CN111968005B

CN111968005B - Regional energy system potential evaluation method and system for implementing same

Info

Publication number: CN111968005B
Application number: CN202010605479.7A
Authority: CN
Inventors: 郑昊; 聂婷; 何子安; 苏灵奇; 杨正; 本·施维格勒
Original assignee: Fa Neng China Energy Technology Co ltd
Current assignee: Fa Neng China Energy Technology Co ltd
Priority date: 2020-06-29
Filing date: 2020-06-29
Publication date: 2023-10-13
Anticipated expiration: 2040-06-29
Also published as: CN111968005A

Abstract

The invention relates to a regional energy system potential evaluation method and a system for realizing the method. The method comprises the following steps: receiving an input target region; receiving one or more regions that demarcate a target region; receiving input regional energy (refrigeration/heat supply) requirements and construction feasibility fraction distribution proportions; if a grid dividing tool is used, extracting the pre-calculated energy requirement and construction feasibility score of the region from a database; if a lasso tool is used, extracting the energy use requirement and construction related data and country design specification data of the region from the database, extracting a building energy requirement and construction feasibility evaluation model matched with the target region from an algorithm library, and respectively calculating the energy requirement and construction feasibility score of the region by using the model based on the data; and (3) carrying out weighted average on the energy demand and the construction feasibility score based on the score distribution proportion, obtaining the engineering potential score of the region and outputting the engineering potential score.

Description

Regional energy system potential evaluation method and system for implementing same

Technical Field

The present invention relates to the field of regional energy, and in particular to a method of assessing the potential of building a regional refrigeration/heating system in a plurality of regions of a target region, a method of optimizing the boundaries of regions suitable for building a regional refrigeration/heating system, a system for implementing these methods and a computer readable medium storing a computer program capable of executing the method.

Background

In recent years, regional energy is becoming more favored as a validated energy solution because of its numerous advantages (e.g., improved energy utilization efficiency, reduced fossil energy consumption, reduced carbon dioxide emissions, etc.), and regional energy systems are being deployed in more cities around the world.

Broadly, the regional energy source is defined as: "in a specific area, various forms and grades of energy sources required by people for production and living are reasonably, integrally and efficiently produced, distributed, utilized and dissipated". In a narrow sense, the regional energy source is defined as: "comprehensive integration of district heating, district cooling, district power, and energy systems to address district energy demands. The area referred to herein may be an administrative division of blocks, various parks, a building group, or the like. ". Among them, especially the regional refrigerating/heating system serving the building refrigerating and heating is continuously emerging in various cities worldwide, continuously developing and continuously improving. The district cooling/heating system generally includes a cooling/heating station constituted by a cooling system for producing chilled water/a heating system for producing hot water, a pipe network for supplying cooling/heating to a service object such as a building, and a heat exchange device.

When planning regional energy systems for a region of larger area (e.g., a county, a city, a province, and even a country), it is important for all stakeholders, particularly for enterprises developing regional energy projects, to quickly and efficiently screen out the region of the region that is suitable for building regional energy systems (i.e., the region with greater potential). Typically, they need to quickly complete screening of potential sites for large regional energy systems at urban scale during the initial stages of project development. However, the relevant data available at this stage is very small and the screening volume and screening range are very large (typically on a city scale).

One common solution to the above problems is to perform qualitative analysis based on interviews, raw statistics, reports, and other relevant information. In this scenario, the research team needs to examine various details within the area, conduct multiple interviews, obtain raw statistics, and then give qualitative decisions based on the personal experience. The traditional engineering method needs a great deal of manpower, material resources and time, the larger the area of the region is, the more time is consumed and the cost is higher, and the evaluation result is largely dependent on the experience and judgment of individuals, so that the evaluation standard inside an investigation team is difficult to subdivide and quantify, and finally, high-quality reference opinion is difficult to provide for potential site selection suitable for building regional energy systems. Thus, the main disadvantage of this method is that it is not suitable for rapid and accurate assessment of large areas.

Secondly, because the data relied on by the method is the non-public data obtained by the investigation team through the on-site investigation of the target area, when the target area changes, the investigation team also needs to carry out investigation again, and new data is collected so as to evaluate the regional energy system potential of the new target area. Thus, another disadvantage of the above method is that it cannot be easily extended to other areas.

Again, there is no intuitive and convenient interaction means in the above method that helps the user understand the analysis results, which makes it impossible for the user to quickly understand and make decisions.

Therefore, there is a need to develop a new regional energy system potential assessment tool.

Disclosure of Invention

One technical problem to be solved by the application is how to help users to quickly and reliably screen out potential areas of a region with high engineering potential, which are suitable for building regional energy systems (particularly regional refrigeration/heating systems), aiming at regions with large areas.

To this end, an embodiment of the present application provides a method of evaluating the potential of building an area refrigeration/heating system in a target area, the method comprising the steps of: a) Receiving a target area input by a user; b) Receiving one or more areas divided by a user for a target area; c) Receiving the distribution proportion of the refrigerating/heating demand fraction and the construction feasibility fraction input by a user; d) If the user uses an automatic meshing tool for dividing the target area in the step B, extracting the pre-calculated refrigeration/heating demand scores and the construction feasibility scores of the one or more areas from a database pre-established for the target area by using a pre-established index; e) If the user uses a lasso tool for dividing the target area in the step B, extracting the energy use requirement and construction related data of the one or more areas and the national design specification data from the database by using the pre-established index, extracting a building cooling/heating requirement assessment model and a construction feasibility assessment model matched with the target area from an algorithm library pre-established for the target area, and calculating the cooling/heating requirement score and the construction feasibility score of the one or more areas by using the models based on the data; f) And (C) carrying out weighted average calculation on the refrigeration/heating demand fraction and the construction feasibility fraction obtained in the step D or E based on the distribution proportion of the refrigeration/heating demand fraction and the construction feasibility fraction received in the step C, obtaining engineering potential fractions of the refrigeration/heating system of the construction area of the one or more areas, and displaying at least the engineering potential fractions of the one or more areas to a user.

In the present application, the term "region" refers to a block, a collection of blocks, or even the country itself, which is divided into areas of a country according to a certain standard (such as a geographic standard, an economic standard, an administrative division standard, etc.), for example, administrative divisions of villages, towns, cities, counties, provinces, regions, allies, states, etc., areas of north China, east China, south China, etc., and economic divisions of northeast economy, east economy, middle economy, west economy, etc. It should be understood that the above list is not exhaustive and that other possibilities exist.

In the present application, the term "potential" is used to characterize whether a region is suitable for building a regional refrigeration/heating system, the greater the potential, the more suitable for building, specifically including but not limited to engineering potential, profitability potential, and energy conservation and emission reduction potential. In an embodiment of the present application, the engineering potential of a region is evaluated from two dimensions, the refrigeration/heating requirement of the region and the construction feasibility of building a refrigeration/heating system in the region; evaluating the profitability potential of a region based on building and operating an overall cost of a regional refrigeration/heating system at the region; the energy saving and emission reduction potential of a certain area is evaluated based on the degree of slowing down the urban heat island effect by the area refrigeration/heating system of the certain area (only applicable to the area refrigeration system) and the resulting reduction of carbon emission.

In the above evaluation method, since different sizes of grid division are performed on the target area in advance, and the refrigeration/heating demand score and the construction feasibility score corresponding to the area represented by each grid are calculated in advance and stored in the database, when a user selects one of preset gears representing the size of the grid to perform grid division on the target area and requests to calculate the engineering potential score of the area represented by each grid under the gear, the required engineering potential score can be obtained by performing weighted average calculation on the refrigeration/heating demand score and the construction feasibility score directly called from the database only based on the distribution proportion of the refrigeration/heating demand score and the construction feasibility score input by the user. Meanwhile, the database also stores intermediate data required for calculation under lasso division, and various models required for calculation under lasso division (used for replacing an evaluation method relying on personal experience) are stored in the algorithm library in advance, so that when a user uses a lasso tool to self-define one or more areas in a target area and requests to calculate the engineering potential score of each area, the engineering potential score of each area can be calculated quickly and accurately by only extracting the corresponding model from the algorithm library according to the required intermediate data extracted from the database. The method greatly saves the time for users to wait for the calculation result and improves the reliability of the calculation result, and can help the users to quickly and reliably screen out the potential areas which have higher engineering potential and are suitable for building the regional refrigerating/heating system aiming at the areas with larger areas, thereby meeting the demands of users (such as universities and construction units) focusing on the engineering potential.

In addition, the method establishes a quantitative and standardized scoring system, so that a user can intuitively feel engineering potential differences of different areas through the differences of scores. Moreover, the method simplifies the input of a user, only the distribution proportion of the refrigerating/heating demand fraction and the construction feasibility fraction is input after the user selects a target area, and the area to be evaluated is selected by an automatic meshing tool or lasso tool, and the rest is automatically completed by the system. Both of these aspects represent a user-friendliness with no associated professional background.

In some embodiments of the present invention, the above method further comprises the steps of: g1 If the user uses an automatic meshing tool for the target zone division in step B), extracting from the database the pre-evaluated profitability potential of the one or more zones using a pre-established index; h1 If in step B the user uses a lasso tool for the target zone, extracting from the database, using a pre-established index, a refrigeration/heating load curve, energy cost data, procurement costs and operating efficiency curves of equipment constituting the zone refrigeration/heating system for each building in the one or more zones, and calculating the profitability potential of the one or more zones based on these data; i1 Displaying the profitability of the one or more areas to the user.

According to the scheme, the potential areas which are suitable for building the regional refrigeration/heating system and have higher profit potential in the areas with larger areas can be quickly and reliably screened out by a user aiming at the areas with larger areas by storing the profit potential results under grid division and the intermediate data required by calculating the profit potential under lasso division in the database, so that the requirements of users (such as investors) focusing on the profit potential are met.

According to some embodiments of the invention, the profitability is characterized by a final cost consisting of investment costs and operating costs of building the district refrigeration/heating system at the one or more districts.

In some embodiments of the present invention, the above method further comprises the steps of: g2 If an automatic meshing tool is used by the user in step B for dividing the target area, extracting the energy saving and emission reduction potential of the one or more areas which are evaluated in advance from the database by using a pre-established index; h2 If in step B the user uses a lasso tool for the target zone, extracting from the database, using a pre-established index, the refrigeration/heating load curve of each building within the one or more zones, the operating efficiency curve of the equipment constituting the zone refrigeration/heating system, the climate data of a typical design day, and calculating the energy saving and emission reduction potential of the one or more zones based on these data; i2 And displaying the energy conservation and emission reduction potential of the one or more areas to a user.

According to the scheme, the energy saving and emission reduction potential results under grid division and the intermediate data required by calculating the energy saving and emission reduction potential under lasso division are stored in the database in advance, so that a user can be helped to quickly and reliably screen out potential areas, which have high energy saving and emission reduction potential and are suitable for building an area refrigeration/heating system, of the area aiming at the area with the large area, and the requirements of users (such as governments) focusing on the energy saving and emission reduction potential are met.

According to some embodiments of the invention, the energy conservation and emission reduction potential is characterized by the amount of carbon emission reduction caused by building regional refrigeration systems in the one or more regions and/or by the extent of mitigation of urban heat island effects or by the amount of carbon emission reduction caused by building regional heating systems in the one or more regions.

According to some embodiments of the invention, the energy usage requirements and construction-related data for the one or more regions include satellite maps, point of interest data, climate data, building footprints, building heights, building categories, water locations, and green space locations within the one or more regions; the national design specification data of the one or more regions includes cooling/heating related design criteria, cooling/heating load calculation related parameters applicable to the one or more regions.

Since these data are public data which are easy to acquire from the internet, data obtained by processing the public data by a mathematical formula, or data estimated by machine learning, the above-mentioned evaluation method can be applied to any region in the world without limitation of the region, and has good expansibility.

According to some embodiments of the invention, the database is built by: determining the boundary of a target area and dividing the target area into a plurality of areas with the same size according to different gears representing the size of a grid by using an automatic grid dividing tool; collecting public raw data within a target area from the internet, the public raw data including satellite map, point of interest data, annual climate data, cooling/heating related design criteria, and cooling/heating load calculation related parameters; using normalized vegetation index and water index to infer water body position and greening position in covered area of each satellite map from the collected green band, infrared band and near infrared band data, and storing the water body position and greening position in the database; dividing the region represented by each grid under each gear by aiming at an automatic grid by using a construction feasibility evaluation model called from the algorithm library, calculating a corresponding construction feasibility score based on a satellite diagram of the region, and storing the construction feasibility score into the database; using the building geometric model which is extracted from the algorithm library and consists of a building occupation area presumption model and a building height presumption model, presuming the building occupation area and the building height of each building in the coverage area of each satellite map from red, green and blue wave band data in the collected satellite maps, and storing the building occupation area and the building height into the database; using the building category presumption model extracted from the algorithm library, presuming the building category of each building according to the socioeconomic characteristics of the inside and the periphery of each building in the target area extracted from the collected interest point data and the building occupation area and the building height of each building and storing the building category in the database; calculating the cooling/heating degree days of the target area from the collected annual climate data and storing the cooling/heating degree days in the database; calculating a refrigerating/heating load curve of each building based on the building occupation area, the building height, the building category, the refrigerating/heating degree days of the target region, the refrigerating/heating related design standard and the refrigerating/heating load calculation related parameters of each building in the target region by using the building refrigerating/heating demand evaluation model extracted from the algorithm library, and storing the refrigerating/heating load curve into the database; and calculating a corresponding cooling/heating demand fraction for the area represented by each grid under each gear by using a building cooling/heating demand evaluation model of each building according to automatic grid division, and storing the cooling/heating demand fraction into the database.

The database building method takes the response rate of the meshing tool and the lasso tool when the data is called into priority. By storing the display data of the grid division tool and the intermediate data of the lasso tool, the calculation times during calling are reduced while the space requirement is reduced, so that the whole database is lighter and more efficient.

According to some embodiments of the invention, the disclosed raw data further includes energy cost data for the target region, the method further comprising the steps of: calculating an operating expense for each building based on the collected energy cost data and the calculated refrigeration/heating load profile for each building, and storing the operating expense in the database; collecting the cost of equipment available in the target area for building the district refrigeration/heating system and the operating efficiency curve of said equipment and storing the cost and operating efficiency curve to said database; calculating a capital expenditure for constructing an area refrigeration/heating system in each area for each area in each gear of the automatic meshing based on the cost and work efficiency curves of the equipment, and storing the capital expenditure in the database; based on the operating and capital expenditures, a final cost of building a regional cooling/heating system in each region is calculated for each region in each gear of the automatic meshing and stored to the database.

In some embodiments of the invention, the method further comprises the steps of: collecting a working efficiency curve of equipment which can be purchased in a target area and is used for constructing an area refrigerating/heating system, and storing the working efficiency curve into the database; collecting climate data for a typical design day for a target area and storing the climate data in the database; the reduction of carbon emissions and/or the reduction of urban heat island effects (applicable to regional refrigeration systems only) caused by building regional refrigeration/heating systems in each region is calculated for each region under each gear of automatic meshing based on the operating efficiency curve of the apparatus and the climate data of the typical design day of the target region, and is stored in the database.

According to some embodiments of the present invention, calculating a cooling/heating demand fraction for an area using a building cooling/heating demand assessment model that matches a target area includes the steps of: extracting the refrigeration/heating load curve of each building in the area from the database by utilizing a pre-established index, and calculating the typical design day time-by-time total refrigeration/heating load requirement of all the buildings in the area; calculating the energy efficiency ratio of the refrigerating/heating system, the utilization rate of the refrigerating/heating unit and the total refrigerating/heating demand of the typical design day based on the total refrigerating/heating load demand of the typical design day time by time; the energy efficiency ratio of the refrigerating/heating system, the utilization rate of the refrigerating/heating unit and the total quantity of refrigerating/heating demands on the typical design day are normalized to be divided into fractions of N, and the average value of the fractions is calculated and taken as the refrigerating/heating demand fraction of the area. Preferably, N is an integer multiple of 10.

Because the energy efficiency ratio of the refrigerating/heating system reflects the operation efficiency of the regional refrigerating/heating station, the utilization rate of the refrigerating/heating unit reflects the return condition of investment equipment, and the total quantity of the refrigerating/heating demand of a typical design day of a region reflects the total quantity of the potential energy demand (potential selling cold/heat profitability) of the region, the three indexes accurately reflect the efficiency and investment economy of the regional refrigerating/heating system based on the attribute of the potential regional energy demand curve, and the system efficiency and the investment economy are the most concerned by various stakeholders in evaluating the energy demand potential of the regional energy system. The three indexes are normalized and averaged to obtain the score, so that the refrigerating/heating requirement of one area can be well represented, and a user without related background knowledge can easily understand and intuitively compare the scores of different areas.

According to some embodiments of the invention, a construction feasibility assessment model that matches the target region calculates a construction feasibility score for the region based on satellite maps for the one or more regions.

According to some embodiments of the present invention, the construction feasibility assessment model matched to the target region is obtained by: preparing a satellite map dataset of the regional scale of the region of the same type as the target region; randomly selecting two satellite images from the satellite image data set, comparing the satellite images with an expert, selecting a satellite image of which the represented area is more suitable for building an area refrigerating/heating system from the two satellite images by the expert, and repeating the steps until the preset times are reached; calculating the construction feasibility score of the area represented by each satellite map according to the expert comparison result; calculating the volume ratio, the building category ratio, the vegetation coverage, the water coverage and the road width of the area represented by each satellite map; carrying out nonlinear fitting modeling on the relation among the volume rate, the building category occupation ratio, the vegetation coverage, the water coverage and the road width of the area represented by the satellite map and the construction feasibility score of the corresponding area to obtain an initial evaluation model; training an initial evaluation model by using a first preset number of satellite graphs, so that the initial evaluation model learns the volume rate, the building category ratio, the vegetation coverage, the water coverage and the relation between the road width and the construction feasibility score of the corresponding area of each satellite graph and automatically adjusts the parameters of the model; and carrying out construction feasibility assessment on a second preset number of satellite images which are not learned by the assessment model after adjustment, comparing the obtained construction feasibility score of the area represented by each satellite image with the construction feasibility score of the same satellite image based on the assessment of an industry expert so as to verify the accuracy of the assessment model, repeating the training step if the accuracy does not reach an expected value, otherwise, storing the adjusted assessment model into the algorithm library as a construction feasibility assessment model matched with a target area.

The trained and verified construction feasibility assessment model can accurately and rapidly give the construction feasibility score of an area based on the satellite map (which is public data) of the area only.

According to some embodiments of the invention, the building footprint estimation model is a deep learning model obtained by: preparing a satellite map training set containing building occupation position marks and red, green and blue wave bands; designing an encoder decoder structure (e.g., a residual network) to semantically segment the satellite image; designing a loss function based on the building boundary and the building floor shape to characterize the gap between the existing model and the ideal state; obtaining a deep learning model to be trained, selecting a satellite map from a training set, training the deep learning model to predict the building occupation area of a building contained in the satellite map, calculating a loss function, adjusting each parameter of the deep learning model through negative feedback, and repeating the steps until the loss function can not descend any more; and taking the deep learning model obtained when the loss function can not descend any more as a building occupied area presumption model, and storing the deep learning model into the algorithm library.

According to some embodiments of the invention, the building height estimation model is a deep learning model obtained by: preparing a digital surface model containing height information and a satellite map training set of red, green and blue wave bands; designing an encoder and decoder structure (e.g., a refining network) to convert the input satellite map data into data containing a predicted altitude; designing a loss function based on building height to characterize the gap between the existing model and the ideal state; obtaining a deep learning model to be trained, selecting a satellite map from a training set, training the deep learning model to predict the building height of a building contained in the satellite map, calculating a loss function, adjusting each parameter of the deep learning model through negative feedback, and repeating the steps until the loss function can not descend any more; and taking the deep learning model obtained when the loss function can not descend any more as a building height presumption model, and storing the deep learning model into the algorithm library.

The above-described deep learning model for estimating the floor area and the height of a building can achieve very high calculation accuracy and granularity.

Embodiments of the present invention also provide a method of optimizing boundaries of an area suitable for building an area refrigeration/heating system, the method comprising the steps of: a) Receiving a target area input by a user; b) Receiving one or more target areas selected by a user in a target area; c) Receiving the distribution proportion of the refrigerating/heating demand fraction and the construction feasibility fraction input by a user; d) Performing a method as described above for assessing the potential of building an area refrigeration/heating system at a target area to obtain engineering potential scores for the one or more target areas; e) Trimming the boundaries of the one or more target areas by automatically adding or subtracting sub-areas to obtain new one or more target areas; f) Repeating the step d, and calculating engineering potential scores of the new one or more target areas; g) Comparing the engineering potential score of the new one or more target areas with the engineering potential score of the one or more target areas prior to adjustment; h) And (c) repeating the steps e, f and g if the engineering potential score of the new one or more target areas is greater than the engineering potential score of the one or more target areas before adjustment, otherwise, displaying the boundary of the new one or more target areas before the last adjustment to the user as an optimized boundary.

According to the method, the boundary of the area with the higher engineering potential score is identified by carrying out automatic iteration on the boundary of the target area which is preliminarily selected by the user and is suitable for the refrigerating/heating system of the building area, so that the user is helped to screen out the on-site selection of the refrigerating/heating system of the building area more quickly and accurately.

Embodiments of the present invention also provide a computer readable medium storing a computer program and a database and algorithm library for use in a method as described above, the computer program being executable by a processor to implement the method as described above.

Embodiments of the present invention also provide a system comprising a memory and a processor, the memory having stored thereon a computer program executable by the processor to implement a method as described above, as well as a database and an algorithm library for use in the method as described above.

Advantageously, the system further comprises a human-machine interaction interface for receiving user input and presenting output of the system to a user. For example, the man-machine interaction interface can be arranged on a touch screen of a mobile terminal (such as a mobile phone and a tablet personal computer), so that a user can easily and conveniently screen potential site selection at any time and any place. The human-machine interface may also be provided on a conventional computer display or any other suitable device.

Advantageously, the memory and processor are located in a cloud server (e.g. an alicloud). The cloud server is lighter, and web page application programs which are calculated on the cloud can be developed to realize the method, so that the time cost and the labor cost which are required for building the local server are greatly reduced, and the user is quickly and efficiently helped to finish screening of potential site selection of the regional refrigerating/heating system in a large area (such as a city) with very low cost.

Drawings

The present invention will be described in further detail with reference to the accompanying drawings. Those skilled in the art will readily appreciate that these drawings are for illustrative purposes only and are not intended to limit the scope of the present invention. The order of the various steps shown in the figures is merely exemplary and they may be performed in an order different from that shown.

FIG. 1 is a flow chart of a method of assessing the potential of building a regional refrigeration/heating system in one or more regions of a target region according to an embodiment of the present invention.

Fig. 2 is a method of optimizing boundaries of an area suitable for building an area refrigeration/heating system according to an embodiment of the present invention.

Fig. 3a to 3c show examples of boundaries obtained with an automatic meshing tool, a lasso tool, a boundary automatic growing tool, respectively, according to an embodiment of the present invention.

FIG. 4 is a flowchart illustrating steps for creating a database for a target region according to an embodiment of the present invention.

FIG. 5 is a flowchart illustrating steps for building an algorithm library for a target locale according to an embodiment of the present invention.

Fig. 6 shows published data used in an evaluation method according to an embodiment of the invention.

Fig. 7 is a diagram of data pipes when a database is built for a target region according to an embodiment of the present invention.

FIG. 8 is a diagram of data pipelines when invoking databases and algorithm libraries according to an embodiment of the present invention.

FIG. 9 is a flowchart illustrating steps for building a geometric model according to an embodiment of the present invention.

FIG. 10 is a flowchart illustrating steps for building a construction category inference model according to an embodiment of the present invention.

Fig. 11 is a schematic diagram of a model for calculating the refrigeration load of a building according to an embodiment of the present invention.

FIG. 12 is a flowchart illustrating the steps of building a construction feasibility assessment model according to an embodiment of the invention.

FIG. 13 is a flowchart illustrating the steps for calculating the final cost of building and operating a district refrigeration/heating system in a district according to an embodiment of the present invention.

FIG. 14 is a flowchart illustrating steps for calculating how much a regional refrigeration system is slowed down to urban heat island effects in a regional building in accordance with an embodiment of the present application.

Fig. 15 is a flowchart showing steps for calculating the amount of carbon emission reduction caused by building a district cooling/heating system in one district according to an embodiment of the present application.

Fig. 16 is a schematic block diagram of a computer system implementing the above method according to an embodiment of the present application.

Detailed Description

Embodiments of the present application provide a method and system for assessing the potential of building an area refrigeration/heating system in one or more areas of a target area. Fig. 1 shows a flow chart of the method 100, and fig. 16 shows a schematic block diagram of a system 50 for implementing the method. In this embodiment, the target area is a city, such as Shanghai, it being understood that the target area may be any other geographical area that is artificially divided according to certain criteria. The method 100 enables a user to know the engineering potential and optional profit potential and energy-saving and emission-reducing potential of building an regional refrigeration/heating system in different regions of a target region in a short time (several seconds), so as to rapidly and accurately screen out one or more high-potential candidate regions of the target region suitable for building the regional refrigeration/heating system, and provide important guidance for site selection decisions of project investors and planners of the regional refrigeration/heating system. As described in the summary section, in the present application, engineering potential is estimated from two dimensions of refrigeration/heating demand and construction feasibility, and profitability of a region is estimated based on the total cost of building and operating a regional refrigeration/heating system in that region; the energy saving and emission reduction potential of a certain area is evaluated based on the degree of slowing down the urban heat island effect by the area refrigeration/heating system of the certain area (only applicable to the area refrigeration system) and the resulting reduction of carbon emission.

The evaluation method 100 is specifically described below with reference to fig. 1.

The method 100 begins at step 101, followed by receiving a target region of interest (e.g., shanghai) entered by a user at a front end of the system 50 (e.g., which may be in the form of a web-based application or software or a man-machine interaction interface based on a mobile device application), in response to which the system presents a map of the target region to the user at the front end, at step 102.

Then, it is determined in step 103 whether the user intends to use a lasso tool (i.e. a tool that allows the user to freely define any enclosed area 14 having a regular or irregular polygonal shape in the target area, see fig. 3 b), and the allocation ratio between the area energy demand (cooling/heating demand in this embodiment) and the fraction of construction feasibility of the area energy item (area cooling/heating system in this embodiment) entered by the user at the front end of the system is received in step 109. Alternatively, the user may embody the respective importance levels of the energy demand and the construction feasibility by dragging the importance slider or by inputting an importance score, and calculate the above-mentioned allocation ratio from the system.

If the determination at step 103 is no, then proceeding to steps 105 and 106, one or more boundaries entered by the user using an automatic meshing tool are received, the boundaries defining one or more areas 12 (see FIG. 3 a), wherein the automatic meshing tool allows the user to divide the target area into a plurality of equally sized grids (i.e., areas) having the same regular shape (e.g., square or rectangle) according to a certain gear, the system may preset several gears representing different grid sizes (in different gears, the target area is divided into several tens, hundreds, thousands, or even tens of thousands of equally sized areas), and may set a certain gear as a default gear (i.e., a gear default by the system when the user does not select).

Next, in step 120, the pre-calculated cooling/heating demand score and construction feasibility score for the one or more zones are extracted from the database 20 previously established for the target zone using the pre-established index. Subsequently, in step 122, based on the distribution ratio of the cooling/heating demand and the construction feasibility score received in step 109, the cooling/heating demand score and the construction feasibility score obtained in step 120 are weighted-averaged to obtain engineering potential scores of the construction area cooling/heating system of the one or more areas, and in step 123, the engineering potential scores are returned to the front end to be presented to the user in an intuitively understandable form, for example, the engineering potential scores of the one or more areas may be presented in a color gradient on a map of the target area, which allows the user to quickly and efficiently screen out high engineering potential areas and further evaluate and investigate these high engineering potential areas. It should be appreciated that in addition to engineering potential scores, the system may also return other characteristic information to the front end that may be of interest to the user of each region. The assessment method 100 then ends at step 124.

Optionally, the method 100 may further extract the estimated profitability and the energy conservation and emission reduction potential of the one or more areas from the database 20 using the pre-established index in step 117, and return the two potentials to the front end for display to the user in step 123.

When a user uses a preset gear of the system to divide a target area automatically, as the system calculates the refrigeration/heating demand scores and the construction feasibility scores, the profit potential and the energy saving and emission reduction potentials of the areas represented by the grids in advance and stores the scores and the construction feasibility scores, the profit potential and the energy saving and emission reduction potentials in a database for all the grids in each gear, when the user requests to check the engineering potential scores of all the areas in a certain gear, only the weighted average calculation of the last step is needed, and when the user requests to check the profit potential and/or the energy saving and emission reduction potentials of all the areas in a certain gear, the user only needs to directly call out from the database, so that the user can receive the feedback result of the system in a very short time. In one example, when a user selects to divide the Shanghai into a plurality of square grids with sides of 4 km, the engineering potential score of the area represented by each grid can be seen after one or two seconds.

If the determination at step 103 is yes, then proceed to step 104 where, at step 104, one or more boundaries entered by the user using the lasso tool are received, the boundaries defining one or more regions. Next, in step 107, energy usage demand and construction related data, country design specification data, energy cost data within the one or more regions are extracted from the database 20 using the pre-established index. Next, in steps 110, 111, a building energy model (in this embodiment, a building cooling/heating demand evaluation model in particular) and a construction feasibility evaluation model that match the target region are extracted from the algorithm library 30 that is previously established for the target region, respectively, and the energy demand (in this embodiment, cooling/heating demand in particular) score and the construction feasibility score of the one or more regions are calculated using the models, respectively, based on the energy usage demand and construction related data, the country design specification data. Subsequently, in step 112, the refrigeration/heating demand score and the construction feasibility score calculated in steps 110, 111 are weighted average calculated based on the distribution ratio of the refrigeration/heating demand and the construction feasibility score received in step 109, to derive an engineering potential score of the construction area refrigeration/heating system of the one or more areas, and in step 118, the engineering potential score is returned to the front end for presentation to the user in a intuitively understandable form. Subsequently, the method 100 ends the evaluation in step 119.

In this embodiment, the energy usage requirements and construction-related data for the one or more regions include satellite maps, point of interest data, climate data, building footprints, building heights, building categories, water locations, and green space locations within the one or more regions; the national design specification data of the one or more regions includes cooling/heating related design criteria (such as related criteria specified in the chinese national standard GB 50736-2012) applicable to the one or more regions, cooling/heating load calculation related parameters (such as related parameters specified in the chinese national standard GB 50736-2012); the energy cost data for the one or more regions includes electricity and natural gas costs for the region. In general, all of the above data are disclosed and available on the internet for any target area, and thus the method of assessing engineering potential can be applied to any area worldwide.

Optionally, the method 100 may also extract, in step 108, the refrigeration/heating load curve, the energy cost data, the procurement costs of the equipment making up the regional refrigeration/heating system, and the operating efficiency curve for each building within the one or more regions from the database 20 using the pre-established index, then calculate, in step 113, a final cost consisting of the investment costs and the operating costs of building the regional refrigeration/heating system in the one or more regions based on these data, evaluate, in step 115, the profitability potential of building the regional refrigeration/heating system in the one or more regions based on the final cost, and return the profitability to the front end for display to the user in step 118.

Optionally, the method 100 may also extract, in step 108, the refrigeration/heating load curves of each building within the one or more zones, the operating efficiency curves of the equipment that constitutes the zone refrigeration/heating system, the climate data for the typical design day from the database 20 using the pre-established index, then calculate, in step 114, based on these data, the amount of carbon emission reduction caused by the construction of the zone refrigeration system at the one or more zones and/or the extent of mitigation of urban heat island effects or the amount of carbon emission reduction caused by the construction of the zone heating system at the one or more zones, evaluate, in step 116, the energy saving and emission reduction potential of the zone refrigeration/heating system at the one or more zones based on the amount of carbon emission reduction and/or the extent of mitigation of urban heat island effects, and return, in step 118, the energy saving and emission reduction potential to the front end for presentation to the user.

In the case of a lasso tool, the one or more areas are user-defined, rather than preset by the system, so that the refrigeration/heating demand scores and the construction feasibility scores, the profitability and the energy saving and emission reduction potentials corresponding to the areas cannot be stored in the database in advance, and on-site calculation is required, so that the time required for feeding back the result to the user is slightly longer than in the case of meshing. However, since intermediate data required for calculation is stored in advance in the database, the calculation process is also quite rapid. The waiting time of the user is related to the size and number of areas that he has selected with the lasso tool. In one example, the user has custom defined an area of 9 square kilometers with a lasso tool, and waits about 1 second to see the engineering potential score for the system to return to the front end.

As can be seen from the above description, the method according to the embodiment of the present invention can meet different user requirements. A typical application mode of the method is that a user firstly utilizes a meshing tool to roughly screen a region with a larger area (such as a certain city) to find out a plurality of potential regions with higher potential scores, then in order to further know the potential of the regions, the user selects meshing of different gears and different refrigerating/heating demands and construction feasibility score distribution ratios for each region, and the potential of the region on different scales is compared. After performing the above operation on each potential region, the user is able to select several candidate regions. Since the candidate region is a regular polygon (typically square or rectangular) that is automatically mesh-partitioned, its boundaries do not necessarily meet the user's expectations. In this case, the user may customize the appropriate boundary using the lasso tool. By previous potential comparisons on different scales, users are generally able to quickly find the appropriate road as a new boundary. By performing steps 104, 107, 110, 111, 112 and/or 104, 108, 113, 115 and/or 104, 108, 114, 116 of the above method, a user can learn engineering potential and/or profitability potential and/or energy conservation and emission reduction potential of the area defined by the new custom boundary and other relevant characteristic information, which can provide important references for site selection decisions of project investors, planners, constructors and approvers of the area refrigeration/heating system.

In reality, there is another user demand, that is, the boundary of the approximate target area selected by the user is optimized, so as to obtain the area with the greatest potential. Fig. 2 illustrates an optimization method 200 that can optimize engineering potential for an area, according to an embodiment of the invention. The method starts in step 201, followed by confirming coverage of the optimized region in step 202, followed by receiving one or more target areas to be optimized entered by a user in step 203. In step 204, engineering potential scores for the one or more target areas are calculated by performing the relevant steps in the assessment method 100 described above. Subsequently, in step 205, the boundaries of the one or more target areas are trimmed by automatically adding or subtracting sub-areas (e.g., adding or subtracting buildings or blocks), resulting in new one or more target areas, and engineering potential scores for the new one or more target areas are calculated by performing the relevant steps in the assessment method 100. In step 206, the engineering potential score of the new target region or regions is compared with the engineering potential score of the target region or regions before trimming, if the former is larger than the latter, step 205 is repeated, otherwise proceeding to step 207, and the boundary of the new target region or regions before the last trimming is presented to the user as the optimized boundary. Subsequently, the boundary optimization method 200 ends at step 208.

In the boundary optimization method 200, the system can dynamically evaluate the increase or decrease of engineering potential for each building or neighborhood zone added or subtracted in order to optimize the boundary of a zone with greater engineering potential based on the target zone initially selected by the user, i.e., to achieve dynamic growth of the boundary. Fig. 3c schematically shows the area 16 defined by the boundaries of such dynamic growth.

Fig. 4 shows the establishment procedure of the database 20 used in the above-described evaluation method 100. The library construction method 300 begins in step 301. Subsequently, in step 302, a target region (e.g., shanghai) for which a database is to be created is selected and partitioned into a plurality of equally sized regions using an automatic meshing tool according to different gear levels representing mesh sizes. Next, satellite map data, point of interest data, energy-related national design specification data (including cooling/heating-related design criteria, and cooling/heating load calculation-related parameters), annual climate data, within the target region are collected from the internet in steps 303, 308, 310, 314, respectively. As shown in fig. 5, these data are public data available to the public over the internet. The database building method according to the embodiment of the invention is not limited by the region because the database building method does not depend on a special database (usually private) of a specific region, has wide adaptability, and can be easily popularized in other regions in the world. It should be understood that the data disclosed on the internet are very much, the data used by the method of the invention are carefully selected by the inventor, the data can be obtained for a common area, and the data can more accurately represent the energy demand condition of each building in the target area and the construction feasibility of regional energy projects after being processed.

In step 304, the water body position and greening position in the coverage area of each satellite map are deduced from the collected green band, infrared band and near infrared band data in the satellite map by using the normalized vegetation index and the water index, and the water body position and greening position are stored in the database 20. In step 305, a construction feasibility assessment model is extracted from the algorithm library 30, and the region represented by each grid in each gear is grid-partitioned for automatic using the model, a corresponding construction feasibility score is calculated based on the satellite map of the region, and the construction feasibility score is stored in the database 20, and then ends in step 306.

In step 307, a building geometry model is extracted from the algorithm library 30, and building geometry information (building floor area and building height) of each building within the coverage area of each satellite map is deduced from the red, green, and blue band data in the satellite map collected in step 303 using the model, and the building geometry information is stored in the database 20.

In step 309, a building class estimation model is extracted from the algorithm library 30, and the building class of each building is estimated from the socioeconomic characteristics of the inside and the periphery of each building in the target region extracted from the point of interest data collected in step 308 and the building occupation area and the building height of each building estimated in step 307 by using the model and stored in the database 20. Among them, point of interest (POI) is a term in geographic information systems, which generally refers to all geographic objects that can be abstracted into points, especially some geographic entities closely related to people's life, such as schools, banks, restaurants, gas stations, hospitals, supermarkets, and the like. The main purpose of the interest points is to describe the addresses of things or events, so that the description capability and the inquiry capability of the positions of the things or events can be enhanced to a great extent, and the accuracy and the speed of geographic positioning are improved. In embodiments of the present invention, points of interest may be used to initially determine a building class for a building, including but not limited to office buildings, hospitals, schools, malls, supermarkets, restaurants, hotels, entertainment venues, industrial sites, and the like.

In step 315, the cooling/heating days of the target region are calculated based on the annual climate data of the target region collected in step 314 and stored in the database 20.

In step 311, a building cooling/heating demand evaluation model of each building in the target area is extracted from the algorithm library 30, and a cooling/heating load curve of each building is calculated based on the building floor area, building height, building type, cooling/heating daily number of the target area, cooling/heating related design standard, cooling/heating load calculation related parameters of each building in the target area, and the cooling/heating load curve is stored in the database 20.

In step 312, using the building cooling/heating demand assessment model for each building in the target area, a corresponding cooling/heating demand fraction is calculated for the area represented by each grid in each gear for automatic meshing and stored in database 20. Subsequently, the method 300 ends in step 313.

Optionally, the method 300 may also collect energy cost data in the target area, such as electricity and natural gas cost charging criteria, in step 316; calculating an operation expense of operating the regional cooling/heating system in the region represented by each grid in each gear for automatic grid division based on the energy cost data and the cooling/heating load curve of each building calculated in step 311 in step 317; in step 318, collecting the cost of equipment available to the target area for construction of the district heating/cooling system and the operating efficiency curve of said equipment, and storing the cost and operating efficiency curve to database 20; in step 319, calculating the capital expenditure for building a district cooling/heating system in the district for the district represented by each grid in each gear of the automatic meshing based on the cost and operating efficiency curves of the plant; in step 320, based on the operating and capital expenditures, calculating a final cost of building and operating an area cooling/heating system in the area for the area represented by each grid in each gear of the automatic grid division, and storing the final cost to the database 20; the method then ends in step 321.

Optionally, the method 300 may also collect climate data for a typical design day for the target area and store the climate data to the database 20 in step 322; calculating a carbon emission reduction amount caused by building the district cooling/heating system in the region for the region represented by each grid under each gear of the automatic meshing based on the work efficiency curve of the equipment for building the district cooling/heating system available in the target region collected in step 318 and the climate data of the typical design day collected in step 322 in step 323, and storing the carbon emission reduction amount to the database 20; the method 300 then ends in step 324.

Optionally, the method 300 may also calculate a degree of mitigation of the urban heat island effect at the regional construction area refrigeration system for the area represented by each grid in each gear of the automatic grid division based on the operating efficiency curve of the equipment for construction area refrigeration system available to the target area collected in step 318 and the climate data of the typical design day collected in step 322 in step 325, and store the degree of mitigation of the urban heat island effect to the database 20; the method 300 then ends in step 326.

A method 400 of creating an algorithm library for a target region according to an embodiment of the present invention is described below with reference to fig. 5.

The method 400 begins at step 401, followed by collecting satellite map data of the target area and corresponding altitude and occupancy tagging data at step 402, and building a building geometry model based on these data at step 406, and storing the building geometry model to the algorithm library 30. Building geometry data (e.g., floor area and height) and corresponding building class information for each building within the target area is collected in step 403, and a building class model is built based on the data and information in step 407 and stored to the algorithm library 30. National design specification data relating to energy in the target region is collected in step 404 and a building energy demand (cooling/heating demand in this embodiment) assessment model is built based on the data in step 408 and stored in the algorithm library 30. Satellite map data of the target region and data of the corresponding expert, which are compared in terms of construction feasibility, are collected in step 405, a construction feasibility assessment model is built based on these data in step 409, and the construction feasibility assessment model is stored in the algorithm library 30. The method 400 then ends at step 410.

Fig. 7 is a data pipeline diagram corresponding to the library construction method 300. How the data pipe implements the above method is briefly described as follows:

step 1: the library interface distributes tasks to the engine after acquiring data collection and analysis requests of the target area.

Step 2: the engine will request that the task be subdivided according to different data types and send to the schedule waiting response.

Step 3: the schedule collates the request list and sends the first task request in the list to the engine.

Step 4: the engine acquires a task request from the timetable and sends a data grabbing request to the crawler script of the corresponding data type.

Step 5: the crawler script captures data from the internet, performs geographic coordinate transformation on the data, screens the data, simplifies processing, and returns the data, log information, and the like to the engine.

Step 6: and after receiving the data grabbing result, the engine submits a feature analysis request to the algorithm library according to the data type, and reports the data grabbing result to the library interface. In the embodiment of the invention, the algorithm in the algorithm library can calculate the daily number of the refrigerating/heating according to the temperature history data of the target area, such as one year or so and combining the use habit of local equipment; the building occupation area and the building height of each building in the coverage area of each satellite map can be deduced from the collected satellite map data by using a machine deep learning model; the vegetation coverage position/water coverage position in the coverage area of each satellite map can be respectively estimated by utilizing the collected satellite map data through a near infrared band and a red light band/green light band; building categories can be inferred by machine learning using building floor space and building height data inferred from satellite map data in combination with POI data. The results of these calculations or inferences are hereinafter collectively referred to as feature data.

Step 7: after the feature data calculation is completed, the algorithm library returns the feature data and the analysis result to the engine.

Step 8: the engine collates the analyzed data into tabular data for storage to the server and simultaneously reports the results to the timetable and the library interface for response.

Step 9: steps 3 through 8 are repeated until a request for a flush is made within the schedule.

Fig. 8 is a data pipeline diagram corresponding to the evaluation method 100. How the data pipe implements the above method is briefly described as follows:

step 1: the user selects one or more regions at the target area at the front end via a meshing or lasso tool. After the front end analyzes the area selected by the user, the front end converts the area into an Http request and sends the Http request to the server to start waiting for response. The server receives the user request sent by the front end, and sends the request to the library interface to start waiting for response.

Step 2: the library interface parses out the specific number and location of regions within the request and sends it to the engine.

Step 3: advantageously, to optimize the user experience and ease server burden, a threshold may be set, for example 20 square kilometers when the area of a certain region selected by the user is greater than the threshold, the engine may reject the request for that region and let the library interface return a prompt that "the area of the selected region is too large"), sending a request arrangement for regions that are suitable for operation to the schedule.

Step 4: the schedule adds the new request list into the existing task request list, if no task request is running, the new task request is sent from the request list to the engine, otherwise, the engine waits for ending the task response.

Step 5: the engine retrieves building data and related characteristic data within the boundary from the database by a search algorithm using a pre-established index according to the region for which the task request is directed.

Step 6: and the engine sends the data called out in the database to an algorithm library to wait for the regional energy characteristic analysis request.

Step 7: after the feature data calculation is completed, the algorithm library returns the potential scores and the feature data that may be of interest to other users to the engine.

Step 8: the engine judges whether all requests sent by all library interfaces are completed, if so, the results of all task requests are integrated and returned to the library interfaces, and the schedule is notified of the completion of the current task. If all the tasks are not completed, the notification schedule sends the next task request, and the 4 th to 8 th steps are repeated until all the tasks are completed.

Step 9: after analysis is completed, the library interface sorts the data sent by the engine into a data format of common internet file transmission such as json file and the like, and the data is transmitted to the front end through the Http request. And the front end displays the potential scores and the feature data possibly interested by other users to the users according to the interface style after receiving the Http response.

Fig. 9 shows a modeling process 500 of the building geometry model mentioned in the above-described banking method 300, wherein the building geometry model is composed of a building floor space estimation model and a building height estimation model, which are both deep learning models. The process begins at step 501, followed by preparing a training set of satellite maps comprising building site markers and red, green, and blue bands for a building site estimation model, and preparing a training set of satellite maps comprising a digital surface model of altitude information and red, green, and blue bands for a building altitude estimation model, step 502.

In step 503, the encoder and decoder architecture (e.g., residual network) is designed to semantically segment the satellite image; in step 504, a loss function (e.g., a maximum distance and an intersection ratio of the predicted building footprint to the real building footprint) is designed based on the building boundaries and the building footprint shape to characterize the gap of the existing model from the ideal state; obtaining a deep learning model to be trained in step 505; in step 506, satellite diagrams and building occupation location labels are selected from the training set, the deep learning model is trained to predict the building occupation area of the building contained in the satellite diagrams, and the result of the loss function is calculated; in step 507, it is determined whether the result of the loss function cannot be lowered, if not, the parameters of the deep learning model are adjusted by negative feedback, and step 506 is repeated, if yes, the deep learning model at this time is used as a building floor space estimation model.

In step 508, the encoder and decoder structure (e.g., a refining network) is designed to convert the input satellite map data into data containing the predicted altitude; in step 509, a loss function (e.g., the mean and variance of the difference between the predicted building height and the actual building height) is designed based on the building height to characterize the existing model's gap from ideal; in step 510, a deep learning model to be trained is acquired; in step 511, satellite images and building height labels are selected from the training set, the deep learning model is trained to predict building heights of buildings contained in the satellite images, and the result of the loss function is calculated; in step 512, it is determined whether the result of the loss function cannot be lowered, if not, the parameters of the deep learning model are adjusted by negative feedback, and step 511 is repeated, if yes, the deep learning model at this time is used as the building height estimation model.

Then in step 513, a building geometry model is built based on the trained building footprint estimation model and the building height estimation model, and stored in the algorithm library 30. Modeling method 500 ends in step 514.

The above-described deep learning model for estimating building footprint and altitude may classify or estimate satellite images pixel-by-pixel by encoder decoder architecture. The encoder can gradually learn texture information, local structure information and overall object information of the image through a plurality of convolution layers and pooling layers to generate a low-resolution characteristic image with semantic characteristics; the decoder may map the low resolution feature image output by the encoder back to the input image size through multiple upsampling layers and convolutional layers. The difference between the training model and the real data is represented by a reasonably designed loss function, and the weight of each parameter in the deep learning model can be continuously adjusted by utilizing negative feedback to continuously improve the precision of the model until the best performance of the model.

Fig. 10 shows a modeling process 1000 of the construction category presumption model mentioned in the above-described banking method 300. The process begins at step 1001, followed by preparing a training set for a building class inference model at step 1002. Next, in step 1003, socioeconomic characteristics of the inside of each building and the periphery thereof in the target region are extracted from the point of interest data, and in step 1004, geometric parameters of the building are extracted from geometric information (building occupation area and building height) of each building in the target region. Then, in step 1005, an initial model based on random forests is built based on the socioeconomic characteristics extracted in step 1003 and the building geometry parameters extracted in step 1004, and the initial model is trained with the training set prepared in step 1002, and the trained model is stored as a building class presumption model in the algorithm library 30.

The building energy model (i.e., the building cooling/heating demand assessment model) mentioned in step 215 of the above-described banking method 300 is described below in conjunction with fig. 11.

The specifications and standards of different countries have different requirements on the calculation method of the refrigeration load of the building, namely, the calculation method of the transfer relation between the heat and the load is different, and the algorithm library according to the embodiment of the invention is internally provided with the load algorithm according to the specifications and standards of different countries and can automatically match the algorithm of the corresponding country according to the target area. For example, according to the requirements of national standard GB50736-2012, the refrigeration load of the building is calculated by adopting a refrigeration load coefficient method, and then when the target area selected by the user is the area of China, the refrigeration load coefficient method in the algorithm library is automatically matched and called. Since the target area generally includes a plurality of buildings, a large amount of calculation is required to calculate a corresponding refrigeration/heating load curve for each building, so in order to reduce waiting time of a user, a model adopted in the calculation needs to be simplified, so that the calculation amount is reduced, and the calculation speed is increased.

Taking a target area of China as an example, how the refrigeration load calculation model according to the embodiment of the invention simplifies the conventional refrigeration load coefficient method model is described below. First, in the refrigeration load calculation model according to the embodiment of the present invention, solar insolation calculation is simplified. Because the directions of the outer walls of the buildings are different, if the sun illumination intensity is calculated for each outer protecting structure of each building, the calculation amount is proportional to the number of the outer protecting structures of the building. If there are ten thousands of cuboid buildings, at least fifty thousand operations are needed. To reduce the amount of computation, the model according to the embodiment of the invention rounds the orientation of each building to a round, for example, 12.1 degrees north to east is approximately 12 degrees. Before the building cold load operation, the illumination intensity is pre-calculated for 24 hours time by time (361 operations are performed). And then when the illumination intensity of the peripheral protection structure is needed during the calculation of the building cold load, the pre-calculation result can be directly taken. The amount of computation can be reduced by two orders of magnitude by this simplification. Secondly, in the refrigeration load calculation model according to the embodiment of the invention, the heat transfer and indoor heat dissipation of the enclosure structure of the same type of building are simplified. The document j. Seem, "Modeling of Heat Transfer in Buildings," University of Wisconsin-Madison,1987 (the content of which is incorporated herein by reference) proposes a wall transfer function calculation method that has high calculation accuracy, but requires discretization of each wall and writing out a state space equation by algebraic operation, followed by matrix transformation. If only the cold load of an individual building needs to be solved, the calculation amount of the method is acceptable, and if the transfer function coefficients need to be solved for all the buildings in the city, the calculation amount is too large. In order to reduce the amount of calculation, in the model according to the embodiment of the present invention, it is assumed that wall structures of the same building class are similar. For the same type of building, typical wall and roof structures of the type of building are selected for calculating transfer function coefficients, and the transfer function coefficients of the type of building wall and roof structures are assumed to be the same. Similarly, to simplify the window heat transfer calculation, it is assumed that the heat transfer coefficients and the total solar transmittance of the same type of architectural window are the same. In addition, for the same type of building, it is assumed that the personnel density, the heat dissipation per unit area, and the illumination heat dissipation are also equal. By the simplification, the calculation amount required by the model according to the embodiment of the invention is greatly reduced. The inventor tests on a computer configured by i7-4850HQ by using the python language, the time for calculating the cooling load of each building 24 hours time by time is only about 0.04 seconds, and the calculation accuracy is relatively good. In addition, the model according to the embodiment of the invention has a plurality of advantages compared with the traditional model established based on the Chinese national standard GB50736-2012, including: the model provided by the embodiment of the invention has more flexibility, can calculate the solar illumination intensity in any city and direction angle, and is not limited to eight directions of east, south, west, north, southeast, southwest, northeast and northwest; the model according to the embodiment of the invention is not limited to wall and roof structures, and can perform heat transfer calculation on any structure; the model according to the embodiment of the invention can also carry out load operation on the building with any load curve.

How the model calculates the cooling load of a building according to an embodiment of the present invention is described in detail below (see fig. 11).

When calculating the cooling load, the building heat should be divided into four parts: (1) The radiation heat obtaining comprises radiation heat obtaining caused by solar illumination penetrating through glass and indoor heat sources; (2) Convection heat generation mainly comes from convection heat generation caused by indoor heat sources and heat generation caused by ventilation of buildings; (3) the enclosure is heated; (4) illuminating to get heat.

To calculate the building cold load caused by different heat sources, the model according to an embodiment of the present invention uses a transfer coefficient method to calculate the cold load. The method utilizes z-transforms to solve for unsteady heat transfer equations. Wherein, the heat q is obtained at the time point theta through the wall body due to indoor and outdoor temperature difference and solar radiation _e,θ Can be obtained from the following

Equation 1/>

The variables in equation 1 are respectively:

a wall area, unit is m ² ，

t _e,θ-nδ The outdoor integrated temperature at the time theta-n delta is given in units of,

q _e,θ-nδ the heat gain at time theta-n delta is calculated from equation 1,

t _rc the indoor design temperature is given in units of,

delta time step in s

b _n ,c _n ,d _n Wall transfer function coefficients.

In this model, the wall transfer function coefficients are solved by the state space equation method set forth in document J.Seem, "Modeling of Heat Transfer in Buildings," University of Wisconsin-Madison, 1987. After the wall structure and the physical characteristics thereof are known, a one-dimensional Fourier unsteady heat conduction equation is discretized by a finite volume method, and the discretized equation is converted into a form of a space state equation, wherein the form is shown as the following formula:

Equation 2

Equation 3 q =c·t+d·t _a

The variables in the above formula are respectively:

t is the temperature vector t of each layer of the wall body of the discretized heat conduction equation ₁ t ₂ … t _n ] ^T The unit is that,

is the first derivative vector of the temperature vector t with respect to time,

t _a is a vector [ t ] consisting of indoor temperature and outdoor comprehensive temperature _rc t _e,θ ] ^T The unit is that,

q is the heat flow received by the inner and outer surfaces of the wallDensity [ q ] _e q _i ] ^T In W/m ² ，

A, B, C, D are coefficient matrices of the discretized one-dimensional heat conduction equation.

The wall transfer coefficients can be deduced from the coefficient matrix of the space state equation, for specific mathematical deductions, please refer to the document J.Seem, "Modeling of Heat Transfer in Buildings," University of Wisconsin-Madison,1987.

The outdoor integrated temperature in equations 1 to 3 can be calculated as follows:

equation 4

Wherein the variables are respectively as follows,

t _o,θ the outdoor dry bulb temperature at time theta is expressed in DEG C

The absorption coefficient of the outer surface of the alpha wall body,

I _t,θ the illumination intensity of the outer surface of the wall body at the moment theta is in W/m ² ，

h ₀ The convection heat transfer coefficient of the outer wall is W/(m) ² K)，

Δq _ir The unit of the long wave radiation heat flux density of the outer wall surface, sky and surrounding environment is W/m ² 。

ASHRAE,1997ASHRAE handbook:Fundamentals.Atlanta,GA:ASHRAE,1997 (the content of which is incorporated herein by reference) for horizontal surfaces The value-3.9K can be taken, which is negligible for vertical surfaces.

The illumination intensity of the outer surface of the wall in the formula 4 can be calculated by the following formula

Equation 5

Wherein the variables are respectively as follows,

I _D sun lightDirect illumination intensity in W/m ² ，

I _d The intensity of scattered sunlight is W/m ² ，

The angle of inclination of the beta wall body, the angle of the vertical plane outer wall is 90 degrees, the angle of the horizontal plane roof is 0 degree,

θ _z the solar altitude angle is set to be the same,

the incident angle of theta sunlight,

ρground reflectivity.

The calculation of the solar altitude and the angle of incidence of sunlight can be referred to in the literature J.A. Duffie and W.A. Beckman, solar Engineering of Thermal Processes,4th ed.Hoboken,New Jersey:Wiley,2013. (the contents of which are incorporated herein by reference).

Heat flux q through window into room _e,θ Can be solved by the steady state method as follows

Equation 6 q _e,θ ＝U·A·(t _rc -t _o )

Wherein the variables are respectively

U window heat transfer coefficient, unit is W/(m) ² K)，

A window area, unit is m ² ，

t _rc The indoor design temperature is given in units of,

t _o outdoor dry bulb temperature in degrees celsius.

Adding the heat obtained from all the walls and windows to obtain the sum which is the heat obtained from the enclosure structure shown in FIG. 11.

The convection heat gain of the building consists of human body convection heat gain and equipment convection heat gain, and the radiation heat gain consists of human body radiation heat gain, equipment radiation heat gain and solar radiation heat gain through windows. Wherein the radiant heat gain of the sun through the window can be calculated from the following

Equation 7 q _s ＝SHGC·I _t

Wherein SHGC is the total solar transmittance of the window.

The heat gain due to ventilation required for the building can be calculated as follows

Equation 8 q _V ＝0.34·n·V _e ·(t _rc -t _o )

Wherein the variables are respectively

n building design ventilation times, the unit is h ^-1 ，

V _e Air volume in building in m ³ 。

After the radiation heat obtaining, convection heat obtaining, building enclosure heat obtaining and illumination heat obtaining are calculated, the refrigeration load caused by the radiation heat obtaining, convection heat obtaining and illumination heat obtaining can be obtained respectively by utilizing the building transfer function, and the refrigeration load is shown as the following formula

Equation 9Q _i,θ ＝v _i,0 ·q _i,θ +v _i,1 ·q _i,θ-δ -w _i,1 ·Q _i,θ-δ

Wherein the variables are respectively

i type of heat gain, r radiant heat gain, c convective heat gain, l illuminated heat gain, e building envelope heat gain

q _i,θ The heat is obtained at the time of theta,

q _i,θ-δ the heat gain at the time theta-delta,

Q _i,θ-δ the cooling load at the moment theta-delta,

v _i,0 ,v _i,1 ,w _i,1 building transfer function coefficients.

From equation 9, the cooling load Q caused by radiation heat gain, enclosure structure and illumination heat gain can be obtained _r 、Q _e And Q is equal to _l Is that

Equation 10Q _r,θ ＝v _r,0 ·q _r,θ +v _r,1 ·q _r,θ-δ -w _r,1 ·Q _r,θ-δ

Equation 11Q _e,θ ＝∑ _k＝1 (v _e,0 q _e,θ,k +v _e,1 q _e,θ-δ,k )-w _e,1 Q _e,θ-δ

Equation 12Q _l,θ ＝v _l,0 q _l,θ +v _l,1 q _l,θ-δ -w _l,1 Q _l,θ-δ

The effect of convective heat on the building can be seen as instantaneous, and therefore, the cold load caused by convective heat is the magnitude of convective heat at that moment.

Equation 13Q _c,θ ＝q _c,θ

Adding all the cold loads to obtain the building cold load at the time theta

Equation 14Q _θ ＝Q _r,θ +Q _l,θ +Q _e,θ +Q _c,θ

From the above formula, it can be seen that the calculation of the cooling load requires the knowledge of the heating and cooling loads at the past points in time, and therefore the method requires iterative calculation. For calculation, the initial value may be set to zero, the time step may be set to 3600 seconds (1 hour), and the outdoor temperature and the direct and scattered sunlight intensities may be set to periodic functions having a period of 24 hours. It was found experimentally that the time-by-time cold load values of the building can converge through seven cycles of iterative calculations (seven days).

How the model calculates the thermal load of a building according to an embodiment of the present invention is described in detail below (see fig. 11).

The thermal load is constituted by heat loss from the enclosure and heat loss from ventilation. Unlike the cold load, the peak heat load occurs at the moment when the heat gain is minimal, so that the heat load can be calculated without considering the heat gain and the heat storage capacity of the building, and only the steady-state heat transfer equation needs to be solved. Therefore, the heat load can be found according to the steady state calculation method in national standard GB 50736-2012. The input quantity required for calculating the heat load is the area of the outer protective structure, the heat transfer coefficient and the outdoor temperature under the design working condition. For a single enclosure, its heat loss can be calculated according to the calculation method of equation 6. The sum of heat loss of all the enclosing structures is the heat loss Q of the overall enclosing structure of the building _T As shown in the following formula

Equation 15Q _T ＝∑ _i U _i ·A _i ·(t _rc -t _o,d )

Wherein the variables are respectively

U _i The heat transfer coefficient of the enclosure structure i is W/(m) ² K)，

A _i The area of the enclosure structure i is m ² ，

t _rc The indoor design temperature is given in units of,

t _o,d outdoor temperature under design conditions is in degrees celsius.

Heat loss Q caused by ventilation required for building _V Can be calculated as follows

Equation 16Q _V ＝0.34·n·V _e ·(t _rc -t _o,d )

Wherein the variables are respectively

n building design ventilation times, the unit is h ^-1 ，

V _e Air volume in building in m ³ 。

The total heat load Q of the building, i.e. the sum of the two

Equation 17Q =q _T +Q _V

If the time-by-time heat load value of the building needs to be solved, the heat load can be calculated according to a cold load calculation model.

A construction feasibility evaluation model establishing method 600 used in the above-described evaluation method 100 is described below with reference to fig. 12.

The method 600 begins at step 601, followed by preparing a satellite map dataset of the regional scale of the region of the same type as the target region in step 602; in step 603, two satellite images are randomly selected from the satellite image data set and are submitted to expert comparison; in step 604, the expert selects a satellite map from the two satellite maps that represents an area more suitable for building an area refrigeration/heating system; in step 605, it is determined whether the number of times compared by the expert reaches the preset number of times, if not, steps 603 and 604 are repeated, if yes, steps 606 and 607 are advanced, in step 606, the volume ratio, the building class ratio, the vegetation coverage, the water coverage and the road width of the area represented by each satellite map are calculated, in step 607, the construction feasibility fraction Q of the area represented by each satellite map is calculated according to the following formula _i ：

Equation 17

Equation 18

Wherein w is _i Representing the number of times an expert selects a picture in a pairwise comparison, l _i Representing the number of times a picture is not selected in the pairwise comparison of an expert, t _i Representing the times that the user cannot judge the quality of one picture with the other picture in the pairwise comparison.Equal to when picture i is preferred over j in total ₁ Picture i is preferred the number of times, +.>Equal to when picture i is not preferred over j in total ₂ Picture i is not preferred for times. Equation 18 calibrates equation 17 by appending the average preference rate and removing the average non-preference rate, effectively integrating all pairwise comparisons of picture result information.

Then, in step 608, nonlinear fitting modeling is performed on the relationship among the volume rate, the building category ratio, the vegetation coverage, the water coverage and the road width of the area represented by the satellite map and the construction feasibility score of the corresponding area, so as to obtain an initial evaluation model; training an initial evaluation model by using a first preset number of satellite graphs, so that the initial evaluation model learns the volume rate, the building category ratio, the vegetation coverage, the water coverage and the relation between the road width and the construction feasibility score of the corresponding area of each satellite graph and automatically adjusts the parameters of the model; and carrying out construction feasibility assessment on a second preset number of satellite images which are not learned by the assessment model after adjustment, comparing the obtained construction feasibility score of the area represented by each satellite image with the construction feasibility score of the same satellite image based on the assessment of an industry expert so as to verify the accuracy of the assessment model, repeating the training step if the accuracy does not reach an expected value, otherwise, storing the adjusted assessment model into an algorithm library 30 as a construction feasibility assessment model matched with a target area.

In practice, the target areas can be classified according to factors such as administrative planning rules, geographic environments and economic development degree, a corresponding machine learning model is trained and verified in advance for the target areas of the same class and is stored in a database, and when a user requests to evaluate construction feasibility for one or more areas of the target areas, the corresponding machine learning model is directly extracted from the database to make a construction feasibility score based on satellite images of the areas.

In the following, it is briefly described with reference to fig. 13 how the final cost of constructing and operating a district cooling/heating system in a district is calculated according to an embodiment of the present invention, which final cost consists of capital expenditure for constructing a district cooling/heating system in the district, operating expenditure for operating a district cooling/heating system, and possibly green evidence equity conversion. The cost calculation method 700 starts at step 701, followed by determining the boundaries of the area to be analyzed at step 702, collecting the procurement cost and operating efficiency curves of the equipment available to the area for constructing the area refrigeration/heating system at step 703, and extracting the building refrigeration/heating load curves for each building within the area from the database 20 at step 704. Next, in step 705, a plurality of different device combination schemes are obtained by permutation and combination. In step 707, the offers for each combination of devices are summed to obtain an initial investment. It is often encountered in projects of regional energy systems that the demand side buildings are not at the same time, but are within the range of stepwise addition to the regional energy system, in which case stepwise addition of equipment according to the increase in load side is required, and step 708 is performed to calculate the subsequent equipment investment by summing the offers of each newly added equipment combination. In step 717, the initial equipment investment obtained in step 707 is added to the subsequent equipment investment obtained in step 708, and the capital expenditure is calculated. Meanwhile, in step 706, the operation policy of each device under a certain combination is preset according to the manual of each device. In step 709, the device is simulated on a time-by-time basis according to each of the operating policies. The time-by-time energy consumption (i.e., power usage) of each device combination is calculated in step 710. In step 712, the power consumption is multiplied by the power rate of the corresponding period to obtain a corresponding equipment operation cost. The calculations of steps 705, 706, 709, 710, 712 are repeated for 8760 hours of the year. In step 711, the associated equipment maintenance costs and labor costs are added as a percentage at the time points specified in the equipment manual for the refurbishment maintenance and replacement required for the different equipment. In step 713, it is determined whether the device operation policy still needs to be optimized, if so, steps 706, 709, 710, 712 are repeated, otherwise proceed to step 714. In step 714, it is determined whether or not the green license rights are acquired, and if not, in step 716, the equipment update maintenance cost and the labor cost obtained in step 711 and the annual operation cost obtained in step 712 are added to obtain the operation expenditure. In step 718, the capital expenditure calculated in step 717 is added to the operating expenditure calculated in step 716 to yield the final cost, which is calculated as follows:

totalcost＝argmin(Capex _i +Opex _i )

Capex _i ＝N _j *p _j

Wherein total cost is total cost, capex _i Initial investment for the ith equipment arrangement combination, opex _i The combined operating costs are arranged for the ith device. N (N) _j For the number of j-th devices, p _j Initial investment for j-th equipment. c _t For the operating cost of the j-th device at time t hours c _jequip For maintenance costs of the j-th apparatus c _jlabor Is the labor cost of the j-th equipment.

If the determination in step 714 is yes, then in step 715 the green license fee is calculated as follows, and then in step 716 the green license fee is charged to the operating expense.

The green certificate, i.e., green power certificate (green electricity certificate), is a green power consumption certificate that can be voluntarily or forcibly purchased in the trade market. The green power certificate subscription work is formally started in China on the 1 st 7 th 2017. The green power certificate transaction meets the green power consumption requirements of the power consumer. The green power certificate transaction system divides green power into two different types of commodities, namely green power certificates and networking electric quantity corresponding to the green power certificates. According to the regulations of the notice about trial renewable energy green power certificate issuing and voluntary purchase transaction system, 1 green power certificate corresponds to green power of 1 000kW.sub.h, through the operation area cooling system, a user can pay the cold energy using fee to add green power cost, and the green power consumption is realized through aggregation. The green electricity procurement cost per kilowatt-hour is calculated as follows:

Wherein s is _i The i green electric power is used for surfing the net, c is the standard pole electricity price of the desulfurization coal-fired unit, r _i Is the discount rate of the ith green electric power energy industry, h _i Adding funds to the ith green electric power energy price to subsidy the amount settlement period d _i And adding funds for the ith green electric power energy price to subsidy the amount of money for a delay payment period. The p thus found _i The lowest economic selling price for the ith green power certificate. Will p _i And multiplying the cold energy consumption of each user to obtain the green electricity purchasing cost of the user.

How to calculate how to mitigate the urban heat island effect by building an area refrigeration system in one area according to an embodiment of the present invention is characterized by comparing the time law and the heat amount of heat release to the urban space by the air conditioning systems before and after building the area refrigeration system in the target area is described below with reference to fig. 14. The method 800 starts in step 801, followed by determining the boundaries of the area to be analyzed in step 803, and collecting in step 802 the operating efficiency curves of the equipment available to the area for building the area refrigeration/heating system. In step 804, typical design day time by time refrigeration load demand data for the area is extracted from database 20, and in step 805, typical design day climate data for the area is extracted from database 20. In step 806, a combination of equipment using ice/water cold storage is calculated based on the typical design day time by time refrigeration load of the area, while in step 807, a combination of equipment is calculated for all building self-contained rooms within the area without area refrigeration. In step 808, the operating strategy of the ice/water cold storage regional refrigeration system baseload and ice making units is optimized according to the ice/water cold storage device combination scheme in step 806, and then a time-by-time heat release profile of the ice/water cold storage regional refrigeration system on a typical design day is calculated based thereon in step 810. Meanwhile, in step 809, a typical design day time-to-time heat release profile for each building in the area is calculated from the equipment combination scheme for cooling all the building's self-contained machine rooms in the area without area cooling, and based thereon in step 811, a typical design day time-to-time heat release profile for all the buildings is calculated. Thereafter, in step 812, the difference in the typical design day time-by-time heat release profile for the regional and non-regional refrigeration systems is calculated and used to characterize the mitigation of urban heat island effects.

The main contribution of the regional refrigeration system using the ice storage and water storage technology is that the time law of releasing heat to urban space by the traditional air conditioning system is changed, in general, the continuous high peak load of the air conditioner occurs in daytime, especially the time point with higher outside air temperature and larger solar load lasts to the latter two hours, regional refrigeration is used for intensively supplying the refrigeration demands of the region, ice or cold water is made at the moment with lower refrigeration demands at night by using the ice storage and water storage technology, the ice or water is stored by using the cold storage technology, the ice or water is reused in daytime, so that a great part of heat released to urban outside space is transferred to the night release, and the position of a regional refrigeration machine room is generally not in the commercial center with serious urban heat island effect of high building density, effectively relieving the urban heat island effect, so that the urban heat island effect is relieved by a single numerical value, and the urban heat island effect is relieved by using the typical design day-by-time curve difference before and after the regional refrigeration system for establishing ice/water storage.

A method 900 of calculating the amount of carbon emission reduction caused by building a district cooling/heating system in a district according to an embodiment of the present invention is described below with reference to fig. 15, in which the contribution of the district cooling/heating system to the reduction of carbon emission is quantified by calculating and comparing the annual carbon emission difference before and after the district cooling/heating system is built in the district. The method 900 starts in step 901, followed by determining the boundaries of the area to be analyzed in step 903, and collecting in step 902 the operating efficiency curves of the equipment available to the area for building the area refrigeration/heating system. In step 904, typical design day time by time refrigeration load demand data for the region is extracted from database 20. Then, in step 905, a device combination scheme corresponding to the total cooling/heating load of the area is calculated, in step 907, a system efficiency curve under different load factors of the area is calculated in combination with a device working efficiency curve, in step 909, a system efficiency curve of the area cooling/heating system in time of year is calculated according to the load factors of the area cooling/heating system in time of year, then in step 911, the system total load in time of year is multiplied to obtain the electricity consumption power of the area cooling/heating system in time of year of the area, and then in step 913, the electricity consumption power is multiplied by a carbon emission coefficient of unit electricity consumption to obtain the time-by-time carbon emission and the total carbon emission caused by the operation of the area cooling/heating system of the area.

The cooling/heating carbon discharge before the zone is established by the zone cooling/heating system is obtained by assuming that each building in the zone is supplied with cooling/heating by a self-established cooling/heating machine room in the building and then calculating the operating carbon discharge of these machine rooms. Specifically, in step 906, a corresponding equipment combination scheme is selected for each building in the cooling/heating unit database, in step 908, a system efficiency curve of the building under different load rates is calculated in combination with the equipment working efficiency curve, then in step 910, a system efficiency curve of the building from time to time is obtained according to the load rates of the building from time to time, in step 912, the system efficiency curve from time to time is multiplied by the cooling/heating demand of the building from time to obtain annual electricity consumption power of the building from time to time, then in step 914, the electricity consumption power from time to time is multiplied by the carbon emission coefficient of unit electricity consumption, so as to obtain carbon emission amount of the building cooling/heating system operation, and the carbon emission amount from time to time and the total carbon emission amount of all buildings from time to time before the building the regional cooling/heating system is built in the region are obtained.

In step 915, the total amount of carbon emissions before and after the zone is established in the zone refrigeration/heating system is subtracted, and the resulting value characterizes the contribution of the zone refrigeration/heating system to the reduction of carbon emissions. The method 900 then ends at step 916.

The invention also relates to a computer readable medium having stored thereon a computer program and a database for use in the above method, the computer program being executable by a processor to implement the various methods as described above. Computer readable media include, but are not limited to, hard disk, floppy disk, optical disk, flash memory, RAM, ROM, and the like.

Fig. 16 shows a system 50 for implementing the methods described above, which may be a computer or mobile terminal of various types, including a memory 501 and a processor 502, the memory 501 having stored thereon a computer program and a database 20 and an algorithm library 30 for use in the methods described above, the computer program being executable by the processor to invoke related data in the database 20 and related models in the algorithm library 30 to implement the methods described above. The system may further comprise a human-machine interaction interface 503 for receiving user inputs and presenting the user with outputs of the system. Advantageously, a human-computer interaction interface is provided on the touch screen 504, and a user can perform various operations such as input by touching and/or a mouse, a keyboard, and the like. The human-machine interface may also be provided on a conventional computer display 505 or any other suitable device. It should be appreciated that the memory 501 and the processor 502 may be located in a local server or cloud server (e.g., alicloud) as desired.

The accompanying drawings and the foregoing description describe non-limiting specific embodiments of the present invention. Some conventional aspects have been simplified or omitted in order to teach the inventive principles. Those skilled in the art will appreciate variations from these embodiments that fall within the scope of the invention. Those skilled in the art will appreciate that the features described above can be combined in various ways to form multiple variations of the invention. Thus, the present invention is not limited to the specific embodiments described above, but only by the claims and their equivalents.

Claims

1. A method of assessing the potential of building a regional refrigeration/heating system in one or more regions of a target region, comprising the steps of:

a) Receiving a target area input by a user;

b) Receiving one or more areas divided by a user for a target area;

c) Receiving the distribution proportion of the refrigerating/heating demand fraction and the construction feasibility fraction input by a user;

d) If the user uses an automatic meshing tool for dividing the target area in the step B, extracting the pre-calculated refrigeration/heating demand scores and the construction feasibility scores of the one or more areas from a database pre-established for the target area by using a pre-established index;

E) If the user uses a lasso tool for dividing the target area in the step B, extracting the energy use requirement and construction related data of the one or more areas and the national design specification data from the database by using the pre-established index, extracting a building cooling/heating requirement assessment model and a construction feasibility assessment model matched with the target area from an algorithm library pre-established for the target area, and calculating the cooling/heating requirement score and the construction feasibility score of the one or more areas by using the models based on the data;

f) And (C) carrying out weighted average calculation on the refrigeration/heating demand fraction and the construction feasibility fraction obtained in the step D or E based on the distribution proportion of the refrigeration/heating demand fraction and the construction feasibility fraction received in the step C, obtaining engineering potential fractions of the refrigeration/heating system of the construction area of the one or more areas, and displaying at least the engineering potential fractions of the one or more areas to a user.

2. The method of claim 1, further comprising the step of:

g1 If the user uses an automatic meshing tool for the target zone division in step B), extracting from the database the pre-evaluated profitability potential of the one or more zones using a pre-established index;

H1 If in step B the user uses a lasso tool for the target zone, extracting from the database, using a pre-established index, a refrigeration/heating load curve, energy cost data, procurement costs and operating efficiency curves of equipment constituting the zone refrigeration/heating system for each building in the one or more zones, and calculating the profitability potential of the one or more zones based on these data;

i1 Displaying the profitability of the one or more areas to the user.

3. The method of claim 2, wherein the profitability is characterized by a final cost consisting of investment costs and operating costs of building regional refrigeration/heating systems in the one or more regions.

4. The method of claim 1, further comprising the step of:

g2 If an automatic meshing tool is used by the user in step B for dividing the target area, extracting the energy saving and emission reduction potential of the one or more areas which are evaluated in advance from the database by using a pre-established index;

h2 If in step B the user uses a lasso tool for the target zone, extracting from the database, using a pre-established index, the refrigeration/heating load curve of each building within the one or more zones, the operating efficiency curve of the equipment constituting the zone refrigeration/heating system, the climate data of a typical design day, and calculating the energy saving and emission reduction potential of the one or more zones based on these data;

I2 And displaying the energy conservation and emission reduction potential of the one or more areas to a user.

5. The method of claim 4, wherein the energy conservation and emission reduction potential is characterized by a reduction in carbon emissions resulting from building a district refrigeration system in the one or more zones and/or a reduction in urban heat island effects or by a reduction in carbon emissions resulting from building a district heating system in the one or more zones.

6. The method of claim 1, wherein the energy usage requirements and construction-related data for the one or more areas comprise satellite maps, point of interest data, climate data, building footprints, building heights, building categories, water locations, and green space locations within the one or more areas; the national design specification data of the one or more regions includes cooling/heating related design criteria, cooling/heating load calculation related parameters applicable to the one or more regions.

7. The method of claim 1, wherein the database is built by:

determining the boundary of a target area and dividing the target area into a plurality of areas with the same size according to different gears representing the size of a grid by using an automatic grid dividing tool;

Collecting public raw data within a target area from the internet, the public raw data including satellite map, point of interest data, annual climate data, cooling/heating related design criteria, and cooling/heating load calculation related parameters;

using normalized vegetation index and water index to infer water body position and greening position in covered area of each satellite map from the collected green band, infrared band and near infrared band data, and storing the water body position and greening position in the database;

dividing the region represented by each grid under each gear by aiming at an automatic grid by using a construction feasibility evaluation model called from the algorithm library, calculating a corresponding construction feasibility score based on a satellite diagram of the region, and storing the construction feasibility score into the database;

using the building geometric model which is extracted from the algorithm library and consists of a building occupation area presumption model and a building height presumption model, presuming the building occupation area and the building height of each building in the coverage area of each satellite map from red, green and blue wave band data in the collected satellite maps, and storing the building occupation area and the building height into the database;

Using the building category presumption model extracted from the algorithm library, presuming the building category of each building according to the socioeconomic characteristics of the inside and the periphery of each building in the target area extracted from the collected interest point data and the building occupation area and the building height of each building and storing the building category in the database;

calculating the cooling/heating degree days of the target area from the collected annual climate data and storing the cooling/heating degree days in the database;

calculating a refrigerating/heating load curve of each building based on the building occupation area, the building height, the building category, the refrigerating/heating degree days of the target region, the refrigerating/heating related design standard and the refrigerating/heating load calculation related parameters of each building in the target region by using the building refrigerating/heating demand evaluation model extracted from the algorithm library, and storing the refrigerating/heating load curve into the database;

and calculating a corresponding cooling/heating demand fraction for the area represented by each grid under each gear by using a building cooling/heating demand evaluation model of each building according to automatic grid division, and storing the cooling/heating demand fraction into the database.

8. The method of claim 7, wherein the public raw data further includes energy cost data for a target region, the method further comprising the steps of:

calculating an operation expense of a cooling/heating system in an area represented by each grid under each gear for automatic grid division based on the collected energy cost data and the calculated cooling/heating load curve of each building;

collecting the cost of equipment available in the target area for building the district refrigeration/heating system and the operating efficiency curve of said equipment and storing the cost and operating efficiency curve to said database;

calculating a capital expenditure for constructing an area refrigeration/heating system in the area for the area represented by each grid in each gear of the automatic grid division based on the cost and work efficiency curves of the apparatus;

based on the operating and capital expenditures, a final cost of building and operating an area cooling/heating system in the area is calculated for the area represented by each grid in each gear of the automatic grid division, and the final cost is stored in the database.

9. The method of claim 7, further comprising the step of:

Collecting a working efficiency curve of equipment which can be purchased in a target area and is used for constructing an area refrigerating/heating system, and storing the working efficiency curve into the database;

collecting climate data for a typical design day for a target area and storing the climate data in the database;

based on the working efficiency curve of the equipment and the climate data of the typical design day of the target area, the reduction of carbon emission and/or the reduction of urban heat island effect caused by building an area refrigerating/heating system in the area are calculated for the area represented by each grid under each gear of automatic grid division, and the reduction of carbon emission and/or the reduction of urban heat island effect are stored in the database.

10. The method of claim 1 or 7, wherein calculating the cooling/heating demand fraction of an area using the building cooling/heating demand assessment model matched to the target area comprises the steps of:

extracting the refrigeration/heating load curve of each building in the area from the database by utilizing a pre-established index, and calculating the typical design day time-by-time total refrigeration/heating load requirement of all the buildings in the area;

Calculating the energy efficiency ratio of the refrigerating/heating system, the utilization rate of the refrigerating/heating unit and the total refrigerating/heating demand of the typical design day based on the total refrigerating/heating load demand of the typical design day time by time;

the energy efficiency ratio of the refrigerating/heating system, the utilization rate of the refrigerating/heating unit and the total quantity of refrigerating/heating demands on the typical design day are normalized to be divided into fractions of N, and the average value of the fractions is calculated and taken as the refrigerating/heating demand fraction of the area.

11. The method of claim 10, wherein N is an integer multiple of 10.

12. The method of claim 1 or 7, wherein the construction feasibility assessment model that matches the target region calculates a construction feasibility score for the region based on satellite maps of the one or more regions.

13. The method of claim 12, wherein the construction feasibility assessment model matching the target region is obtained by:

preparing a satellite map dataset of the regional scale of the region of the same type as the target region;

randomly selecting two satellite images from the satellite image data set, comparing the satellite images with an expert, selecting a satellite image of which the represented area is more suitable for building an area refrigerating/heating system from the two satellite images by the expert, and repeating the steps until the preset times are reached;

Calculating the construction feasibility score of the area represented by each satellite map according to the expert comparison result;

calculating the volume ratio, the building category ratio, the vegetation coverage, the water coverage and the road width of the area represented by each satellite map;

carrying out nonlinear fitting modeling on the relation among the volume rate, the building category occupation ratio, the vegetation coverage, the water coverage and the road width of the area represented by the satellite map and the construction feasibility score of the corresponding area to obtain an initial evaluation model;

training an initial evaluation model by using a first preset number of satellite graphs, so that the initial evaluation model learns the volume rate, the building category ratio, the vegetation coverage, the water coverage and the relation between the road width and the construction feasibility score of the corresponding area of each satellite graph and automatically adjusts the parameters of the model;

and carrying out construction feasibility assessment on a second preset number of satellite images which are not learned by the assessment model after adjustment, comparing the obtained construction feasibility score of the area represented by each satellite image with the construction feasibility score of the same satellite image based on the assessment of an industry expert so as to verify the accuracy of the assessment model, repeating the training step if the accuracy does not reach an expected value, otherwise, storing the adjusted assessment model into the algorithm library as a construction feasibility assessment model matched with a target area.

14. The method of claim 7, wherein the building footprint estimation model is a deep learning model obtained by:

preparing a satellite map training set containing building occupation position marks and red, green and blue wave bands;

designing an encoder decoder structure to semantically segment the satellite image;

designing a loss function based on the building boundary and the building floor shape to characterize the gap between the existing model and the ideal state;

obtaining a deep learning model to be trained, selecting a satellite map from a training set, training the deep learning model to predict the building occupation area of a building contained in the satellite map, calculating a loss function, adjusting each parameter of the deep learning model through negative feedback, and repeating the steps until the loss function can not descend any more;

and taking the deep learning model obtained when the loss function can not descend any more as a building floor space presumption model.

15. The method of claim 7, wherein the building height estimation model is a deep learning model obtained by:

preparing a digital surface model containing height information and a satellite map training set of red, green and blue wave bands;

Designing an encoder and decoder structure to convert the input satellite map data into data containing a predicted altitude;

designing a loss function based on building height to characterize the gap between the existing model and the ideal state;

obtaining a deep learning model to be trained, selecting a satellite map from a training set, training the deep learning model to predict the building height of a building contained in the satellite map, calculating a loss function, adjusting each parameter of the deep learning model through negative feedback, and repeating the steps until the loss function can not descend any more;

and taking the deep learning model obtained when the loss function can not descend any more as a building height presumption model.

16. A method of optimizing boundaries of an area suitable for building an area refrigeration/heating system, comprising the steps of:

a) Receiving a target area input by a user;

b) Receiving one or more target areas selected by a user in a target area;

d) Performing the method of claim 1 to obtain engineering potential scores for the one or more target areas;

e) Trimming the boundaries of the one or more target areas by automatically adding or subtracting sub-areas to obtain new one or more target areas;

f) Repeating the step d, and calculating engineering potential scores of the new one or more target areas;

g) Comparing the engineering potential score of the new one or more target areas with the engineering potential score of the one or more target areas prior to adjustment;

h) And (c) repeating the steps e, f and g if the engineering potential score of the new one or more target areas is greater than the engineering potential score of the one or more target areas before adjustment, otherwise, displaying the boundary of the new one or more target areas before the last adjustment to the user as an optimized boundary.

17. A computer readable medium, characterized in that it stores a computer program and databases and algorithm libraries for use in the method according to any of claims 1-16, the computer program being executable by a processor to implement the method according to any of claims 1-16.

18. A system comprising a memory and a processor, wherein the memory has stored thereon a computer program executable by the processor to implement the method of any one of claims 1-16, and a database and algorithm library for use in the method of any one of claims 1-16.

19. The system of claim 18, further comprising a human-machine interaction interface for receiving user input and presenting output of the system to a user.

20. The system of claim 18 or 19, wherein the memory and processor are located in a cloud server.