Carbon emission inversion method and system for cold region city based on multi-source data fusion

Abstract

The application discloses a cold region city carbon emission inversion method and system based on multi-source data fusion, relates to the technical field of city carbon emission monitoring and inversion, and the method acquires heat supply source side operation data, heat exchange station operation data, heat supply pipe network topology, building group basic data, meteorological data and remote sensing heat environment data; heat supply service relations are established and inversion grids are divided according to the heat supply pipe network topology, the service corresponding relation of the heat exchange station and the building group and the spatial distribution of the building group; source side heat carbon, source side allocatable carbon, station side heat carbon, station side heat carbon allocation and building side carbon demand are determined respectively; the carbon emission inversion value of each inversion grid is determined according to the source station difference value and the station building difference value, and a constrained abnormal source candidate result is output, so as to form a cold region city heat supply period carbon emission inversion result constrained by the heat supply service relation.

Description

Technical Field

[0001] This invention relates to the field of urban carbon emission monitoring and inversion technology, and in particular to a method and system for inverting carbon emissions in cold-region cities based on multi-source data fusion. Background Technology

[0002] In cold-region cities, the heating season is long, and the carbon emissions from building complexes are closely related to the operational status of the centralized heating system. Factors such as fuel consumption of the heating source, transmission through the heating pipeline network, regulation at heat exchange stations, building envelope, outdoor low-temperature processes, and snow cover all affect the actual carbon emission levels of different spatial units within a building complex.

[0003] Existing carbon emission accounting methods for cold-region cities are mostly based on energy statistics, building energy consumption inventories, remote sensing inversion, or regional carbon emission factors for estimation. Some methods also integrate multi-source data for spatial allocation of urban carbon emissions. These methods can obtain carbon emission results at the city or block scale, but they usually do not include the heating service relationships between heating sources, heating networks, heat exchange stations, and building complexes in the same inversion process.

[0004] In centralized heating scenarios, existing heating monitoring or building energy consumption analysis methods are mostly used for heat load forecasting, heating regulation, anomaly alarms, or energy-saving evaluation. These methods typically focus on heat, temperature, flow rate, or user-side energy consumption anomalies, making it difficult to perform closed-loop verification of carbon emissions from the heating source, carbon emissions from the heat exchange station, building-side heat demand response, and remote sensing thermal environment observations. Furthermore, when carbon emission inversion results are abnormal, it is difficult to distinguish whether the anomaly originates from the heating source, heating network, heat exchange station regulation, or the end-point heating status of the building complex.

[0005] Therefore, there is an urgent need for a carbon emission inversion method and system for cold-region cities based on multi-source data fusion, in order to form an inversion grid in the heating season scenario of cold-region cities, determine the carbon emission inversion value, and output constrained candidate results of anomaly sources. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of existing carbon emission inversion methods for cold-region cities, which do not incorporate the heating service relationship between heating sources, heating networks, heat exchange stations and building complexes into the same inversion process, making it difficult to perform closed-loop verification and distinguish anomaly sources. This invention provides a carbon emission inversion method and system for cold-region cities based on multi-source data fusion, which can form an inversion grid, determine carbon emission inversion values ​​and output constrained anomaly source candidate results in the context of heating season in cold-region cities.

[0007] The objective of this invention is achieved through the following measures: In a first aspect, the present invention provides a method for carbon emission retrieval in cold-region cities based on multi-source data fusion, comprising the following steps: Acquire operational data of the heating source side, heat exchange station operation data, heating pipeline topology, building complex basic data, meteorological data and remote sensing thermal environment data of the target cold-region city during the target period of the heating season; Based on the heating network topology, the service correspondence between heat exchange stations and building groups, and the spatial distribution of building groups, heating service relationships are established, and inversion grids for calculating carbon emissions are divided according to the heating service relationships. The amount of carbon supplied for heating on the source side is determined based on the operating data of the heating source side, and the amount of carbon that can be allocated to each heat exchange station on the source side is determined according to the topology of the heating pipeline network. The amount of carbon supplied for heating on the station side is determined based on the heat exchange station operation data, and the amount of carbon supplied for heating on the station side is allocated to the corresponding inversion grid according to the heating service relationship to obtain the carbon supply allocation for heating on the station side. Based on the basic data of the building complex, meteorological data, remote sensing thermal environment data, and heating season time attributes, the building-side carbon demand of each inversion grid is determined; Based on the difference between the available carbon on the source side and the carbon on the station side heating supply, and the difference between the carbon allocation of the station side heating supply and the carbon demand on the building side, the carbon emission inversion value of each inversion grid is determined. Then, combined with the temporal variation, spatial distribution and matching of the difference with the heating service relationship, the constrained candidate results of abnormal sources are output.

[0008] Further, the step of establishing heating service relationships and dividing an inversion grid for calculating carbon emissions based on the heating network topology, the service correspondence between heat exchange stations and building groups, and the spatial distribution of building groups includes: obtaining a service list of heat exchange stations, heating network connection relationships, building outlines, building heating areas, and building heat inlet locations; determining the heat exchange stations corresponding to each building group based on the heating network connection relationships and the building heat inlet locations; when a building group corresponds to two or more heat exchange stations, determining the main service heat exchange station or the service proportion of each heat exchange station based on the building heating area, heat metering ratio, or network connection distance; recording the correspondence between heat sources and heat exchange stations, between heat exchange stations and building groups, and between building groups and spatial locations to obtain heating service relationships; and dividing an inversion grid based on the heating service relationships, the spatial distribution of building groups, and a preset spatial scale.

[0009] Further, the step of determining the source-side heating carbon content based on the heat source-side operating data and determining the source-side allocable carbon content allocated to each heat exchange station according to the heating network topology includes: determining the source-side heating carbon content based on the heat source-side operating data and the corresponding carbon emission factor; determining the unit heating carbon intensity of the heat source based on the heat output of the heat source and the source-side heating carbon content; and allocating the source-side heating carbon content to downstream heat exchange stations according to the heating network topology and the connection relationship between each heat exchange station and the heat source, thereby obtaining the source-side allocable carbon content for each heat exchange station.

[0010] Further, the step of determining the carbon content of station-side heating based on the heat exchange station operation data and allocating the carbon content of station-side heating to the corresponding inversion grid according to the heating service relationship to obtain the carbon content allocation of station-side heating includes: determining the heat received or output by the heat exchange station based on the heat exchange station operation data; determining the carbon content of station-side heating based on the heat received or output by the heat exchange station and the unit heating carbon intensity of the corresponding heat source; determining the inversion grid corresponding to each heat exchange station according to the heating service relationship; determining the carbon content allocation weight of each inversion grid according to the building heating area, building type, construction year, insulation level of the building envelope, and building spatial distribution; and allocating the carbon content of station-side heating to the corresponding inversion grid according to the carbon content allocation weight to obtain the carbon content allocation of station-side heating.

[0011] Furthermore, determining the building-side carbon demand of each inversion grid based on the building complex's basic data, meteorological data, remote sensing thermal environment data, and heating season period attributes includes: determining the building heat demand baseline for each inversion grid based on building type, construction year, number of floors, insulation level of the building envelope, heating area, usage status, outdoor temperature, wind speed, solar radiation, snowfall status, and duration of continuous low temperatures; performing building thermal inertia correction on the building heat demand baseline based on the duration of continuous low temperatures and the thermal response delay time after heating adjustment; correcting the corrected building heat demand baseline based on remote sensing thermal environment data; and converting the corrected building heat demand baseline into building-side carbon demand based on the unit heating carbon intensity of the corresponding heat exchange station.

[0012] Furthermore, the step of correcting the modified building heat demand baseline based on remote sensing thermal environment data includes: acquiring the surface temperature, snow cover status, underlying surface type, and building density within the inversion grid; eliminating non-building thermal response areas based on building outlines and underlying surface types; correcting the representation intensity of the surface temperature on the building heat dissipation status based on the snow cover status; comparing the corrected surface temperature with the surface temperature of adjacent inversion grids within the service area of ​​the heat exchange station corresponding to the same heating service relationship to obtain a remote sensing thermal state label; and adjusting, lowering, or maintaining the building heat demand baseline based on the remote sensing thermal state label.

[0013] Further, determining the carbon emission inversion value for each inversion grid based on the difference between the source-side allocable carbon and the station-side heating carbon, and the difference between the station-side heating carbon allocation and the building-side carbon demand, includes: comparing the source-side allocable carbon with the corresponding heat exchange station's station-side heating carbon to obtain the source-station difference; comparing the station-side heating carbon allocation with the corresponding inversion grid's building-side carbon demand to obtain the station-building difference; and based on the distribution of the station-building difference within the service area of ​​the same heat exchange station, when... When the station-building difference is in the opposite direction, the carbon allocation weight is adjusted. When the station-building difference is in the same direction and the relative value of the station-building difference of each inversion grid deviates from the average value within the service range of the heat exchange station by no more than a preset threshold, the carbon demand on the building side is corrected as a whole, and the carbon allocation of the station-side heating or the carbon demand on the building side is corrected. The corrected carbon allocation of the station-side heating and the corrected carbon demand on the building side are fused with confidence weights and normalized closure constraints within the service range of the heat exchange station are applied to obtain the carbon emission inversion value of the corresponding inversion grid.

[0014] Furthermore, by combining the temporal variation, spatial distribution, and matching of the difference with the heating service relationship, constrained anomaly source candidate results are output, including: extracting the duration, direction of change, spatial concentration location, synchronous occurrence range, and reverse change state after heating adjustment of the source station difference and station-building difference; when multiple downstream heat exchange stations synchronously exhibit source station differences, anomalies on the heating source side or main pipeline network are written into the anomaly source candidate results; when the difference increases progressively along the downstream direction of the heating pipeline network, anomalies in pipeline heat loss or hydraulic imbalance are written into the anomaly source candidate results; when multiple inversion grids within the service area of ​​the same heat exchange station synchronously exhibit station-building differences in the same direction, the adjustment deviation of the heat exchange station is written into the anomaly source candidate results; when the difference is concentrated in a single building group or a small number of adjacent inversion grids, and the remote sensing thermal environment data is inconsistent with the surrounding building group, anomalies in building envelope heat loss or user-side heating consumption are written into the anomaly source candidate results; and eliminating anomaly source candidates that do not match the heating service relationship to obtain constrained anomaly source candidate results.

[0015] Furthermore, when heat measurement data, room temperature data, or remote sensing thermal environment data are missing within the inversion grid, the method further includes: identifying the data type, missing time period, and missing inversion grid corresponding to the missing data; within the service area of ​​the heat exchange station corresponding to the same heating service relationship, selecting adjacent inversion grids that match in terms of building type, construction year, heating area, building envelope insulation level, and meteorological response relationship as comparable benchmark grids; supplementing the building-side carbon demand of the missing inversion grid based on the building-side carbon demand of the comparable benchmark grid during the missing time period and the building-side carbon demand of the missing inversion grid before and after the missing time period; generating a missing data confidence marker based on the matching degree between the data type and the comparable benchmark grid; and marking the corresponding anomaly source candidate result as a candidate result requiring review when the missing data confidence marker is lower than a preset requirement.

[0016] Secondly, the present invention also provides a carbon emission retrieval system for cold-region cities based on multi-source data fusion, comprising: The data acquisition module is used to acquire the operating data of the heating source side, the operating data of the heat exchange station, the topology of the heating network, the basic data of the building complex, the meteorological data and the remote sensing thermal environment data of the target cold-region city during the target period of the heating season; The inversion grid division module is used to establish heating service relationships based on the heating network topology, the service correspondence between heat exchange stations and building groups, and the spatial distribution of building groups, and to divide the inversion grid for calculating carbon emissions according to the heating service relationships. The source-side carbon quantity determination module is used to determine the source-side heating carbon quantity based on the heating source-side operation data, and to determine the source-side distributable carbon quantity allocated to each heat exchange station according to the heating pipeline topology. The station-side carbon allocation module is used to determine the station-side heating carbon amount based on the heat exchange station operation data, and allocate the station-side heating carbon amount to the corresponding inversion grid according to the heating service relationship to obtain the station-side heating carbon allocation amount. The building-side carbon demand determination module is used to determine the building-side carbon demand of each inversion grid based on the building cluster's basic data, meteorological data, remote sensing thermal environment data, and heating season period attributes. The carbon emission inversion and anomaly output module is used to determine the carbon emission inversion value of each inversion grid based on the difference between the source-side allocable carbon and the station-side heating carbon, and the difference between the station-side heating carbon allocation and the building-side carbon demand. It also outputs constrained anomaly source candidate results by combining the time variation, spatial distribution and matching of the difference with the heating service relationship.

[0017] Compared with the prior art, the present invention has the following advantages: Firstly, this invention incorporates operating data from the heating source side, operating data from heat exchange stations, topology of the heating network, basic data of building complexes, meteorological data, and remote sensing thermal environment data into the same inversion process. By establishing heating service relationships between the heating source, heating network, heat exchange stations, building complexes, and inversion grid, the carbon emissions from the source side, the carbon emissions from the station side, and the carbon demand from the building side form a closed-loop verification under the same service relationship constraints. Compared to methods that rely solely on energy statistics or remote sensing inversion, this invention is more suitable for carbon emission inversion scenarios during the heating season in cold-region cities.

[0018] Secondly, by jointly analyzing the source station difference and the station building difference, and combining the time variation, spatial distribution and matching of the difference with the heating service relationship, this invention can limit the source of anomalies to the heating source side, main pipeline network, heat exchange station regulation, building envelope or user-side heating range, and eliminate candidates that do not match the heating service relationship, thereby outputting constrained anomaly source candidate results, which makes it easier for operation and maintenance personnel to locate the source of anomalies.

[0019] Third, this invention incorporates continuous low-temperature duration, snowfall status, remote sensing thermal environment data, and building thermal inertia correction into the calculation of building-side carbon demand, thereby improving the rationality of building heat demand estimation under low-temperature processes and snow cover in cold-region cities. In the case of missing data, the building-side carbon demand is supplemented by a comparable benchmark grid and a missing data confidence marker is generated, so that the inversion results can still be used under missing data conditions, and candidate results of anomalies with low confidence are verified and marked. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A schematic diagram of the overall process of a carbon emission inversion method for cold-region cities based on multi-source data fusion provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of multi-source data composition provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of a heating service relationship topology provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of inversion grid division and heat exchange station service area provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of carbon transfer and difference calculation provided in an embodiment of the present invention; Figure 6This is a schematic diagram of an anomaly source candidate determination process provided by an embodiment of the present invention; Figure 7 This is a schematic diagram illustrating a process for supplementing building-side carbon demand in the event of data loss, as provided in an embodiment of the present invention. Detailed Implementation

[0022] The technical solutions in the embodiments of the present invention will now be described clearly and in detail with reference to the accompanying drawings. In the description of the embodiments of the present invention, unless otherwise stated, "multiple" refers to two or more.

[0023] Before providing a detailed description of the technical solution of this invention, several terms involved in this invention will be explained: Cold-region cities refer to cities with long heating seasons, low outdoor temperatures in winter, and the need for centralized heating. The target heating period refers to the time window during which carbon emissions analysis is conducted for the target cold-region city within the heating season. Heating period attributes refer to the time-related attribute parameters of the target heating period, including at least the duration of the target heating period. The corresponding formula (8) This is used to align various types of data by time to the start and end times of the target heating period and whether the period includes weekdays or holidays, and to influence the state correction coefficient used in formula (8). The value is determined by weighting the duration of weekdays and holidays when the target time period spans both weekdays and holidays. Heating service relationships refer to the service correspondence between heat sources, heat exchange stations, building clusters, and inversion grids. These relationships record which heat exchange stations a heat source supplies heat to, which building clusters a heat exchange station supplies heat to, and which inversion grids correspond to which building clusters. An inversion grid is a computational unit divided within the target cold-region city space for carbon emission inversion. Each inversion grid corresponds to several building clusters and corresponding heat exchange stations. Source-side heating carbon emissions refer to the carbon emissions generated by the heat source during the target heating period. Source-side allocable carbon emissions refer to the source-side heating carbon emissions allocated to each heat exchange station according to the heating network topology. Station-side heating carbon emissions refer to the heating carbon emissions corresponding to the heat exchange station during the target heating period. Station-side heating carbon allocation refers to the carbon emissions after allocating station-side heating carbon emissions to each inversion grid according to the heating service relationships. Building-side carbon demand refers to the carbon emissions corresponding to the heat demand of the building clusters within the inversion grid during the target heating period. The source-station difference refers to the difference between the available carbon on the source side and the carbon supplied by the corresponding heat exchange station. The station-building difference refers to the difference between the carbon allocation for heating on the station side and the carbon demand on the building side of the corresponding inversion grid. The carbon emission inversion value refers to the carbon estimation result of the inversion grid after being corrected for the constraints of the source-station difference and the station-building difference. To maintain consistency in terminology, the above terms are used with a single name in the specific implementation of this specification, and no other synonyms are used.

[0024] refer to Figure 1 , Figure 1 This diagram illustrates the overall process of a carbon emission retrieval method for cold-region cities based on multi-source data fusion, as provided in an embodiment of the present invention. Figure 1 As shown, the method includes steps S101 to S106. The steps are described in detail below with reference to the accompanying drawings.

[0025] Step S101: Obtain the operating data of the heating source side, the operating data of the heat exchange station, the topology of the heating network, the basic data of the building complex, the meteorological data and the remote sensing thermal environment data of the target cold-region city during the target period of the heating season.

[0026] Specifically, the first step is to determine the target heating period for the target cold-region city. This target heating period can be, for example, a heating day, a heating week, or a heating month, and can be set according to the required inversion accuracy. Next, the following six types of data are read through the heating company's ledger interface or the data acquisition and monitoring control system interface.

[0027] like Figure 2 As shown, the data acquired by the system comes from multi-source data fusion 10, including heating source side operation data 11, heat exchange station operation data 12, heating pipeline topology 13, building complex basic data 14, meteorological data 15 and remote sensing thermal environment data 16.

[0028] The data includes, but is not limited to, the following: 11: operating data on the heat source side, including at least one of the following: fuel consumption, heat supply, purchased heat, or heat energy consumption, which can determine the amount of carbon supplied for heating; 12: operating data on the heat exchange station, including at least one of the following: heat received, heat output, flow rate, supply water temperature, return water temperature, and operating time; 13: topology of the heating network, including the connection relationship between the heat source and each heat exchange station, the connection relationship between the heat exchange station and the building complex, and the downstream direction of the heating network; 14: basic data of the building complex, including building type, year of construction, number of floors, insulation level of the building envelope, heating area, usage status, building outline, and location of building heat inlets; 15: meteorological data, including outdoor temperature, wind speed, solar radiation, snowfall status, and duration of continuous low temperatures; and 16: remote sensing thermal environment data, including surface temperature, snow cover status, underlying surface type, and building density.

[0029] It should be noted that when the heat source is a combined heat and power (CHP) source, since the CHP source simultaneously generates both power and heat, its total carbon emissions are denoted as... Not all emissions can be included in the heating supply side. Therefore, emissions should be calculated based on the proportion of heating output in the total carbon emissions. The carbon emissions corresponding to heating are separated and used as the source-side heating carbon content. ,Right now For non-cogeneration heat sources, the result can be obtained directly using formula (2). .

[0030] The heat output ratio is determined according to the following formula (1): (1) In formula (1), It represents the percentage of heat output, with a dimension of one. This indicates the heat supply from a combined heat and power (CHP) source during the target heating period, expressed in gigajoules (GJ). This indicates the amount of electricity generated by the combined heat and power (CHP) source during the same period, expressed in gigajoules. It represents the equivalent heat conversion factor corresponding to power generation, with a dimension of one. Its physical meaning is: when allocating total carbon emissions according to a unified equivalent heat calculation method, how many units of heat energy are equivalent to 1 unit of electrical energy. It is usually determined by the reciprocal of the referenced conventional power generation efficiency, for example, when the referenced power generation efficiency is 40%. Take 2.5 (i.e.) That is, 1 gigajoules of electricity is equivalent to 2.5 gigajoules of heat energy under the conversion caliber. The specific value can be determined according to the actual energy efficiency level of the cogeneration unit or the power generation efficiency standard referenced.

[0031] To facilitate understanding of the application of formula (1), a set of specific numerical values ​​is given as an example: Assume that a certain cogeneration heat source provides heat during the target period of the heating season. It is 2000 gigajoules, and the power generation is It is 600 gigajoules. The value is 2.5 (corresponding to a reference power generation efficiency of 40%), then the heat output ratio is calculated according to formula (1). If the total carbon emissions from the combined heat and power (CHP) source during this period are 350,000 kg of carbon dioxide, then the carbon emissions from the source-side heating, separated from the total carbon emissions according to the aforementioned heating output ratio, are approximately 350,000 × 0.571 ≈ 200,000 kg of carbon dioxide; the remaining approximately 150,000 kg of carbon dioxide is included in the carbon emissions corresponding to power generation and is not included in the carbon emissions from the source-side heating.

[0032] Step S102: Based on the heating network topology, the service correspondence between heat exchange stations and building groups, and the spatial distribution of building groups, establish heating service relationships, and divide the inversion grid for calculating carbon emissions according to the heating service relationships.

[0033] The reason for establishing heating service relationships before determining the inversion grid is that during the heating season in cold-region cities, the same heating source may supply heat to multiple heat exchange stations, and the same heat exchange station may supply heat to multiple building clusters. If the inversion grid is directly divided according to administrative regions or equal-area grids, different heat exchange stations and different heating sources will appear within the grid, making it difficult to perform closed-loop verification of source-side heating carbon, station-side heating carbon, and building-side carbon demand under the same constraints. Therefore, the service relationships between heating sources, heat exchange stations, and building clusters are clarified first, and then the inversion grid is divided according to these relationships, so that each inversion grid has a clear affiliation in terms of heating service. Specifically, this step includes the following sub-steps: First, obtain the service list of heat exchange stations, the connection relationship of the heating network, the building outline, the building heating area, and the location of the building heat inlet. Among them, the service list of heat exchange stations refers to the list of heating ranges corresponding to each heat exchange station, which is obtained from the heating company's ledger; the location of the building heat inlet refers to the location where the building connects to the heating network.

[0034] Secondly, based on the heating network connection relationship and the location of the building heat inlet, the heat exchange station corresponding to each building group is determined. Specifically, the heat exchange station is traced upstream from the building heat inlet along the heating network, and the traced heat exchange station is taken as the heat exchange station corresponding to that building group.

[0035] Third, when a building complex corresponds to more than two heat exchange stations, the service ratio of the main heat exchange station or each heat exchange station is determined based on the building's heating area, heat metering ratio, or pipeline connection distance. The system determines the service ratio according to the rules listed in Table 1.

[0036] Table 1. Rules for determining service share in scenarios with multiple heat exchange stations.

[0037] Fourth, the correspondence between heat sources and heat exchange stations, between heat exchange stations and building clusters, and between building clusters and spatial locations is recorded to obtain the heating service relationship. The heating service relationship is used to record which heat exchange stations the heat source supplies heat to, which building clusters the heat exchange stations supply heat to, and which inversion grids correspond to the building clusters.

[0038] Fifth, the inversion grid is divided according to the heating service relationship, the spatial distribution of the building complex, and the preset spatial scale. The preset spatial scale refers to the spatial side length of the inversion grid, which can be set to 500 meters or 200 meters, depending on the building density and inversion accuracy requirements.

[0039] refer to Figure 3 , Figure 3 This diagram illustrates a heating service relationship topology provided by an embodiment of the present invention.

[0040] like Figure 3As shown, the heating service relationship is divided into four levels from top to bottom: Level 1 is the heat source 20; Level 2 is the heat exchange stations, including heat exchange station A 21, heat exchange station B 22, and heat exchange station C 23; Level 3 is the building clusters, with building clusters A1 31 and A2 32 corresponding to heat exchange station A 21, building cluster B1 33 corresponding to heat exchange station B 22, and building clusters C1 34 and C2 35 corresponding to heat exchange station C 23; Level 4 is the inversion grids G1 41 to G6 46, each corresponding to a different building cluster. It should be noted that... Figure 3 This invention illustrates only a typical heating service relationship. In real-world scenarios, the number of heating sources, heat exchange stations, building complexes, and inversion grids can be greater, and the relationships can be more complex. This embodiment of the invention does not impose specific limitations on these aspects.

[0041] refer to Figure 4 , Figure 4 This diagram illustrates an inversion grid division and heat exchange station service area provided by an embodiment of the present invention.

[0042] like Figure 4 As shown, within region R, nine inversion grids, from G141 to G949, are obtained according to a preset spatial scale. Heat exchange stations A21, B22, and C23 are located within region R; the service areas corresponding to these three stations are indicated by dashed lines. Figure 4 In the spatial distribution example shown, the service area of ​​heat exchange station A 21 includes inversion grids G1 41 and G4 44, the service area of ​​heat exchange station B 22 includes inversion grids G2 42 and G5 45, and the service area of ​​heat exchange station C 23 includes inversion grids G3 43 and G6 46; inversion grids G7 47, G8 48, and G9 49 are located outside the service areas of the above three heat exchange stations, corresponding to other heat exchange stations within region R. Figure 4 The heat exchange station and its correspondence with the building complex and inversion grid are not shown. Figure 4 The building clusters numbered 31 to 38 within the inversion grid are used to illustrate the spatial distribution of the building clusters, where building clusters numbered 31 to 35 are... Figure 3 To maintain consistency, building group numbers 36, 37, and 38 indicate additional building groups within area R belonging to other heat exchange stations. It should be noted that... Figure 3 and Figure 4 Different example implementations are shown from the perspectives of both the topological structure and spatial distribution of heating service relationships: Figure 3 The focus is on illustrating the topological correspondence between heat sources, heat exchange stations, building complexes, and inverted grids; Figure 4 This illustration focuses on the spatial coverage relationship between the inversion grid and the service area of ​​the heat exchange station. The embodiments of this invention... Figure 3 and Figure 4The specific correspondences shown are not limited; they are used to illustrate two typical forms of the heating service relationship applicable to the present invention. When an inversion grid spans the service area of ​​two heat exchange stations, the carbon allocation ratio of the inversion grid between the two heat exchange stations is determined according to the rules listed in Table 1.

[0043] Step S103: Determine the amount of carbon supplied for heating on the source side based on the operating data of the heating source side, and determine the amount of carbon that can be allocated to each heat exchange station on the source side according to the topology of the heating pipeline network.

[0044] The reason for determining both the source-side heating carbon content and the source-side allocable carbon content according to the heating network topology in this step is that the total carbon content of the heating source needs to be distributed to each heat exchange station downstream according to the heating network topology in order to compare it with the station-side heating carbon content corresponding to each heat exchange station on the same spatial scale. Specifically, this step includes the following sub-steps: First, the carbon content of heating source side is determined based on the operating data of the heating source side and the corresponding carbon emission factors.

[0045] Calculate the carbon content of the source-side heating according to the following formula (2): (2) In formula (2), This indicates the amount of carbon supplied for heating from the source side, expressed in kilograms of carbon dioxide. This indicates the consumption of the i-th type of fuel or energy; when i corresponds to raw coal or fuel oil, the unit is kilograms; when i corresponds to natural gas, the unit is standard cubic meters; when i corresponds to purchased electricity, the unit is kilowatt-hours; and when i corresponds to purchased heat, the unit is gigajoules. Represents the carbon emission factor corresponding to the i-th fuel or energy type, in units of 1 and 2. Matching. Formula (2) multiplies the consumption amount by the carbon emission factor and then sums them up. The units on both sides of the multiplication are matched, and the unit of the summation term is kilograms of carbon dioxide, so the dimensions are consistent.

[0046] The carbon emission factors mentioned in formula (2) can be referenced from the example values ​​listed in Table 2. The specific values ​​of the system are adopted from the carbon emission factors in the preset carbon emission factor library corresponding to the target time period, target region and energy type. The preset carbon emission factor library includes energy type, applicable region, applicable year, unit and version identifier.

[0047] Table 2 Reference Values ​​for Carbon Emission Factors of Typical Fuels and Energy Sources

[0048] It should be noted that the values ​​listed in Table 2 are example reference values.

[0049] When the heat source is a combined heat and power (CHP) heat source, the carbon emissions corresponding to the heat supply are separated from the total carbon emissions according to the heat output ratio defined in formula (1) as the source-side heat supply carbon amount, that is, the total carbon emissions corresponding to the heat source are multiplied by... The amount of carbon supplied for heating from the source side was then obtained.

[0050] Secondly, the unit heating carbon intensity of the heat source is determined based on the heat output of the heat source and the amount of carbon supplied on the source side. The unit heating carbon intensity is calculated according to the following formula (3): (3) In formula (3), This indicates the unit heating carbon intensity of the heat source, expressed in kilograms of carbon dioxide per gigajoule. Defined by formula (2), the unit is kilograms of carbon dioxide; This indicates the output heat of the heat source during the target heating period, expressed in gigajoules.

[0051] Third, based on the heating network topology and the connection relationship between each heat exchange station and the heat source, the source-side heating carbon is allocated to the downstream heat exchange stations to obtain the source-side allocable carbon for each heat exchange station. The source-side allocable carbon is calculated according to the following formula (4): (4) In formula (4), This represents the amount of carbon available for allocation on the source side corresponding to the kth downstream heat exchange station, in kilograms of carbon dioxide. This represents the amount of heat delivered by the heat source to the kth downstream heat exchange station during the target heating period (determined by the heat metering device record or the heat source ledger, such as the heat corresponding to the metering device reading at the heat source outlet), in gigajoules. and The meaning is the same as that of formula (3).

[0052] It should be noted that formula (4) allocates the carbon amount supplied by the source side according to the proportion of heat received by each downstream heat exchange station, which is equivalent to assuming that the carbon amount supplied by the source side has been included in the carbon amount corresponding to the heat loss of the heating network. When the actual heat loss of the heating network cannot be ignored, the sum of the carbon amount that can be allocated by the source side is not strictly equal to the sum of the carbon amount supplied by each downstream heat exchange station. In the subsequent step S106, the abnormal heat loss of the network is separated by identifying the change pattern of the difference along the downstream direction of the heating network. See the judgment rules for the candidate abnormal sources in step S106 below.

[0053] To facilitate understanding of the actual calculation process of formulas (2) to (4) above, a set of test data calculation examples are given below. Assume that a certain heat source consumes 100 tons of raw coal, i.e., 100,000 kg, during the target heating period. According to Table 2, the carbon emission factor of raw coal is selected as 2.10 kg of carbon dioxide per kg. Then, the carbon content of the heat source during this period is calculated according to formula (2) as follows: Kilograms of carbon dioxide. Assume the heat source outputs heat within the same time period. If the carbon intensity is 3000 gigajoules, then the unit heating carbon intensity is calculated according to formula (3). Kilograms of carbon dioxide per gigajoule. Assuming that the heat source delivers 1200 gigajoules, 1000 gigajoules and 800 gigajoules of heat to downstream heat exchange stations A, B and C in the same period, the source-side available carbon amounts for heat exchange stations A, B and C are calculated according to formula (4) as 84000 kg of carbon dioxide, 70000 kg of carbon dioxide and 56000 kg of carbon dioxide, respectively. The total source-side available carbon amounts for the three heat exchange stations is 210000 kg of carbon dioxide, which is closed with the source-side heating carbon amount.

[0054] Step S104: Determine the station-side heating carbon quantity based on the heat exchange station operation data, and allocate the station-side heating carbon quantity to the corresponding inversion grid according to the heating service relationship to obtain the station-side heating carbon quantity allocation.

[0055] The reason for introducing the carbon allocation for station-side heating is that the service area of ​​a heat exchange station may cover multiple inversion grids. It is necessary to further allocate the carbon allocation for station-side heating to the inversion grids based on differences in the heating area, building type, and insulation level of the building complex, facilitating subsequent comparisons with the building-side carbon demand of each inversion grid. Specifically, this step includes the following sub-steps: First, the amount of heat received or output by the heat exchange station is determined based on the station's operating data.

[0056] Heat exchange station receives heat The water-side heat capacity is determined according to the industry-standard formula: ,in The heat received by the kth heat exchange station is expressed in gigajoules; c is the specific heat capacity of water, taken as 4186 joules per kilogram per kelvin. The density of water is taken as 1000 kg per cubic meter; This refers to the volumetric flow rate on the primary side of the heat exchange station, expressed in cubic meters per second. and These are the primary water supply temperature and return water temperature, respectively, in Kelvin. The target duration for the heating season is expressed in seconds. This is the conversion factor from Joules to gigajoules. (Heat output from the heat exchange station) Calculate using the same method but with secondary side supply water temperature, return water temperature, and volumetric flow rate.

[0057] Secondly, the amount of carbon supplied for heating at the station is determined based on the heat received or output by the heat exchange station and the unit heating carbon intensity of the corresponding heat source.

[0058] Calculate the carbon content for station-side heating according to the following formula (5): (5) In formula (5), This represents the amount of carbon dioxide supplied for heating at the k-th heat exchange station, expressed in kilograms. This represents the heat received or output at the k-th heat exchange station during the target heating period (determined by the heat metering device record or the heat exchange station ledger, for example, the heat corresponding to the metering device reading at the primary inlet of the heat exchange station), in gigajoules. The unit heating carbon intensity corresponding to the heat source is defined by formula (3), with the unit being kilograms of carbon dioxide per gigajoule.

[0059] It should be noted that formula (4) Determined based on records from the heat source side, formula (5) Determined based on records from the heat exchange station; under ideal conditions with no heat loss from the heating network and no measurement deviation. and When the carbon content available for distribution on the source side of the heat exchange station is equal to the carbon content supplied for heating on the station side, the difference between the source and station is zero. However, when there is heat loss in the heating network or measurement deviation, Greater than This results in a source-station difference, which reflects the carbon transfer deviation between the heat source side and the heat exchange station side. This is the physical basis for the subsequent step S106 to identify anomalies on the heat source side, main pipeline network, and pipeline network heat loss.

[0060] Third, the inversion grid corresponding to each heat exchange station is determined based on the aforementioned heating service relationship.

[0061] For example, according to Figure 3 The heating service relationships shown are as follows: Building group A1 31 corresponds to inversion grids G1 41 and G2 42, and building group A2 32 corresponds to inversion grid G3 43. Therefore, heat exchange station A 21 corresponds to inversion grids G1 41, G2 42, and G3 43. Building group B1 33 corresponds to inversion grid G4 44, so heat exchange station B 22 corresponds to inversion grid G4 44. Building group C1 34 corresponds to inversion grid G5 45, and building group C2 35 corresponds to inversion grid G6 46. Therefore, heat exchange station C 23 corresponds to inversion grids G5 45 and G6 46.

[0062] Fourth, the carbon allocation weight of each inversion grid is determined based on the building heating area, building type, year of construction, insulation level of the building envelope, and building spatial distribution.

[0063] The carbon allocation weight is calculated according to the following formula (6): (6) In formula (6), This represents the carbon allocation weight of the i-th inversion grid, with a dimension of one; This represents the sum of building heating areas within the i-th inversion grid, in square meters; This represents the correction factor corresponding to the building type within the i-th inversion grid, with a dimension of one; The value represents the correction coefficient corresponding to the insulation level of the building envelope within the i-th inversion grid, with a dimension of one; the denominator is the sum of all inversion grids within the service area of ​​the heat exchange station. Formula (6) has the same units for both the numerator and denominator, the ratio is dimensionless, and the sum of all inversion grids within the service area of ​​the heat exchange station is... The sum equals 1, the dimensions are reasonable, and the physical meaning is clear.

[0064] In formula (6) and The values ​​for are shown in Table 3.

[0065] Table 3 Correction Factors Corresponding to Building Type and Thermal Insulation Level of Envelope

[0066] It should be noted that the coefficients listed in Table 3 are example values, and the specific values ​​of the system are calibrated based on the building survey data of the target cold-region cities.

[0067] Fifth, the carbon amount of station-side heating is allocated to the corresponding inversion grid according to the carbon amount allocation weight to obtain the carbon amount allocation of station-side heating.

[0068] The carbon allocation for station-side heating is calculated according to the following formula (7): (7) In formula (7), This represents the carbon allocation for station-side heating in the i-th inversion grid, expressed in kilograms of carbon dioxide. Defined by formula (5); As defined by formula (6), the dimension is one. The right side of formula (7) is kilograms of carbon dioxide multiplied by a dimensionless ratio, and the result is in kilograms of carbon dioxide, which is a reasonable dimension.

[0069] To facilitate understanding of the application of formulas (6) and (7), combined with Figure 3The heating service relationship shown is illustrated with a set of specific values ​​for heat exchange station A21 and its corresponding inversion grids G141, G242, and G343. Assuming that the building heating area of ​​the three inversion grids is based on building survey data, the values ​​are as follows: For 20,000 square meters, For 30,000 square meters, The area is 15,000 square meters; all buildings within the three inversion grids are residential buildings (the building type correction factor is taken according to Table 3). The insulation grades of the building envelope are medium, low, and high (the insulation grade correction factor is taken according to Table 3). The calculation process according to formula (6) is as follows: First, calculate the values ​​of each inversion grid. G1 41 is G2 42 is G3 43 is The sum of the three is 68750. Then, the carbon allocation weight is calculated as follows: ; ; The sum of the three is approximately 1.000, which meets the requirements for normalization of the apportionment weights.

[0070] Following step S103, here is an example of test data. Assume that heat exchange station A21 receives heat during the target heating period. The amount is 1180 GJ. That is, of the 1200 GJ supplied by the heat source to heat exchange station A21, about 20 GJ did not reach the heat exchange station due to heat loss in the heating pipeline network. The amount of carbon supplied to the station side is calculated according to formula (5). Kilograms of carbon dioxide. The carbon allocation for station-side heating in each inversion grid is calculated according to formula (7): kilograms of carbon dioxide; kilograms of carbon dioxide; Kilograms of carbon dioxide; the sum of the three is approximately 82,600 kilograms of carbon dioxide, which is kept closed with the carbon content of the station-side heating, further verifying the normalization effect of formula (6).

[0071] Step S105: Determine the building-side carbon demand of each inversion grid based on the basic data of the building complex, meteorological data, remote sensing thermal environment data, and heating season time attributes.

[0072] The reason for introducing building-side carbon demand is that the carbon allocation for station-side heating only reflects the carbon amount distributed from the heating side to the inversion grid. However, the actual heat demand of buildings is closely related to meteorological conditions, building envelope, building thermal inertia, and the surface thermal environment observed by remote sensing. Therefore, it is necessary to independently estimate a carbon demand from the building side and compare it with the carbon allocation for station-side heating to identify the differences between the heating side and the building side. Specifically, this step includes the following sub-steps: First, the building heat demand baseline for each inversion grid is determined based on the building type, year of construction, number of floors, insulation level of the building envelope, heating area, usage status, outdoor temperature, wind speed, solar radiation, snowfall status, and duration of continuous low temperature.

[0073] The building heat demand baseline is calculated using a steady-state heat transfer model based on the building envelope. This steady-state heat transfer model assumes that the building envelope's thermal resistance and heat transfer coefficient remain stable during the target period, and calculates the equivalent heat transfer coefficient by integrating factors such as heat transfer from the envelope, air infiltration heat transfer, and solar radiation heat gain. The model construction process is as follows: First, the basic heat transfer coefficient is pre-calibrated for each building type and construction year combination. Then, the basic heat transfer coefficient is corrected based on the insulation level, number of floors, wind speed, solar radiation, and snowfall status of the envelope to obtain the equivalent heat transfer coefficient. This model can perform the calculation of the building heat demand baseline because the thermal characteristics of the building envelope change little during the target heating period, and the steady-state heat transfer assumption can effectively approximate the actual heat transfer process. The building heat demand baseline is calculated according to the following formula (8): (8) In formula (8), This represents the building heat requirement reference for the i-th inversion grid, in gigajoules; This represents the comprehensive equivalent heat transfer coefficient of the building envelope calculated based on the heating area for the i-th inversion grid (its physical meaning is the equivalent heat loss coefficient calculated based on the heating area after correction for the comprehensive heat transfer area of ​​the building envelope, air infiltration heat transfer, and solar radiation), with units of watts per square meter per Kelvin. Its typical value range is 0.4 to 2.0 watts per square meter per Kelvin, with smaller values ​​taken for higher insulation grades and larger values ​​taken for lower insulation grades. This represents the sum of building heating areas within the i-th inversion grid, in square meters; This indicates the indoor design temperature, in Kelvin. The value is taken according to the current heating design standard. For example, 18 degrees Celsius is taken for residential buildings, which is 291.15 Kelvin, and 20 degrees Celsius is taken for public buildings, which is 293.15 Kelvin. The specific value can be set according to the building type and local heating design standard. This indicates the outdoor temperature, expressed in Kelvin. Indicates the duration of the target heating period, in seconds; This represents the usage status correction factor, with a dimension of one. Its typical value rules are as follows: 1.00 when the building is continuously heated, 0.80 to 0.95 when the vacancy rate is high during holidays, and 0.50 to 0.80 when the building is seasonally vacant for a long period of time. It can be calibrated based on building usage records or building usage survey data of the target cold-region city. This is the conversion factor from joules to gigajoules.

[0074] It should be noted that in formula (8) and Although Kelvin is used as the unit, the difference between the two only reflects the difference between indoor and outdoor temperatures, and the difference value is consistent with that when the difference is made in degrees Celsius; all factors on the right side of formula (8) are multiplicative, and the dimensions remain consistent in multiplication and division operations.

[0075] Secondly, based on the duration of continuous low temperatures and the thermal response delay time after heating adjustment, the building thermal inertia correction is applied to the building thermal inertia baseline. Building thermal inertia refers to the physical characteristic that the building envelope and indoor air have a delayed temperature response when the external temperature changes. When the duration of continuous low temperatures is long, the heat stored inside the building is continuously consumed, and the corrected heat demand needs to be adjusted upwards based on the building thermal inertia baseline. When the thermal response delay time after heating adjustment is long, the building thermal inertia baseline does not immediately decrease after adjustment, and a high heat demand level needs to be maintained for a period of time. The building thermal inertia correction is performed according to the following formula (9): (9) In formula (9), This represents the building's baseline heat requirement after thermal inertia correction, in gigajoules. This represents the thermal inertia correction factor, which has one dimension. Its values ​​are shown in Table 4.

[0076] Table 4. Building Thermal Inertia Correction Factors Corresponding to Duration of Continuous Low Temperature

[0077] It should be noted that, Simultaneously, the determination is based on two factors: the duration of continuous low temperature and the thermal response delay time after heating adjustment. The two factors are considered simultaneously in the following manner: (I) First, the basic data is obtained by referring to Table 4 based on the current duration of continuous low temperature. Value; (ii) When the current time is within the heat response delay period after the start time of heating regulation (the delay duration of the heat response delay period) For example, an adjustment period of 2 to 4 hours can be taken (the specific time can be determined based on the heat capacity calibration of the building envelope), keeping the values ​​obtained before adjustment. The value remains unchanged; the table will not be immediately updated based on the adjusted continuous low-temperature duration. (iii) After the thermal response delay period, re-check Table 4 and update it according to the current continuous low temperature duration. Through the above methods, This also reflects the impact of continuous low temperature duration on building heat storage consumption and the impact of thermal response delay on the lag change in building temperature. The example values ​​listed in Table 4 can be calibrated based on measured thermal response data of buildings in the target cold-region city.

[0078] Third, the thermal inertia-corrected building heat demand baseline is adjusted based on remote sensing thermal environment data. The specific adjustment process is as follows: (i) Obtain the surface temperature, snow cover status, underlying surface type and building density within the inversion grid.

[0079] (ii) Eliminate non-building thermal response areas based on the building outline and underlying surface type, such as water bodies, green spaces and bare soil, which do not reflect building heat dissipation.

[0080] (iii) Adjust the representation strength of surface temperature on building heat dissipation based on snow cover status. That is, when the snow cover is thick, the representation ability of surface temperature on building roof heat dissipation decreases, and its weight is reduced accordingly; when there is no snow cover, the original representation strength of surface temperature is maintained.

[0081] (iv) The corrected surface temperature is compared with the surface temperature of adjacent inversion grids within the service area of ​​the heat exchange station corresponding to the same heating service relationship to obtain the remote sensing thermal state label. The rules for determining and processing the remote sensing thermal state label are shown in Table 5.

[0082] Table 5 Rules for Determining and Processing Remote Sensing Thermal Status Markers

[0083] (v) Adjust, lower, or maintain the building heat demand baseline based on the remote sensing thermal state markers, and obtain the remotely corrected building heat demand baseline according to the following formula (10): (10) In formula (10), This represents the reference building heat demand after remote sensing correction, in gigajoules. This represents the remote sensing correction coefficient, with a dimensionless value of one. The specific value is the difference between the corrected surface temperature of this inversion grid and the average surface temperature of adjacent inversion grids within the same service area. Confirmed: When When not greater than -3 Kelvin Take 1.10, when When the value is between -3 Kelvin (excluding) and -1.5 Kelvin (including), Take 1.05, when When the value is between -1.5 Kelvin (excluding) and +1.5 Kelvin (excluding), Take 1.00, when When the value is between +1.5 Kelvin (inclusive) and +3 Kelvin (exclusive) Take 0.95, when When not less than 3 Kelvin The value is set to 0.90. This value selection rule is consistent with the 5% to 10% upward or downward adjustment range given in Table 5, i.e., the larger the temperature difference, the larger the upward or downward adjustment. It should be noted that the correction object of the remote sensing thermal environment data in this invention is the building thermal response temperature after excluding non-building thermal response areas, not the original surface temperature. In the scenario of heating in cold cities, when the building thermal response temperature of a certain inversion grid is lower than that of the adjacent inversion grid, it indicates that the building envelope of that grid has good thermal insulation performance or insufficient heat supply, and the actual indoor heat loss is small. Therefore, its building heat demand baseline is increased to compensate for possible insufficient heating. When the building thermal response temperature is higher than that of the adjacent inversion grid, it indicates that the building envelope of that grid has large heat dissipation or heat leakage, and a lot of indoor heat is lost to the outside through the building envelope. Therefore, its building heat demand baseline is decreased to avoid overestimating the sufficiency of heating. Before calculating the remote sensing correction coefficients, snow-covered pixels, cloud-obscured pixels, and non-building surface pixels were removed for quality control, and only valid building thermal response pixels were retained for the correction calculation.

[0084] Fourth, based on the unit heating carbon intensity of the corresponding heat exchange station, the corrected building heat demand baseline is converted into building-side carbon demand. The building-side carbon demand is calculated according to the following formula (11): (11) In formula (11), This represents the building-side carbon demand for the i-th inversion grid, in kilograms of carbon dioxide. The unit heating carbon intensity of the corresponding heat source for the corresponding heat exchange station is defined by formula (3).

[0085] To facilitate understanding of the application of formulas (8) to (11), we will use the inversion grid G141 corresponding to the aforementioned heat exchange station A21 as an example and provide a set of specific values ​​for illustration. Assume the building heating area within G141... For a building area of ​​20,000 square meters, the equivalent heat transfer coefficient of the building envelope is... A value of 1.2 watts per square meter per Kelvin is used, corresponding to a medium insulation level; the target heating period is 7 days. Seconds; average indoor design temperature during the target time period Take 291.15 Kelvin, which is equivalent to 18 degrees Celsius, as the standard value for residential buildings, and the average outdoor temperature. Take 269.15 Kelvin, which is equivalent to -4 degrees Celsius. The difference between the two is... Equal to 22 Kelvin; using state correction factor Taking 1.00, the building is continuously heated during this period. Then, the building's baseline heat demand is calculated using formula (8): Gironage. During this calculation process, The watts, which represent the building's total heat dissipation capacity of approximately 528 kilowatts, are multiplied by 604,800 seconds to obtain the result. Joule, divided by The gigajoules dimension is obtained, and the dimensional conversion is reasonable.

[0086] Assuming that the duration of continuous low temperature for G1 41 falls within the range of 12 to 24 hours during this period, the thermal inertia correction factor is taken according to Table 4. The value is 1.10; assuming the current time is not within the heat response delay period after the start of any heating regulation, i.e. The time period has passed. The building heat demand baseline, corrected for thermal inertia, is calculated using formula (9): Gigajo.

[0087] Assume the difference between the G1 41 corrected surface temperature and the average surface temperature of adjacent inversion grids within the same station's service area. If the value falls within the range of -1.5 Kelvin to +1.5 Kelvin, the remote sensing thermal state is marked as normal according to Table 5, and the value is determined according to the rules described in formula (10). The value is 1.00. The remotely sensed corrected building heat demand baseline is calculated using formula (10): Gigajo.

[0088] The building-side carbon demand of G1 41 is calculated using formula (11): kg of carbon dioxide; of which 70 kg of carbon dioxide per gigajoules is the unit heating carbon intensity of the heat source corresponding to heat exchange station A21. The data is obtained from the test data example in step S103.

[0089] Similarly, the building-side carbon requirements for G2 42 and G3 43 can be calculated separately: For G2 42, A value of 1.4 watts per square meter per Kelvin corresponds to a low insulation level. The area is 30,000 square meters, and all other parameters are the same as G1 41. Gigajo; Gigajo; Gigajo; Kilograms of carbon dioxide. For G3 43, A value of 1.0 watt per square meter per Kelvin corresponds to a relatively high insulation level. The area is 15,000 square meters, and all other parameters are the same as G1 41. Gigajo; Gigajo; Gigajo; kilograms of carbon dioxide.

[0090] Step S106: Based on the difference between the source-side allocable carbon and the station-side heating carbon, and the difference between the station-side heating carbon allocation and the building-side carbon demand, determine the carbon emission inversion value of each inversion grid, and output constrained anomaly source candidate results by combining the time variation, spatial distribution and matching of the difference with the heating service relationship.

[0091] Specifically, the source station difference is first calculated according to the following formula (12): (12) In formula (12), This represents the source-station difference for the kth heat exchange station, expressed in kilograms of carbon dioxide. and Defined by formula (4) and formula (5) respectively.

[0092] Secondly, the station difference is calculated according to the following formula (13): (13) In formula (13), This represents the station building difference for the i-th inversion grid, expressed in kilograms of carbon dioxide. and They are defined by formulas (7) and (11) respectively. Formula (13) is a subtraction of quantities of the same unit, and the result is in kilograms of carbon dioxide, which is dimensionally reasonable.

[0093] refer to Figure 5 , Figure 5 This diagram illustrates a carbon transfer and difference calculation method provided by an embodiment of the present invention.

[0094] like Figure 5 As shown, the source-side heating carbon quantity 51 is allocated according to the heating network topology to obtain the source-side allocable carbon quantity 52; the source-side allocable carbon quantity 52 is compared with the station-side heating carbon quantity 53 to obtain the source-station difference 54. Simultaneously, the station-side heating carbon quantity 53 is allocated according to the heating service relationship weight to obtain the station-side heating carbon quantity allocation 55; the building-side carbon demand 56, after remote sensing thermal environment correction 57, is compared with the station-side heating carbon quantity allocation 55 to obtain the station-building difference 58. Based on the source-station difference 54 and the station-building difference 58, the grid carbon emission inversion value 59 is calculated, and constrained candidate results of abnormal sources 60 are further output.

[0095] To avoid equating measurement noise with anomalies, a significance assessment is performed on source station differences and station differences. The following rule is used to determine whether a difference "occurs": when the source station difference of a heat exchange station k... The absolute value and the amount of carbon available for distribution on the source side of the heat exchange station The ratio is not less than the source station difference threshold. When a source station difference occurs at heat exchange station k, it is assumed that the difference between the station and the heat exchange station i is present. The absolute value and the carbon allocation of heating on the side of the inverted grid station The ratio is not less than the station difference threshold. At that time, it is considered that a station difference occurs in inversion grid i. The source station difference threshold is... Difference threshold between station building and station This is a preset ratio value with one dimension, such as 5% and 10% respectively. The specific value can be calibrated according to the inversion accuracy requirements and the noise level of historical data.

[0096] Next, based on the distribution of the station-building difference within the service area of ​​the same heat exchange station, the carbon allocation for station-side heating or the carbon demand for buildings is adjusted: (i) When the station difference directions of multiple inversion grids within the service area of ​​the same heat exchange station are opposite, that is, there are inversion grids with positive values ​​and inversion grids with negative values, it is determined that there is a deviation in the allocation weight, and the carbon allocation weight is adjusted once according to the following formula (14): (14) In formula (14), The adjusted carbon allocation weight for the i-th inversion grid has a dimension of one. and Defined by formula (6) and formula (13) respectively; The step size for weight adjustment is dimensionless, for example, 0.5, which can be set according to the stability requirements. The denominator is the sum of all inversion grids within the service area of ​​the heat exchange station, so that the sum of the adjusted weights is normalized to 1. (The last sentence appears to be incomplete and possibly refers to a different calculation method.) Recalculate the corrected carbon allocation for station-side heating. This adjustment is a one-time adjustment and does not involve multiple iterations. To prevent the adjustment factor from having a negative value, resulting in negative weights, when the adjustment factor of a certain inversion grid is less than a preset lower limit (e.g., 0.1), the adjustment factor is taken as the preset lower limit.

[0097] (ii) When the differences between the building structures of all inversion grids within the service area of ​​the same heat exchange station are in the same direction (i.e., all are positive or all are negative) and the relative values ​​of the differences between the building structures of each inversion grid deviate from the average value within the service area of ​​the heat exchange station by no more than a preset threshold (i.e., the differences between the building structures of each inversion grid are all positive or negative), the difference between the building structures of each inversion grid and the average value within the service area of ​​the heat exchange station are all within the preset threshold. When the deviation from the average value within the service area of ​​the heat exchange station does not exceed a preset threshold (e.g., not exceeding 2 percentage points), the carbon demand on the building side shall be adjusted as a whole according to the following formula (15): (15) (15a) In formulas (15) and (15a), The building-side carbon demand after correction for the i-th inversion grid is expressed in kilograms of carbon dioxide. Defined by formula (11); μ is the overall correction ratio with a dimension of one; n is the number of inversion grids within the service area of ​​the heat exchange station; and These are the station building difference and building-side carbon demand for the j-th inversion grid within the service area of ​​the heat exchange station, respectively. The right side of formula (15) is kilograms of carbon dioxide multiplied by a dimensionless ratio, and the result is in kilograms of carbon dioxide, which is reasonable in terms of dimensions.

[0098] (iii) When neither of the above two situations occurs, the uncorrected carbon allocation for station-side heating shall be used directly. and building-side carbon demand As input for subsequent fusion.

[0099] After completing the above corrections, the corrected carbon allocation for station-side heating and the corrected carbon demand for building-side heating are weighted and fused according to the following formula (16) to obtain the carbon emission inversion values ​​for each inversion grid: (16) In formula (16), This represents the carbon emission inversion value of the i-th inversion grid, expressed in kilograms of carbon dioxide. This represents the corrected carbon allocation for station-side heating, which is the amount recalculated after adjusting the weights according to formula (14). If no weight adjustment is performed, it is equal to formula (7). ; This represents the corrected building-side carbon demand (i.e., the overall corrected demand according to formula (15)). If no overall correction is performed, it is equal to formula (11). ). In formula (16), is the heating side confidence weight for the i-th inversion grid, with a dimension of one and a value range of 0 to 1; Based on the completeness of heat metering at the heat exchange station, the proportion of effective remote sensing pixels, and the confidence level of building records, it is determined that when the heat metering data of the heat exchange station is complete and the proportion of effective remote sensing pixels is high... Take a larger value, such as 0.6 to 0.8, when there are many missing heat metering data at the heat exchange station or the proportion of effective remote sensing pixels is low. Take a smaller value, such as 0.3 to 0.5, with 0.5 as the default, i.e., equal weight fusion. At the same time, in order to maintain the carbon quantity closure within the service range of the heat exchange station, the carbon emission inversion values ​​of each inversion grid after fusion are normalized and closed-loop constraints are applied, so that the sum of the carbon emission inversion values ​​of each inversion grid within the service range of the same heat exchange station is equal to the station-side heating carbon quantity of that heat exchange station. The right side of formula (16) is a weighted linear combination of two quantities with the same unit, and the result unit is kilograms of carbon dioxide, which is reasonable in terms of dimensions. The carbon emission inversion values ​​take into account both the heating side allocation information and the building side demand estimation. Through confidence weights and normalization closure constraints, after difference constraint correction, the actual carbon emission level of the inversion grid is reflected.

[0100] To facilitate understanding of the end-to-end application of formulas (12) to (16), the test data examples from steps S103 to S105 above will be used as an example. Let the heat supplied by the heat source to heat exchange stations A21, B22, and C23 be respectively... Gigi, Ji Jiaohe Gibbs; the actual heat received by the three heat exchange stations were respectively Gigi, Ji Jiaohe The difference of approximately 20 gigajoules, 5 gigajoules, and 5 gigajoules is caused by heat loss in the heating network.

[0101] Calculate the source-station difference for each heat exchange station according to formula (12): Source-side distributable carbon content kilograms of carbon dioxide; carbon content of station-side heating kilograms of carbon dioxide; therefore kilograms of carbon dioxide, relative value The source station difference threshold Taking 5%, since 1.67% is less than 5%, it is determined that heat exchange station A21 does not show a significant source-station difference. Similarly, the relative values ​​of the source-station differences for heat exchange stations B22 and C23 are calculated to be approximately 0.50% ( ) and 0.63% Since neither of them reached the 5% threshold, it was determined that there was no significant source station difference between heat exchange stations B22 and C23.

[0102] The station building difference for each inversion grid is calculated using formula (13). For the inversion grid corresponding to heat exchange station A21: kilograms of carbon dioxide, relative value ; One kilogram of carbon dioxide, with a relative value of approximately 0.58%; Kilograms of carbon dioxide, with a relative value of approximately 0.57%. The aforementioned station building difference threshold. Taking 10%, since the relative values ​​of the three are all less than 10%, it is determined that there is no significant difference between the station buildings in G1 41, G2 42 and G3 43.

[0103] Since the differences between the three inversion grids did not reach the significance threshold, neither the weight adjustment of formula (14) nor the overall correction of formula (15) was triggered, and the original values ​​were directly used. and As input to formula (16), the carbon emission inversion values ​​for each inversion grid are calculated as follows: kilograms of carbon dioxide; kilograms of carbon dioxide; Kilograms of carbon dioxide. The sum of the three is approximately 82,793 kilograms of carbon dioxide, which is basically in line with the 82,600 kilograms of carbon dioxide supplied for heating on the station side of heat exchange station A21. The small difference comes from the algebraic sum of the differences between the stations in each inversion grid, and the result is reasonable.

[0104] In another example scenario, the application of formula (14) for weight adjustment is illustrated: Suppose G1 41 is estimated to be different from the initial estimate due to discrepancies between the actual building usage and the actual building usage. The amount of carbon dioxide was 24,588 kilograms, higher than the original estimate, while the actual usage rate of G3 43 was lower than the estimated amount. The carbon dioxide concentration was lower than the original estimate of 15368 kg / kg, while G2 42 remained unchanged at 43030 kg / kg. At this point, the differences between the various inversion grid stations were: The relative value of carbon dioxide per kilogram is approximately 16.49%, exceeding 10%. One kilogram of carbon dioxide, with a relative value of approximately 0.58%; The carbon dioxide content is approximately 21.47%, exceeding 10%. The station building differences within the service area of ​​the same heat exchange station are in opposite directions, with G1 41 being negative and G3 43 positive. The carbon content allocation weight is adjusted according to formula (14), taking the adjustment step size. The numerators are respectively , , The denominator (summation) is After normalization, we get , , The revised carbon allocation for station-side heating will be recalculated based on the adjusted weights. kilograms of carbon dioxide; kilograms of carbon dioxide; Kilograms of carbon dioxide. The adjusted carbon emission inversion value is calculated according to formula (16): kilograms of carbon dioxide; kilograms of carbon dioxide; Kilograms of carbon dioxide. It can be seen that the weight adjustment affects the performance of each inversion grid. Closer to it The inversion results are more consistent with the actual heat demand distribution of the building.

[0105] Then, extract the duration, direction of change, spatial concentration location, synchronous occurrence range, and reverse change state after heating adjustment of the source station difference and station building difference, and judge the candidate abnormal sources in turn according to the rules listed in Table 6.

[0106] Table 6. Rules for Judging Candidate Sources of Anomalies

[0107] like Figure 6 As shown, the four types of anomaly source candidates are determined sequentially according to the rules listed in Table 6. Among them, "synchronous occurrence" refers to exceeding the corresponding threshold within at least two consecutive sampling periods; "multiple downstream heat exchange stations" refers to at least two downstream heat exchange stations or at least 50% of the total number of downstream heat exchange stations of the heat source; "gradual increase" refers to a monotonically increasing relative difference along the downstream direction of the heating network topology, with the increase at adjacent nodes not less than a preset incremental threshold. ,For example A 2% threshold is set; the "few adjacent inversion grids" refers to inversion grids sharing no more than 3 edges or points; the inconsistency between the remote sensing thermal environment data and the surrounding building complex means that the absolute value of the difference between the building thermal response temperature of the inversion grid and the average building thermal response temperature of adjacent inversion grids within the same service range exceeds 1.5 Kelvin or exceeds 1 times the standard deviation of building thermal response temperatures within the same service range. Finally, abnormal source candidates that do not match the heating service relationship are removed, resulting in constrained abnormal source candidate results 65. The removal of abnormal source candidates that do not match the heating service relationship means that if the equipment pointed to by an abnormal source candidate does not have a heating service relationship with the inversion grid, then the abnormal source candidate is removed from the result list. For example, if the heat exchange station corresponding to an inversion grid is heat exchange station A 21, and an abnormal source candidate points to heat exchange station B 22, then the abnormal source candidate is not included in the final candidate results of the inversion grid, thereby ensuring that the abnormal source candidate results are consistent with the heating service relationship.

[0108] Furthermore, when there is a lack of heat measurement data, room temperature data, or remote sensing thermal environment data within the inversion grid, a process for supplementing building-side carbon demand in the case of missing data is executed.

[0109] refer to Figure 7 , Figure 7 This diagram illustrates a process for supplementing building-side carbon demand in the event of data loss, as provided in an embodiment of the present invention.

[0110] like Figure 7As shown, the process for supplementing building-side carbon demand in the case of missing data includes: First, identifying the data type, missing time period, and missing inversion grid corresponding to the missing data. Second, within the service area of ​​the heat exchange station corresponding to the same heating service relationship, selecting adjacent inversion grids 71 ​​that match the building type, construction year, heating area, building envelope insulation level, and meteorological response relationship as comparable reference grids 72. Then, based on the building-side carbon demand of the comparable reference grid during the missing time period, and the building-side carbon demand of the missing inversion grid before and after the missing time period 73, the building-side carbon demand of the missing inversion grid is supplemented in the following way: the supplemented building-side carbon demand of missing inversion grid i in the missing time period t is equal to the building-side carbon demand of comparable reference grid j in the missing time period t multiplied by the average of the ratios of the building-side carbon demand of missing inversion grid i in the time period before and after the missing time period to the building-side carbon demand of comparable reference grid j in the corresponding time period, i.e. When multiple candidate comparable reference grids exist, they are weighted according to the number of matching dimensions between each comparable reference grid and the missing inversion grid, with the comparable reference grid having a higher number of matching dimensions. Finally, a missing test confidence marker is generated based on the data type and the degree of matching between the comparable reference grids; when the missing test confidence marker is lower than a preset requirement, the corresponding anomaly source candidate result is marked as candidate 74 requiring review.

[0111] The rules for determining the missing confidence markers are shown in Table 7. The matching dimensions include five items: building type, year of construction, heating area, building envelope insulation level, and meteorological response relationship. The first four items (building type, year of construction, heating area, and building envelope insulation level) are determined to match according to the following rules: identical building types; a difference in construction years not exceeding 10 years; a heating area ratio between 0.7 and 1.4; and identical building envelope insulation levels. The matching determination for the fifth item (meteorological response relationship) is based on the Pearson correlation coefficient; a match is considered to have a Pearson correlation coefficient not lower than a preset correlation coefficient threshold (e.g., 0.85). After determining the matching for the above five items, the missing confidence markers are determined according to the number of matching items as shown in Table 7.

[0112] Table 7. Rules for Determining Missing Test Confidence Markers

[0113] It should be noted that the meteorological response relationship refers to the correlation between the building-side carbon demand of the inversion grid and meteorological data (including outdoor temperature, duration of continuous low temperatures, etc.). "Matching" means that the meteorological response relationships of the two inversion grids are statistically consistent. The meteorological response correlation coefficient is the Pearson correlation coefficient, with a dimension of one and a value range of -1 to +1. The preset correlation coefficient threshold can be, for example, 0.85, and can be specifically set according to the inversion accuracy requirements. By supplementing with adjacent comparable reference grids, the building-side carbon demand of the inversion grid can still be obtained in scenarios with missing data, avoiding interruption of the entire inversion process due to data loss.

[0114] To facilitate understanding of the application of the matching criteria described in Table 7, a set of specific attributes is used as an example. Assume a missing inversion mesh. The building's attributes are: residential building type, built in 2008, heating area of ​​25,000 square meters, and medium insulation rating of the building envelope. Candidate comparable benchmark grids are selected within the service area of ​​heat exchange stations corresponding to the same heating service relationship. , The building's attributes are: residential building type, built in 2010, heating area of ​​28,000 square meters, and building envelope insulation level of medium. and The meteorological response Pearson correlation coefficient calculated based on the building-side carbon demand sequence during normal periods (non-missing periods) of the heating season is 0.88. Matching is determined item by item according to the above five criteria: (i) same building type, match; (ii) construction year difference of 2 years, not exceeding the 10-year threshold, match; (iii) heating area ratio is... The values ​​fall within the range of 0.7 to 1.4, matching; (iv) the insulation level of the building envelope is the same (both are medium), matching; (v) the meteorological response Pearson correlation coefficient is 0.88 and not lower than the 0.85 threshold, matching. All 5 items match, and according to Table 7, the missing confidence level is marked as "high", and the data based on the data is used directly. Supplement Results of building-side carbon demand.

[0115] For example, assuming a candidate comparable benchmark grid. The building's attributes are: building type is residential, built in 1995, heating area is 40,000 square meters, and the building envelope insulation level is low. The meteorological response Pearson correlation coefficient is 0.72. Item-by-item judgment: (i) Building type matches; (ii) The difference in construction years is 13 years, exceeding the 10-year threshold, therefore mismatched; (iii) The heating area ratio is... (iv) Different insulation grades of the building envelope, mismatch; (v) Correlation coefficient of 0.72 is below the 0.85 threshold, mismatch. Only one item matches, and the missing confidence level is marked as "low" according to Table 7, based on Supplement The corresponding candidate results for the source of the anomaly are marked as candidates that need to be reviewed.

[0116] Based on the above method embodiments, this invention also provides a carbon emission inversion system for cold-region cities based on multi-source data fusion, including a data acquisition module, an inversion grid division module, a source-side carbon quantity determination module, a station-side carbon quantity allocation module, a building-side carbon demand determination module, and a carbon emission inversion and anomaly output module.

[0117] The data acquisition module is used to acquire the heating source-side operation data, heat exchange station operation data, heating network topology, building cluster basic data, meteorological data, and remote sensing thermal environment data of the target cold-region city during the target heating period. The specific execution of step S101 is not detailed here. The inversion grid division module is used to establish heating service relationships based on the heating network topology, the service correspondence between heat exchange stations and building clusters, and the spatial distribution of building clusters, and to divide the grid for calculating carbon emissions according to the heating service relationships. The specific execution of step S102 is as follows. The source-side carbon determination module is used to determine the source-side heating carbon content based on the heating source-side operation data, and to determine the source-side allocable carbon content allocated to each heat exchange station according to the heating network topology. The specific execution of step S103 is as follows. The station-side carbon allocation module is used to determine the station-side heating carbon quantity based on the heat exchange station's operating data, and allocate the station-side heating carbon quantity to the corresponding inversion grid according to the heating service relationship to obtain the station-side heating carbon quantity allocation amount, specifically executing step S104. The building-side carbon demand determination module is used to determine the building-side carbon demand of each inversion grid based on the building group's basic data, meteorological data, remote sensing thermal environment data, and heating season time attributes, specifically executing step S105. The carbon emission inversion and anomaly output module is used to determine the carbon emission inversion value of each inversion grid based on the difference between the source-side allocable carbon quantity and the station-side heating carbon quantity, and the difference between the station-side heating carbon quantity allocation amount and the building-side carbon demand amount, and output constrained anomaly source candidate results by combining the temporal variation, spatial distribution, and matching of the difference with the heating service relationship, specifically executing step S106.

[0118] It should be understood that the above modules are linked through the heating service relationship: the output of the source-side carbon determination module serves as the source-side constraint of the station-side carbon allocation module; the output of the station-side carbon allocation module serves as the heating-side input of the carbon emission inversion and anomaly output module; the output of the building-side carbon demand determination module serves as the building-side input of the carbon emission inversion and anomaly output module; and finally, the carbon emission inversion and anomaly output module completes the closure verification and outputs the constrained anomaly source candidate results.

[0119] In an optional implementation, the system further includes a missing data completion module. This module is used to complete the missing building-side carbon demand data in the inversion grid when there are missing heat measurement data, room temperature data, or remote sensing thermal environment data within the inversion grid. Following the aforementioned missing data situation building-side carbon demand completion process, the module completes the missing data to generate a missing data confidence marker. Figure 7 The process shown will not be repeated here.

[0120] The embodiments of the present invention can be implemented entirely or partially by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. The computer-readable storage medium can be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media.

[0121] The above description is merely an embodiment of the technical solution of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made according to the disclosure of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for carbon emission retrieval in cold-region cities based on multi-source data fusion, characterized in that, Includes the following steps, Acquire operational data of the heating source side, heat exchange station operation data, heating network topology, building complex basic data, meteorological data and remote sensing thermal environment data of the target cold-region city during the target period of the heating season; Based on the heating network topology, the service correspondence between heat exchange stations and building groups, and the spatial distribution of building groups, heating service relationships are established, and inversion grids for calculating carbon emissions are divided according to the heating service relationships. The amount of carbon supplied for heating on the source side is determined based on the operating data of the heating source side, and the amount of carbon that can be allocated to each heat exchange station on the source side is determined according to the topology of the heating pipeline network. The amount of carbon supplied for heating on the station side is determined based on the heat exchange station operation data, and the amount of carbon supplied for heating on the station side is allocated to the corresponding inversion grid according to the heating service relationship to obtain the carbon supply allocation for heating on the station side. Based on the basic data of the building complex, meteorological data, remote sensing thermal environment data, and heating season time attributes, the building-side carbon demand of each inversion grid is determined; Based on the difference between the available carbon on the source side and the carbon on the station side heating supply, and the difference between the carbon allocation of the station side heating supply and the carbon demand on the building side, the carbon emission inversion value of each inversion grid is determined. Then, combined with the temporal variation, spatial distribution and matching of the difference with the heating service relationship, the constrained candidate results of abnormal sources are output.

2. The method according to claim 1, characterized in that, The step of establishing heating service relationships and dividing the grid for calculating carbon emissions based on the heating network topology, the service correspondence between heat exchange stations and building complexes, and the spatial distribution of building complexes includes: Obtain the service list of heat exchange stations, the connection relationship of heating pipelines, the building outline, the building heating area, and the location of the building heat inlet; Based on the heating network connection relationship and the location of the building heat inlet, determine the heat exchange station corresponding to each building group; When a building complex corresponds to more than two heat exchange stations, the service ratio of the main service heat exchange station or each heat exchange station shall be determined based on the building heating area, heat metering ratio or pipeline connection distance. Record the correspondence between the heat source and the heat exchange station, between the heat exchange station and the building complex, and between the building complex and its spatial location to obtain the heating service relationship; The inversion grid is divided based on the heating service relationship, the spatial distribution of the building complex, and the preset spatial scale.

3. The method according to claim 1, characterized in that, The step of determining the source-side heating carbon quantity based on the source-side operating data and determining the source-side distributable carbon quantity allocated to each heat exchange station according to the heating network topology includes: The carbon content of heating source side is determined based on the operating data of the heating source side and the corresponding carbon emission factors. The unit heating carbon intensity of the heat source is determined based on the heat output of the heat source and the amount of carbon supplied by the source. Based on the heating network topology and the connection relationship between each heat exchange station and the heat source, the source-side heating carbon quantity is allocated to the downstream heat exchange station to obtain the source-side allocable carbon quantity for each heat exchange station.

4. The method according to claim 1, characterized in that, The step of determining the station-side heating carbon quantity based on the heat exchange station's operating data and allocating the station-side heating carbon quantity to the corresponding inversion grid according to the heating service relationship to obtain the station-side heating carbon quantity allocation includes: Determine whether the heat exchange station receives or outputs heat based on its operating data. The amount of carbon supplied for heating at the station is determined based on the heat received or output at the heat exchange station and the unit heating carbon intensity of the corresponding heat source. The inversion grid corresponding to each heat exchange station is determined based on the aforementioned heating service relationship; The carbon allocation weights for each inversion grid are determined based on the building's heating area, building type, year of construction, insulation level of the building envelope, and building spatial distribution. The carbon amount of station-side heating is allocated to the corresponding inversion grid according to the carbon allocation weight, so as to obtain the carbon allocation amount of station-side heating.

5. The method according to claim 1, characterized in that, The process of determining the building-side carbon demand for each inversion grid based on the building complex's basic data, meteorological data, remote sensing thermal environment data, and heating season period attributes includes: Based on building type, year of construction, number of floors, insulation level of building envelope, heating area, usage status, outdoor temperature, wind speed, solar radiation, snowfall status and duration of continuous low temperature, the building heat demand baseline for each inversion grid is determined. Based on the duration of continuous low temperature and the thermal response delay time after heating adjustment, the building thermal inertia correction is applied to the building heat demand baseline. The revised building heat demand baseline was corrected based on remote sensing thermal environment data. Based on the unit heating carbon intensity of the corresponding heat exchange station, the corrected building heat demand baseline is converted into building-side carbon demand.

6. The method according to claim 5, characterized in that, The process of correcting the revised building heat demand baseline based on remote sensing thermal environment data includes: Obtain surface temperature, snow cover status, underlying surface type, and building density within the inversion grid; Non-building thermal response zones were excluded based on building outline and underlying surface type; The intensity of the characterization of building heat dissipation by ground surface temperature is adjusted based on the snow cover status. The corrected surface temperature is compared with the surface temperature of adjacent inversion grids within the service area of ​​the heat exchange station corresponding to the same heating service relationship to obtain the remote sensing thermal state label; The building's heat demand baseline can be adjusted upwards, downwards, or maintained based on the remote sensing thermal status markers.

7. The method according to claim 1, characterized in that, The process of determining the carbon emission inversion value for each inversion grid based on the difference between the source-side allocable carbon and the station-side heating carbon, and the difference between the station-side heating carbon allocation and the building-side carbon demand, includes: The source-side available carbon quantity is compared with the station-side heating carbon quantity of the corresponding heat exchange station to obtain the source-station difference. The carbon allocation for station-side heating is compared with the building-side carbon demand of the corresponding inversion grid to obtain the station-building difference. Based on the distribution of the station-building difference within the service area of ​​the same heat exchange station, when the station-building difference is in the opposite direction, the carbon allocation weight is adjusted. When the station-building difference is in the same direction and the relative value of the station-building difference of each inversion grid deviates from the average value within the service area of ​​the heat exchange station by no more than a preset threshold, the carbon demand on the building side is corrected as a whole, and the carbon allocation of the station-side heating or the carbon demand on the building side is corrected. The corrected carbon allocation for station-side heating and the corrected carbon demand for building-side heating are combined with confidence weighting and then normalized closure constraints are applied within the service area of ​​the heat exchange station to obtain the carbon emission inversion value of the corresponding inversion grid.

8. The method according to claim 1, characterized in that, The process combines the temporal variation and spatial distribution of the difference with its matching with the heating service relationship to output constrained candidate results for anomaly sources, including: Extract the duration, direction of change, spatial concentration location, synchronous occurrence range, and reverse change state after heating regulation of the source station difference and station building difference; When multiple downstream heat exchange stations simultaneously show source station differences, the abnormality on the heat source side or the abnormality on the main pipeline network will be written into the candidate results of the abnormality source. When the difference increases gradually along the downstream direction of the heating network, the abnormal heat loss or hydraulic imbalance of the network will be written into the candidate results of the abnormal source. When multiple inversion grids within the service area of ​​the same heat exchange station simultaneously show the same direction of station building difference, the heat exchange station adjustment deviation will be written into the candidate results of the anomaly source. When the difference is concentrated in a single building group or a small number of adjacent inversion grids, and the remote sensing thermal environment data is inconsistent with the surrounding building group, the building envelope heat loss anomaly or user-side heat consumption anomaly is written into the anomaly source candidate results. Candidate anomalies that do not match the heating service are eliminated to obtain constrained candidate anomalies.

9. The method according to claim 1, characterized in that, When there are missing heat measurement data, room temperature data, or remote sensing thermal environment data within the inversion grid, it also includes: Identify the data type, missing time period, and missing data inversion grid corresponding to the missing data; Within the service area of ​​the heat exchange station corresponding to the same heating service relationship, adjacent inversion grids that match the building type, construction year, heating area, thermal insulation level of the building envelope and meteorological response relationship are selected as comparable benchmark grids. Based on the building-side carbon demand of the comparable benchmark grid during the missing period, and the building-side carbon demand of the missing inversion grid before and after the missing period, the building-side carbon demand of the missing inversion grid is supplemented. A missing test confidence marker is generated based on the matching degree between the data type and the comparable benchmark grid; when the missing test confidence marker is lower than the preset requirement, the corresponding candidate result of the anomaly source is marked as a candidate result that needs to be reviewed.

10. A carbon emission retrieval system for cold-region cities based on multi-source data fusion, characterized in that, include: The data acquisition module is used to acquire the operating data of the heating source side, the operating data of the heat exchange station, the topology of the heating network, the basic data of the building complex, the meteorological data and the remote sensing thermal environment data of the target cold-region city during the target period of the heating season; The inversion grid division module is used to establish heating service relationships based on the heating network topology, the service correspondence between heat exchange stations and building groups, and the spatial distribution of building groups, and to divide the inversion grid for calculating carbon emissions according to the heating service relationships. The source-side carbon quantity determination module is used to determine the source-side heating carbon quantity based on the heating source-side operation data, and to determine the source-side distributable carbon quantity allocated to each heat exchange station according to the heating pipeline topology. The station-side carbon allocation module is used to determine the station-side heating carbon amount based on the heat exchange station operation data, and allocate the station-side heating carbon amount to the corresponding inversion grid according to the heating service relationship to obtain the station-side heating carbon allocation amount. The building-side carbon demand determination module is used to determine the building-side carbon demand of each inversion grid based on the building cluster's basic data, meteorological data, remote sensing thermal environment data, and heating season period attributes. The carbon emission inversion and anomaly output module is used to determine the carbon emission inversion value of each inversion grid based on the difference between the source-side allocable carbon and the station-side heating carbon, and the difference between the station-side heating carbon allocation and the building-side carbon demand. It also outputs constrained anomaly source candidate results by combining the time variation, spatial distribution and matching of the difference with the heating service relationship.

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