A method and system for evaluating regional balance of infrastructure based on multi-dimensional data

Abstract

The present invention discloses a method and system for assessing regional infrastructure balance based on multidimensional data. This method and system belongs to the field of data processing technology and includes multidimensional data integration and spatial environment modeling. After calculating regional supply capacity and regional demand using multidimensional data, the regional supply and demand balance is evaluated and scored, and corresponding strategies are output based on the scores and regional population conditions. This method and system for assessing regional infrastructure balance based on multidimensional data introduces a dynamic spatial service area modeling mechanism for infrastructure to truly reflect the effective radiation capacity of infrastructure within and outside the region for the population. Combined with a population micro-behavior modeling mechanism, it accurately depicts the supply and demand relationship and establishes a dynamic regional balance assessment and feedback mechanism, updating balance evaluation results and strategic recommendations in real time.

Description

Technical Field

[0001] The present invention belongs to the field of data analysis technology, and in particular relates to a method and system for evaluating regional balance of infrastructure based on multi-dimensional data. Background Art

[0002] In modern urban and regional development, infrastructure (such as healthcare, education, transportation, and energy) serves as a key factor supporting regional socioeconomic operations. Its layout and distribution directly impact regional equity, coordination, and sustainable development. Assessing the regional balance of infrastructure has become a crucial tool for governments and planning agencies at all levels in formulating regional development policies, optimizing public resource allocation, and improving the quality of public services. Existing balance assessment methods are mostly based on traditional spatial statistics, weighted scoring, the Analytic Hierarchy Process (AHP), or per capita indicators. These methods typically use administrative boundaries as assessment units. Based on data such as the number of infrastructure facilities, service capacity, and population size within a region, per capita infrastructure resource indicators are calculated across regions as a measure of balance. However, in reality, the use and distribution of regional infrastructure exhibits a high degree of spatial heterogeneity and dynamic population behavior. The actual service capacity of infrastructure is constrained by various factors, such as terrain barriers, transportation accessibility, and service levels. The "service radius" of the same facility varies significantly across different geographical environments. Simply dividing by administrative boundaries fails to truly reflect the service spillover effects of infrastructure on surrounding areas. On the other hand, existing assessment methods generally ignore the cross-regional mobility, service preferences, and selection behaviors of people when actually using infrastructure. Different groups, influenced by factors such as travel distance, service quality, and economic costs, exhibit a significant "unbalanced use" phenomenon. That is, even if infrastructure resources are evenly distributed spatially, actual usage by people can still lead to overloaded facilities in some areas and underutilized facilities in others. Furthermore, some existing methods only conduct static assessments at the current point in time and fail to dynamically reflect the evolution of regional equilibrium under the influence of multiple factors such as infrastructure supply and demand, changes in population mobility, and policy interventions. Consequently, they lack foresight and strategic guidance.

[0003] Therefore, existing technologies have the following major flaws when assessing regional infrastructure balance: (1) the division of assessment units is too rigid, making it difficult to truly reflect the service capacity of facility space; (2) there is a lack of dynamic modeling of people's actual usage behavior, which makes it impossible to reveal the complex interactive relationship between infrastructure supply and actual demand; and (3) there is a lack of dynamic prediction of future balance trends, which makes it impossible to provide effective decision-making support. These problems seriously restrict the scientific nature and operability of the assessment results, resulting in the risk of decision-making lags or resource mismatches in infrastructure planning and adjustment.

[0004] To this end, we propose an infrastructure regional balance assessment method and system based on multi-dimensional data to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to solve the problem of lack of dynamic modeling evaluation in the existing technology, and to propose a method and system for infrastructure regional balance evaluation based on multi-dimensional data.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for evaluating regional infrastructure balance based on multi-dimensional data, including:

[0008] S1: Collect multi-dimensional raw data, integrate the multi-dimensional raw data into a data set after preprocessing, and build a data graph model with each area and facility as a node and the traffic connections and impact relationships between areas as edges;

[0009] S2: Calculate the influence of each facility on any area based on the data graph model, delineate the dynamic service area of ​​each facility based on the influence, and generate the corresponding spatial supply matrix to represent the comprehensive service volume obtained by the area from each facility;

[0010] S3: Calculate the demand for facilities in each region by taking the spatial supply matrix, the population attributes of each region, and the facility service area as input, and screen the facilities that are actually accessible in the region, and output the dynamic actual demand matrix for each region;

[0011] S4: Taking the spatial supply matrix and the dynamic actual demand matrix as input, complete the supply and demand matching analysis of infrastructure services in the region, and quantify it to output a sequence of regional balance indicators;

[0012] S5: Divide the balance indicator sequences of different regions into different intervals corresponding to different strategy levels, and output the corresponding response strategy based on the basic population size of the region.

[0013] Preferably, the multi-dimensional original data in step S1 includes infrastructure data, transportation network data, terrain data and population data.

[0014] Preferably, the influence in step S2 is calculated by weighting the travel time or distance from the region to the facility, the ratio of the transportation cost of the facility's location, and the ratio of the service supply and demand pressure.

[0015] Preferably, the demand for facilities in step S3 is calculated by weighting and balancing the population base, travel activity, space supply, traffic accessibility, subjective preferences and service scope.

[0016] Preferably, the supply and demand matching analysis in step S4 evaluates the basic deviation between supply and demand by calculating the normalized cumulative degree of supply and demand of all facilities in the region, and obtains the balance score of the region through calculation after multi-dimensional adjustment and correction of the basic deviation.

[0017] Preferably, the regional balance score is obtained by amplifying the imbalance by weighted summing of negative factors, including the basic deviation between supply and demand, the region's dependence on external services, and the overload of facilities, and converting it into a standardized score, i.e., a regional balance index sequence, by the inverse.

[0018] Preferably, in step S5, by respectively setting the first equilibrium threshold, the second equilibrium threshold, and the population critical threshold, the corresponding policy levels are as follows:

[0019] When the balance score sequence is lower than the first balance threshold and the population is lower than the critical population threshold, the strategy level is expansion priority, and infrastructure is added or expanded;

[0020] When the balance score sequence is higher than or equal to the first balance threshold and lower than the second balance threshold and / or the population is lower than the critical population threshold, the strategy level is structural adjustment, optimizing the layout of existing facilities;

[0021] When the balance score sequence is higher than the second balance threshold, the strategy level is elastic redundancy and no new facilities are added.

[0022] An infrastructure regional balance assessment system based on multi-dimensional data, including:

[0023] The data collection module is configured to collect multi-dimensional raw data, integrate the multi-dimensional raw data into a data set after pre-processing, and establish a data graph model with each area and facility as a node and the traffic connections and impact relationships between areas as edges;

[0024] The supply and demand calculation module is configured to calculate the influence of each facility on any region based on a data graph model, delineate the dynamic service area of ​​each facility based on the influence, and generate a corresponding spatial supply matrix to represent the total amount of service that the region receives from each facility. The module uses the spatial supply matrix, the population attributes of each region, and the facility service area as input to calculate the regional demand for facilities, screen the facilities that are actually accessible in the region, and output a dynamic actual demand matrix for each region.

[0025] The supply and demand balance assessment module is configured to take the spatial supply matrix and the dynamic actual demand matrix as input, complete the supply and demand matching analysis of infrastructure services in the region, and quantify it to output a sequence of regional balance indicators;

[0026] The strategy generation module is set to divide the balance indicator sequences of different regions into different intervals corresponding to different strategy levels, and output the corresponding response strategy based on the basic population size of the region.

[0027] In summary, the technical effects and advantages of the present invention are as follows: the infrastructure regional balance assessment method and system based on multi-dimensional data, based on multi-source heterogeneous data, innovatively introduces a dynamic spatial service area modeling mechanism for infrastructure, which can fully consider a variety of spatial factors such as terrain, traffic accessibility, and facility supply capacity, dynamically construct the true service scope of each facility, and truly reflect the effective radiation capacity of infrastructure inside and outside the region to the population; at the same time, the system combines the crowd micro-behavior modeling mechanism to simulate the cross-regional flow, preference selection and use behavior of different groups for infrastructure inside and outside the service area, accurately portray the dynamic supply and demand relationship of "people-facilities-space", and significantly improve the authenticity and accuracy of the assessment results. In addition, the present invention further establishes a dynamic assessment and feedback mechanism for regional balance, which supports real-time updating of balance evaluation results and strategic recommendations under the influence of dynamic factors such as supply and demand relationships, population changes, and resource adjustments. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is a flow chart of the method of the present invention;

[0029] Figure 2 Schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0031] like Figure 1 As shown in FIG, a method for evaluating regional infrastructure balance based on multi-dimensional data includes:

[0032] S1: Collect multi-dimensional raw data, integrate the multi-dimensional raw data into a data set after preprocessing, and build a data graph model with each area and facility as a node and the traffic connections and impact relationships between areas as edges;

[0033] S2: Calculate the influence of each facility on any area based on the data graph model, delineate the dynamic service area of ​​each facility based on the influence, and generate the corresponding spatial supply matrix to represent the comprehensive service volume obtained by the area from each facility;

[0034] S3: Calculate the demand for facilities in each region by taking the spatial supply matrix, the population attributes of each region, and the facility service area as input, and screen the facilities that are actually accessible in the region, and output the dynamic actual demand matrix for each region;

[0035] S4: Taking the spatial supply matrix and the dynamic actual demand matrix as input, complete the supply and demand matching analysis of infrastructure services in the region, and quantify it to output a sequence of regional balance indicators;

[0036] S5: Divide the balance indicator sequences of different regions into different intervals corresponding to different strategy levels, and output the corresponding response strategy based on the basic population size of the region.

[0037] The details are as follows:

[0038] Step 1: Multi-dimensional data integration and spatial environment modeling

[0039] In this step, we integrate multi-dimensional data such as region, infrastructure, transportation network, terrain and population to build a comprehensive data graph model of "space-facility-population" , providing data support for subsequent steps such as spatial service area modeling, crowd behavior simulation, and balance assessment.

[0040] Data integration and graph model construction:

[0041] Goal: Build a multi-dimensional data graph , which includes a comprehensive representation of factors such as facilities, transportation, population, and terrain.

[0042] Data Collection:

[0043] Infrastructure data: This includes the location, capacity, and service types of facilities such as hospitals, schools, and transportation hubs (for example, Hospital A has 200 beds, School B has 500 places, etc.);

[0044] Transportation network data: including the spatial location and accessibility of road networks, bus routes, subway lines, etc. (for example, the walking time from area 1 to area 2 is 15 minutes, and the public transportation time from area 1 to area 3 is 30 minutes);

[0045] Topographic data: for example, slope, geographical obstacles, population density, etc. (for example, there is a large hill between area A and area B, and it takes 40 minutes to walk);

[0046] Population data: socioeconomic information such as the total population, age structure, and income level of each area (e.g., Area 1 has a population of 5,000, 80% of whom are young people).

[0047] Integrate data:

[0048] After pre-processing (such as standardization and normalization), these data will be integrated into a unified data set and provided to the subsequent steps. Each area and facility will be used as a node in the graph ( ), while the traffic connections and influence relationships between regions will serve as edges ( ) are added to the diagram.

[0049] Example: Suppose there are three areas A, B, and C, each with different facilities and transportation connections:

[0050] Area A has a hospital and a school, area B has a school, and area C has multiple transportation hubs.

[0051] The transportation network connects Area A and Area B (10 minutes walk), and Area B and Area C (15 minutes walk).

[0052] Weighted impact factor and data graph construction:

[0053] In order to truly depict the relationship between regions and the influence of facilities, we introduced a weighted impact factor , the influence between each pair of adjacent regions is calculated using the following formula:

[0054] ;

[0055] in:

[0056] : Indicates area For the region influence;

[0057] :area and region The transportation distance or time between them indicates “transportation accessibility”;

[0058] 、 :Represents the area and The population size (which can be population density, reflecting the intensity of demand);

[0059] :area The service capacity of internal infrastructure, such as the number of hospital beds and school degrees, reflects service capabilities;

[0060] , , : Weighting factor, used to adjust the weights of different factors.

[0061] Examples:

[0062] Assume that the distance between area A and area B is 15 minutes walking, the population of area A is 5000, the population of area B is 3000, area A has one hospital and one school with a total service capacity of 200 beds and 500 degrees, and area B has only one school with a total service capacity of 300 degrees. Assume that we give the weight , , ,So:

[0063] minutes (walking time);

[0064] , ;

[0065] (Total service capacity of the facility), .

[0066] Substitute the formula for calculation :

[0067] ;

[0068] ;

[0069] ;

[0070] This weighting method can dynamically adjust the influence between regions based on transportation accessibility, population distribution, and infrastructure service capacity.

[0071] Node attribute vector construction:

[0072] Attribute vector for each area and facility Factors such as facilities, population, transportation, and terrain will be comprehensively considered. The specific formula is:

[0073] ;

[0074] Examples:

[0075] For Region A:

[0076] = [number of hospital beds = 200, number of school places = 500];

[0077] = 5000 people;

[0078] = 15 minutes walk to Area B;

[0079] = 200 (facility service capacity).

[0080] So the attribute vector of region A for:

[0081] ;

[0082] Step 2: Infrastructure dynamic service area modeling

[0083] In this step, our goal is to build a multi-dimensional data graph model based on the data provided in step 1. , dynamically calculate the service area for each infrastructure , and generate the corresponding space supply matrix The core innovation of this step lies in the design of dynamic influencing factors. Combined with the "infrastructure regional balance assessment" scenario targeted by the plan, the supply capacity of each region is dynamically adjusted through a weighted influence model, thereby providing a more realistic supply environment for subsequent population behavior modeling and balance assessment.

[0084] The input of this step is the multidimensional space graph output from step 1 , which includes the node set of regions and facilities, the connecting edges, and the attribute vector of each node .

[0085] based on , calculate each facility For any area Influence , defined as follows:

[0086] ;

[0087] in:

[0088] :facility For the region service impact;

[0089] :area To the facility travel time or distance, indicating accessibility (unit: minutes or kilometers);

[0090] :facility The location's accessibility to the transportation network, typically expressed as connectivity or average transit time (e.g., number of buses / subways, road density);

[0091] :facility Unit service cost, such as maintenance fee or construction budget (unit: 10,000 yuan);

[0092] :facility Service capacity, such as the number of beds or degrees (unit: person);

[0093] :area The population of (unit: person);

[0094] : Smoothing constant, to prevent division by zero, usually ;

[0095] : Weighting coefficients set by experience (such as ), which is used to adjust the contribution of each item to influence.

[0096] This formula innovatively introduces Item, reflecting the supply-demand pressure ratio, while traditional models often ignore this dynamic item; and add , which measures the benefits of transportation services per unit of input.

[0097] Based on all , define the dynamic service area of ​​each facility :

[0098] ;

[0099] in:

[0100] :facility The service area represents the set of all areas that it can effectively radiate;

[0101] : Service impact threshold (can be set by facility level or experience, indicating the minimum effective service standard, such as ).

[0102] Finally, for each area Calculate the supply matrix , which represents the comprehensive service volume obtained by the region from various facilities:

[0103] ;

[0104] in:

[0105] :area spatial supply intensity (unit: impact score);

[0106] :Indicator function, if , then it is 1, otherwise it is 0, and only the contribution of facilities that actually belong to the service area is counted.

[0107] Through this mechanism, all facilities’ service capabilities for the region are comprehensively formed. .

[0108] Step 3: Dynamic demand modeling based on micro-crowd behavior

[0109] The goal of this step is to generate the spatial supply matrix generated in step 2. Based on the population attributes and facility service areas of each area , build a spatial behavior response model and output the dynamic actual demand matrix of each area , which is used to characterize the real usage tendency of people towards infrastructure services under multi-dimensional influencing factors.

[0110] For modeling area About facilities The real demand , we design a behavior-aware demand generation function:

[0111] ;

[0112] in:

[0113] :area About facilities Actual usage demand (number of people);

[0114] :area Total population;

[0115] : The travel activity of regional population (such as Indicates that the population in the region has high mobility);

[0116] :area Total space supply intensity;

[0117] :area To the facility Traffic access time;

[0118] :Crowd to facilities Type preference coefficient;

[0119] : Small constant to prevent division by zero (such as );

[0120] :Service area indicator function, if In the facility If it is within the service area, it is 1, otherwise it is 0;

[0121] : Behavioral response weight coefficient, balancing the influence of accessibility orientation and subjective preference orientation (such as ).

[0122] The formula expresses:

[0123] The basic demand of the population is determined by Decide;

[0124] Supply-distance ratio It means “distance perception under the premise of supply availability”;

[0125] Subjective preference Whether in the service area Together they determine whether the population will actively choose this facility type;

[0126] The combination of the two factors determines the intensity of the facility response of the population.

[0127] Final output dynamic demand matrix , each element Indicates the “actual number of people who intend to use” the facility in the area:

[0128] ;

[0129] in It is a region A collection of practically accessible facilities.

[0130] Through the above design, we have built a real, dynamic, and spatially aware population demand modeling mechanism, providing indispensable "demand-side modeling support" for regional infrastructure balance assessment.

[0131] Step 4: Dynamic assessment of the balance of supply and demand integration

[0132] The core goal of this step is to obtain the regional supply matrix output in step 2. The dynamic demand matrix output from step 3 Build a fusion relationship between them, complete the supply and demand matching analysis of infrastructure services in the region, and quantify the regional "Dynamic equilibrium level" in infrastructure use Unlike traditional supply-demand gap analysis, this step combines factors such as cross-regional service flows, population activity, and facility carrying capacity to propose a "Supply-Demand Structural Deviation Index" (SDSDI) model with integrated structure and dynamic correction to accurately depict the degree of imbalance between infrastructure supply and usage behavior in different regions.

[0133] The input variables include:

[0134] Regional Supply Matrix , indicating facilities For the region service intensity;

[0135] Regional Demand Matrix , indicating the area Actual facilities Use requirements;

[0136] Regional population travel activity coefficient (from step 3);

[0137] Regional facility load risk factor , reflecting the long-term load-bearing intensity of infrastructure in the region (e.g., the normalized value of the average annual load ratio of facilities);

[0138] Cross-region service outflow ratio , defined as the region The proportion of the population's demand for "non-regional facilities" reflects the region's reliance on external facilities. The calculation formula is as follows:

[0139] ;

[0140] in:

[0141] Indicates area The collection of facilities within the jurisdiction;

[0142] Indicates area About facilities actual needs;

[0143] is a small constant that prevents the denominator from being zero.

[0144] This item reflects the "spillover dependence" of regional services and is an important dimension for assessing regional structural imbalances.

[0145] To assess the “basic deviation” between supply and demand, we define the regional supply and demand structure deviation value , indicating that in the area The normalized cumulative degree of supply and demand offsets for all relevant facilities in:

[0146] ;

[0147] in:

[0148] :area The unit supply and demand deviation index is usually in the range of between;

[0149] : All pairs of regions the collection of facilities that generate services;

[0150] :facility For the region supply intensity;

[0151] :area About facilities The actual demand intensity;

[0152] : Smoothing factor to prevent division by zero (recommended );

[0153] : Facility response importance factor, defined as:

[0154] ;

[0155] Indicates that the facility is in all affected areas The service weight of the facilities.

[0156] This formula not only considers the proportional shift of the supply and demand difference, but also The introduction of a weighting for the impact of "main facilities" in services makes the evaluation more sensitive to imbalances in the main service sources. This structural design can reflect the real-world scenario where "imbalances in key facilities are more important."

[0157] In calculating Finally, we further define the “dynamic balance score of behavior-structure fusion” , by making multi-dimensional adjustments to the basic deviation, the following index model is constructed:

[0158] ;

[0159] in:

[0160] :area Balance score, range , the larger the value, the higher the balance;

[0161] : Deviation of basic supply and demand structure;

[0162] : Population travel activity coefficient (from step 3);

[0163] : Service spillover coefficient, which indicates the degree of regional dependence on external services;

[0164] : Regional facility load pressure coefficient (such as annual average utilization rate), whose square term strengthens the nonlinear increase of high pressure risk;

[0165] : Weighted adjustment factor (for example, , , ) to adapt to urban policy orientation.

[0166] For example: suppose a region The parameters are as follows:

[0167] (slight shift in supply and demand structure);

[0168] (high travel activity area);

[0169] (40% of people use external services);

[0170] (The facility has entered a high load risk);

[0171] Set the adjustment coefficient , , ,but:

[0172] ;

[0173] This balance score suggests that the region as a whole has shown a serious imbalance between supply and demand, with both behavioral and facility problems coexisting.

[0174] The output result is a sequence of regional balance indicators , which will serve as the basic support for generating sorting, grading and regulation suggestions in subsequent optimization feedback.

[0175] In summary, this step constructs a quantitative scheme that is highly adaptable to the infrastructure spatial imbalance assessment scenario through the integration of multi-dimensional adjustment and structural index. It is the core technical node supporting systematic evaluation in this scheme.

[0176] Step 5: Dynamic feedback and strategy recommendation generation

[0177] This step aims to evaluate the regional balance score output from the previous step. This analysis is conducted based on a rule-based interpretation and combined with the region's basic attributes to form structured and categorized strategic recommendations, which serve as the response output of the entire patent system, completing the "modeling-assessment-optimization" closed loop. We propose an "evaluation-driven strategy generation rule set" to achieve differentiated regulatory recommendations for balanced outcomes, avoiding further modeling or numerical optimization, and ensuring clarity, reproducibility, and operability of strategy generation.

[0178] The only input of this step is the balance score sequence output from the previous step ,in:

[0179] ∈ (0, 1], indicating the region Dynamic supply and demand balance score;

[0180] The lower it is, the greater the imbalance between supply and demand in the region.

[0181] Design a rule set based on the scoring interval to directly The value segment is divided into several strategic levels, combined with the regional Basic population size (determined in step 1), output the response strategy recommendation category :

[0182] ;

[0183] in:

[0184] :area Corresponding strategy category suggestions;

[0185] : Balance threshold (such as , );

[0186] : Critical population size, used to distinguish high-population from low-population areas (e.g. );

[0187] Specific recommendations for each strategy category are as follows:

[0188] Expansion priority ( Very low + high population):

[0189] Strategic recommendations: Build or expand infrastructure, prioritize the introduction of supply points within the region, and shorten service distances;

[0190] Supporting measures: establish new sites, increase the capacity of existing facilities, and encourage local use.

[0191] Structural adjustments (medium score or small population area):

[0192] Strategic recommendations: Optimize the layout of existing facilities and improve resource utilization efficiency;

[0193] Supporting measures: Relieve redundant / insufficient resources through facility integration and cross-regional collaboration.

[0194] Resilience and Redundancy (High-scoring Areas):

[0195] Strategic recommendations: No new facilities, retain flexible space;

[0196] Supporting measures: Strengthen behavioral monitoring, set up early warning mechanisms, and maintain flexible scheduling.

[0197] The technical solutions in the above-mentioned embodiments of the present application have at least the following technical effects or advantages: based on multi-source heterogeneous data, an innovative infrastructure dynamic spatial service area modeling mechanism is introduced, which can fully consider a variety of spatial factors such as terrain, traffic accessibility, and facility supply capacity, dynamically construct the real service scope of each facility, and truly reflect the effective radiation capacity of infrastructure inside and outside the region to the population; at the same time, the system combines the crowd micro-behavior modeling mechanism to simulate the cross-regional flow, preference selection and use behavior of different groups for infrastructure inside and outside the service area, accurately depict the dynamic supply and demand relationship of "people-facilities-space", and significantly improve the authenticity and accuracy of the evaluation results. In addition, the present invention further establishes a dynamic evaluation and feedback mechanism for regional balance, which supports real-time updating of balance evaluation results and strategic recommendations under the influence of dynamic factors such as supply and demand relationships, population changes, and resource adjustments.

[0198] The present application also provides an infrastructure regional balance assessment system based on multi-dimensional data, such as Figure 2 Shown, including:

[0199] The data collection module is configured to collect multi-dimensional raw data, integrate the multi-dimensional raw data into a data set after pre-processing, and establish a data graph model with each area and facility as a node and the traffic connections and impact relationships between areas as edges;

[0200] The supply and demand calculation module is configured to calculate the influence of each facility on any region based on a data graph model, delineate the dynamic service area of ​​each facility based on the influence, and generate a corresponding spatial supply matrix to represent the total amount of service that the region receives from each facility. The module uses the spatial supply matrix, the population attributes of each region, and the facility service area as input to calculate the regional demand for facilities, screen the facilities that are actually accessible in the region, and output a dynamic actual demand matrix for each region.

[0201] The supply and demand balance assessment module is configured to take the spatial supply matrix and the dynamic actual demand matrix as input, complete the supply and demand matching analysis of infrastructure services in the region, and quantify it to output a sequence of regional balance indicators;

[0202] The strategy generation module is set to divide the balance indicator sequences of different regions into different intervals corresponding to different strategy levels, and output the corresponding response strategy based on the basic population size of the region.

[0203] The technical solutions in the above-mentioned embodiments of the present application have at least the following technical effects or advantages: based on multi-source heterogeneous data, an innovative infrastructure dynamic spatial service area modeling mechanism is introduced, which can fully consider a variety of spatial factors such as terrain, traffic accessibility, and facility supply capacity, dynamically construct the real service scope of each facility, and truly reflect the effective radiation capacity of infrastructure inside and outside the region to the population; at the same time, the system combines the crowd micro-behavior modeling mechanism to simulate the cross-regional flow, preference selection and use behavior of different groups for infrastructure inside and outside the service area, accurately depict the dynamic supply and demand relationship of "people-facilities-space", and significantly improve the authenticity and accuracy of the evaluation results. In addition, the present invention further establishes a dynamic evaluation and feedback mechanism for regional balance, which supports real-time updating of balance evaluation results and strategic recommendations under the influence of dynamic factors such as supply and demand relationships, population changes, and resource adjustments.

[0204] The working principle is as follows: By introducing the dynamic spatial service area modeling mechanism of infrastructure, the real service range of the facility is dynamically constructed based on multi-dimensional data, and the dynamic supply and demand relationship is constructed in combination with crowd behavior for dynamic evaluation.

[0205] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for evaluating regional balance of infrastructure based on multi-dimensional data, characterized in that: The process includes the following steps: S1: Collect multi-dimensional raw data, integrate the multi-dimensional raw data into a data set after preprocessing, and build a data graph model with each area and facility as a node and the traffic connections and impact relationships between areas as edges; S2: Calculate the influence of each facility on any region based on the data graph model, delineate the dynamic service area of ​​each facility based on the influence, and generate a corresponding spatial supply matrix to represent the comprehensive service volume obtained by the region from each facility; the influence is calculated by weighting the travel time or distance from the region to the facility, the transportation cost ratio of the facility's location, and the service supply and demand pressure ratio; S3: Calculate the demand for facilities in each region by taking the spatial supply matrix, the population attributes of each region, and the facility service area as input, and screen the facilities that are actually accessible in the region, and output the dynamic actual demand matrix for each region; The dynamic actual demand matrix is ​​used to describe the actual usage tendency of the population towards infrastructure services under multi-dimensional influencing factors; S4: Taking the spatial supply matrix and the dynamic actual demand matrix as input, complete the supply and demand matching analysis of infrastructure services in the region, and quantify it to output a sequence of regional balance indicators; S5: Divide the balance indicator sequences of different regions into different intervals, where the different intervals correspond to different strategy levels, and output corresponding response strategies based on the basic population size of the region.

2. The method for evaluating regional infrastructure balance based on multi-dimensional data according to claim 1, characterized in that: The multi-dimensional original data in step S1 includes infrastructure data, transportation network data, terrain data and population data.

3. The method for evaluating regional infrastructure balance based on multi-dimensional data according to claim 1, characterized in that: The demand for facilities in step S3 is calculated by weighting and balancing population base, travel activity, space supply, traffic accessibility, subjective preference and service scope.

4. The method for evaluating regional infrastructure balance based on multi-dimensional data according to claim 1, characterized in that: The supply and demand matching analysis in step S4 evaluates the basic deviation between supply and demand by calculating the normalized cumulative degree of supply and demand of all facilities in the region, and obtains the regional balance score through calculation after multi-dimensional adjustment and correction of the basic deviation.

5. The method for evaluating regional infrastructure balance based on multi-dimensional data according to claim 4 is characterized in that: The balance score of the region is obtained by amplifying the imbalance by weighted summing of negative factors, including the basic deviation between supply and demand, the region's dependence on external services and the overload of facilities, and converting it into a standardized score, i.e., a sequence of regional balance indicators, through the inverse.

6. The method for evaluating regional infrastructure balance based on multi-dimensional data according to claim 1, characterized in that: In step S5, by setting the first equilibrium threshold, the second equilibrium threshold, and the population critical threshold, the corresponding policy levels are as follows: When the balance score sequence is lower than the first balance threshold and the population is higher than the critical population threshold, the strategy level is expansion priority, and the strategy recommendation is to add or expand infrastructure; When the balance score sequence is higher than or equal to the first balance threshold and lower than the second balance threshold and / or the population is lower than the critical population threshold, the strategy level is structural adjustment and the strategy recommendation is to optimize the layout of existing facilities; When the balance score sequence is higher than the second balance threshold, the strategy level is elastic redundancy and the strategy recommendation is not to add new facilities.

7. A regional infrastructure balance assessment system based on multi-dimensional data, characterized by: include: The data collection module is configured to collect multi-dimensional raw data, integrate the multi-dimensional raw data into a data set after pre-processing, and establish a data graph model with each area and facility as a node and the traffic connections and impact relationships between areas as edges; The supply and demand calculation module is configured to calculate the influence of each facility on any region based on a data graph model, delineate the dynamic service area of ​​each facility based on the influence, and generate a corresponding spatial supply matrix to represent the comprehensive service volume obtained by the region from each facility. The module uses the spatial supply matrix, the population attributes of each region, and the facility service area as input to calculate the regional demand for facilities, and screens the facilities that are actually accessible to the region to output a dynamic actual demand matrix for each region. The influence is calculated by weighting the travel time or distance from the region to the facility, the transportation cost ratio of the facility location, and the service supply and demand pressure ratio. The dynamic actual demand matrix is ​​used to characterize the actual usage tendency of the population for infrastructure services under multi-dimensional influencing factors. The supply and demand balance assessment module is configured to take the spatial supply matrix and the dynamic actual demand matrix as input, complete the supply and demand matching analysis of infrastructure services in the region, and quantify it to output a sequence of regional balance indicators; The strategy generation module is configured to divide the balance indicator sequences of different regions into different intervals, where the different intervals correspond to different strategy levels, and output corresponding response strategies based on the basic population size of the region.

Images