A smart site selection analysis method and system based on a large model and industry characteristics

Abstract

The application relates to a wisdom site selection analysis method and system based on a large model and industrial characteristics, and relates to the technical field of wisdom site selection, which comprises the following steps: step 100, collecting demand information of a user; step 101, extracting an industrial type from the demand information; step 102, determining industrial demand in response to the industrial type, and extracting area demand from the demand information; step 103, determining a site block in combination with the industrial demand and the area demand; and step 104, generating and displaying an industrial site selection suggestion based on the site block. The application has the effects of improving the accuracy of industrial site selection and selecting more suitable site blocks according to different industrial types.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent site selection, in particular to an intelligent site selection analysis method and system based on large models and industrial characteristics. BACKGROUND

[0002] Industrial site selection refers to the strategic decision-making process of an enterprise or industrial park selecting the optimal geographic location to layout production, research and development, warehousing, sales and other core businesses in a specific area based on its development needs, resource endowments, cost benefits, policy environment, market potential and other multidimensional factors.

[0003] With the rise of geographic information systems (GIS), big data and artificial intelligence technology, industrial site selection analysis has gradually shifted from traditional manual research to digital assisted decision-making. Existing industrial site selection systems generally limit plots in terms of plot area range, infrastructure distance and other factors, and then evaluate multiple plots that meet the restrictions from the perspectives of traffic factors and environmental factors to screen plots that meet the conditions. The effectiveness of such systems is relatively effective in simple site selection scenarios, enabling basic automated screening and visualization, such as quickly excluding plots that do not meet area or distance requirements.

[0004] Existing industrial site selection systems rely on manually hard-coded general rules to screen plots, but different industries may have special requirements when selecting sites, such as integrated circuits requiring specific wastewater treatment and air transportation. When screening plots required by different industries through uniform requirements, the industrial characteristics may be overlooked, leading to generalized screening results and resulting in site selection bias or omission of the best plots. SUMMARY

[0005] To improve the accuracy of industrial site selection and select more suitable plots according to different industry types, the present application provides an intelligent site selection analysis method and system based on large models and industrial characteristics.

[0006] In a first aspect, the present application provides an intelligent site selection analysis method based on large models and industrial characteristics, which adopts the following technical solution:

[0007] An intelligent site selection analysis method based on large models and industrial characteristics, comprising:

[0008] Step 100: Collecting user demand information;

[0009] Step 101: Extracting an industry type from the demand information;

[0010] Step 102: Determining industrial requirements in response to the industry type and extracting area requirements from the demand information;

[0011] Step 103: Determining a site selection plot in combination with the industrial requirements and area requirements;

[0012] Step 104: generating and displaying an industry site selection suggestion based on the site block.

[0013] By using the above technical solution, the special requirements are retrieved according to the type of the industry to be selected, so that a more perfect industry site selection condition is generated by combining the special requirements of the industry and the conventional requirements of the user for the industry area, and then a more suitable block can be selected according to different types of industries, thereby improving the accuracy of industry site selection.

[0014] Optionally, it further comprises:

[0015] Step 105: determining the number of blocks based on the site block;

[0016] Step 106: when the number of blocks is greater than 1, determining a dominant industry according to the site block;

[0017] Step 107: determining an industry matching degree in response to the dominant industry, and determining an expressway distance according to the site block;

[0018] Step 108: determining a traffic convenience degree in response to the expressway distance, and retrieving a block contour according to the site block;

[0019] Step 109: determining a block regularity degree in response to the block contour, and retrieving a block maturity and a planning compliance degree according to the site block;

[0020] Step 110: calculating a block evaluation value by combining the industry matching degree, the traffic convenience degree, the block regularity degree, the block maturity and the planning compliance degree;

[0021] Step 111: selecting the site block based on the block evaluation value.

[0022] By using the above technical solution, when multiple blocks are screened out, each block is evaluated and quantified as a block evaluation value from five aspects of the industry matching degree, the block maturity, the traffic convenience degree, the planning compliance degree and the block regularity degree, so that the most suitable block for the user is selected according to the block evaluation value.

[0023] Optionally, it further comprises:

[0024] Step 112: when the number of blocks is greater than 1, determining an upstream industry according to the type of the industry;

[0025] Step 113: determining an upstream distance by combining the upstream industry and the site block;

[0026] Step 114: determining a cluster coefficient in response to the upstream distance and the traffic convenience degree;

[0027] Step 115: updating the plot evaluation value according to the cluster coefficient.

[0028] By adopting the technical scheme, when multiple plots are screened out, the plot with the most easily transported raw materials is selected according to the difficulty of transporting raw materials required by industrial production, so as to reduce the situation that production delay is caused by delay and error of raw material transportation, and further improve the stability of industrial production.

[0029] Optionally, the anti-disaster site selection method further comprises:

[0030] Step 200: determining an impact disaster according to the industrial type;

[0031] Step 201: calling a plot disaster record based on the site plot and the impact disaster;

[0032] Step 202: determining a disaster coefficient according to the plot disaster record, and determining a disaster threshold according to the industrial type;

[0033] Step 203: updating the plot evaluation value according to the disaster coefficient when the disaster coefficient is greater than the disaster threshold.

[0034] By adopting the technical scheme, when the candidate plot is screened out according to the industrial site selection condition, the historical disaster situation of the region where the candidate plot is located is called, and the impact of the disaster on the industry is evaluated, so that the plot with the smallest disaster impact is selected, and the situation that the industry is damaged due to the disaster is reduced.

[0035] Optionally, the anti-disaster site selection method further comprises:

[0036] Step 204: determining a transportation route in combination with the upstream industry and the site plot when the disaster coefficient is not greater than the disaster threshold;

[0037] Step 205: calling a route disaster record based on the transportation route;

[0038] Step 206: determining a route coefficient according to the route disaster record;

[0039] Step 207: determining a secondary selection distance in combination with the upstream industry and the site plot when the route coefficient is greater than a preset transportation threshold;

[0040] Step 208: updating the cluster coefficient according to the secondary selection distance, the upstream distance and the traffic convenience.

[0041] By adopting the technical scheme, the route for transporting raw materials is planned according to the address of the industry, and the historical disaster situation on the route is called, so that other positions capable of transporting raw materials are selected when the disaster affects transportation, and the situation that industrial production is delayed due to the impact of the disaster on raw material transportation is reduced.

[0042] Optionally, the disaster-resistant site selection method further comprises:

[0043] Step 209: determining the transportation duration in response to the transportation route when the route coefficient is greater than the preset transportation threshold;

[0044] Step 210: determining the barrier duration in combination with the route coefficient and the transportation duration;

[0045] Step 211: determining the storage demand according to the barrier duration and the industry type;

[0046] Step 212: updating the site plot in response to the storage demand.

[0047] By adopting the above technical solution, when disasters easily affect raw material transportation, appropriate raw material storage is selected according to the influence of disasters on raw material transportation, and the storage demand of the site plot is obtained according to the raw material storage, so that the industry can continue production through the stored raw materials when disasters affect raw material transportation, thereby reducing the situation of production delay of the industry caused by disasters affecting raw material transportation.

[0048] Optionally, the environmental-friendly site selection method further comprises:

[0049] Step 300: determining the building material table in response to the area demand and the industry type;

[0050] Step 301: determining the transportation emission according to the building material table and the site plot, and calling the plot image based on the site plot;

[0051] Step 302: identifying the earthwork amount from the plot image;

[0052] Step 303: determining the earthwork emission in response to the earthwork amount;

[0053] Step 304: determining the construction emission value in combination with the transportation emission and the earthwork emission;

[0054] Step 305: updating the plot evaluation value according to the construction emission value.

[0055] By adopting the above technical solution, the building materials required by the industry are estimated according to the area of the industry, the carbon emission of transporting the building materials is estimated according to the location of the industry, and the carbon emission of cleaning the plot is identified from the image of the site plot, and then the total carbon emission of building the industry is calculated, so that the plot with the minimum carbon emission is selected, and the environmental-friendliness of building the industry is improved.

[0056] Optionally, the environmental-friendly site selection method further comprises:

[0057] Step 306: determining a power structure according to the site block;

[0058] Step 307: determining a power coefficient in response to the power structure;

[0059] Step 308: determining a production emission in combination with the power coefficient and the industry type, and determining a recycling coefficient according to the building material table;

[0060] Step 309: determining a demolition emission in combination with the recycling coefficient and the area requirement;

[0061] Step 310: determining a total emission value according to the transportation emission, the earthwork emission, the production emission and the demolition emission, and determining an emission threshold in response to the industry type;

[0062] Step 311: generating and displaying a high-carbon emission warning based on the total emission value when the total emission value is greater than the emission threshold.

[0063] By adopting the above technical solution, the power supply structure of the industry at the location of the candidate site block is called to calculate the carbon emission of the industry production, and the carbon emission of the demolished industry is estimated according to the area of the industry, and then the carbon emission of the entire life cycle of the industry is calculated in combination with the carbon emissions of the built industry, the industry production and the demolished industry, and the user is reminded in time when the carbon emission of the entire life cycle of the industry is too high.

[0064] Optionally, the environmental protection site selection method further comprises:

[0065] Step 312: calling a resource distribution based on the site block when the total emission value is greater than the emission threshold;

[0066] Step 313: determining a power generation efficiency in response to the resource distribution;

[0067] Step 314: determining an emission reduction efficiency according to the power generation efficiency and the power coefficient;

[0068] Step 315: determining an emission reduction emission in combination with the emission reduction efficiency and the production emission;

[0069] Step 316: determining an external power source based on the site block if the emission reduction emission is greater than the emission threshold;

[0070] Step 317: generating and displaying an environmental protection emission reduction suggestion in combination with the external power source and the emission reduction efficiency.

[0071] By adopting the above technical solution, when the carbon emission of the industry is too high, the natural resource conditions of the location where the industry is located are called according to the location, so that natural resources are used for power generation when the natural resources are abundant, and water power, wind power and photovoltaic power and other power sources are called from other places when the natural resources are scarce, so as to reduce the carbon emission of the industry and improve the environmental protection of the industry.

[0072] In a second aspect, the application provides a smart site selection analysis system based on a large model and industrial characteristics, which adopts the following technical solution:

[0073] A smart site selection analysis system based on a large model and industrial characteristics comprises:

[0074] A collection module is configured to collect demand information.

[0075] A memory is configured to store the program of any of the smart site selection analysis methods based on a large model and industrial characteristics.

[0076] A processor, and the program in the memory can be loaded and executed by the processor.

[0077] By adopting the above technical solution, the corresponding special demand is retrieved according to the type of the industry to be selected, so that a more perfect industrial site selection condition is generated by combining the special demand of the industry and the conventional demand of the user for the industrial area, and then a more suitable plot can be selected according to different types of industries, thereby improving the accuracy of industrial site selection.

[0078] In summary, the application has at least one of the following beneficial technical effects:

[0079] 1. The corresponding special demand is retrieved according to the type of the industry to be selected, so that a more perfect industrial site selection condition is generated by combining the special demand of the industry and the conventional demand of the user for the industrial area, and then a more suitable plot can be selected according to different types of industries, thereby improving the accuracy of industrial site selection.

[0080] 2. When multiple plots are screened, each plot is evaluated and quantified as a plot evaluation value from five aspects of industrial matching degree, plot maturity, traffic convenience, planning compliance, and plot regularity, so that the most suitable plot for the user is selected according to the plot evaluation value.

[0081] 3. When multiple plots are screened, the plot with the easiest transportation of raw materials is selected according to the difficulty of raw material transportation required by industrial production, so as to reduce the situation that production delay is caused by raw material transportation delay and error, and thereby improve the stability of industrial production. BRIEF DESCRIPTION OF DRAWINGS

[0082] Figure 1 is a flowchart of a smart site selection analysis method based on a large model and industrial characteristics;

[0083] Figure 2 is a flowchart of an anti-disaster site selection method;

[0084] Figure 3 is a flowchart of an environmental protection site selection method. DETAILED DESCRIPTION

[0085] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0086] Referring to Figure 1 A wisdom site selection analysis method based on a large model and industry characteristics, comprising:

[0087] Step 100: Collecting demand information of a user.

[0088] The demand information refers to the requirements of the user for the industry, and the demand information includes the type of industry required by the user for site selection and the area requirement. The information of the user is generally collected by a sound collector as the demand information, and the collection method of the demand information is selected by the staff according to the actual situation, which is not described here.

[0089] Step 101: Extracting the type of industry from the demand information.

[0090] The type of industry is the type of industry required by the user for site selection, which is contained in the demand information such as integrated circuits. The type of industry can be determined by a user intention analysis module. The user intention analysis module integrates a large language model (LLM, such as the Qianwen series model), which can analyze the intention of the demand information through prompt engineering technology. After the demand information is tokenized, the model outputs the structured JSON format of the intention elements (such as {“industry type”:“integrated circuits”,“area range”:[45,55]}). The module interacts with other modules through an API interface (such as RESTful service), supports multi-round dialogue to clarify the intention, and the determination method of the type of industry is selected by the staff according to the actual situation, which is not described here.

[0091] Step 102: Determining the industry demand in response to the type of industry and extracting the area demand from the demand information.

[0092] The industry demand refers to the special demand corresponding to the type of industry, for example, integrated circuits have demand for sewage treatment and space transportation. It is generally required that the address is less than or equal to 500 meters away from the sewage pipe network, and less than or equal to 30000 meters away from the airport. The industry demand can be called from an industry database. The industry database refers to a database recording different types of industries and their corresponding industry demands.

[0093] The industrial database refers to extracting and structuring the demand knowledge of each industry for infrastructure from expert literature, industrial standards and historical cases by using knowledge graph technology (such as Neo4j graph database), forming a triple knowledge representation (such as node “industry type”-relationship “demand”-node “infrastructure condition”, for example, “integrated circuit-polluted water pipe network demand-distance≤500m”), and the knowledge base supports dynamic updating, and the corresponding industrial demand is generally retrieved by SPARQL query language.

[0094] The area demand is the minimum area value required by the industry included in the above demand information, and the determination method of the area demand can be determined by the user intention analysis module.

[0095] Step 103: determining the site block in combination with the industrial demand and the area demand.

[0096] The site block refers to the industrial candidate block position selected according to the industrial demand and the area demand. The industrial demand and the area demand can be combined into a block query instruction by the intelligent condition generation module, and the site block can be queried from the basic database construction module according to the block query instruction by the spatial query and screening module.

[0097] The intelligent condition generation module refers to dynamically assembling the industrial demand and the area demand by using a rule engine (such as Drools) (such as logical AND / OR operation to generate “gyfsgdjl≤500 AND gksljl≤1000”). The module processes complex constraints (such as priority sorting: industry matching>infrastructure>area), and outputs SQL-like query condition strings.

[0098] The spatial query and screening module refers to using a spatial query engine (such as PostGIS functions ST_Distance, ST_Within), combining the block query instruction to perform a joint query (attribute filtering+spatial analysis) from the basic database, for example, calculating the Euclidean distance or buffer intersection of the block and the infrastructure, and the query result is a candidate block list (including attributes such as xzqmc, tdmj, fl, etc.), supporting paging and sorting.

[0099] The basic database is constructed by a basic database construction module, which collects original spatial data and attribute data from multi-source data interfaces (such as the API of the Land and Resources Bureau, the database of the Environmental Protection Bureau, and the road network data of the transportation department) through an ETL (Extract-Transform-Load) tool (such as Apache Airflow combined with a Python script), including park leading industry information, sewage pipe network distribution, transportation station location, aviation height restriction, ecological protection red line boundary, high-speed road network, port terminal, railway line, and plot vector data (such as plot area, boundary coordinates), and performs data cleaning (removing noise, handling missing values), coordinate system unification (converting to WGS84 coordinate system), and standardization processing (mapping fields to a unified format, such as unified units in meters) after collection. Finally, it is stored in a spatial database (such as PostgreSQL extended PostGIS, supporting spatial indexing), and the module outputs a standardized basic data set as the data support layer of the system.

[0100] Step 104: generating and displaying an industry site selection suggestion based on the site block.

[0101] The industry site selection suggestion refers to the information presented to the user, and the determination method of the industry site selection suggestion is known to those skilled in the art and will not be repeated here.

[0102] According to the type of the industry to be selected, the corresponding special requirements are retrieved, so as to generate more perfect industry site selection conditions by combining the special requirements of the industry and the conventional requirements of the user for the area of the industry, and then more suitable plots can be selected according to different types of industries, improving the accuracy of industry site selection.

[0103] A smart site selection analysis method based on a large model and industry characteristics further comprises:

[0104] Step 105: determining the number of plots based on the site block.

[0105] The number of plots refers to the number of plots queried according to the plot query instruction, and the determination method of the number of plots is known to those skilled in the art and will not be repeated here.

[0106] Step 106: when the number of plots is greater than 1, determining the leading industry according to the site block.

[0107] The number of plots greater than 1 represents the existence of multiple plots to be selected, and the leading industry refers to the leading industry of the park where the site block is located. The leading industry corresponding to the site block can be queried from the basic database, and the determination method of the leading industry is known to those skilled in the art and will not be repeated here.

[0108] Step 107: determining an industrial matching degree in response to the dominant industry, and determining a freeway distance according to the site block.

[0109] The industrial matching degree is a value for showing the matching degree of the park where the site block is located and the industry to be constructed. When the dominant industry is consistent with the industry type, the industrial matching degree is 1, otherwise the industrial matching degree is 0.

[0110] The freeway distance is the distance value of the site block from a high-speed road. The freeway distance can be determined from a basic database. The determination method of the freeway distance is known to those skilled in the art, and will not be described here.

[0111] Step 108: determining a traffic convenience degree in response to the freeway distance, and retrieving a block contour according to the site block.

[0112] The traffic convenience degree is a value for showing the traffic situation of the site block. Generally, the traffic convenience degree is calculated by the formula: traffic convenience degree = 1 / (1 - freeway distance / 1000). The freeway distance is normalized by the formula.

[0113] The block contour is the edge shape of the site block. The block contour can be determined from a basic database. The determination method of the block contour is known to those skilled in the art, and will not be described here.

[0114] Step 109: determining a block regularity degree in response to the block contour, and retrieving a block maturity and a planning compliance degree according to the site block.

[0115] The block regularity degree is a value for showing the boundary rectangle ratio of the site block. The block regularity degree can be calculated by the formula: block regularity degree = 1 / [block perimeter / (2*sqrt(π*block area))]. The block perimeter is the contour length of the site block, and the block area is the area value of the site block. The determination method of the block perimeter and the block area is known to those skilled in the art, and will not be described here.

[0116] The block maturity is a value for showing the construction degree of the block. When the site block is directly available, the block maturity is 1.0. When the site block is available in the near future, the block maturity is 0.8. When the site block is a reserve block, the block maturity is 0.6. The block maturity can be queried from a basic database. The determination method of the block maturity is known to those skilled in the art, and will not be described here.

[0117] The planning compliance degree is a value for showing whether the site block needs to avoid ecological red lines. When the site block needs to avoid ecological red lines, the planning compliance degree is 0, otherwise the planning compliance degree is 1. The planning compliance degree can be queried from a basic database. The determination method of the planning compliance degree is known to those skilled in the art, and will not be described here.

[0118] Step 110: Calculate the plot evaluation value in combination with the industry matching degree, traffic convenience, plot regularity, plot maturity and planning compliance.

[0119] The plot evaluation value refers to a numerical value used to show the good or bad degree of the selected plot, and the multi-dimensional evaluation module can be used to calculate the plot evaluation value.

[0120] The multi-dimensional evaluation module refers to using a weighted sum algorithm (Weighted Sum Model) to quantitatively score the candidate plot from five dimensions: industry matching degree (weight 30%); plot maturity (weight 25%); traffic convenience (weight 20%); planning compliance (weight 15%); plot regularity (weight 10%), and the module outputs a sorted plot score list

[0121] Step 111: Select the site plot based on the plot evaluation value.

[0122] When multiple plots are screened, each plot is evaluated and quantified as a plot evaluation value from five aspects of industry matching degree, plot maturity, traffic convenience, planning compliance and plot regularity, so as to select the most suitable plot for the user according to the plot evaluation value.

[0123] A smart site selection analysis method based on a large model and industry characteristics, further comprising:

[0124] Step 112: When the number of plots is greater than 1, determine the upstream industry according to the industry type.

[0125] The upstream industry refers to an industry location that supplies raw materials for the industry site selection required by the user, and the corresponding upstream location of the industry type can be queried from the upstream data table, and the nearest upstream location to the site plot is selected as the upstream industry. The upstream data table refers to a data table recording different industry types and their corresponding upstream locations.

[0126] Step 113: Determine the upstream distance in combination with the upstream industry and the site plot.

[0127] The upstream distance is the distance value between the upstream industry and the site plot, and the determination method of the upstream distance is well known to those skilled in the art, which is not described here.

[0128] Step 114: Determine the cluster coefficient in response to the upstream distance and the traffic convenience.

[0129] The cluster coefficient is a value used to show the difficulty of raw material transportation. The cluster coefficient is smaller when the upstream distance is larger and the traffic convenience is smaller. The cluster coefficient corresponding to the upstream distance and the traffic convenience can be obtained from a cluster data table.

[0130] Step 115: updating the plot evaluation value according to the cluster coefficient.

[0131] When multiple plots are screened, the sum of the original plot evaluation value and the cluster coefficient is calculated as a new plot evaluation value, so that the plot with the easiest raw material transportation is selected according to the raw material transportation difficulty required by industrial production, thereby reducing the production delay caused by raw material transportation delay and error, and improving the stability of industrial production.

[0132] Reference Figure 2 The anti-disaster site selection method comprises the following steps:

[0133] Step 200: determining the impact disaster in response to the industry type.

[0134] The impact disaster refers to a disaster that has an impact on the industry. For example, typhoon and flood are prone to affect food and pharmaceutical production industries, causing food or medicine to be damp. Earthquake is prone to affect energy production industry, causing energy transmission line to be damaged. The impact disaster corresponding to the industry type can be obtained from a disaster correspondence table. The disaster correspondence table is a data table recording different industry types and their corresponding impact disasters.

[0135] Step 201: retrieving plot disaster records based on the selected plot and the impact disaster.

[0136] The plot disaster record refers to the degree and time of the impact disaster occurring on the selected plot. The plot disaster record corresponding to the selected plot and the impact disaster can be obtained from a record data table. The record data table is a data table recording different selected plots and impact disasters and their corresponding plot disaster records.

[0137] Step 202: determining a disaster coefficient in response to the plot disaster record, and determining a disaster threshold value according to the industry type.

[0138] The disaster coefficient is a value used to show the impact of the disaster on the selected plot. The sum of the degrees of the disasters is calculated as a total degree, and then the quotient of the total degree and the disaster interval is calculated as the disaster coefficient. The disaster interval refers to the interval between the earliest time and the latest time in the plot disaster record. The method for determining the disaster coefficient is known to those skilled in the art and is not described here.

[0139] The disaster threshold value refers to a value for judging the influence of a disaster on an industry. The disaster threshold value corresponding to an industry type can be queried from a threshold value data table. The threshold value data table refers to a data table recording different industry types and their corresponding disaster threshold values.

[0140] Step 203: When the disaster coefficient is greater than the disaster threshold value, updating the plot evaluation value according to the disaster coefficient.

[0141] When the disaster coefficient is greater than the disaster threshold value, it means that the influence of the disaster on the industry is greater. At this time, the quotient of the plot evaluation value and the disaster coefficient is calculated as the new plot evaluation value.

[0142] After the candidate plot is screened according to the industry site selection condition, the historical disaster situation of the region where the candidate plot is located is called, and the influence of the disaster on the industry is evaluated, so as to select the plot with the smallest disaster influence, thereby reducing the situation that the industry is damaged due to disasters.

[0143] The disaster-resistant site selection method further comprises:

[0144] Step 204: When the disaster coefficient is not greater than the disaster threshold value, determining a transportation route in combination with the upstream industry and the site selection plot.

[0145] When the disaster coefficient is not greater than the disaster threshold value, it means that the influence of the disaster on the industry is smaller. The transportation route refers to the route of transporting raw materials from the upstream industry to the site selection plot. The determination method of the transportation route is known to those skilled in the art, and is not described here.

[0146] Step 205: Calling a route disaster record based on the transportation route.

[0147] The route disaster record refers to the degree and time of various disasters occurring on the transportation route. The calling method of the route disaster record is referred to the above step 201, and is not described here.

[0148] Step 206: Determining a route coefficient in response to the route disaster record.

[0149] The route coefficient refers to a value for showing the degree of influence of a disaster on a transportation route. The determination method of the route coefficient is referred to the above step 202, and is not described here.

[0150] Step 207: When the route coefficient is greater than a preset transportation threshold value, determining a secondary selection distance in combination with the upstream industry and the site selection plot.

[0151] The transportation threshold refers to the maximum route coefficient of the vehicle that can continue transportation on the transportation route. The transportation threshold can be selected by the staff according to the actual situation, and will not be repeated here. The route coefficient greater than the transportation threshold represents that it is difficult to transport raw materials from the upstream industry to the site plot. The secondary selection distance refers to the distance between the site plot and the nearest upstream position of the site plot except the upstream industry. The determination method of the secondary selection distance is well known to those skilled in the art, and will not be repeated here.

[0152] Step 208: updating the cluster coefficient in response to the secondary selection distance, upstream distance and traffic convenience.

[0153] According to the address of the industry, the route of transporting raw materials is planned, and the historical disaster situation on the route is called to select other positions that can transport raw materials when the disaster affects transportation, thereby reducing the situation that the production of the industry is delayed due to the influence of the disaster on the transportation of raw materials.

[0154] The disaster-resistant site selection method further comprises:

[0155] Step 209: determining the transportation duration in response to the transportation route when the route coefficient is greater than the preset transportation threshold.

[0156] The transportation duration refers to the shortest duration required for the vehicle to travel according to the transportation route. The determination method of the transportation duration is well known to those skilled in the art, and will not be repeated here.

[0157] Step 210: determining the blocking duration in combination with the route coefficient and the transportation duration.

[0158] The blocking duration refers to the maximum duration of the disaster blocking the transportation of raw materials. The length of the time period in which the route coefficient is greater than the transportation threshold can be calculated as the disaster duration, and the maximum value of the disaster duration greater than the transportation duration can be selected as the blocking duration.

[0159] Step 211: determining the storage demand according to the blocking duration and the type of the industry.

[0160] The storage demand refers to the volume of raw materials required to maintain normal production of the industry to avoid the difficulty of transporting raw materials caused by the disaster, i.e. the volume of raw materials required for the production of the industry during the blocking duration. The storage demand corresponding to the blocking duration and the type of the industry can be queried from the storage correspondence table. The storage correspondence table refers to a data table recording different blocking durations, types of industries and their corresponding storage demands.

[0161] Step 212: updating the site plot in response to the storage demand.

[0162] When the disaster is likely to affect the raw material transportation, the appropriate raw material storage quantity is selected according to the influence of the disaster on the raw material transportation, and the storage demand of the site plot is obtained according to the raw material storage quantity, so that the industry can continue production through the stored raw materials when the disaster affects the raw material transportation, thereby reducing the situation of production delay of the industry caused by the disaster affecting the raw material transportation.

[0163] With reference to Figure 3 , the environmental site selection method comprises:

[0164] Step 300: determining a building material table in response to the area demand and the industry type.

[0165] The building material table refers to a data table recording the types and quantities of building materials required for building an industry plant. The building material table corresponding to the area demand and the industry type can be queried from a material corresponding table. The material corresponding table refers to a data table recording different area demands, industry types, and corresponding building material tables.

[0166] Step 301: determining a transportation emission according to the building material table and the site plot, and calling a plot image based on the site plot.

[0167] The transportation emission refers to the carbon emission of transporting the building materials. The source address corresponding to the building material table can be queried from a source corresponding table, and the carbon emission of transporting the building materials from the source address to the site plot can be queried from a transportation corresponding table. The source address refers to the address where the building materials are stored. The source corresponding table refers to a data table recording different building material types and corresponding source addresses. The transportation corresponding table refers to a data table recording different building material types, building material quantities, and transportation distances and corresponding transportation emissions. The transportation distance refers to the route distance from the source address to the site plot.

[0168] The plot image refers to a picture of the site plot. The calling method of the plot image is known to those skilled in the art, and will not be described here.

[0169] Step 302: identifying the earthwork quantity from the plot image.

[0170] The earthwork quantity refers to the excavation quantity and backfill quantity required for cleaning the site plot. The uneven position and size of the plot can be identified from the plot image through image recognition technology, and the earthwork quantity is matched. The determination method of the earthwork quantity is known to those skilled in the art, and will not be described here.

[0171] Step 303: determining an earthwork emission in response to the earthwork quantity.

[0172] The earthwork emission refers to the carbon emission of cleaning the site block. The earthwork emission corresponding to the earthwork quantity can be queried from the earthwork data table. The earthwork data table refers to a data table recording different earthwork quantities and corresponding earthwork emissions.

[0173] Step 304: determining the construction emission value in combination with the transportation emission and the earthwork emission.

[0174] The construction emission value refers to the total carbon emission of the construction industry, that is, the sum of the transportation emission and the earthwork emission. The determination method of the construction emission value is known to those skilled in the art, and will not be repeated here.

[0175] Step 305: updating the block evaluation value according to the construction emission value.

[0176] The new block evaluation value is generally calculated by the formula (block evaluation value*0.85+construction emission value*0.15). The building materials needed for the construction of the industry are estimated according to the area of the industry, and the carbon emission of transporting the building materials is estimated according to the location of the industry. The carbon emission of cleaning the site block is identified from the image of the site block, and the total carbon emission of building the industry is calculated, so as to select the site block with the minimum carbon emission and improve the environmental protection of the construction of the industry.

[0177] The environmental protection site selection method further comprises:

[0178] Step 306: determining the power structure according to the site block.

[0179] The power structure refers to the power supply structure of the site block, that is, the proportion of various power sources such as wind power, photovoltaic power, thermal power, etc. The determination method of the power structure is known to those skilled in the art, and will not be repeated here.

[0180] Step 307: determining the power coefficient in response to the power structure.

[0181] The power coefficient refers to the carbon emission of producing unit power. The calculation method of the power coefficient is known to those skilled in the art, and will not be repeated here.

[0182] Step 308: determining the production emission in combination with the power coefficient and the industry type, and determining the recycling coefficient according to the building material table.

[0183] The production emission refers to the carbon emission of the power consumed during the production of the industry. The production power corresponding to the industry type can be queried from the production corresponding table, and the product of the production power and the power coefficient is calculated as the production emission. The production power refers to the power needed for the production of the industry in one year.

[0184] The recycling coefficient refers to the unit carbon emission during the factory building demolition and construction waste treatment. The recycling coefficient corresponding to the building material table can be queried from the recycling data table. The recycling data table refers to the data table recording different building material tables and their corresponding recycling coefficients.

[0185] Step 309: Determine the demolition emission by combining the recycling coefficient and the area requirement.

[0186] The demolition emission refers to the total carbon emission during the factory building demolition and construction waste treatment. The demolition emission corresponding to the recycling coefficient and the area requirement can be queried from the demolition corresponding table. The demolition corresponding table refers to the data table recording different recycling coefficients and area requirements and their corresponding demolition emissions.

[0187] Step 310: Determine the total emission value according to the transportation emission, earthwork emission, production emission and demolition emission, and determine the emission threshold value in response to the industry type.

[0188] The total emission value refers to the carbon emission of the entire life cycle of the industry, i.e. the sum of the transportation emission, earthwork emission, production emission and demolition emission. The determination method of the total emission value is known to those skilled in the art, and will not be described here.

[0189] The emission threshold value refers to the maximum carbon emission specified by the industry. The emission threshold value corresponding to the industry type can be queried from the limit corresponding table. The limit corresponding table refers to the data table recording different industry types and their corresponding emission threshold values. The data in the limit corresponding table can be formed by statistics of the data in the relevant documents such as “Carbon Emission Limit for Heavy Pollution Industries”.

[0190] Step 311: When the total emission value is greater than the emission threshold value, generate and display a high carbon emission warning based on the total emission value.

[0191] The total emission value greater than the emission threshold value represents that the carbon emission of the industry is too large. The high carbon emission warning refers to the information for warning the user of the existence of the situation of excessive carbon emission. The determination method of the high carbon emission warning is known to those skilled in the art, and will not be described here.

[0192] According to the location of the candidate site, the power supply structure of the industry is retrieved, so as to calculate the carbon emission of the industry production, and estimate the carbon emission of the demolished industry according to the area of the industry. Then, the carbon emission of the entire life cycle of the industry is calculated by combining the carbon emissions of building the industry, the industry production and demolishing the industry, and the user is reminded in time when the carbon emission of the entire life cycle of the industry is too high.

[0193] The environmental protection site selection method further comprises:

[0194] Step 312: When the total emission value is greater than the emission threshold value, retrieve the resource distribution based on the selected site.

[0195] The resource distribution refers to the distribution of natural resources of the site block, i.e. the distribution of light, wind and water power. The method for obtaining the resource distribution is well known to those skilled in the art and will not be described here.

[0196] Step 313: determining the power generation efficiency in response to the resource distribution.

[0197] The power generation efficiency refers to the efficiency of power generation by natural resources. The method for determining the power generation efficiency is well known to those skilled in the art and will not be described here.

[0198] Step 314: determining the emission reduction efficiency according to the power generation efficiency and the power coefficient.

[0199] The emission reduction efficiency refers to a value for showing the ability of power generation by natural resources to reduce carbon emissions. The emission reduction coefficient corresponding to the power generation efficiency can be obtained from an emission reduction table, and the product of the emission reduction coefficient and the power coefficient is taken as the emission reduction efficiency. The emission reduction coefficient refers to the unit carbon emission reduction of power generated by natural resources. The emission reduction table refers to a data table recording different power generation efficiencies and their corresponding emission reduction coefficients.

[0200] Step 315: determining the emission reduction discharge by combining the emission reduction efficiency and the production discharge.

[0201] The emission reduction discharge refers to the carbon emission after power generation by natural resources. The product of the emission reduction efficiency and the production discharge is taken as the power generation discharge, and the difference between the total discharge value and the power generation discharge is taken as the emission reduction discharge.

[0202] Step 316: determining the external power source based on the site block if the emission reduction discharge is greater than the discharge threshold.

[0203] The emission reduction discharge greater than the discharge threshold means that the carbon emission of the industry after power generation by natural resources is still too high. The external power source refers to a source address of clean energy such as wind power, photovoltaic power and hydropower from the outside world. The closest source to the site block can be selected as the external power source. The method for determining the external power source is well known to those skilled in the art and will not be described here.

[0204] Step 317: generating and displaying the environmental protection emission reduction suggestion by combining the external power source and the emission reduction efficiency.

[0205] The environmental protection emission reduction suggestion refers to information for showing the external power source and the emission reduction efficiency to the user. The method for determining the environmental protection emission reduction suggestion is well known to those skilled in the art and will not be described here.

[0206] When the carbon emission of the industry is too high, the natural resource condition of the location where the industry is located is called to utilize the natural resource to generate electricity when the natural resource is rich, and to call the power from other places such as water power, wind power and photovoltaic power when the natural resource is scarce, so as to reduce the carbon emission of the industry and improve the environmental protection of the industry.

[0207] Based on the same inventive concept, the embodiment of the present application provides a smart site selection analysis system based on a large model and industry characteristics, comprising:

[0208] The acquisition module is configured to acquire the demand information.

[0209] The memory is configured to store the program of any of the smart site selection analysis methods based on the large model and the industry characteristics.

[0210] The processor, and the program in the memory can be loaded and executed by the processor.

[0211] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional modules is exemplified, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is 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 refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0212] The above is only the preferred embodiment of the present application, and the protection scope of the present application is not limited to the above-mentioned embodiments. Any technical solution falling within the concept of the present application shall be deemed to fall within the protection scope of the present application. It should be noted that, for ordinary skilled persons in the art, some improvements and refinements without departing from the principles of the present application shall also be deemed to fall within the protection scope of the present application.

Claims

1. A smart site selection analysis method based on large models and industry characteristics, characterized in that, Comprising: Step 100: collecting demand information of a user; Step 101: extracting industry type from the demand information; Step 102: determining industry demand in response to the industry type, and extracting area demand from the demand information; Step 103: determining site block in combination with the industry demand and the area demand; Step 104: generating and displaying industry site selection suggestion based on the site block; Further comprising: Step 105: determining number of blocks based on the site block; Step 106: determining dominant industry according to the site block when the number of blocks is greater than 1; Step 107: determining industry matching degree in response to the dominant industry, and determining expressway distance according to the site block; Step 108: determining traffic convenience in response to the expressway distance, and calling block contour according to the site block; Step 109: determining block regularity in response to the block contour, and calling block maturity and planning compliance according to the site block; Step 110: calculating block evaluation value in combination with the industry matching degree, the traffic convenience, the block regularity, the block maturity and the planning compliance; Step 111: selecting the site block based on the block evaluation value; Further comprising an environmental protection site selection method, the environmental protection site selection method comprising: Step 300: determining building material table in response to the area demand and the industry type; Step 301: determining transportation emission according to the building material table and the site block, and calling block image based on the site block; Step 302: identifying earthwork quantity from the block image; Step 303: determining earthwork emission in response to the earthwork quantity; Step 304: determining construction emission value in combination with the transportation emission and the earthwork emission; Step 305: updating the block evaluation value according to the construction emission value.

2. The wisdom site selection analysis method based on a large model and industry characteristics according to claim 1, characterized in that, Further comprising: Step 112: determining upstream industry according to the industry type when the number of blocks is greater than 1; Step 113: determining upstream distance in combination with the upstream industry and the site block; Step 114: determining cluster coefficient in response to the upstream distance and the traffic convenience; Step 115: updating the block evaluation value according to the cluster coefficient.

3. The wisdom site selection analysis method based on a large model and industry characteristics according to claim 2, characterized in that, Further comprising a disaster resistance site selection method, the disaster resistance site selection method comprising: Step 200: determining impact disaster in response to the industry type; Step 201: calling block disaster record based on the site block and the impact disaster; Step 202: determining disaster coefficient in response to the block disaster record, and determining disaster threshold value according to the industry type; Step 203: updating the block evaluation value according to the disaster coefficient when the disaster coefficient is greater than the disaster threshold value.

4. The wisdom site selection analysis method based on a large model and industry characteristics according to claim 3, characterized in that, The disaster resistance site selection method further comprising: Step 204: determining transportation route in combination with the upstream industry and the site block when the disaster coefficient is not greater than the disaster threshold value; Step 205: calling route disaster record based on the transportation route; Step 206: determining route coefficient in response to the route disaster record; Step 207: determining secondary site distance in combination with the upstream industry and the site block when the route coefficient is greater than a preset transportation threshold value; Step 208: updating the cluster coefficient in response to the secondary selected distance, upstream distance, and traffic convenience.

5. The wisdom site selection analysis method based on large models and industry characteristics according to claim 4, characterized in that, The disaster-resistant site selection method further comprises: Step 209: determining the transportation time in response to the transportation route when the route coefficient is greater than a preset transportation threshold; Step 210: determining the barrier time in combination with the route coefficient and transportation time; Step 211: determining the storage demand according to the barrier time and industry type; Step 212: updating the site plot in response to the storage demand.

6. The wisdom site selection analysis method based on a large model and industry characteristics according to claim 1, characterized in that, The environmentally-friendly site selection method further comprises: Step 306: determining the power structure according to the site plot; Step 307: determining the power coefficient in response to the power structure; Step 308: determining the production emission in combination with the power coefficient and industry type, and determining the recycling coefficient according to the building material table; Step 309: determining the demolition emission in combination with the recycling coefficient and area demand; Step 310: determining the total emission value according to the transportation emission, earthwork emission, production emission, and demolition emission, and determining the emission threshold in response to the industry type; Step 311: generating and displaying a high carbon emission warning based on the total emission value when the total emission value is greater than the emission threshold.

7. The wisdom site selection analysis method based on a large model and industry characteristics according to claim 6, characterized in that, The environmentally-friendly site selection method further comprises: Step 312: calling the resource distribution based on the site plot when the total emission value is greater than the emission threshold; Step 313: determining the power generation efficiency in response to the resource distribution; Step 314: determining the emission reduction efficiency according to the power generation efficiency and power coefficient; Step 315: determining the emission reduction emission in combination with the emission reduction efficiency and production emission; Step 316: determining the external power source based on the site plot if the emission reduction emission is greater than the emission threshold; Step 317: generating and displaying an environmentally-friendly emission reduction suggestion in combination with the external power source and emission reduction efficiency.

8. A smart site selection analysis system based on large models and industry characteristics, characterized in that, Comprise: The acquisition module is used to acquire demand information; The memory is used to store the program of the intelligent site selection analysis method based on large models and industry characteristics according to any one of claims 1-7; The processor, the program in the memory can be loaded and executed by the processor.

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