For the j-th criterion layer, the first... The overall contribution of each indicator to the entire region; N is the total number of spatial units; The effective weight value of the j-th criterion layer; For the j-th criterion layer, the first... Effective weight values ​​for each indicator; For the i-th spatial unit, the j-th criterion layer, the... The standardized value of the indicator; For the i-th spatial unit, the j-th criterion layer, the... Deviation of the indicator; For the j-th criterion layer in the entire region, the first... The standardized average of the indicators; For the i-th spatial unit, the j-th criterion layer, the... The degree of obstruction of the indicator; The diagnostic module uses preset judgment rules to comprehensively compare the calculated contribution, deviation, and obstacle values ​​of each indicator. It then selects indicators whose obstacle value is higher than a preset obstacle threshold, whose contribution value reaches a preset contribution threshold, and whose deviation exceeds a preset deviation range. These indicators are then used as the core constraint indicators for the entire region and each spatial unit. Based on the criterion layer dimension, indicator attribute characteristics, and obstacle causes of the core constraint indicators, the module quantifies and classifies obstacle types into resource-shortage, operationally inefficient, insufficiently supported, market-weak, and spatially mismatched types, and outputs the obstacle factor dataset for each spatial unit.

9. The forest health and wellness industry competitiveness assessment and path planning system according to claim 1, characterized in that, The output module contains a preset planning parameter library that stores a set of industry upgrading strategies, implementation parameter thresholds and spatial adaptation rules that correspond one-to-one with each indicator item and each type of obstacle. During the operation phase, the output module, based on the obstacle factor dataset and spatial feature dataset, completes the parameterized matching of core constraint indicators with the preset planning parameter library using the following formula: ； In the formula: The degree of matching between the core constraint index of the i-th spatial unit and the k-th group of planning parameters in the preset planning parameter library; The comprehensive obstacle degree is the core constraint index of the i-th spatial unit; This represents the total number of core constraint indicators for this space unit. The obstacle degree is the z-th core constraint indicator of the i-th spatial unit; is the effective weight value corresponding to the z-th core constraint indicator; This represents the overall competitiveness score of the i-th spatial unit. Let be the obstacle fit coefficient for the k-th group of planning parameters; Improve the fit coefficient for the competitiveness of the k-th group of planning parameters; For maintenance items; The output module sorts the planning parameters in the preset planning parameter library from high to low according to their matching degree, and selects the top N sets of planning parameters with a matching degree greater than a preset matching threshold as candidate parameter sets. Then, combined with the spatial clustering characteristics, gradient differentiation characteristics, and hot and cold spot distribution characteristics in the spatial feature dataset, the candidate parameter sets are subjected to spatial constraint screening according to preset spatial compatibility rules, and planning parameters that are incompatible with the spatial characteristics of the spatial unit are eliminated. Then, the screening candidate parameter sets are subjected to implementation feasibility verification according to preset feasibility verification dimensions, and planning parameters that do not meet the preset implementation conditions are eliminated, resulting in the final adapted parameter set. Subsequently, according to preset targeted matching rules, a one-to-one correspondence is established between the final adapted parameter set and the core constraint indicators of the spatial unit, generating industrial upgrading path entries that can directly solve the shortcomings of the corresponding core constraint indicators. Finally, based on the obstacle degree and effective weight value of the core constraint indicators, a corresponding quantitative control threshold is matched for each industrial upgrading path entry, generating a sub-item implementation parameter set.

10. A forest-based health and wellness industry competitiveness assessment and pathway planning system according to claim 1, characterized in that, The acquisition module is interconnected with a setting module via a wireless network. The setting module is interconnected with a verification module and a parsing module via a wireless network. The parsing module is interconnected with a diagnostic module and an output module via a wireless network.

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