Intelligent site selection analysis method and system based on large model and industrial characteristics

Abstract

The invention relates to an intelligent site selection analysis method and system based on a large model and industrial characteristics, and relates to the technical field of intelligent site selection, and the method comprises the steps: 100, collecting the demand information of a user; 101, extracting an industry type from the demand information; step 102, determining an industrial demand in response to the industrial type, and extracting an area demand from the demand information; step 103, determining a site selection plot in combination with the industrial demand and the area demand; and 104, generating and displaying an industrial site selection suggestion based on the site selection plot. The method has the advantages that the accuracy of industrial site selection is improved, and more suitable land parcels can be selected according to different industrial types.

Description

Technical Field

[0001] This invention relates to the field of smart site selection technology, and in particular to a smart site selection analysis method and system based on large models and industry characteristics. Background Technology

[0002] Industrial site selection refers to the strategic decision-making process by which enterprises or industrial parks choose the optimal geographical location within a specific region to conduct core businesses such as production, R&D, warehousing, and sales, based on multiple factors such as their own development needs, resource endowment, cost-effectiveness, policy environment, and market potential.

[0003] With the rise of Geographic Information Systems (GIS), big data, and artificial intelligence technologies, industrial site selection analysis has gradually shifted from traditional manual surveys to digitally assisted decision-making. Existing industrial site selection systems generally restrict land parcels based on factors such as plot size and distance from infrastructure. They then evaluate multiple parcels that meet the restrictions based on transportation and environmental factors to select the ones that meet the criteria. Such systems are quite effective in simple site selection scenarios, enabling basic automated screening and visualization, such as quickly eliminating parcels that do not meet the area or distance requirements.

[0004] Existing industrial site selection systems rely on manually hard-coded general rules to screen land parcels. However, different industries may have special requirements when selecting sites, such as the specific needs of integrated circuits for wastewater treatment and air transportation. When screening land parcels required by different industries through uniform requirements, it is easy to overlook the characteristics of the industries and lead to the generalization of screening results, resulting in site selection bias or omission of the best land parcels. Summary of the Invention

[0005] To improve the accuracy of industrial site selection and enable the selection of more suitable plots based on different industry types, this invention provides a smart site selection analysis method and system based on large models and industry characteristics.

[0006] In a first aspect, the present invention provides a smart site selection analysis method based on large-scale models and industry characteristics, employing the following technical solution: A smart site selection analysis method based on large-scale models and industry characteristics includes: Step 100: Collect user needs information; Step 101: Extract industry type from the demand information; Step 102: Determine industry demand in response to the industry type, and extract area demand from the demand information; Step 103: Determine the site selection plot based on the aforementioned industry demand and area requirements; Step 104: Generate and display industry site selection suggestions based on the selected site.

[0007] By adopting the above technical solution, the corresponding special requirements are retrieved according to the type of industry that needs to be located. This allows for the generation of more comprehensive industrial site selection conditions by combining the special requirements of the industry with the user's general requirements for industrial area. Consequently, more suitable plots can be selected based on different industry types, thereby improving the accuracy of industrial site selection.

[0008] Optional, also includes: Step 105: Determine the number of land parcels based on the selected site; Step 106: When the number of land parcels is greater than 1, determine the dominant industry based on the selected land parcels; Step 107: Determine the industry matching degree in response to the leading industry, and determine the expressway distance based on the selected site; Step 108: Determine traffic convenience in response to the expressway distance, and retrieve the site outline based on the selected site; Step 109: Determine the regularity of the plot in response to the plot outline, and retrieve the plot maturity and planning compliance based on the selected plot; Step 110: Calculate the land parcel evaluation value by combining the industry matching degree, transportation convenience, land parcel regularity, land parcel maturity, and planning compliance. Step 111: Select the site based on the site evaluation value.

[0009] By adopting the above technical solution, when multiple plots of land are selected, each plot is evaluated and quantified into a plot evaluation value from five aspects: industry matching degree, plot maturity, transportation convenience, planning compliance and plot regularity. The most suitable plot for the user is then selected based on the plot evaluation value.

[0010] Optional, also includes: Step 112: When the number of land parcels is greater than 1, determine the upstream industry based on the industry type; Step 113: Determine the upstream distance by combining the upstream industries and the selected site; Step 114: Determine the clustering coefficient in response to the upstream distance and accessibility; Step 115: Update the land parcel evaluation value according to the cluster coefficient.

[0011] By adopting the above technical solution, when multiple plots of land are selected, the plot with the easiest raw material transportation is chosen according to the difficulty of transporting raw materials required for industrial production. This reduces the possibility of production delays caused by delays or errors in raw material transportation, thereby improving the stability of industrial production.

[0012] Optionally, it also includes a disaster-resistant site selection method, which includes: Step 200: Determine the impacting disaster in response to the industry type; Step 201: Retrieve the site disaster records based on the selected site and the disaster impacts; Step 202: Determine the disaster coefficient in response to the disaster record of the land parcel, and determine the disaster threshold according to the industry type; Step 203: When the disaster coefficient is greater than the disaster threshold, update the land parcel evaluation value according to the disaster coefficient.

[0013] By adopting the above technical solution, after candidate plots are selected according to the industrial site selection conditions, the historical disaster situation of the area where the candidate plots are located is retrieved, and the impact of disasters on the industry is assessed. In this way, the plot with the least disaster impact is selected, thereby reducing the damage to the industry caused by disasters.

[0014] Optionally, the disaster-resistant site selection method further includes: Step 204: When the disaster coefficient is not greater than the disaster threshold, determine the transportation route by combining the upstream industry and the selected site; Step 205: Retrieve route disaster records based on the transportation route; Step 206: Determine route coefficients in response to the route disaster records; Step 207: When the route coefficient is greater than the preset transportation threshold, determine the secondary distance by combining the upstream industry and the selected site; Step 208: Update the cluster coefficient in response to the secondary distance, upstream distance, and accessibility.

[0015] By adopting the above technical solution, the route for transporting raw materials is planned according to the address of the industry, and the historical disaster information of the route is retrieved. In this way, when the transportation is affected by the disaster, other locations that can transport raw materials can be selected, thereby reducing the delay in industrial production caused by the impact of the disaster on the transportation of raw materials.

[0016] Optionally, the disaster-resistant site selection method further includes: Step 209: When the route coefficient is greater than the preset transportation threshold, determine the transportation time in response to the transportation route; Step 210: Determine the blocking duration by combining the route coefficient and transportation time; Step 211: Determine warehousing requirements based on the duration of the blockage and the industry type; Step 212: Update the site selection in response to the warehousing demand.

[0017] By adopting the above technical solution, when disasters are likely to affect the transportation of raw materials, an appropriate amount of raw material storage can be selected according to the impact of the disaster on the transportation of raw materials, and the storage requirements of the selected site can be obtained according to the amount of raw material storage. In this way, when disasters affect the transportation of raw materials, the industry can continue to produce through the stored raw materials, thereby reducing the situation where the industry's production is delayed due to the impact of disasters on the transportation of raw materials.

[0018] Optionally, it also includes an environmentally friendly site selection method, which includes: Step 300: Determine the building materials list in response to the area requirements and industry type; Step 301: Determine transportation emissions based on the building materials list and the selected site, and retrieve site images based on the selected site; Step 302: Identify the earthwork volume from the plot image; Step 303: Determine earthwork discharge in response to the earthwork volume; Step 304: Determine the construction emissions value by combining the transportation emissions and earthwork emissions; Step 305: Update the land parcel evaluation value based on the construction emissions value.

[0019] By adopting the above technical solution, the building materials required for the industry are estimated according to the industrial area, and the carbon emissions of transporting building materials are estimated according to the location of the industry. At the same time, the carbon emissions of clearing the site are identified from the image of the selected site, and the total carbon emissions of building the industry are calculated. Thus, the site with the lowest carbon emissions is selected, thereby improving the environmental friendliness of industrial construction.

[0020] Optionally, the environmentally friendly site selection method further includes: Step 306: Determine the power structure based on the selected site; Step 307: Determine the power coefficient in response to the power structure; Step 308: Determine production emissions by combining the power coefficient and industry type, and determine the recycling coefficient according to the building materials table; Step 309: Determine the demolition and discharge requirements based on the aforementioned recycling coefficient and area requirements; Step 310: Determine the total emissions value based on the transportation emissions, earthwork emissions, production emissions, and demolition emissions, and determine the emissions threshold in response to the industry type; Step 311: When the total emissions value is greater than the emission threshold, generate and display a high carbon emission warning based on the total emissions value.

[0021] By adopting the above technical solution, the power supply structure of the industry is retrieved according to the location of the candidate plot, thereby calculating the carbon emissions of the industry's production, estimating the carbon emissions of demolishing the industry based on the area of ​​the industry, and then combining the carbon emissions of building the industry, producing the industry, and demolishing the industry to calculate the carbon emissions of the entire life cycle of the industry, and promptly reminding users when the carbon emissions of the entire life cycle of the industry are too high.

[0022] Optionally, the environmentally friendly site selection method further includes: Step 312: When the total emissions exceed the emissions threshold, retrieve the resource distribution based on the selected site. Step 313: Determine the power generation efficiency in response to the resource distribution; Step 314: Determine the emission reduction efficiency based on the power generation efficiency and the power coefficient; Step 315: Determine the emission reduction based on the emission reduction efficiency and production emissions; Step 316: If the emission reduction exceeds the emission threshold, determine the external power source based on the selected site. Step 317: Generate and display environmental protection and emission reduction recommendations by combining the external power source and emission reduction efficiency.

[0023] By adopting the above-mentioned technical solutions, when the carbon emissions of an industry are too high, the natural resources of the local area can be accessed according to the location of the industry. When natural resources are relatively abundant, they can be used to generate electricity, and when natural resources are scarce, hydropower, wind power, and photovoltaic power can be transferred from other places, thereby reducing the carbon emissions of the industry and improving its environmental friendliness.

[0024] Secondly, this application provides a smart site selection analysis system based on large-scale models and industry characteristics, employing the following technical solution: A smart site selection analysis system based on large-scale models and industry characteristics includes: The data acquisition module is used to collect demand information. The memory is used to store the program of any of the above-mentioned smart location analysis methods based on large models and industry characteristics; The processor is the unit of memory that allows programs to be loaded and executed by the processor.

[0025] By adopting the above technical solution, the corresponding special requirements are retrieved according to the type of industry that needs to be located. This allows for the generation of more comprehensive industrial site selection conditions by combining the special requirements of the industry with the user's general requirements for industrial area. Consequently, more suitable plots can be selected based on different industry types, thereby improving the accuracy of industrial site selection.

[0026] In summary, this application includes at least one of the following beneficial technical effects: 1. Retrieve relevant special requirements according to the type of industry to be selected, and generate more complete industrial site selection conditions by combining the special requirements of the industry with the user's general requirements for industrial area. This will enable the selection of more suitable plots of land according to different industry types and improve the accuracy of industrial site selection. 2. When multiple plots are selected, each plot is evaluated and quantified into a plot evaluation value based on five aspects: industry matching degree, plot maturity, transportation convenience, planning compliance, and plot regularity. The most suitable plot for the user is then selected based on the plot evaluation value. 3. When multiple plots of land are selected, the plot with the easiest raw material transportation should be chosen based on the difficulty of transporting the raw materials required for industrial production. This will reduce the possibility of production delays caused by delays or errors in raw material transportation, thereby improving the stability of industrial production. Attached Figure Description

[0027] Figure 1 This is a flowchart of a smart site selection analysis method based on large models and industry characteristics; Figure 2 This is a flowchart of disaster mitigation site selection methods; Figure 3 This is a flowchart of environmentally friendly site selection methods. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0029] Reference Figure 1 A smart site selection analysis method based on large-scale models and industry characteristics includes: Step 100: Collect user needs information.

[0030] Demand information refers to the user's requirements for the industry. Demand information includes the type of industry and area requirements of the site to be selected by the user. Generally, the user's information is collected by recording device as demand information. The method of collecting demand information is selected by the staff according to the actual situation, and will not be elaborated here.

[0031] Step 101: Extract the industry type from the demand information.

[0032] The industry type refers to the type of industry that the user needs to select a location, such as integrated circuits, contained in the demand information. The industry type can be determined by the user intent parsing module. The user intent parsing module integrates a large language model (LLM, such as the Qianwen series model) and can parse the intent of the demand information through prompt engineering technology. After the demand information is tokenized, the model outputs the intent elements in structured JSON format (such as {"Industry Type": "Integrated Circuits", "Area Range": [45, 55]}). This module interacts with other modules through API interfaces (such as RESTful services) and supports multi-turn dialogue to clarify intent. The method for determining the industry type is selected by the staff according to the actual situation, and will not be elaborated here.

[0033] Step 102: Determine industry demand in response to the industry type, and extract area demand from the demand information.

[0034] Industry demand refers to the specific needs corresponding to an industry type. For example, integrated circuits have needs for sewage treatment and aerospace transportation. Generally, the location is required to be less than or equal to 500 meters away from the sewage pipe network and less than or equal to 30,000 meters away from the airport. Industry demand can be retrieved from the industry database, which is a database that records different industry types and their corresponding industry demands.

[0035] Among them, the industry database refers to the use of knowledge graph technology (such as Neo4j graph database) to extract and structure the knowledge of infrastructure needs of various industries from expert literature, industry standards and historical cases, forming a triple knowledge representation (such as node "industry type" - relation "demand" - node "infrastructure conditions", for example "integrated circuit - sewage pipe network demand - distance ≤ 500 meters"). The knowledge base supports dynamic updates and generally uses the SPARQL query language to retrieve the industry demand corresponding to the industry type.

[0036] The area requirement is the minimum area required by the industry contained in the above requirement information. The method for determining the area requirement can be determined through the user intent parsing module mentioned above.

[0037] Step 103: Determine the site selection plot based on the aforementioned industry demand and area requirements.

[0038] Site selection refers to the location of potential industrial sites selected based on industry demand and area requirements. The intelligent condition generation module can first combine industry demand and area requirements into a site query command, and then the spatial query and filtering module can query the site selection from the basic database construction module according to the site query command.

[0039] The intelligent condition generation module refers to dynamically assembling industry and area requirements using a rule engine (such as Drools) (e.g., generating "gyfsgdjl<=500ANDgksljl<=1000" using logical AND / OR operations). This module handles complex constraints (such as priority ranking: industry matching > infrastructure > area) and outputs SQL-like query condition strings.

[0040] The spatial query and filtering module refers to using a spatial query engine (such as PostGIS functions ST_Distance and ST_Within) to perform joint queries (attribute filtering + spatial analysis) from the basic database in conjunction with land parcel query commands. For example, it can calculate the Euclidean distance or buffer intersection between land parcels and infrastructure. The query result is a list of candidate land parcels (containing attributes such as xzqmc, tdmj, fl, etc.), which supports pagination and sorting.

[0041] The basic database is constructed by a basic database construction module. This module uses ETL (Extract-Transform-Load) tools (such as Apache Airflow combined with Python scripts) to collect raw spatial and attribute data from multiple data interfaces (such as the API of the Ministry of Land and Resources, the database of the Environmental Protection Bureau, and road network data of the transportation department). This includes information on the leading industries in the park, the distribution of sewage pipe networks, the location of transportation stations, aviation height restrictions, ecological protection red line boundaries, expressways, ports and wharves, railway lines, and land parcel vector data (such as land parcel area and boundary coordinates). After collection, the data is cleaned (noise removal and missing value processing), converted to the WGS84 coordinate system, and standardized (fields are mapped to a unified format, such as a unified unit of meters). Finally, the data is stored in a spatial database (such as PostgreSQL extended with PostGIS, supporting spatial indexing). This module outputs a standardized basic dataset, which serves as the data support layer of the system.

[0042] Step 104: Generate and display industry site selection suggestions based on the selected site.

[0043] Industry site selection recommendations refer to information presented to users regarding potential land parcels. The methods for determining industry site selection recommendations are common knowledge among those in the field and will not be elaborated upon here.

[0044] By retrieving the specific requirements of the industry to be located, and combining the specific requirements of the industry with the user's general requirements for the area of ​​the industry, more comprehensive industrial site selection conditions are generated. This allows for the selection of more suitable plots of land based on different industry types, thereby improving the accuracy of industrial site selection.

[0045] A smart site selection analysis method based on large-scale models and industry characteristics also includes: Step 105: Determine the number of plots based on the selected site.

[0046] The number of land parcels refers to the number of land parcels retrieved according to the land parcel query command. The method for determining the number of land parcels is common knowledge among those in the field and will not be elaborated here.

[0047] Step 106: When the number of plots is greater than 1, determine the dominant industry based on the selected plots.

[0048] A number greater than 1 indicates that there are multiple candidate plots. The dominant industry refers to the dominant industry of the park where the selected plot is located. The dominant industry corresponding to the selected plot can be found from the basic database. The method for determining the dominant industry is common knowledge among those in the field and will not be elaborated here.

[0049] Step 107: In response to the leading industry, determine the industry matching degree and determine the expressway distance based on the selected site.

[0050] Industry matching degree refers to a numerical value used to show the degree of matching between the park where the selected site is located and the industry to be built. When the dominant industry and industry type are consistent, the industry matching degree is 1; otherwise, the industry matching degree is 0.

[0051] The distance to the expressway refers to the distance between the selected site and the expressway. The distance to the expressway can be determined from the basic database. The method for determining the distance to the expressway is common knowledge to those in the field and will not be elaborated here.

[0052] Step 108: Determine traffic accessibility in response to the expressway distance, and retrieve the site outline based on the selected site.

[0053] Traffic convenience refers to a numerical value used to show the traffic conditions of a selected site. It is generally calculated using the formula: Traffic Convenience = 1 / (1 - Distance to Expressway / 1000), with the distance to the expressway being normalized using the formula.

[0054] The plot outline refers to the edge shape of the selected plot. The plot outline can be determined from the basic database. The method for determining the plot outline is common knowledge to those in the field and will not be elaborated here.

[0055] Step 109: In response to the plot outline, determine the plot regularity, and retrieve the plot maturity and planning compliance based on the selected plot.

[0056] The regularity of a plot refers to the ratio of the boundary rectangle of the selected plot. It can be expressed by the formula: Plot regularity = 1 / [Plot perimeter / (2 * sqrt(π * plot area))]. The plot perimeter is the outline length of the selected plot, and the plot area is the area value of the selected plot. The methods for determining the plot perimeter and plot area are common knowledge in the field and will not be elaborated here.

[0057] Land maturity refers to a numerical value used to show the development level of a land parcel. When the selected land parcel is directly available, the land maturity is 1.0; when the selected land parcel is soon available, the land maturity is 0.8; and when the selected land parcel is a reserve land parcel, the land maturity is 0.6. The land maturity can be queried from the basic database. The method for determining the land maturity is common knowledge to those in the field and will not be elaborated here.

[0058] Planning compliance refers to a numerical value used to show whether a site needs to avoid the ecological red line. When a site needs to avoid the ecological red line, the planning compliance is 0; otherwise, the planning compliance is 1. The planning compliance can be queried from the basic database. The method for determining the planning compliance is common knowledge in this field and will not be elaborated here.

[0059] Step 110: Calculate the land parcel evaluation value by combining the industry matching degree, transportation convenience, land parcel regularity, land parcel maturity, and planning compliance degree.

[0060] The land parcel evaluation value is a numerical value used to show the quality of the candidate land parcels. The land parcel evaluation value can be calculated using a multi-dimensional evaluation module.

[0061] The multi-dimensional evaluation module employs a weighted sum model to quantitatively score candidate land parcels across five dimensions: industry matching (30%), land maturity (25%), transportation convenience (20%), planning compliance (15%), and land regularity (10%). The module outputs a ranked list of land parcel scores. Step 111: Select the site based on the site evaluation value.

[0062] When multiple plots are selected, each plot is evaluated and quantified into a plot evaluation value based on five aspects: industry matching degree, plot maturity, transportation convenience, planning compliance, and plot regularity. The most suitable plot for the user is then selected based on the plot evaluation value.

[0063] A smart site selection analysis method based on large-scale models and industry characteristics also includes: Step 112: When the number of land parcels is greater than 1, determine the upstream industry based on the industry type.

[0064] Upstream industries refer to the locations of industries that supply raw materials to the industries that users need to select. The upstream locations corresponding to the industry types can be found from the upstream data table. Then, the upstream location closest to the selected site can be selected as the upstream industry. The upstream data table is a data table that records different industry types and their corresponding upstream locations.

[0065] Step 113: Determine the upstream distance by combining the upstream industries and the selected site.

[0066] The upstream distance is the distance between the upstream industry and the selected site. The method for determining the upstream distance is common knowledge among those in the field and will not be elaborated here.

[0067] Step 114: Determine the clustering coefficient in response to the upstream distance and accessibility.

[0068] The cluster coefficient is a numerical value used to show the ease or difficulty of transporting raw materials. The greater the upstream distance and the lower the transportation convenience, the smaller the cluster coefficient. The cluster coefficient corresponding to the upstream distance and transportation convenience can be found in the cluster data table. The cluster data table is a data table that records different upstream distances and transportation convenience and their corresponding cluster coefficients.

[0069] Step 115: Update the land parcel evaluation value according to the cluster coefficient.

[0070] When multiple plots are selected, the sum of the original plot evaluation value and the cluster coefficient is calculated as the new plot evaluation value. This allows the plot with the easiest raw material transportation to be selected based on the difficulty of transporting raw materials required for industrial production. This reduces the possibility of production delays caused by delays or errors in raw material transportation and improves the stability of industrial production.

[0071] Reference Figure 2 Disaster mitigation site selection methods include: Step 200: Determine the impact of disasters in response to the industry type.

[0072] Disasters that affect an industry are those that have an impact on it. For example, typhoons and floods can affect the food and pharmaceutical production industries, causing food or pharmaceuticals to become damp. Earthquakes can affect the energy production industry, causing damage to energy transmission lines. You can look up the disasters that affect an industry type in the disaster correspondence table. The disaster correspondence table is a data table that records different industry types and their corresponding disasters.

[0073] Step 201: Retrieve the site disaster records based on the selected site and the disaster impacts.

[0074] Site disaster records refer to the degree and timing of disasters that occur on the selected site. Site disaster records corresponding to the selected site and the disasters can be queried from the record data table. The record data table is a data table that records different site sites, disasters, and their corresponding site disaster records.

[0075] Step 202: Determine the disaster coefficient in response to the disaster record of the land parcel, and determine the disaster threshold according to the industry type.

[0076] The disaster coefficient is a numerical value that shows the degree of impact of a disaster on a selected site. It can be calculated by first summing the degree of disasters as the total degree, and then calculating the quotient of the total degree and the disaster interval as the disaster coefficient. The disaster interval refers to the interval between the earliest and latest times in the disaster record of the site. The method for determining the disaster coefficient is common knowledge in the field and will not be elaborated here.

[0077] Disaster thresholds are numerical values ​​used to determine the impact of disasters on industries. The disaster thresholds corresponding to different industry types can be retrieved from the threshold data table. The threshold data table is a data table that records different industry types and their corresponding disaster thresholds.

[0078] Step 203: When the disaster coefficient is greater than the disaster threshold, update the land parcel evaluation value according to the disaster coefficient.

[0079] A disaster coefficient greater than the disaster threshold indicates that the disaster has a significant impact on the industry. In this case, the quotient of the land parcel evaluation value and the disaster coefficient is used as the new land parcel evaluation value.

[0080] After selecting candidate sites based on industry site selection criteria, historical disaster information for the areas where the candidate sites are located is retrieved, and the impact of disasters on industries is assessed. In this way, the site with the least impact from disasters is selected, thereby reducing the damage to industries caused by disasters.

[0081] Disaster mitigation site selection methods also include: Step 204: When the disaster coefficient is not greater than the disaster threshold, determine the transportation route by combining the upstream industry and the selected site.

[0082] A disaster coefficient not exceeding the disaster threshold indicates that the disaster has a relatively small impact on the industry. The transportation route refers to the route by which the upstream industry transports raw materials, that is, the various routes from the upstream industry to the selected site. The method for determining the transportation route is common knowledge among those in the field and will not be elaborated here.

[0083] Step 205: Retrieve route disaster records based on the transportation route.

[0084] Route disaster records refer to the severity and timing of various disasters that occur along the transportation route. The method for retrieving route disaster records is the same as step 201 above, and will not be repeated here.

[0085] Step 206: Determine the route coefficients in response to the route disaster record.

[0086] The route coefficient is a numerical value used to show the degree of impact of a disaster on a transportation route. The method for determining the route coefficient is the same as step 202 above, and will not be repeated here.

[0087] Step 207: When the route coefficient is greater than the preset transportation threshold, determine the secondary distance by combining the upstream industry and the selected site.

[0088] The transportation threshold refers to the maximum route coefficient that a vehicle can continue transporting along the route. The transportation threshold can be selected by staff based on the actual situation, and will not be elaborated here. A route coefficient greater than the transportation threshold means that raw materials are difficult to transport from upstream industries to the selected site. The secondary selection distance refers to the distance between the selected site and the nearest upstream location other than the upstream industry. The method for determining the secondary selection distance is common knowledge in the field and will not be elaborated here.

[0089] Step 208: Update the cluster coefficient in response to the secondary distance, upstream distance, and accessibility.

[0090] Based on the industry's location, routes for transporting raw materials are planned, and historical disaster information along the routes is retrieved. This allows for the selection of alternative locations for transporting raw materials when disasters affect transportation, thereby reducing production delays caused by disasters impacting raw material transport.

[0091] Disaster mitigation site selection methods also include: Step 209: When the route coefficient is greater than the preset transportation threshold, determine the transportation time in response to the transportation route.

[0092] Transportation time refers to the shortest time required for a vehicle to travel along a transportation route. The method for determining transportation time is common knowledge among those in the field and will not be elaborated here.

[0093] Step 210: Determine the blocking duration by combining the route coefficient and transportation time.

[0094] The duration of disruption refers to the maximum duration during which a disaster can disrupt the transportation of raw materials. It can be determined by first counting the length of time during which the route coefficient is greater than the transportation threshold as the duration of the disaster, and then selecting the maximum value among the durations of disasters that are greater than the transportation duration as the duration of disruption.

[0095] Step 211: Determine warehousing needs based on the duration of the blockage and the industry type.

[0096] Storage demand refers to the volume of raw materials required for an industry to maintain normal production when disasters make it difficult to transport raw materials. In other words, it is the volume of raw materials required for an industry to produce during the duration of the disruption. The storage demand corresponding to the duration of the disruption and the industry type can be found in the storage correspondence table. The storage correspondence table is a data table that records different durations of disruption and industry types and their corresponding storage demands.

[0097] Step 212: Update the site selection in response to the warehousing demand.

[0098] When disasters are likely to affect the transportation of raw materials, an appropriate amount of raw material storage is selected according to the impact of the disaster on the transportation of raw materials, and the storage requirements of the selected site are obtained according to the amount of raw material storage. In this way, when the disaster affects the transportation of raw materials, the industry can continue to produce through the stored raw materials, thereby reducing the situation where the disaster affects the transportation of raw materials and causes delays in industrial production.

[0099] Reference Figure 3 Environmental site selection methods include: Step 300: Determine the building materials list in response to the area requirements and industry type.

[0100] A building materials list is a data table that records the types and quantities of building materials needed to construct industrial plants. You can look up the building materials list corresponding to the area requirements and industry type from the material correspondence table. The material correspondence table is a data table that records different area requirements and industry types and their corresponding building materials lists.

[0101] Step 301: Determine transportation emissions based on the building materials list and the site selection, and retrieve site images based on the site selection.

[0102] Transportation emissions refer to the carbon emissions from transporting building materials. The source address corresponding to the building materials can be found in the source correspondence table, and the carbon emissions from transporting the building materials from the source address to the selected site can be found in the transportation correspondence table. The source address refers to the address where the building materials are stored. The source correspondence table is a data table that records different types of building materials and their corresponding source addresses. The transportation correspondence table is a data table that records different types of building materials, the quantity of building materials, and the transportation distance and their corresponding transportation emissions. The transportation distance refers to the route distance from the source address to the selected site.

[0103] A site image refers to a picture of a selected site. The method for obtaining site images is common knowledge to those in the field and will not be elaborated here.

[0104] Step 302: Identify the earthwork volume from the plot image.

[0105] Earthwork volume refers to the amount of excavation and backfill required to clear the selected site. Image recognition technology can be used to identify the unevenness and size of the site from the site image and match the earthwork volume. The method for determining the earthwork volume is common knowledge in the field and will not be elaborated here.

[0106] Step 303: Determine earthwork discharge in response to the earthwork volume.

[0107] Earthwork emissions refer to the carbon emissions from cleaning up the selected site. The earthwork emissions corresponding to the earthwork volume can be found in the earthwork data table, which records different earthwork volumes and their corresponding earthwork emissions.

[0108] Step 304: Determine the construction emissions value by combining the transportation emissions and earthwork emissions.

[0109] Construction emissions refer to the total carbon emissions of the construction industry, which is the sum of transportation emissions and earthwork emissions. The method for determining construction emissions is common knowledge in the field and will not be elaborated here.

[0110] Step 305: Update the land parcel evaluation value based on the construction emissions value.

[0111] The new land parcel evaluation value is generally calculated using the formula (land parcel evaluation value * 0.85 + construction emissions value * 0.15). The building materials required for the industry are estimated based on the industrial area, and the carbon emissions from transporting these materials are estimated according to the industry's location. Simultaneously, the carbon emissions from clearing the site are identified from images of the selected site. This process is used to calculate the overall carbon emissions from building the industry, thereby selecting the site with the lowest carbon emissions and improving the environmental friendliness of industrial construction.

[0112] Environmental site selection methods also include: Step 306: Determine the power structure based on the selected site.

[0113] The power structure refers to the power supply structure of the selected site, that is, the proportion of various power sources such as wind power, photovoltaic power, and thermal power. The method for determining the power structure is common knowledge among those in the field and will not be elaborated here.

[0114] Step 307: Determine the power coefficient in response to the power structure.

[0115] The electricity coefficient refers to the carbon emissions per unit of electricity produced. The calculation method for the electricity coefficient is common knowledge among those in the field and will not be elaborated here.

[0116] Step 308: Determine production emissions by combining the power coefficient and industry type, and determine the recycling coefficient according to the building materials table.

[0117] Production emissions refer to the carbon emissions from the electricity consumed during industrial production. The production electricity corresponding to the industry type can be found by querying the production correspondence table, and then the product of production electricity and the electricity coefficient is calculated as production emissions. Production electricity refers to the electricity required for industrial production in one year.

[0118] The recycling coefficient refers to the unit carbon emission during factory demolition and construction waste disposal. The recycling coefficient corresponding to the building material table can be found in the recycling data table. The recycling data table is a data table that records different building material tables and their corresponding recycling coefficients.

[0119] Step 309: Determine the demolition and discharge based on the recycling coefficient and area requirements.

[0120] Demolition emissions refer to the total carbon emissions from factory demolition and construction waste disposal. The demolition emissions corresponding to the recycling coefficient and area requirement can be found in the demolition correspondence table, which is a data table that records different recycling coefficients and area requirements and their corresponding demolition emissions.

[0121] Step 310: Determine the total emissions value based on the transportation emissions, earthwork emissions, production emissions, and demolition emissions, and determine the emissions threshold in response to the industry type.

[0122] Gross emissions refer to the carbon emissions of an industry throughout its entire life cycle, which is the sum of transportation emissions, earthwork emissions, production emissions, and demolition emissions. The method for determining gross emissions is common knowledge to those in the field and will not be elaborated here.

[0123] Emission thresholds refer to the maximum carbon emissions stipulated for an industry. The emission thresholds corresponding to different industry types can be found in the limit correspondence table. The limit correspondence table is a data table that records different industry types and their corresponding emission thresholds. Data from relevant documents such as the "Carbon Emission Limits for Heavily Polluting Industries" can be compiled from the limit correspondence table.

[0124] Step 311: When the total emissions value is greater than the emission threshold, generate and display a high carbon emission warning based on the total emissions value.

[0125] Total emissions exceeding the emission threshold indicates excessive carbon emissions from an industry. High carbon emission warnings are information used to alert users to excessive carbon emissions. The methods for determining high carbon emission warnings are common knowledge in the field and will not be elaborated here.

[0126] The system retrieves the power supply structure of the industries in the candidate site locations to calculate the carbon emissions of industrial production. It also estimates the carbon emissions of demolishing industries based on their area. Finally, it combines the carbon emissions from building, producing, and demolishing industries to calculate the carbon emissions of the entire industry lifecycle and promptly alerts users when the carbon emissions throughout the industry lifecycle are too high.

[0127] Environmental site selection methods also include: Step 312: When the total emissions exceed the emissions threshold, retrieve the resource distribution based on the selected site.

[0128] Resource distribution refers to the distribution of natural resources of the selected site, namely the distribution of sunlight, wind power and water power. The methods for obtaining resource distribution information are common knowledge in this field and will not be elaborated here.

[0129] Step 313: Determine the power generation efficiency in response to the resource distribution.

[0130] Power generation efficiency refers to the efficiency of generating electricity from natural resources. The methods for determining power generation efficiency are common knowledge among those in the field and will not be elaborated here.

[0131] Step 314: Determine the emission reduction efficiency based on the power generation efficiency and the power coefficient.

[0132] Emission reduction efficiency refers to a numerical value used to demonstrate the ability of generating electricity from natural resources to reduce carbon emissions. The emission reduction coefficient corresponding to the power generation efficiency can be found in the emission reduction correspondence table. The quotient of the emission reduction coefficient and the power coefficient is then calculated as the emission reduction efficiency. The emission reduction coefficient refers to the reduction of carbon emissions per unit of electricity generated from natural resources. The emission reduction correspondence table is a data table that records different power generation efficiencies and their corresponding emission reduction coefficients.

[0133] Step 315: Determine the emission reduction based on the emission reduction efficiency and production emissions.

[0134] Emission reduction refers to the reduction of carbon emissions after using natural resources for power generation. The product of emission reduction efficiency and production emissions can be calculated as power generation emissions, and the difference between the total emissions and power generation emissions can be calculated as emission reduction emissions.

[0135] Step 316: If the emission reduction is greater than the emission threshold, determine the external power source based on the selected site.

[0136] Emissions reduction exceeding the emission threshold indicates that carbon emissions from industries generating electricity using natural resources are still too high. External power sources refer to the sources of clean energy such as wind power, photovoltaic power, and hydropower that are drawn from the outside. The source closest to the selected site can be selected as the external power source. The method for determining the external power source is common knowledge in the field and will not be elaborated here.

[0137] Step 317: Generate and display environmental protection and emission reduction recommendations by combining the external power source and emission reduction efficiency.

[0138] Environmental protection and emission reduction recommendations refer to information used to present users with information on external power sources and emission reduction efficiency. The methods for determining environmental protection and emission reduction recommendations are common knowledge in the field and will not be elaborated here.

[0139] When an industry's carbon emissions are too high, the local natural resources can be assessed based on the industry's location. When natural resources are abundant, they can be used to generate electricity. When natural resources are scarce, hydropower, wind power, and photovoltaic power can be sourced from other regions, thereby reducing the industry's carbon emissions and improving its environmental friendliness.

[0140] Based on the same inventive concept, embodiments of the present invention provide a smart site selection analysis system based on large-scale models and industry characteristics, comprising: The data acquisition module is used to collect demand information. The memory is used to store the program of any of the above-mentioned smart location analysis methods based on large models and industry characteristics; The processor is the unit of memory that allows programs to be loaded and executed by the processor.

[0141] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0142] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A smart site selection analysis method based on large-scale models and industry characteristics, characterized in that, include: Step 100: Collect user needs information; Step 101: Extract industry type from the demand information; Step 102: Determine industry demand in response to the industry type, and extract area demand from the demand information; Step 103: Determine the site selection plot based on the aforementioned industry demand and area requirements; Step 104: Generate and display industry site selection suggestions based on the selected site.

2. The intelligent site selection analysis method based on large-scale models and industry characteristics according to claim 1, characterized in that, Also includes: Step 105: Determine the number of land parcels based on the selected site; Step 106: When the number of land parcels is greater than 1, determine the dominant industry based on the selected land parcels; Step 107: Determine the industry matching degree in response to the leading industry, and determine the expressway distance based on the selected site; Step 108: Determine traffic convenience in response to the expressway distance, and retrieve the site outline based on the selected site; Step 109: Determine the regularity of the plot in response to the plot outline, and retrieve the plot maturity and planning compliance based on the selected plot; Step 110: Calculate the land parcel evaluation value by combining the industry matching degree, transportation convenience, land parcel regularity, land parcel maturity, and planning compliance. Step 111: Select the site based on the site evaluation value.

3. The intelligent site selection analysis method based on large-scale models and industry characteristics according to claim 2, characterized in that, Also includes: Step 112: When the number of land parcels is greater than 1, determine the upstream industry based on the industry type; Step 113: Determine the upstream distance by combining the upstream industries and the selected site; Step 114: Determine the clustering coefficient in response to the upstream distance and accessibility; Step 115: Update the land parcel evaluation value according to the cluster coefficient.

4. The intelligent site selection analysis method based on large-scale models and industry characteristics according to claim 3, characterized in that, It also includes disaster-resistant site selection methods, which include: Step 200: Determine the impacting disaster in response to the industry type; Step 201: Retrieve the site disaster records based on the selected site and the disaster impacts; Step 202: Determine the disaster coefficient in response to the disaster record of the land parcel, and determine the disaster threshold according to the industry type; Step 203: When the disaster coefficient is greater than the disaster threshold, update the land parcel evaluation value according to the disaster coefficient.

5. The intelligent site selection analysis method based on large-scale models and industry characteristics according to claim 4, characterized in that, The disaster-resistant site selection method also includes: Step 204: When the disaster coefficient is not greater than the disaster threshold, determine the transportation route by combining the upstream industry and the selected site; Step 205: Retrieve route disaster records based on the transportation route; Step 206: Determine route coefficients in response to the route disaster records; Step 207: When the route coefficient is greater than the preset transportation threshold, determine the secondary distance by combining the upstream industry and the selected site; Step 208: Update the cluster coefficient in response to the secondary distance, upstream distance, and accessibility.

6. The intelligent site selection analysis method based on large-scale models and industry characteristics according to claim 5, characterized in that, The disaster-resistant site selection method also includes: Step 209: When the route coefficient is greater than the preset transportation threshold, determine the transportation time in response to the transportation route; Step 210: Determine the blocking duration by combining the route coefficient and transportation time; Step 211: Determine warehousing requirements based on the duration of the blockage and the industry type; Step 212: Update the site selection in response to the warehousing demand.

7. The intelligent site selection analysis method based on large-scale models and industry characteristics according to claim 3, characterized in that, It also includes environmentally friendly site selection methods, which include: Step 300: Determine the building materials list in response to the area requirements and industry type; Step 301: Determine transportation emissions based on the building materials list and the selected site, and retrieve site images based on the selected site; Step 302: Identify the earthwork volume from the plot image; Step 303: Determine earthwork discharge in response to the earthwork volume; Step 304: Determine the construction emissions value by combining the transportation emissions and earthwork emissions; Step 305: Update the land parcel evaluation value based on the construction emissions value.

8. The intelligent site selection analysis method based on large-scale models and industry characteristics according to claim 7, characterized in that, The environmentally friendly site selection method also includes: Step 306: Determine the power structure based on the selected site; Step 307: Determine the power coefficient in response to the power structure; Step 308: Determine production emissions by combining the power coefficient and industry type, and determine the recycling coefficient according to the building materials table; Step 309: Determine the demolition and discharge requirements based on the aforementioned recycling coefficient and area requirements; Step 310: Determine the total emissions value based on the transportation emissions, earthwork emissions, production emissions, and demolition emissions, and determine the emissions threshold in response to the industry type; Step 311: When the total emissions value is greater than the emission threshold, generate and display a high carbon emission warning based on the total emissions value.

9. The intelligent site selection analysis method based on large-scale models and industry characteristics according to claim 8, characterized in that, The environmentally friendly site selection method also includes: Step 312: When the total emissions exceed the emissions threshold, retrieve the resource distribution based on the selected site. Step 313: Determine the power generation efficiency in response to the resource distribution; Step 314: Determine the emission reduction efficiency based on the power generation efficiency and the power coefficient; Step 315: Determine the emission reduction based on the emission reduction efficiency and production emissions; Step 316: If the emission reduction exceeds the emission threshold, determine the external power source based on the selected site. Step 317: Generate and display environmental protection and emission reduction recommendations by combining the external power source and emission reduction efficiency.

10. A smart site selection analysis system based on large-scale models and industry characteristics, characterized in that, include: The data acquisition module is used to collect demand information. A memory for storing a program of a smart location analysis method based on large models and industry characteristics as described in any one of claims 1 to 9; The processor is the unit of memory that allows programs to be loaded and executed by the processor.

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