Methods, devices, equipment and media for calculating emission inventories of food service sources
By obtaining business license registration information and network point data from catering enterprises, and combining grid-based statistics with the scale and cuisine information of catering enterprises, the problem of uncertainty and high information acquisition cost in the calculation of emission inventory in the catering industry has been solved, and more accurate calculation of catering source emissions and improved spatial resolution have been achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BEIJING MUNICIPAL ENVIRONMENTAL MONITORING CENT
- Filing Date
- 2023-02-16
- Publication Date
- 2026-07-17
AI Technical Summary
Existing methods for calculating emissions inventories in the catering industry suffer from significant uncertainty in results or high information acquisition costs, especially when the distribution of catering businesses differs from the distribution of the population in urban spatial allocation, leading to substantial errors.
By acquiring the business license registration information and network point data of catering enterprises in the target area, they are assigned to grids based on their business addresses. Combining enterprise size and cuisine information, the pollutant emissions of various types of catering enterprises are calculated. Large restaurants are matched one by one, while small restaurants are processed in a grid-based manner to form files of business addresses, cuisines and sizes.
It has improved the accuracy and spatial resolution of the emission inventory of catering sources, reduced the uncertainty of the accounting results, lowered the cost of information acquisition and updating, and realized the transformation from total emissions at the city scale to point source accounting.
Smart Images
Figure CN116151568B_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of environmental protection technology, and in particular to a method, apparatus, electronic equipment and medium for calculating emission inventories of catering sources. Background Technology
[0002] There are two main technologies for calculating emission inventories in the catering industry: one is to use emission coefficients based on population size and resident population data, and the other is to obtain information on catering enterprises in the region through a census, and calculate based on different scales (i.e., the number of stoves) and cuisine information, as well as their corresponding emission factors.
[0003] The former method is relatively simple, but the results are highly uncertain, and it is usually used to calculate total emissions at the city scale. Its urban spatial allocation is mainly based on the number of permanent residents in administrative districts. Some research or patents use network POI (Point of Information) location data for allocation. In reality, catering businesses are generally concentrated in urban core areas and commercial complexes, and their distribution characteristics differ somewhat from the urban population distribution. Therefore, using the permanent resident population of administrative districts as the basis for spatial allocation of catering business emissions has a large margin of error. While using network POI data allocation can characterize the distribution of catering businesses, their emissions depend on key parameters such as scale and cuisine. Therefore, using total emission calculation combined with network POI data allocation can improve spatial resolution, but it cannot solve the problem of high emission uncertainty.
[0004] The latter type of accounting result has relatively less uncertainty and more precise spatial allocation, but it requires a large amount of survey work. Moreover, catering businesses are frequently mobile, so it is costly to conduct comprehensive surveys regularly and data acquisition is difficult to guarantee. Summary of the Invention
[0005] In view of the above problems, the present invention provides a method for calculating the emission inventory of catering sources to solve the above technical problems.
[0006] One aspect of this disclosure provides a method for calculating a food service source emissions inventory, comprising: under authorized conditions, acquiring all business license registration information and network point data of food service enterprises within a target area, wherein the business license registration information of food service enterprises includes the name, business address, and size of the food service enterprise, and the network point data includes the name, business address, and cuisine of the food service enterprise; matching the business license registration information and network point data of food service enterprises of extra-large and large sizes; dividing the target area into multiple grids, and allocating all the business license registration information of food service enterprises and the network point data to each grid based on the business address; and based on the grid... The data includes restaurant business license registration information and network location data. It calculates the number of restaurants of various sizes within the grid, and the number of restaurants belonging to various cuisines within each size category. Specifically, based on the matched data of extra-large and large restaurant business license registration information and network location data, it calculates the number of extra-large and large restaurants and the number of their respective cuisines. Based on the number of restaurants of various sizes, the number of restaurants belonging to various cuisines within each size category, and air pollutant emission factors, it calculates the air pollutant emissions of restaurants of various sizes and cuisines within the grid.
[0007] According to embodiments of this disclosure, the matching of catering enterprise business license registration information and network location data of extra-large and large scale includes: matching the catering enterprise names of the catering enterprise business license registration information and the network location data; and filtering catering enterprise business license registration information and network location data of extra-large and large scale catering enterprises based on the catering enterprise names that are successfully matched.
[0008] According to embodiments of this disclosure, the method further includes: calculating the distance difference between the business addresses of the business license registration information and network location data of the extra-large and large catering enterprises, and filtering the business license registration information and network location data whose distance difference is less than a preset distance; calculating the similarity between the business addresses and the catering enterprise names of the business license registration information and network location data of the extra-large and large catering enterprises, and filtering the business license registration information and network location data whose business address name similarity is greater than a second threshold and whose catering enterprise name similarity is greater than a first threshold, and recording them as the final business license registration information and network location data of the extra-large and large catering enterprises.
[0009] According to embodiments of this disclosure, the process of counting the number of catering enterprises of various sizes within the grid based on the business license registration information and network point data of the catering enterprises in the grid, and counting the number of catering enterprises belonging to various cuisines within each size of catering enterprise, includes: based on the business license registration information and network point data of the remaining catering enterprises other than the extra-large and large catering enterprises, counting the first proportion of medium-sized, small, and micro-small-sized catering enterprises within the grid, and calculating the product of the first proportion and the total number of catering enterprises within the grid to obtain the number of medium-sized, small, and micro-small-sized catering enterprises within the grid; based on the cuisine information included in the network point data of medium-sized, small, and micro-small-sized catering enterprises, counting the second proportion of catering enterprises of different cuisines within each size of catering enterprise, and calculating the product of the second proportion and the total number of catering enterprises within the grid to obtain the number of medium-sized, small, and micro-small-sized catering enterprises of various cuisines within the grid.
[0010] According to embodiments of this disclosure, the calculation of air pollutant emissions from catering enterprises of various sizes and cuisines within the grid, based on the number of catering enterprises of various sizes, the number of catering enterprises of various cuisines within each size, and air pollutant emission factors, includes: obtaining the oil consumption and first air pollutant emission factors of catering enterprises of various sizes and cuisines, and calculating the first air pollutant emissions of catering enterprises of various sizes and cuisines; obtaining the number of stoves and second air pollutant emission factors of catering enterprises of various sizes and cuisines, and calculating the second air pollutant emissions of catering enterprises of various sizes and cuisines.
[0011] According to embodiments of this disclosure, calculating the first air pollutant emissions of catering enterprises of various sizes and cuisines includes:
[0012]
[0013] Among them, P i S represents the first air pollutant emission of cuisine i. j N represents the amount of oil used by a restaurant of size j serving cuisine i. i The number of catering enterprises of size j representing cuisine i, and A representing the first air pollutant emission factor per unit of oil consumption.
[0014] According to embodiments of this disclosure, calculating the second air pollutant emissions of catering enterprises of various sizes and cuisines includes:
[0015]
[0016] Among them, Q in represents the second air pollutant emission of cuisine i. j T represents the number of stoves in a catering enterprise of size j representing cuisine i. j Ni represents the working hours of a j-sized catering enterprise of cuisine i, Ni represents the number of j-sized catering enterprises of cuisine i, and B represents the second atmospheric pollutant emission factor per unit stove working hour.
[0017] The second aspect of this disclosure provides a device for calculating emission inventory of catering sources, comprising: a data acquisition module, used to acquire, under authorized conditions, all catering business license registration information and network point data within a target area, wherein the catering business license registration information includes the catering business name, business address, and scale, and the network point data includes the catering business name, business address, and cuisine; a data matching module, used to match catering business license registration information and network point data of extra-large and large scale; and a grid division module, used to divide the target area into multiple grids and allocate all the catering business license registration information and network point data to each grid based on the business address; The classification module is used to count the number of catering enterprises of various sizes within the grid based on the business license registration information and network point data of the catering enterprises in the grid, and to count the number of catering enterprises of various cuisines within each size of catering enterprise. Specifically, based on the matched business license registration information and network point data of extra-large and large catering enterprises, the module counts the number of extra-large and large catering enterprises and the number of their respective cuisines. The calculation and statistics module is used to calculate the air pollutant emissions of catering enterprises of various sizes and cuisines in the grid based on the number of catering enterprises of various sizes, the number of catering enterprises of various cuisines within each size of catering enterprise, and the air pollutant emission factor.
[0018] A third aspect of this disclosure provides an electronic device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the various steps of the food service source emission inventory calculation method of any of the first aspects.
[0019] A fourth aspect of this disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the food service source emission inventory calculation methods of the first aspect.
[0020] The above-described at least one technical solution adopted in the embodiments of this disclosure can achieve the following beneficial effects:
[0021] The emission inventory calculation method for catering sources provided in this disclosure integrates the technical solution of combining business license registration information and network point data. To merge the scale information of business license registration information and the cuisine information of network point data into a single set of data, a gridded statistical method based on address information is adopted, thereby solving the problem of difficulty in matching and integrating the characteristic parameters of multi-source data. To further improve the accuracy of the calculation results, this method differentiates between large and small restaurants: large restaurants are matched one-by-one, while small restaurants are matched using a gridded approach, and matching function program code has been developed. This creates a file of the operating address, cuisine, and scale of each large restaurant, while simultaneously improving the accuracy of emission calculation for large restaurants.
[0022] This method addresses several key issues. First, it solves the problems of high cost and difficulty in information acquisition and updating. Second, it provides information on the scale and cuisine of catering enterprises needed for calculating the atmospheric pollutant emission inventory from catering sources, reducing the uncertainty of the calculation results compared to methods based on population emission coefficients. Third, it provides information on the addresses of catering enterprises, significantly improving the spatial resolution of the atmospheric pollutant emission inventory from catering sources and shifting the current calculation of atmospheric pollutant emission inventories from the area source form of total emissions at the city scale to point source accounting. Attached Figure Description
[0023] To gain a more complete understanding of this disclosure and its advantages, reference will now be made to the following description taken in conjunction with the accompanying drawings, wherein:
[0024] Figure 1 The illustration shows a schematic diagram of a method for calculating emission inventories of catering sources provided in an embodiment of this disclosure;
[0025] Figure 2 This schematic diagram illustrates a structural block diagram of a food service source emission inventory calculation device provided in an embodiment of the present disclosure;
[0026] Figure 3 The schematic diagram illustrates a structural block diagram of an electronic device provided in an embodiment of the present disclosure. Detailed Implementation
[0027] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.
[0028] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.
[0029] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.
[0030] The accompanying drawings show some block diagrams and / or flowcharts. It should be understood that some blocks or combinations thereof in the block diagrams and / or flowcharts can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when executed by the processor, these instructions can create means for implementing the functions / operations described in these block diagrams and / or flowcharts.
[0031] Therefore, the technology disclosed herein can be implemented in hardware and / or software (including firmware, microcode, etc.). Additionally, the technology disclosed herein can take the form of a computer program product stored on a computer-readable medium, which can be used by or in conjunction with an instruction execution system. In the context of this disclosure, a computer-readable medium can be any medium capable of containing, storing, transmitting, propagating, or transmitting instructions. For example, a computer-readable medium can include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, apparatuses, or propagation media. Specific examples of computer-readable media include: magnetic storage devices, such as magnetic tape or hard disk drives (HDDs); optical storage devices, such as optical discs (CD-ROMs); memories, such as random access memory (RAM) or flash memory; and / or wired / wireless communication links.
[0032] Figure 1 The illustration shows a schematic diagram of a method for calculating the emission inventory of catering sources provided in an embodiment of this disclosure.
[0033] Figure 1 As shown in the embodiments of this disclosure, a method for calculating the emission inventory of catering sources is provided, including S1 to S5.
[0034] S1, under authorization, obtain all catering business license registration information and network outlet data within the target area. Catering business license registration information includes the catering business name, business address and scale, and network outlet data includes the catering business name, business address and cuisine.
[0035] Cuisine includes Chinese food, fast food, hot pot, foreign restaurants, and pastries and beverages; the size of catering enterprises is divided into extra-large, large, medium, small, and micro.
[0036] S2 matches business license registration information and network point data for extra-large and large catering enterprises.
[0037] In this embodiment, the matching of catering enterprise operation license registration information and network point data of extra-large and large scale includes S210~S220.
[0038] S210, the name of a catering enterprise that matches the business license registration information and online store category data.
[0039] S220, based on the catering enterprise name successfully matched catering enterprise business license registration information and network outlet data, filters catering enterprise business license registration information and network outlet data of the scale of extra-large and large catering enterprises.
[0040] Among these methods, matching the restaurant names in the business license registration information and online restaurant category data can be done by determining whether the restaurant names in the matching business license registration information and online restaurant category data are the same; if the restaurant names are the same, the match is successful. Alternatively, matching the restaurant names in the business license registration information and online restaurant category data can be done by calculating the similarity of the restaurant names; if the similarity is greater than a preset threshold, the match is successful.
[0041] S220 may also include S221 to S222.
[0042] S221, calculate the distance difference between the business license registration information and network location data of extra-large and large catering enterprises, and filter the business license registration information and network location data of extra-large and large catering enterprises whose distance difference is less than a preset distance. For example, the distance difference between the business license registration information and network location data is less than 2km.
[0043] S222, calculate the name similarity between the business address and the name of the catering enterprise in the business license registration information and network outlet data of extra-large and large catering enterprises. Select the business license registration information and network outlet data of enterprises with a business address name similarity greater than the first threshold and a catering enterprise name similarity greater than the second threshold as successfully matched data, and record them as the final business license registration information and network outlet data of extra-large and large catering enterprises.
[0044] S3 divides the target area into multiple grids and distributes all catering business license registration information and network point data to each grid based on the business address.
[0045] Optionally, the grid size can be 1km×1km, or the size can be enlarged or reduced according to the actual size of the target area and the fine-grained requirements.
[0046] S4, based on grid-based business license registration information and network point data of catering enterprises, counts the number of catering enterprises of various sizes within the grid, and counts the number of catering enterprises of various cuisines within each size.
[0047] Among them, based on the matching of the business license registration information and network outlet data of extra-large and large catering enterprises, the number of extra-large and large catering enterprises and the number of their respective cuisines are statistically analyzed.
[0048] Furthermore, based on the business license registration information and network point data of catering enterprises other than the aforementioned extra-large and large catering enterprises, a first proportion of medium-sized, small, and micro-sized catering enterprises within the grid is calculated, and the product of the first proportion and the total number of catering enterprises within the grid is calculated to obtain the number of medium-sized, small, and micro-sized catering enterprises within the grid. According to the cuisine information included in the network point data of medium-sized, small, and micro-sized catering enterprises, a second proportion of catering enterprises of different cuisines within each size is calculated, and the product of the second proportion and the total number of catering enterprises within the grid is calculated to obtain the number of medium-sized, small, and micro-sized catering enterprises of various cuisines within the grid. In this embodiment, a differentiated processing method is adopted for large restaurants and small restaurants; that is, large restaurants are matched one-to-one, while small restaurants are matched in a grid-based manner, and matching function program code has been developed. This creates a file of the operating address, cuisine, and size of each large restaurant, while improving the accuracy of emissions calculation for large restaurants.
[0049] S5 calculates the air pollutant emissions of catering enterprises of various sizes and of various cuisines within each size grid, based on the number of catering enterprises of various sizes and the number of catering enterprises of various cuisines within each size grid, as well as the air pollutant emission factors.
[0050] In this embodiment, a first air pollutant emission factor calculated based on oil consumption can be obtained based on literature review, and a second air pollutant emission factor calculated based on the number of stoves can be obtained. Simultaneously, the oil consumption and number of stoves of catering enterprises of various sizes and cuisines within the grid are obtained.
[0051] S5 includes S510 to S520.
[0052] S510: Obtain the oil consumption and primary air pollutant emission factors of catering enterprises of various sizes and cuisines, and calculate the primary air pollutant emissions of catering enterprises of various sizes and cuisines.
[0053] The calculation of primary air pollutant emissions from catering businesses of various sizes and cuisines includes:
[0054]
[0055] Among them, P i S represents the first air pollutant emission of cuisine i. j N represents the amount of oil used by a restaurant of size j serving cuisine i. i The number of catering enterprises of size j representing cuisine i, and A representing the first air pollutant emission factor per unit of oil consumption.
[0056] S520 obtains the number of stoves and the second air pollutant emission factor for catering enterprises of various sizes and cuisines, and calculates the second air pollutant emissions of catering enterprises of various sizes and cuisines.
[0057] The calculation of secondary air pollutant emissions from catering enterprises of various sizes and cuisines includes:
[0058]
[0059] Among them, Q i n represents the second air pollutant emission of cuisine i. j T represents the number of stoves in a catering enterprise of size j representing cuisine i. j Ni represents the working hours of a j-sized catering enterprise of cuisine i, Ni represents the number of j-sized catering enterprises of cuisine i, and B represents the second atmospheric pollutant emission factor per unit stove working hour.
[0060] The emission inventory calculation method for catering sources provided in this disclosure integrates the technical solution of combining business license registration information and network point data. To merge the scale information of business license registration information and the cuisine information of network point data into a single set of data, a gridded statistical method based on address information is adopted, thereby solving the problem of difficulty in matching and integrating the characteristic parameters of multi-source data. To further improve the accuracy of the calculation results, this method differentiates between large and small restaurants: large restaurants are matched one-by-one, while small restaurants are matched using a gridded approach, and matching function program code has been developed. This creates a file of the operating address, cuisine, and scale of each large restaurant, while simultaneously improving the accuracy of emission calculation for large restaurants.
[0061] This method addresses several key issues. First, it solves the problems of high cost and difficulty in information acquisition and updating. Second, it provides information on the scale and cuisine of catering enterprises needed for calculating the atmospheric pollutant emission inventory from catering sources, reducing the uncertainty of the calculation results compared to methods based on population emission coefficients. Third, it provides information on the addresses of catering enterprises, significantly improving the spatial resolution of the atmospheric pollutant emission inventory from catering sources and shifting the current calculation of atmospheric pollutant emission inventories from the area source form of total emissions at the city scale to point source accounting.
[0062] Figure 2 The schematic diagram illustrates a structural block diagram of a food service source emission inventory calculation device provided in an embodiment of this disclosure.
[0063] like Figure 2 As shown in the figure, the catering source emission inventory calculation device 200 provided in this embodiment includes a data acquisition module 210, a data matching module 220, a grid division module 230, a data classification module 240, and a calculation and statistics module 250.
[0064] The data acquisition module 210 is used, under authorized conditions, to acquire all catering business license registration information and network outlet data within the target area. The catering business license registration information includes the catering business name, business address and scale, and the network outlet data includes the catering business name, business address and cuisine.
[0065] The data matching module 220 is used to match the business license registration information and network point data of catering enterprises of extra-large and large scale.
[0066] The grid division module 230 is used to divide the target area into multiple grids and allocate all catering business license registration information and network point data to each grid based on the business address.
[0067] The data classification module 240 is used to collect grid-based catering business license registration information and network point data to count the number of catering businesses of various sizes within the grid, and to count the number of catering businesses of various cuisines within each size. Specifically, based on the matched extra-large and large catering business license registration information and network point data, the module counts the size and cuisine of extra-large and large catering businesses.
[0068] The calculation and statistics module 250 is used to calculate the air pollutant emissions of catering enterprises of various sizes and of various cuisines in the grid based on the number of catering enterprises of various sizes, the number of catering enterprises of various cuisines in each size, and the air pollutant emission factors.
[0069] It is understood that the data acquisition module 210, data matching module 220, mesh generation module 230, data classification module 240, and calculation and statistics module 250 can be implemented in one module, or any one of these modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in one module. According to embodiments of the present invention, at least one of the data acquisition module 210, data matching module 220, mesh generation module 230, data classification module 240, and calculation and statistics module 250 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable manner of integrating or packaging the circuitry, or as hardware or firmware implementations, or as appropriate combinations of software, hardware, and firmware implementations. Alternatively, at least one of the data acquisition module 210, data matching module 220, grid partitioning module 230, data classification module 240, and calculation and statistics module 250 can be at least partially implemented as a computer program module, which can perform the functions of the corresponding module when the program is run by a computer.
[0070] Figure 3 The schematic diagram illustrates a structural block diagram of an electronic device provided in an embodiment of the present disclosure.
[0071] like Figure 3 As shown, the electronic device described in this embodiment includes: electronic device 300 including processor 310 and computer-readable storage medium 320. This electronic device 300 can perform the functions described above (see reference 310). Figure 1 The described method enables the detection of specific operations.
[0072] Specifically, processor 310 may include, for example, a general-purpose microprocessor, an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. Processor 310 may also include onboard memory for caching purposes. Processor 310 may be used for executing reference... Figure 1 The method flow described according to embodiments of this disclosure refers to a single processing unit or multiple processing units performing different actions.
[0073] Computer-readable storage medium 320 may be any medium capable of containing, storing, transmitting, propagating, or transmitting instructions. For example, readable storage media may include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, apparatuses, or propagation media. Specific examples of readable storage media include: magnetic storage devices such as magnetic tape or hard disk drives (HDDs); optical storage devices such as optical discs (CD-ROMs); memories such as random access memory (RAM) or flash memory; and / or wired / wireless communication links.
[0074] Computer-readable storage medium 320 may include computer program 321, which may include code / computer-executable instructions that, when executed by processor 310, cause processor 310 to perform, for example, the above-described combination. Figure 1 The described method and any variations thereof.
[0075] Computer program 321 can be configured to have computer program code, for example, including computer program modules. For example, in an exemplary embodiment, the code in computer program 321 may include one or more program modules, such as 321A, module 321B, ... It should be noted that the division and number of modules are not fixed. Those skilled in the art can use appropriate program modules or combinations of program modules according to the actual situation. When these combinations of program modules are executed by processor 310, the processor 310 can perform, for example, the above-described combinations... Figures 1-2 The described method and any variations thereof.
[0076] According to an embodiment of the present invention, at least one of the data acquisition module 210, data matching module 220, grid partitioning module 230, data classification module 240, and calculation and statistics module 250 can be implemented as a reference. Figure 3 The described computer program module, when executed by processor 310, can perform the corresponding operations described above.
[0077] This disclosure also provides a computer-readable medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.
[0078] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.
[0079] Although this disclosure has been shown and described with reference to specific exemplary embodiments thereof, those skilled in the art will understand that various changes in form and detail may be made to this disclosure without departing from the spirit and scope of the disclosure as defined by the appended claims and their equivalents. Therefore, the scope of this disclosure should not be limited to the above embodiments, but should be defined not only by the appended claims, but also by their equivalents.
Claims
1. A method for calculating an emissions inventory of food service sources, characterized in that, include: Under authorized conditions, obtain all catering business license registration information and network location data within the target area. The catering business license registration information includes the catering business name, business address and scale, and the network location data includes the catering business name, business address and cuisine. Matching business license registration information and network location data for extra-large and large catering enterprises; The target area is divided into multiple grids, and all the business license registration information of the catering enterprises and the network point data are allocated to each grid based on the business address; Based on the business license registration information and network point data of the catering enterprises in the grid, the number of catering enterprises of various sizes in the grid is counted, and the number of catering enterprises of various cuisines in each size is counted. Among them, based on the business license registration information and network point data of extra-large and large catering enterprises obtained by matching, the size and cuisine of extra-large and large catering enterprises are counted. Based on the number of catering enterprises of various sizes and the number of catering enterprises of various cuisines within each size, as well as the air pollutant emission factors, the air pollutant emissions of catering enterprises of various sizes and cuisines in the grid are calculated. The matching scale includes the business license registration information and network location data of extra-large and large catering enterprises, including: Match the restaurant business license registration information with the restaurant name in the network point data; Based on the successful matching of catering enterprise names with the catering enterprise business license registration information and network location data, the catering enterprise business license registration information and network location data of the scale of extra-large and large catering enterprises are filtered; The data includes the registration information of catering business licenses and network point categories based on the grid, the number of catering businesses of various sizes within the grid, and the number of catering businesses belonging to various cuisines within each size category, including: Based on the business license registration information and network point data of the remaining catering enterprises other than the extra-large and large catering enterprises, the first proportion of medium-sized, small and micro-sized catering enterprises in the grid is calculated, and the product of the first proportion and the total number of catering enterprises in the grid is calculated to obtain the number of medium-sized, small and micro-sized catering enterprises in the grid. Based on the cuisine information included in the network point data of medium-sized, small-sized, and micro-sized catering enterprises, the second proportion of catering enterprises of different cuisines in each type of catering enterprise is calculated, and the product of the second proportion and the total number of catering enterprises in the grid is calculated to obtain the number of medium-sized, small-sized, and micro-sized catering enterprises of various cuisines in the grid.
2. The method according to claim 1, characterized in that, The method further includes: calculating the distance difference between the business addresses of the extra-large and large catering enterprises’ business license registration information and network location data, and filtering the extra-large and large catering enterprises’ business license registration information and network location data whose distance difference is less than a preset distance; Calculate the name similarity of the business addresses and the name similarity of the catering enterprises for the extra-large and large catering enterprises. Filter out the business license registration information and network location data of enterprises whose business address name similarity is greater than a first threshold and whose catering enterprise name similarity is greater than a second threshold. Record these as the final business license registration information and network location data of the extra-large and large catering enterprises.
3. The method according to claim 1, characterized in that, The calculation of air pollutant emissions from catering enterprises of various sizes and cuisines within the grid, based on the number of catering enterprises of different sizes, the number of catering enterprises of different cuisines within each size, and air pollutant emission factors, includes: Obtain the oil consumption and primary air pollutant emission factor of catering enterprises of various sizes and cuisines, and calculate the primary air pollutant emissions of catering enterprises of various sizes and cuisines. Obtain the number of stoves and the second air pollutant emission factor for catering enterprises of various sizes and cuisines, and calculate the second air pollutant emissions of catering enterprises of various sizes and cuisines.
4. The method according to claim 3, characterized in that, The process of obtaining the oil consumption and primary air pollutant emission factors of catering enterprises of various sizes and cuisines, and calculating the primary air pollutant emissions of catering enterprises of various sizes and cuisines, includes: Among them, P i S represents the first air pollutant emission of cuisine i. j N represents the amount of oil used by a restaurant of size j serving cuisine i. i denoted by j, the number of catering enterprises of scale j in cuisine i, A represents the first air pollutant emission factor per unit of oil consumption, and m represents the total number of catering enterprise scale levels in cuisine i.
5. The method according to claim 3, characterized in that, The process of obtaining the number of stoves and the second air pollutant emission factor for catering enterprises of various sizes and cuisines, and calculating the second air pollutant emissions of catering enterprises of various sizes and cuisines, includes: Among them, Q i n represents the second air pollutant emission of cuisine i. j T represents the number of stoves in a catering enterprise of size j representing cuisine i. j Ni represents the working hours of a j-sized catering enterprise of cuisine i, Ni represents the number of j-sized catering enterprises of cuisine i, B represents the second atmospheric pollutant emission factor per unit stove working hour, and m represents the total number of catering enterprise scale levels of cuisine i.
6. A device for calculating emission inventory of catering sources, applied to the method as described in any one of claims 1 to 5, characterized in that, include: The data acquisition module is used, under authorized conditions, to acquire all catering business license registration information and network outlet data within the target area. The catering business license registration information includes the catering business name, business address, and scale, and the network outlet data includes the catering business name, business address, and cuisine. The data matching module is used to match the business license registration information and network point data of extra-large and large catering enterprises; The grid division module is used to divide the target area into multiple grids and allocate all the business license registration information of the catering enterprises and the network point data to each grid based on the business address; The data classification module is used to count the number of catering enterprises of various sizes within the grid based on the catering enterprise business license registration information and network point data, and to count the number of catering enterprises of various cuisines within each size of catering enterprise. Specifically, based on the matched catering enterprise business license registration information and network point data of extra-large and large catering enterprises, the module counts the size and cuisine of extra-large and large catering enterprises. The calculation and statistics module is used to calculate the air pollutant emissions of catering enterprises of various sizes and of various cuisines in the grid based on the number of catering enterprises of various sizes, the number of catering enterprises of various cuisines in each size, and the air pollutant emission factor.
7. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements each step of the method for calculating the emission inventory of catering sources according to any one of claims 1 to 5.
8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements each step of the method for calculating the emission inventory of catering sources according to any one of claims 1 to 5.