Business building updating evaluation and treatment system

Abstract

The invention discloses a business building updating evaluation and treatment system, and the system comprises a data collection module which is used for obtaining the position information, building attribute data and operation attribute data of a target business building; the zone bit attribute data generation module is used for generating zone bit attribute data; the location evaluation module is used for analyzing the location attribute data and generating a location score; the building evaluation module analyzes the building attribute data and generates a building score; the operation evaluation module is used for analyzing the operation attribute data and generating an operation score; the updating demand calculation module is used for calculating a building updating demand level according to a formula; the visual report generation module is used for generating a digital report containing a comprehensive evaluation matrix graph, a diagnosis conclusion and an updating strategy suggestion; and the governance scheme recommendation module is used for outputting a customized action scheme. According to the invention, through integration of position, building and operation data, a standardized evaluation framework is established, and multi-dimensional quantitative analysis of building values is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of big data processing platforms, and in particular relates to a commercial building renewal evaluation and diagnosis system. Background Art

[0002] Traditional building assessment systems rely on a single data source. Traditional methods only conduct assessments based on individual indicators such as rent, building age and location, and especially lack the impact of the building's location. It is difficult to quantify the impact of location on the comprehensive evaluation of buildings. Traditional methods rely on manual calculations, making it difficult to quickly process large-scale building data and conduct spatial analysis. Traditional methods usually only provide independent assessments or diagnoses to identify problems, but lack governance analysis to provide suggestions for solving problems. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a commercial building renewal evaluation and treatment system in view of the deficiencies in the above-mentioned prior art, break through the traditional data silos, build a cross-dimensional dynamic data pipeline, solve the problems of insufficient building information integrity and difficult data fusion, create a real-time report generation system, and greatly shorten the time spent on complex building group analysis (1000+ buildings).

[0004] To solve the above technical problems, the technical solution adopted by the present invention is: a commercial building renewal evaluation and diagnosis system, including a data acquisition module for obtaining location information, building attribute data and operation attribute data of a target commercial building; A location attribute data generation module generates location attribute data based on the location information of the target commercial building, wherein the location attribute data includes strategic location information, traffic condition information, public supporting facilities information and landscape resource information; The building attribute data includes information on the construction year, building size, property charge information and landmark image information; The operational attribute data includes occupancy rate information, unit tax information, unit rent information and resident enterprise information; The location assessment module analyzes the location attribute data according to the preset location attribute evaluation rules and generates a location score; The building assessment module analyzes the building attribute data and generates a building score according to the preset building attribute evaluation rules; The operation evaluation module analyzes the operation attribute data and generates an operation score according to the preset operation attribute evaluation rules; The update demand calculation module calculates the building update demand level according to the formula "building update demand level X = building score + operation score - location score"; Visual report generation module, used to generate digital reports including comprehensive evaluation matrix, diagnosis conclusions and update strategy recommendations; The governance plan recommendation module, based on digital reports, matches the preset building renewal governance strategy library and outputs customized action plans.

[0005] In the above system, the location attribute data generation module includes: The strategic location information generation unit is used to match the building location with the urban planning layer through a spatial overlay algorithm to determine whether it belongs to the "strategic development zone", "core functional area" or "emerging expansion area"; The traffic condition information generation unit calls the Internet map API to calculate the navigation distance from the building to the rail transit station and the expressway entrance in real time, and divides the traffic convenience level according to the preset threshold; The public supporting facilities information generation unit uses the Internet map API to calculate the navigation distance from the building to educational facilities, medical facilities and commercial facilities in real time, and divides the public supporting facilities into convenience levels according to preset thresholds; The landscape resource information generation unit calls the Internet map API to calculate the navigation distance from buildings to rivers and parks in real time, and divides the landscape convenience level according to preset thresholds.

[0006] In the above system, the scoring rules of the location assessment module include the following steps: (a) Set four location indicators and their weights: strategic location, transportation conditions, public facilities, and landscape resources; (b) Score each indicator according to the preset rules: Strategic location: divided by ring level, weight 30%, score 0-3; Traffic conditions: distance to rail transit, weighted 25%, score 0-3; distance to expressway, weighted 10%, score 0-3; Public facilities: distance to cultural and sports facilities, weighted 10%, score 0-3; commercial popularity, weighted 15%, score 0-3; Landscape resources: by distance to parks / water bodies, weighted 10%, score 0-3; (c) Multiply the scores of each indicator by the corresponding weight and add them together to generate a comprehensive location score, ranging from 0 to 3 points; (d) The location level is divided according to the score: 2-3 points are classified as Class A, 1-2 points are classified as Class B, and 0-1 points are classified as Class C.

[0007] In the above system, the scoring rules of the building assessment module include the following steps: (a) Setting four building indicators and their weights: construction year, building size, property charges, and landmark image; (b) Score each indicator according to the preset rules: Construction Year: divided by completion year, weight 30%, score 0-3; Building size: divided by building area, weight 30%, score 0-3; Property charges: divided by unit price range, weighted 30%, score 0-3; Landmark image: determines whether it is a super high-rise / special landmark, with a weight of 10% and a score of 0 or 3; (c) Multiply the scores of each indicator by the corresponding weight and add them together to generate a comprehensive building score, ranging from 0 to 3 points; (d) Building grades are based on the score: 2-3 points for Class A, 1-2 points for Class B, and 0-1 points for Class C.

[0008] In the above system, the scoring rules of the operation evaluation module include the following steps: (a) Set four operating indicators and their weights: occupancy rate, unit tax, unit rent, and resident enterprises; (b) Score each indicator according to the preset rules: Occupancy rate: divided by proportion interval, weight 35%, score 0-3; Unit tax: divided by tax density, weight 35%, score 0-3; Unit rent: divided by rent range, weighted 25%, score 0-3; Enterprises settled in: Determine whether there are Fortune 500 / unicorn enterprises, weighted 5%, score 0 or 3; (c) Multiply the scores of each indicator by the corresponding weight and add them together to generate a comprehensive operational score, with a score range of 0-3 points; (d) Operation level is divided according to the score: 2-3 points are graded as A, 1-2 points are graded as B, and 0-1 points are graded as C.

[0009] The above system, updating the demand calculation module, further includes executing the following steps: s1. After obtaining the location score, building score and operation score, the scores of each dimension are graded, 2 to 3 points (including 2 points) are graded as A, 1 to 2 points (including 1 point) are graded as B, 0 to 1 points are graded as C, and a triangular radar chart is formed; s2. Generate the renewal demand intensity classification according to the building renewal demand level X and the dimension classification.

[0010] In the above system, the governance solution recommendation module includes: The preset policy library contains three types of governance solutions: Space transformation: facade renovation, floor function reorganization, green energy-saving transformation; Investment promotion optimization: tax incentive package, targeted enterprise introduction list, rental ladder pricing model; Supporting facilities upgrade: additional shuttle buses, increased commercial facilities density, and deployment of smart building systems; Automatically match solutions based on priority tags and provide customized industry recommendations based on the information of settled companies.

[0011] In the above system, the visualization report generation module further comprises: Generate dynamic interactive GIS maps with the following layers overlaid: Building score heat map, real-time status of traffic network, and planning strategic zone boundaries; The data drilling function is embedded in the comprehensive evaluation matrix, which supports clicking on building points to view detailed diagnosis conclusions; Automatically generate editable project checklists with building ownership, renovation budget estimates, and ROI projections.

[0012] The above system further comprises: The data preprocessing module is used to clean and standardize the collected raw data, including: interpolating missing values ​​in building attribute data, using the mean value of neighboring building attributes or planning indicator constraint filling; Outlier detection is performed on abnormal values ​​in the operational attribute data, and correction is made through the box plot method or Z-score standardization; Convert the location information into WGS84 coordinate system and perform spatial alignment check with the urban planning layer; Dynamic data interface module, connecting to Internet map API and government data platform, real-time synchronization of the following data: Dynamic correction of navigation distance caused by changes in traffic conditions; New POI facilities provide real-time updates of public supporting indicators; The strategic location weights triggered by planning layer adjustments are recalculated.

[0013] The above system has a multi-level permission management module that allocates data operation permissions based on user roles: Government administrator rights: can modify the scoring thresholds and weights in the algorithm, and view sensitive data of all buildings in the entire domain; General user rights: can upload structured building data for evaluation, view building diagnostic reports and matching governance plans; The offline analysis engine performs the following operations using locally cached data in an offline environment: Estimated traffic condition score based on historical navigation data; Call the pre-trained semantic model to continue generating landmark image scores; Output the basic version diagnostic report and mark it with the label "Pending network synchronization update".

[0014] Compared with the prior art, the present invention has the following advantages: 1. Full-dimensional evaluation system: By integrating location, building, and operation data, a standardized evaluation framework is established to achieve multi-dimensional quantitative analysis of building value.

[0015] 2. Dynamic diagnosis and governance closed loop: Formulate and calculate the update demand level (X=building + operation-location), directly link the evaluation results to the governance strategy, and form a full-chain decision support of "analysis-diagnosis-governance".

[0016] 3. Automated report generation: Automatically output matrix diagrams, diagnostic conclusions and strategic recommendations through digital reports, reducing the time for manual report preparation by more than 90%.

[0017] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a system architecture diagram of the present invention.

[0019] Figure 2 This is a diagram of the commercial building evaluation system of the present invention.

[0020] Figure 3 A radar chart for evaluating commercial buildings of the present invention.

[0021] Figure 4 This is a requirement classification diagram for updating and improving the present invention.

[0022] Figure 5 This is the building comprehensive evaluation conclusion matrix diagram of the present invention. DETAILED DESCRIPTION

[0023] like Figure 1 As shown, a commercial building renewal evaluation and diagnosis system includes a data acquisition module for acquiring location information, building attribute data, and operation attribute data of a target commercial building; A location attribute data generation module generates location attribute data based on the location information of the target commercial building, wherein the location attribute data includes strategic location information, traffic condition information, public supporting facilities information and landscape resource information; The building attribute data includes information on the construction year, building size, property charge information and landmark image information; The operational attribute data includes occupancy rate information, unit tax information, unit rent information and resident enterprise information; The location assessment module analyzes the location attribute data according to the preset location attribute evaluation rules and generates a location score; The building assessment module analyzes the building attribute data and generates a building score according to the preset building attribute evaluation rules; The operation evaluation module analyzes the operation attribute data and generates an operation score according to the preset operation attribute evaluation rules; The update demand calculation module calculates the building update demand level according to the formula "building update demand level X = building score + operation score - location score"; Visual report generation module, used to generate digital reports including comprehensive evaluation matrix, diagnosis conclusions and update strategy recommendations; The governance plan recommendation module, based on digital reports, matches the preset building renewal governance strategy library and outputs customized action plans.

[0024] It should be noted that in actual implementation, data governance is required after the various data of the target commercial buildings are collected. For example: data cleaning, verifying the field format through regular expressions (such as retaining the unit of "yuan / ㎡·day" in the rent field), and eliminating building records with a missing rate of >30%; spatial processing, calling the Amap reverse geocoding API to convert the building address into longitude and latitude coordinates, building a spatial index to accelerate retrieval, and for buildings with missing coordinates, using the neighboring building coordinate interpolation method (inverse distance weighted method) to estimate the location, with the error controlled within ±20m; data fusion, linking scattered Excel tables such as property charges and occupancy rates through UUIDs to form a standardized building archive table containing 50+ fields.

[0025] Instructions for collecting building attribute data: Extract fields such as construction year and building area from the government real estate registration database, and complete missing data through the electronic archives of planning licenses.

[0026] The property fee data is captured by crawlers from the public bidding platform, and regular expressions are used to clean unstructured text.

[0027] Instructions for collecting operational attribute data: The occupancy rate information is connected to the SQL database of the building property management system and is updated synchronously on a quarterly basis.

[0028] The company’s registration information is obtained by OCR recognition of photos of company notice boards, and the company name and industry classification are verified in combination with industrial and commercial registration data.

[0029] In this embodiment, the location attribute data generation module includes: The strategic location information generation unit is used to match the building location with the urban planning layer through a spatial overlay algorithm to determine whether it belongs to the "strategic development zone", "core functional area" or "emerging expansion area"; The traffic condition information generation unit calls the Internet map API to calculate the navigation distance from the building to the rail transit station and the expressway entrance in real time, and divides the traffic convenience level according to the preset threshold; The public supporting facilities information generation unit uses the Internet map API to calculate the navigation distance from the building to educational facilities, medical facilities and commercial facilities in real time, and divides the public supporting facilities into convenience levels according to preset thresholds; The landscape resource information generation unit calls the Internet map API to calculate the navigation distance from buildings to rivers and parks in real time, and divides the landscape convenience level according to preset thresholds.

[0030] It should be noted that the urban planning layer is mainly composed of spatial point data and spatial surface data; Spatial point data is obtained in the following ways: using Amap POI API to capture coordinate data of rail transit stations, expressway entrances, commercial facilities, etc., and integrating OpenStreetMap road network data to clean up redundant nodes (such as filtering non-public facility POIs); Spatial surface data are obtained by: using GIS spatial merging technology to process the vector boundaries of planning functional areas, and combining satellite images to calibrate the scope of parks and river water bodies (e.g., using NDWI water body index to assist in identification); Establish a unified coordinate system to encode and map names and categories (e.g. classify "mall / shopping center" into the commercial facility category); The urban planning layer is pre-calculated in a grid-based manner. For example, the city (the entire Shanghai area) is divided into several 100m×100m grids. Based on Spark distributed computing, the location indicators of each grid are pre-generated, including strategic location indicators, traffic condition indicators, public supporting indicators and landscape resource indicators. The building coordinates are matched to the pre-calculated grid cells through spatial topological analysis, and the location score is obtained in seconds.

[0031] In this embodiment, the scoring rules of the location assessment module, taking Shanghai as an example, can be seen in the following table:

[0032] In this embodiment, the scoring rules of the building assessment module, taking Shanghai as an example, can be seen in the following table:

[0033] In this embodiment, the scoring rules of the operation evaluation module, taking Shanghai as an example, can be seen in the following table:

[0034] It should be noted that if Figure 2As shown in the figure, an evaluation system consisting of "three dimensions and twelve indicators" is formed, in which the location evaluation dimension reflects the evaluation of commercial buildings in terms of environmental value and social support, the operation evaluation dimension reflects the evaluation of commercial buildings in terms of social contribution and usage status, and the architectural evaluation dimension reflects the evaluation of commercial buildings in terms of material space and its own value.

[0035] Each indicator is scored from 0 to 3 points, and the scores of each indicator are added up according to the weight to obtain a comprehensive evaluation score for different dimensions of each building.

[0036] In this embodiment, updating the demand calculation module further includes executing the following steps: s1. After obtaining the location score, building score and operation score, the scores of each dimension are graded, 2 to 3 points (including 2 points) are graded as A, 1 to 2 points (including 1 point) are graded as B, 0 to 1 points are graded as C, and a triangular radar chart is formed; s2. Generate the renewal demand intensity classification according to the building renewal demand level X and the dimension classification.

[0037] It should be noted that if Figure 3 As shown, after obtaining the evaluation scores of each dimension, the scores of each dimension are graded, 2 to 3 points (including 2 points) are graded as A, 1 to 2 points (including 1 point) are graded as B, 0 to 1 points are graded as C, and a triangular radar chart is formed.

[0038] It should be said that, generally speaking, the better the location environment of a business building, but the worse the construction and operation conditions, the stronger the demand for renewal and improvement; conversely, the worse the location environment, but the better the construction and operation conditions, the weaker the demand for renewal and improvement.

[0039] Therefore, the difference between the sum of the building and operation scores and the location score is used to calculate the strength of the renewal and improvement demand. The calculation result is between +3 and -1, and is divided into five levels from high to low, such as Figure 4 As shown in the figure, the update and improvement requirements are from weak to strong, namely A+3, B+2, C+1, D±0, and E-1, among which: A+3 grade commercial buildings represent commercial buildings with the weakest demand for renewal and upgrading, and are exemplary and leading commercial buildings; B+2 grade commercial buildings represent commercial buildings with the weakest demand for renovation and upgrading, and are advantage-enhancing commercial buildings, which means it is recommended to strengthen the advantages of these commercial buildings; C+1 grade commercial buildings represent commercial buildings with medium demand for renovation and upgrading, and are supportive commercial buildings, which means that it is recommended to support the renovation and upgrading of such commercial buildings; Commercial buildings with a D±0 grade represent commercial buildings with the second strongest demand for renewal and improvement, and are commercial buildings that encourage renewal; E-1 grade commercial buildings represent the commercial buildings with the strongest demand for renovation and upgrading, and are key renovation-type commercial buildings.

[0040] The renovation and improvement strategies required for a building are determined by the building and operation ratings. If the building rating is low, the renovation and improvement direction of the building tends to be space renovation and renovation; if the operation rating is low, the renovation and improvement direction of the building tends to be functional operation improvement; if the difference between the two is not big, the two strategies will jointly promote the renovation and improvement of the building.

[0041] like Figure 5 As shown in the figure, from the two dimensions of the strength of renewal and improvement demand and the renewal and transformation strategy, a commercial building renewal and improvement strategy matrix can be formed. The X-axis is the strength of renewal and improvement demand, the weaker the demand is to the left, and the stronger the demand is to the right. The Y-axis is the renewal and improvement direction, upward for functional operation improvement, and downward for space transformation and renewal. There are 27 permutations and combinations of the building comprehensive evaluation triangle radar chart.

[0042] Incorporating the comprehensive evaluation conclusion matrix, the radar charts of similar conclusions are merged, with a total of 17 matrix positions, namely Figure 3 and Figure 4 The combined result forms Figure 5 .

[0043] In this embodiment, the governance solution recommendation module includes: The preset policy library contains three types of governance solutions: Space transformation: facade renovation, floor function reorganization, green energy-saving transformation; Investment promotion optimization: tax incentive package, targeted enterprise introduction list, rental ladder pricing model; Supporting facilities upgrade: additional shuttle buses, increased commercial facilities density, and deployment of smart building systems; Automatically match solutions based on priority tags and provide customized industry recommendations based on the information of settled companies.

[0044] It should be noted that in actual implementation, when constructing the strategy knowledge map, a three-dimensional classification system is first established: governance type (space / investment attraction / supporting facilities) × intervention intensity (transformation / optimization / upgrade) × cost range (low / medium / high); Establish a development case feature extraction engine to automatically extract 200+ key decision factors from historical successful cases Deploy a semantic understanding model to achieve two-way conversion between natural language policy entries and structured data, further establish an intelligent matching engine, build a multi-objective optimization model, balance the three factors of renovation cost, expected benefits and implementation difficulty, develop a context-aware reasoning mechanism, and adjust the weight of the scheme in combination with regional policy orientation (such as green building subsidies); establish a design scheme conflict detection algorithm to automatically avoid implementation conflicts between strategies (such as the time conflict between facade renovation and energy-saving construction); Finally, a customized solution generation system was constructed, a modular strategy component library was established, a flexible combination mode of "basic solution + optional plug-in" was supported, a BIM model interface was integrated so that the key space renovation plan could directly output a three-dimensional construction preview, and an investment return simulator was developed to predict the probability distribution of economic benefits of different solutions based on the Monte Carlo method.

[0045] Through the deep collaboration of the intelligent matching engine and the knowledge graph, a governance decision support system covering the entire life cycle has been built. Compared with the traditional experience-driven model, it has achieved a qualitative breakthrough in the scientific nature of the solutions, response speed and predictability of implementation effects, providing a decision-making intelligence center with self-evolution capabilities for urban renewal.

[0046] In this embodiment, the visualization report generation module further includes: Generate dynamic interactive GIS maps with the following layers overlaid: Building score heat map, real-time status of traffic network, and planning strategic zone boundaries; The data drilling function is embedded in the comprehensive evaluation matrix, which supports clicking on building points to view detailed diagnosis conclusions; Automatically generate editable project checklists with building ownership, renovation budget estimates, and ROI projections.

[0047] It should be noted that the visualization report generation module builds a dynamic interactive GIS map by calling ArcGIS API for JavaScript. In the specific implementation: Thermal map rendering: Based on WebGL technology, the building score (0-3 points) is converted into RGBA color values, and the Gaussian kernel density algorithm is used to generate a thermal map with a resolution of 500m. The transparency is dynamically adjusted with the data confidence.

[0048] Real-time traffic overlay: Connect to the Amap Traffic API, synchronize traffic vector slices every 5 minutes, generate a three-color status layer of "congestion-slow traffic-unblocked traffic" through road network topology analysis, and perform spatial overlay analysis with building locations.

[0049] Data drilling function: Leaflet.js framework is used to develop click interaction. When the user clicks the building coordinate point, an AJAX request is triggered to call the Spring Boot microservice, and the detailed diagnostic data of the building (including historical scoring trends and enterprise composition pie charts) is retrieved from MongoDB and dynamically rendered in the floating pop-up window in SVG format.

[0050] Project list generation: An editable XLSX file is automatically generated based on the Apache POI library. The renovation budget estimation uses Monte Carlo simulation (1000 iterations). The input variables include the volatility of building material prices (σ=15%) and the growth rate of labor costs (μ=5%). The cost forecast value under the 95% confidence interval is output.

[0051] In this embodiment, it also includes: The data preprocessing module is used to clean and standardize the collected raw data, including: interpolating missing values ​​in building attribute data, using the mean value of neighboring building attributes or planning indicator constraint filling; Outlier detection is performed on abnormal values ​​in the operational attribute data, and correction is made through the box plot method or Z-score standardization; Convert the location information into WGS84 coordinate system and perform spatial alignment check with the urban planning layer; Dynamic data interface module, connecting to Internet map API and government data platform, real-time synchronization of the following data: Dynamic correction of navigation distance caused by changes in traffic conditions; New POI facilities provide real-time updates of public supporting indicators; The strategic location weights triggered by planning layer adjustments are recalculated.

[0052] It should be noted that the data preprocessing module is deployed on the Hadoop cluster and performs the following processing flow: Missing value filling: For buildings with missing construction years, the KNN algorithm (k=5) is used to find buildings within a 1km range in the spatial index and calculate the mode of their completion years to fill in the missing values. If there is a constraint of plot ratio>5.0 in the planning and control regulations, it will be automatically marked as "pending demolition and reconstruction" status.

[0053] Outlier cleaning: For the unit rent field, an improved box plot method is used, and the upper and lower limits [Q1-1.5IQR, Q3+1.5IQR] are calculated using Tukey's fences formula. For values ​​that exceed the range, a manual review process is initiated. If the review fails, the industry average is used instead.

[0054] Coordinate system conversion: Use the PROJ library to batch convert Baidu BD09 coordinate system to WGS84, and start Gauss-Krüger projection correction for points with conversion residuals > 0.0001° to ensure that the spatial overlay error with the planning layer is <3 meters.

[0055] Dynamic data synchronization: A real-time data pipeline is established through the Kafka message queue. When it is detected that the navigation distance of Amap has changed by more than 10%, the Flink streaming computing engine is triggered to re-evaluate the traffic condition score and update the cached results in Redis.

[0056] In this embodiment, a multi-level permission management module allocates data operation permissions according to user roles: Government administrator rights: can modify the scoring thresholds and weights in the algorithm, and view sensitive data of all buildings in the entire domain; General user rights: can upload structured building data for evaluation, view building diagnostic reports and matching governance plans; The offline analysis engine performs the following operations using locally cached data in an offline environment: Estimated traffic condition score based on historical navigation data; Call the pre-trained semantic model to continue generating landmark image scores; Output the basic version diagnostic report and mark it with the label "Pending network synchronization update".

[0057] It should be noted that the multi-level permission management system is implemented based on Spring Security OAuth 2.0: Government administrator authority: After verifying that the IP address belongs to the government extranet segment through the ABAC (Attribute-Based Access Control) model, the PostGIS service can be called to modify the spatial weight parameters of the planning layer. Sensitive data queries require secondary biometric authentication.

[0058] The offline analysis engine uses the SQLite embedded database to store data snapshots for the last 30 days. When the network is disconnected: Traffic score calculation: Markov chain prediction based on historical navigation data, with the average value of the morning peak period of 8:00-9:00 as the benchmark value Landmark recognition: Load the pre-trained ResNet-50 model (TensorFlow Lite format), extract features from the locally cached street view images, and identify landmark buildings when the output layer softmax value is > 0.7 Report generation: Add a striking watermark "Offline version - Data end [YYYYMMDD]" to the PDF document header, automatically trigger difference synchronization after network recovery, and ensure data integrity through CRC32 check.

[0059] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent structural change made to the above embodiment based on the technical essence of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A commercial building renovation evaluation and treatment system, characterized in that: include: A data collection module, used to obtain location information, building attribute data and operation attribute data of a target commercial building; A location attribute data generation module generates location attribute data based on the location information of the target commercial building, wherein the location attribute data includes strategic location information, traffic condition information, public supporting facilities information and landscape resource information; The building attribute data includes information on the construction year, building size, property charge information and landmark image information; The operational attribute data includes occupancy rate information, unit tax information, unit rent information and resident enterprise information; The location assessment module analyzes the location attribute data according to the preset location attribute evaluation rules and generates a location score; The building assessment module analyzes the building attribute data and generates a building score according to the preset building attribute evaluation rules; The operation evaluation module analyzes the operation attribute data and generates an operation score according to the preset operation attribute evaluation rules; The update demand calculation module calculates the building update demand level according to the formula "Building Update Demand Level X = Building Score + Operation Score - Location Score"; Visual report generation module, used to generate digital reports including comprehensive evaluation matrix, diagnosis conclusions and update strategy recommendations; The governance plan recommendation module, based on digital reports, matches the preset building renewal governance strategy library and outputs customized action plans.

2. A commercial building renovation evaluation and treatment system according to claim 1, characterized in that: The location attribute data generation module includes: The strategic location information generation unit is used to match the building location with the urban planning layer through a spatial overlay algorithm to determine whether it belongs to the "strategic development zone", "core functional area" or "emerging expansion area"; The traffic condition information generation unit calls the Internet map API to calculate the navigation distance from the building to the rail transit station and the expressway entrance in real time, and divides the traffic convenience level according to the preset threshold; The public supporting facilities information generation unit uses the Internet map API to calculate the navigation distance from the building to educational facilities, medical facilities and commercial facilities in real time, and divides the public supporting facilities into convenience levels according to preset thresholds; The landscape resource information generation unit calls the Internet map API to calculate the navigation distance from buildings to rivers and parks in real time, and divides the landscape convenience level according to preset thresholds.

3. A commercial building renovation evaluation and treatment system according to claim 1, characterized in that: The scoring rules of the location assessment module include the following steps: (a) Set four location indicators and their weights: strategic location, transportation conditions, public facilities, and landscape resources; (b) Score each indicator according to the preset rules: Strategic location: divided by ring level, weight 30%, score 0-3; Traffic conditions: distance to rail transit, weighted 25%, score 0-3; distance to expressway, weighted 10%, score 0-3; Public facilities: distance to cultural and sports facilities, weighted 10%, score 0-3; commercial popularity, weighted 15%, score 0-3; Landscape resources: by distance to parks / water bodies, weighted 10%, score 0-3; (c) Multiply the scores of each indicator by the corresponding weight and add them together to generate a comprehensive location score, ranging from 0 to 3 points; (d) The location level is divided according to the score: 2-3 points are classified as Class A, 1-2 points are classified as Class B, and 0-1 points are classified as Class C.

4. A commercial building renovation evaluation and treatment system according to claim 1, characterized in that: The scoring rules of the building assessment module include the following steps: (a) Setting four building indicators and their weights: construction year, building size, property charges, and landmark image; (b) Score each indicator according to the preset rules: Construction Year: divided by completion year, weight 30%, score 0-3; Building size: divided by building area, weight 30%, score 0-3; Property charges: divided by unit price range, weighted 30%, score 0-3; Landmark image: determines whether it is a super high-rise / special landmark, with a weight of 10% and a score of 0 or 3; (c) Multiply the scores of each indicator by the corresponding weight and add them together to generate a comprehensive building score, ranging from 0 to 3 points; (d) Building grades are based on the score: 2-3 points for Class A, 1-2 points for Class B, and 0-1 points for Class C.

5. A commercial building renovation evaluation and treatment system according to claim 1, characterized in that: The scoring rules of the operation evaluation module include the following steps: (a) Set four operating indicators and their weights: occupancy rate, unit tax, unit rent, and resident enterprises; (b) Score each indicator according to the preset rules: Occupancy rate: divided by proportion interval, weight 35%, score 0-3; Unit tax: divided by tax density, weight 35%, score 0-3; Unit rent: divided by rent range, weighted 25%, score 0-3; Enterprises settled in: Determine whether there are Fortune 500 / unicorn enterprises, weighted 5%, score 0 or 3; (c) Multiply the scores of each indicator by the corresponding weight and add them together to generate a comprehensive operational score, with a score range of 0-3 points; (d) Operation level is divided according to the score: 2-3 points are graded as A, 1-2 points are graded as B, and 0-1 points are graded as C.

6. A commercial building renovation evaluation and treatment system according to claim 1, characterized in that: Updating the demand calculation module also includes performing the following steps: s1. After obtaining the location score, building score and operation score, the scores of each dimension are graded, with 2 to 3 points as grade A, 1 to 2 points as grade B, and 0 to 1 points as grade C, and a triangular radar chart is formed; s2. Generate the renewal demand intensity classification according to the building renewal demand level X and the dimension classification.

7. A commercial building renovation evaluation and treatment system according to claim 1, characterized in that: The governance solution recommendation module includes: The preset policy library contains three types of governance solutions: Space transformation: facade renovation, floor function reorganization, green energy-saving transformation; Investment promotion optimization: tax incentive package, targeted enterprise introduction list, rental ladder pricing model; Supporting facilities upgrade: additional shuttle buses, increased commercial facilities density, and deployment of smart building systems; Automatically match solutions based on priority tags and provide customized industry recommendations based on the information of settled companies.

8. A commercial building renovation evaluation and treatment system according to claim 1, characterized in that: The visualization report generation module further includes: Generate dynamic interactive GIS maps with the following layers overlaid: Building score heat map, real-time status of traffic network, and planning strategic zone boundaries; The data drilling function is embedded in the comprehensive evaluation matrix, which supports clicking on building points to view detailed diagnosis conclusions; Automatically generate editable project checklists with building ownership, renovation budget estimates, and ROI projections.

9. A commercial building renovation evaluation and treatment system according to claim 1, characterized in that: Also includes: The data preprocessing module is used to clean and standardize the collected raw data, including: interpolating missing values ​​in building attribute data, using the mean value of neighboring building attributes or planning indicator constraint filling; Outlier detection is performed on abnormal values ​​in the operational attribute data, and correction is made through the box plot method or Z-score standardization; Convert the location information into WGS84 coordinate system and perform spatial alignment check with the urban planning layer; Dynamic data interface module, connecting to Internet map API and government data platform, real-time synchronization of the following data: Dynamic correction of navigation distance caused by changes in traffic conditions; New POI facilities provide real-time updates of public supporting indicators; The strategic location weights triggered by planning layer adjustments are recalculated.

10. A commercial building renovation evaluation and treatment system according to claim 1, characterized in that: Multi-level permission management module, assigning data operation permissions based on user roles: Government administrator rights: can modify the scoring thresholds and weights in the algorithm, and view sensitive data of all buildings in the entire domain; General user rights: can upload structured building data for evaluation, view building diagnostic reports and matching governance plans; The offline analysis engine performs the following operations using locally cached data in an offline environment: Estimated traffic condition score based on historical navigation data; Call the pre-trained semantic model to continue generating landmark image scores; Output the basic version diagnostic report and mark it with the "Pending network synchronization update" label.

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