Dynamic early warning method for carbon emission, terminal equipment and storage medium

Abstract

The invention is suitable for the technical field of carbon emission accounting, and discloses a dynamic early warning method for carbon emission, terminal equipment and a storage medium. The dynamic early warning method for carbon emission comprises the following steps: acquiring carbon emission data of a target area in real time; calculating an actual emission value of the early warning target according to the carbon emission data; comparing the actual emission value with a target value corresponding to the early warning target; and if the actual emission value exceeds the target value, generating and outputting early warning information. According to the invention, the core problems of low real-time performance and weak dynamic performance of carbon emission early warning are solved.

Description

Technical Field

[0001] This invention belongs to the field of carbon emission accounting technology, and in particular relates to a dynamic early warning method, terminal equipment and storage medium for carbon emissions. Background Technology

[0002] Carbon management presents challenges in the process of urbanization.

[0003] Traditional carbon emission accounting relies on manual reporting and annual or monthly statistical data, resulting in long update cycles and the use of static thresholds, lacking the ability to dynamically adjust targets. For example, when some warning targets are exceeded, historical data must be manually retrieved for comparison, demonstrating the low real-time performance of traditional carbon emission warnings. A new technological approach is needed to address these technical problems. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a dynamic early warning method, terminal device and storage medium for carbon emissions, which can solve the problem of low real-time performance of carbon emission early warning in related technologies.

[0005] The first aspect of this invention provides a dynamic early warning method for carbon emissions, comprising: Real-time acquisition of carbon emission data for the target area; Based on the carbon emission data, calculate the actual emission values ​​of the early warning targets; Compare the actual emission values ​​with the target values ​​corresponding to the early warning targets; If the actual emission value exceeds the target value, an early warning message will be generated and output.

[0006] Optionally, in a first implementation of the first aspect of the present invention, before the step of comparing the actual emission value with the target value corresponding to the warning target, the method further includes: Obtain GDP data and building area data for the target area; Based on the GDP data and the building area data, the target value corresponding to the early warning target is determined.

[0007] Optionally, in a second implementation of the first aspect of the present invention, the step of calculating the actual emission value of the early warning target based on the carbon emission data includes: The target area is used as the early warning target, and the GDP data of the target area is obtained; Based on the GDP data and the carbon emission data, the actual carbon emission value per unit of GDP is calculated as the actual emission value of the early warning target.

[0008] Optionally, in a third implementation of the first aspect of the present invention, the step of calculating the actual emission value of the warning target based on the carbon emission data includes: The buildings in the target area are used as the early warning targets, and the building area data of the target area is obtained; Based on the building area data and the carbon emission data, the actual carbon emission value per unit area is calculated as the actual emission value of the early warning target.

[0009] Optionally, in a fourth implementation of the first aspect of the present invention, the step of calculating the actual emission value of the warning target based on the carbon emission data includes: The population of the target area is used as the early warning target, and carbon sink data of the target area is obtained; Based on the carbon sink data and the carbon emission data, calculate the actual net carbon emissions and obtain the population data of the target area; Based on the population data and the carbon emission data, the actual per capita carbon emission value is calculated as the actual emission value of the early warning target.

[0010] Optionally, in a fifth implementation of the first aspect of the present invention, the step of calculating the actual emission value of the warning target based on the carbon emission data includes: The traffic roads in the target area are used as the early warning targets, and traffic flow data in the target area is obtained; Based on the traffic flow data and the carbon emission data, the actual carbon emission value per unit traffic flow is calculated as the actual emission value of the early warning target.

[0011] Optionally, in a sixth implementation of the first aspect of the present invention, after the step of calculating the actual carbon emission value per unit traffic flow as the actual emission value of the warning target based on the traffic flow data and the carbon emission data, the method further includes: Calculate the target actual carbon emission value for each road based on the actual carbon emission value per unit traffic flow. The warning information is output based on the road information of each road, and the road information includes at least one of road grade, road length, road traffic volume, and road carbon emissions.

[0012] Optionally, in a seventh implementation of the first aspect of the present invention, the step of calculating the actual emission value of the warning target based on the carbon emission data includes: Select early warning targets, wherein the optional early warning targets include the target area and unit targets in the target area. The unit targets include traffic roads, population, and buildings. The buildings include commercial buildings, office buildings, hotel buildings, cultural buildings, educational buildings, medical and health buildings, sports buildings, transportation buildings, residential buildings, and special buildings. The characteristic buildings are obtained by custom calibration. Based on the carbon emission data, calculate the actual emission value of the early warning target.

[0013] Secondly, embodiments of the present invention provide a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described dynamic early warning method for carbon emissions.

[0014] Thirdly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described dynamic early warning method for carbon emissions.

[0015] Fourthly, embodiments of the present invention provide a computer program product that, when run on a terminal device, causes the terminal device to execute the aforementioned dynamic early warning method for carbon emissions.

[0016] The beneficial effects of this invention compared to existing technologies are as follows: by integrating real-time data acquisition, automated calculation, dynamic comparison, and immediate early warning, a closed-loop dynamic early warning system is formed. This abandons the passive mode of traditional carbon management, significantly improving early warning response speed; dynamic target values ​​replace static thresholds, enhancing adaptability to regional changes; and automated processes reduce reliance on manual intervention. It directly solves the core problems of low real-time performance and weak dynamism in carbon emission early warning. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, 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.

[0018] Figure 1 This is a schematic diagram of one embodiment of the dynamic early warning method for carbon emissions in this invention. Figure 2 This is a schematic diagram of a specific embodiment of the dynamic early warning method for carbon emissions in this invention, prior to step S103. Figure 3 This is a schematic diagram of a specific embodiment of step S102 of the dynamic early warning method for carbon emissions in this invention. Figure 4 This is a schematic diagram of another specific embodiment of step S102 of the dynamic early warning method for carbon emissions in this invention. Figure 5 This is a schematic diagram of one embodiment of the terminal device in this invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are protected by this invention.

[0020] It should be noted that the terms "comprising," "including," and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this invention, are intended to cover non-exclusive inclusion. For example, a process, method, terminal, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices. In the claims, specification, and accompanying drawings of this invention, relational terms such as "first" and "second" are used merely to distinguish one entity / operation / object from another entity / operation / object, and do not necessarily require or imply any such immediate relationship or order between these entities / operations / objects.

[0021] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0022] Carbon management presents challenges in the process of urbanization.

[0023] Traditional carbon emission accounting relies on manual reporting and annual or monthly statistical data, resulting in long update cycles and the use of static thresholds, lacking the ability to dynamically adjust targets. For example, when some warning targets are exceeded, historical data must be manually retrieved for comparison, demonstrating the low real-time performance of traditional carbon emission warnings. A new technological approach is needed to address these technical problems.

[0024] In view of this, embodiments of the present invention provide a dynamic early warning method, terminal device, and storage medium for carbon emissions. By integrating real-time data acquisition, automated calculation, dynamic comparison, and immediate early warning, a closed-loop dynamic early warning system is formed. This abandons the passive mode of traditional carbon management, significantly improving the early warning response speed; dynamic target values ​​replace static thresholds, enhancing adaptability to regional changes; and automated processes reduce reliance on manual intervention. It directly solves the core problems of low real-time performance and weak dynamism in carbon emission early warning.

[0025] To illustrate the technical solution of the present invention, specific embodiments are described below.

[0026] Figure 1 This diagram illustrates a flowchart of a dynamic early warning method for carbon emissions provided by an embodiment of the present invention. This method can be applied to terminal devices. Terminal devices can be mobile phones, tablets, laptops, ultra-mobile personal computers (UMPCs), netbooks, etc.

[0027] Specifically, the aforementioned dynamic early warning method for carbon emissions may include the following steps S101 to S104.

[0028] Step S101: Acquire carbon emission data of the target area in real time.

[0029] In embodiments of the present invention, dynamic data of various carbon emission sources within the target area are continuously collected through IoT sensors, energy monitoring equipment, or real-time data interfaces, including real-time monitoring values ​​of direct emission sources such as industrial facilities, transportation systems, and building energy consumption.

[0030] Optionally, the collected raw data can be subjected to noise filtering, outlier correction, and format standardization to form a structured carbon emission dataset, providing reliable input for subsequent calculations.

[0031] Step S102: Calculate the actual emission value of the early warning target based on the carbon emission data.

[0032] In embodiments of the present invention, based on the type of early warning target (such as the entire region, a building complex, or a transportation network), a differentiated algorithm model is used to calculate the carbon emission intensity per unit GDP (total carbon emissions / GDP), the carbon emission per unit area (total carbon emissions / building area), and the carbon emission per unit traffic flow (total carbon emissions / traffic flow).

[0033] Specifically, actual emissions values ​​are dynamically adjusted as real-time data is updated to avoid the lag of static calculations. For example, traffic carbon emissions calculations incorporate traffic flow sensor data to reflect emissions fluctuations caused by road congestion in real time.

[0034] Optionally, early warning targets can be selected. These targets may include the target area and unit targets within the target area. Unit targets include roads, population, and buildings. Buildings include commercial buildings, office buildings, hotels, cultural buildings, educational buildings, medical and health buildings, sports buildings, transportation buildings, residential buildings, and special buildings. The characteristic buildings are obtained through custom calibration. Based on the carbon emission data, the actual emission values ​​of the early warning targets are calculated. A three-tiered early warning target system is pre-set, including a macro-level (target area), a meso-level (unit targets), and a micro-level (buildings). Through the custom calibration function, high-emission characteristic buildings (such as laboratories and semiconductor factories) can be included in the monitoring system, preventing standard algorithms from underestimating their emission responsibility.

[0035] Step S103: Compare the actual emission value with the target value corresponding to the warning target.

[0036] In embodiments of the present invention, the target value is not a fixed threshold, but is dynamically generated by combining economic indicators (GDP), spatial data (building area), or environmental factors (carbon sinks). For example, a flexible target value adapted to the current stage of development can be generated by using a correlation model between historical carbon emission trends and GDP growth rates.

[0037] The difference or ratio analysis between the actual emission value and the dynamic target value is used to determine the degree of exceedance (e.g., if the actual value is greater than 105% of the target value, it is considered a slight exceedance, and if it is greater than 120%, it is considered a severe exceedance).

[0038] The comparison results trigger an immediate logical judgment. If the actual value exceeds the target value, the early warning generation process is initiated; otherwise, the process returns to the data collection stage to continue monitoring.

[0039] Step S104: If the actual emission value exceeds the target value, then generate and output early warning information.

[0040] In embodiments of the present invention, warning levels are divided according to the extent of exceeding the standard (such as yellow, orange, and red), and corresponding warning signal text, visual charts, or alarm instructions are generated.

[0041] Optionally, structured early warning reports can be pushed to the regulatory platform via API, and the responsible persons can be notified via SMS, email or mobile terminal to ensure the timeliness of information delivery.

[0042] The beneficial effects of this invention compared to existing technologies are as follows: by integrating real-time data acquisition, automated calculation, dynamic comparison, and immediate early warning, a closed-loop dynamic early warning system is formed. This abandons the passive mode of traditional carbon management, significantly improving early warning response speed; dynamic target values ​​replace static thresholds, enhancing adaptability to regional changes; and automated processes reduce reliance on manual intervention. It directly solves the core problems of low real-time performance and weak dynamism in carbon emission early warning.

[0043] Traditional carbon management methods use fixed thresholds (such as an annual carbon emission cap), which cannot respond to quarterly or monthly economic fluctuations or changes in construction progress, causing early warning results to deviate from actual needs. Based on this, the present invention proposes an optional embodiment.

[0044] Reference Figure 2 , Figure 2 This is a schematic diagram of a specific embodiment of the dynamic early warning method for carbon emissions before step S103 in this invention. The following specific implementation methods also include steps before step S103: Step S201: Obtain GDP data and building area data for the target area.

[0045] In embodiments of the present invention, by accessing regional economic statistics databases, building energy management systems (such as BMS), and geographic information systems (GIS), macroeconomic indicators such as GDP total and growth rate of the target area, as well as spatial scale data such as total building area and building area by type (such as commercial / industrial / residential) are collected in real time. The raw data is filtered for outliers (such as removing extreme fluctuations in GDP statistics) and standardized in terms of units (such as converting building area to square meters) to ensure the reliability of the input data.

[0046] Step S202: Determine the target value corresponding to the early warning target based on the GDP data and the building area data.

[0047] In this embodiment of the invention, total carbon emission weights are allocated based on the intensity of regional economic activity reflected by GDP data; simultaneously, spatial carrying capacity density is quantified by combining building area data. Using a linear regression model or machine learning algorithm (such as multiple linear regression), GDP and building area are used as independent variables to output dynamic target values.

[0048] Optionally, when quarterly GDP data is updated or new buildings are completed, a target value recalculation process can be triggered to avoid the lag in traditional annual target setting. Carbon emission intensity constraints are transformed into a hard upper limit on the target value to ensure that dynamic results comply with regional emission reduction policy requirements.

[0049] In this embodiment of the invention, GDP data captures changes in economic activity, and building area data reflects changes in space utilization, so that the target value is adapted to the actual development status of the region in real time, which can avoid false alarms or omissions caused by fixed thresholds.

[0050] Traditional carbon management methods suffer from drawbacks such as limited accounting dimensions and slow response at the regional level. Therefore, this invention proposes an alternative embodiment.

[0051] Reference Figure 3 , Figure 3 This is a schematic diagram of a specific embodiment of step S102 of the dynamic early warning method for carbon emissions in this invention. Step S102 further includes the following specific implementation methods: Step S1021: The target area is used as the early warning target, and the GDP data of the target area is obtained.

[0052] In the embodiments of the present invention, the warning object is the entire "target area" (such as an industrial park or administrative region), which is different from micro-units such as individual buildings or traffic roads.

[0053] By connecting to government economic statistics platforms, enterprise ERP systems, or satellite remote sensing data streams, the system dynamically collects GDP totals and growth rates for target areas. Data update frequencies support hourly / daily levels to ensure timeliness matching with carbon emission data. GDP data is spatially mapped according to administrative boundaries or geographical grids to ensure complete overlap with the regional scope of carbon emission data and avoid statistical bias.

[0054] Step S1022: Based on the GDP data and the carbon emission data, calculate the actual carbon emission value per unit of GDP as the actual emission value of the early warning target.

[0055] In an embodiment of the present invention, the total real-time carbon emissions are divided by the GDP value of the same period to generate a standardized indicator.

[0056] To address the lag in GDP statistics (such as quarterly releases), high-frequency substitute indicators (such as electricity consumption and freight volume) are introduced to construct a regression model for real-time interpolation and prediction of GDP data, ensuring the continuity of calculations.

[0057] The standard deviation threshold method is used to remove outliers in carbon emissions or GDP caused by data acquisition failures, thus preventing extreme values ​​from distorting the calculation results.

[0058] The current carbon emissions per unit of GDP are linked with historical values ​​for the same period last year (e.g., the same week last year) and planned target values ​​(e.g., carbon neutrality pathway indicators) to form a dynamic reference system. A structured data object containing total carbon emissions, GDP baseline, and carbon intensity value is generated for direct use by subsequent comparison modules, avoiding duplicate calculations.

[0059] In this embodiment of the invention, upgrading annual or quarterly GDP data to near real-time data streams can shorten the carbon intensity accounting cycle and significantly improve monitoring timeliness.

[0060] Traditional building carbon management uses fixed-period matching of building area and carbon emission data, which fails to capture quarterly operational changes. Furthermore, the early warning threshold settings do not incorporate core parameters such as building area and functional zoning, resulting in early warning results that are out of sync with the actual operational status of the building. Based on this, the present invention proposes an optional embodiment.

[0061] Reference Figure 4 , Figure 4 This is a schematic diagram of another specific embodiment of step S102 of the dynamic early warning method for carbon emissions in this invention. Step S102 further includes the following specific implementation: Step S1023: Use the buildings in the target area as the warning targets and obtain the building area data of the target area.

[0062] In embodiments of the present invention, specific buildings (such as commercial complexes, hospitals, or schools) that need to be monitored within a target area are identified and locked, distinguishing them from macro-targets such as the overall area or traffic roads.

[0063] By connecting with Building Information Modeling (BIM), Geographic Information System (GIS), or Building Management System (BMS), core spatial indicators such as the total building area, functional zoning area (such as office area, computer room, public area), and greenable area of ​​the target building can be extracted in real time.

[0064] Linking building area data with carbon emission monitoring cycles (such as hourly / daily) ensures that the spatiotemporal scales of area data and carbon emission data are consistent, avoiding calculation distortion due to statistical discrepancies.

[0065] Step S1024: Based on the building area data and the carbon emission data, calculate the actual carbon emission value per unit area as the actual emission value of the early warning target.

[0066] In an embodiment of the present invention, the total real-time building carbon emissions are divided by the total building area during the same period to generate a standardized intensity index.

[0067] For multi-functional buildings (such as hotels with guest rooms, restaurants, and conference areas), weights are allocated based on energy density.

[0068] Optionally, outliers caused by sensor malfunctions (such as sudden carbon emission peaks) can be removed through box plot analysis.

[0069] In this embodiment of the invention, carbon emissions from a macro-region are broken down to individual buildings and even functional zones, allowing for precise identification of high-energy-consuming entities. By using carbon emission intensity per unit area, inefficient building types can be identified, providing a quantitative basis for prioritizing renovations.

[0070] Traditional regional carbon management uses fixed-period (e.g., annual) carbon emission statistics, failing to incorporate carbon sink absorption variables in real time, leading to early warning results that deviate from actual environmental contributions. Based on this, the present invention proposes an optional embodiment.

[0071] Step S102 further includes the following specific implementation methods: Step S1025: Use the population of the target area as the early warning target, and obtain the carbon sink data of the target area.

[0072] In the embodiments of the present invention, the early warning target is a regional "population" set (such as permanent residents and migrant population), which is different from physical targets such as total economic output or building units, highlighting the responsibility of human activities for carbon emissions.

[0073] By utilizing satellite remote sensing, forest resource databases, or urban green space monitoring systems, real-time carbon absorption data (such as forest carbon sequestration, wetland carbon storage, and green space purification capacity) of target areas is acquired to form a dynamic baseline value for ecological offsetting. Data update frequency supports daily or weekly updates to ensure timely matching with carbon emission fluctuations.

[0074] Carbon sink data is mapped to regional grids based on population distribution density (e.g., matching carbon sink data of nearby parks to high-density residential areas) to avoid spatial misalignment between ecological benefits and population activities.

[0075] Step S1026: Calculate the actual net carbon emissions based on the carbon sink data and the carbon emission data, and obtain the population data of the target area.

[0076] In an embodiment of the present invention, a carbon balance algorithm engine is used to subtract the amount of carbon sink absorption during the same period from the total real-time carbon emissions to generate a "net carbon emission value", which quantifies the actual net output of greenhouse gases in the region.

[0077] Optionally, population size and distribution can be dynamically collected through mobile signaling, household registration systems, or IoT devices, and weight adjustments can be made for sudden population changes (such as temporary population surges caused by large-scale events).

[0078] Optionally, the net carbon emissions can be divided by the total population in the same period to generate a "tons of CO2 per person" indicator, so as to achieve comparability of carbon emission intensity in regions of different sizes.

[0079] Step S1027: Calculate the actual per capita carbon emission value as the actual emission value of the early warning target based on the population data and the carbon emission data.

[0080] In this embodiment of the invention, per capita carbon emissions are linked to historical averages and international standard values ​​to form a dynamic evaluation coordinate system. The output is a quadruple data entity containing population size, total carbon emissions, net emissions, and per capita intensity, which can be directly accessed by the early warning comparison module.

[0081] In this embodiment of the invention, the introduction of dynamic offsetting of carbon sink data can transform "gross emissions" into "net emissions," avoiding the distortion of early warnings caused by ignoring contributions to ecological restoration (such as forest cities being misjudged as high-carbon areas). Furthermore, by using per capita carbon emission intensity indicators, the correlation between population size, migration patterns, and emissions can be revealed, providing a basis for differentiated regulation.

[0082] Traditional traffic carbon management relies on overall regional emission statistics (such as annual average traffic carbon emissions in a city), which cannot pinpoint specific high-emission road sections. Therefore, this invention proposes an alternative embodiment.

[0083] Following step S1027, the following specific implementation methods are also included: Step S1028: Use the traffic roads in the target area as the warning target and obtain the traffic flow data of the target area.

[0084] In an embodiment of the present invention, the absolute total carbon emissions of each road are calculated based on the actual emissions per unit of traffic flow and in conjunction with real-time traffic flow data. The calculation formula is as follows: Actual road emissions = Emissions per unit of traffic flow * Real-time traffic flow of the road segment.

[0085] For example: If the unit traffic flow emission value of a certain road section is 0.5 kg CO2 / standard vehicle, and the current traffic flow is 10,000 standard vehicles / hour, then the total emission of CO2 in the current period is 5 tons.

[0086] Traffic flow changes are collected in real time through IoT devices (such as geomagnetic sensors and cameras), and the total emissions are updated every 15 minutes to avoid calculation errors caused by traffic flow fluctuations.

[0087] Step S1029: Based on the traffic flow data and the carbon emission data, calculate the actual carbon emission value per unit traffic flow as the actual emission value of the early warning target.

[0088] In an embodiment of the present invention, road grade, road length, real-time traffic flow, and carbon emissions are constructed into a "road carbon emission profile" data object, for example: {Road ID: R015, Grade: Expressway, Length: 18km, Traffic flow: 12,000 vehicles / hour, Emissions: 7.2 tons CO2}.

[0089] In this embodiment of the invention, overall regional emissions are broken down into specific road segments, overcoming the blind spot where regional averages mask high-emission road segments. By integrating characteristics such as road grade and length, high-impact road segments requiring priority remediation can be identified.

[0090] like Figure 5 The diagram illustrates a terminal device according to an embodiment of the present invention. The terminal device 500 may include a processor 501, a memory 502, and a computer program 503 stored in the memory 502 and executable on the processor 501, such as a dynamic carbon emission early warning program. When the processor 501 executes the computer program 503, it implements the steps described in the various dynamic carbon emission early warning embodiments.

[0091] A computer program can be divided into one or more modules / units. One or more modules / units are stored in memory 502 and executed by processor 501 to complete the present invention. One or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in a terminal device.

[0092] The terminal device may include, but is not limited to, processor 501 and memory 502. Those skilled in the art will understand that... Figure 5 This is merely an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, a terminal device may also include input / output devices, network access devices, buses, etc.

[0093] The processor 501 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0094] The memory 502 can be an internal storage unit of the terminal device, such as the hard drive or RAM of the terminal device. The memory 502 can also be an external storage device of the terminal device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 502 can include both internal and external storage units of the terminal device. The memory 502 is used to store computer programs and other programs and data required by the terminal device. The memory 502 can also be used to temporarily store data that has been output or will be output.

[0095] It should be noted that, for the sake of convenience and brevity, the structure of the terminal device described above can also be referred to the specific description of the structure in the method embodiment, which will not be repeated here.

[0096] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps in the above-described dynamic early warning method for carbon emissions.

[0097] This invention provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the steps in the aforementioned dynamic early warning method for carbon emissions.

[0098] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0099] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for various specific applications, but such implementations should not be considered beyond the scope of this invention.

[0100] In the embodiments provided by this invention, it should be understood that the disclosed terminal devices and methods can be implemented in other ways. For example, the terminal device embodiments described above are merely illustrative. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0101] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0102] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0103] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0104] The embodiments described above are merely illustrative of the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A dynamic early warning method for carbon emissions, characterized in that, include: Real-time acquisition of carbon emission data for the target area; Based on the carbon emission data, calculate the actual emission values ​​of the early warning targets; Compare the actual emission values ​​with the target values ​​corresponding to the early warning targets; If the actual emission value exceeds the target value, an early warning message will be generated and output.

2. The dynamic early warning method for carbon emissions as described in claim 1, characterized in that, Before the step of comparing the actual emission value with the target value corresponding to the warning target, the method further includes: Obtain GDP data and building area data for the target area; Based on the GDP data and the building area data, the target value corresponding to the early warning target is determined.

3. The dynamic early warning method for carbon emissions as described in claim 1, characterized in that, The step of calculating the actual emission value of the early warning target based on the carbon emission data includes: The target area is used as the early warning target, and the GDP data of the target area is obtained; Based on the GDP data and the carbon emission data, the actual carbon emission value per unit of GDP is calculated as the actual emission value of the early warning target.

4. The dynamic early warning method for carbon emissions as described in claim 1, characterized in that, The step of calculating the actual emission value of the early warning target based on the carbon emission data includes: The buildings in the target area are used as the early warning targets, and the building area data of the target area is obtained; Based on the building area data and the carbon emission data, the actual carbon emission value per unit area is calculated as the actual emission value of the early warning target.

5. The dynamic early warning method for carbon emissions as described in claim 1, characterized in that, The step of calculating the actual emission value of the early warning target based on the carbon emission data includes: The population of the target area is used as the early warning target, and carbon sink data of the target area is obtained; Based on the carbon sink data and the carbon emission data, calculate the actual net carbon emissions and obtain the population data of the target area; Based on the population data and the carbon emission data, the actual per capita carbon emission value is calculated as the actual emission value of the early warning target.

6. The dynamic early warning method for carbon emissions as described in claim 1, characterized in that, The step of calculating the actual emission value of the early warning target based on the carbon emission data includes: The traffic roads in the target area are used as the early warning targets, and traffic flow data in the target area is obtained; Based on the traffic flow data and the carbon emission data, the actual carbon emission value per unit traffic flow is calculated as the actual emission value of the early warning target.

7. The dynamic early warning method for carbon emissions as described in claim 6, characterized in that, After the step of calculating the actual carbon emission value per unit of traffic flow as the actual emission value of the early warning target based on the traffic flow data and the carbon emission data, the method further includes: Calculate the target actual carbon emission value for each road based on the actual carbon emission value per unit traffic flow. The warning information is output based on the road information of each road, and the road information includes at least one of road grade, road length, road traffic volume, and road carbon emissions.

8. The dynamic early warning method for carbon emissions as described in claim 1, characterized in that, The step of calculating the actual emission value of the early warning target based on the carbon emission data includes: Select early warning targets, wherein the optional early warning targets include the target area and unit targets in the target area. The unit targets include traffic roads, population, and buildings. The buildings include commercial buildings, office buildings, hotel buildings, cultural buildings, educational buildings, medical and health buildings, sports buildings, transportation buildings, residential buildings, and special buildings. The characteristic buildings are obtained by custom calibration. Based on the carbon emission data, calculate the actual emission value of the early warning target.

9. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the dynamic early warning method for carbon emissions as claimed in any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the dynamic early warning method for carbon emissions as described in any one of claims 1 to 8.

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