A method and system for monitoring industrial carbon emissions

Abstract

This invention discloses an industrial carbon emission monitoring method and system, relating to the field of industrial carbon emission monitoring. It includes: a preset module for pre-setting a unified spatiotemporal reference logic for collecting carbon emission and air pollutant emission data at fixed industrial emission points, and generating a synchronous acquisition trigger control signal based on the logic; and an acquisition module for responding to the synchronous acquisition trigger control signal and simultaneously acquiring carbon emission characteristic parameters and air pollutant emission characteristic parameters at the corresponding emission points. This invention ensures that the synchronously acquired data is tamper-proof and accurately verifiable throughout the entire process by irreversibly coupling and binding the data in a spatiotemporal manner. Furthermore, based on multi-dimensional feature decomposition and multiple compliance checks, it accurately extracts effective co-source emission coupling features for precise correction and positioning of the emission source spatial coordinates.

Description

Technical Field

[0001] This invention relates to the field of industrial carbon emission monitoring technology, specifically to a method and system for monitoring industrial carbon emissions. Background Technology

[0002] Currently, PM2.5 / O3 atmospheric compound pollution and carbon emissions share a high degree of common origin, and coordinated governance at the regional and industry scales is the core direction for ecological and environmental protection.

[0003] The invention patent application with application number 202211300104.5 discloses a method and system for processing carbon emissions. The application aims to solve the problem that "existing carbon emission monitoring methods are inaccurate and inefficient".

[0004] However, existing methods for monitoring industrial carbon emissions have not achieved a precise match between the spatiotemporal correlation of carbon emissions and air pollutant emissions, making it difficult to simultaneously identify co-emission sources of high carbon and high pollution from industry.

[0005] To this end, we propose a method and system for monitoring industrial carbon emissions. Summary of the Invention

[0006] In view of the above-mentioned shortcomings of the existing technology, the present invention provides an industrial carbon emission monitoring method and system, which can effectively solve the problems of the existing technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions; This invention discloses an industrial carbon emission monitoring system, comprising: The system comprises the following modules: a preset module for establishing a unified spatiotemporal reference logic for collecting carbon emission and air pollutant emission data at fixed industrial emission points, and generating a trigger control signal for synchronous acquisition based on this logic; an acquisition module for responding to the synchronous acquisition trigger control signal and simultaneously acquiring carbon emission characteristic parameters and air pollutant emission characteristic parameters at the corresponding emission points; a binding module for coupling and binding the synchronously acquired carbon emission characteristic parameters and air pollutant emission characteristic parameters with the corresponding unified spatiotemporal reference parameters; an extraction module for performing decomposition and extraction of co-source emission coupling features based on the unified spatiotemporal reference for the coupled carbon emission and air pollutant emission parameters; a location identification module for performing spatial location, collaborative emission attribute assignment, and unique identification processing for the corresponding emission points based on the extracted co-source emission coupling features; and an output module for performing differentiated storage and distribution to preset receiving terminals for the identified collaborative emission source data and coupled full-link spatiotemporal matching data. The preset module is interconnected with the acquisition module via a wireless network, the binding module is interconnected with the extraction module via a wireless network, the positioning identifier module is interconnected with the output module via a wireless network, the positioning identifier module and the output module are interconnected with the extraction module via a wireless network, and the acquisition module is interconnected with the binding module via a wireless network.

[0008] Furthermore, during the operation phase of the preset module, a unified spatiotemporal reference grid covering all fixed emission points is constructed by taking a preset global time reference as the core and combining the spatial coordinate parameters of each emission point. Based on the node timing of the unified spatiotemporal reference grid, a synchronous acquisition trigger control signal corresponding to each emission point is generated. Each grid node within the unified spatiotemporal reference grid corresponds to a unique spatiotemporal reference parameter, which includes a global timestamp and spatial coordinates of the point. The triggering sequence of the trigger control signal is aligned with the global timestamp of the corresponding grid node.

[0009] Furthermore, the acquisition module receives a synchronous acquisition trigger control signal, locks the acquisition window corresponding to the global timestamp, and synchronously issues an acquisition execution command. Based on the command, it acquires carbon emission characteristic parameters of the emission point within the locked acquisition window, and synchronously acquires atmospheric pollutant emission characteristic parameters of the same emission point within the same locked acquisition window. Among them, carbon emission characteristic parameters include carbon dioxide volume concentration, methane volume concentration, flue gas flow rate, flue gas temperature, and flue gas pressure; atmospheric pollutant emission characteristic parameters include nitrogen oxide mass concentration, sulfur dioxide mass concentration, particulate matter mass concentration, and volatile organic compound mass concentration.

[0010] Furthermore, the binding module extracts the unified spatiotemporal reference parameters corresponding to the synchronously acquired data, generates a spatiotemporal anchoring operator uniquely corresponding to this set of acquired data, and irreversibly couples and binds the synchronously acquired carbon emission characteristic parameters and air pollutant emission characteristic parameters with the unified spatiotemporal reference parameters based on the spatiotemporal anchoring operator. The calculation logic of the coupling and binding follows the following: ; In the formula: This is the coupled and bound collaborative emission dataset; Generates functions for spatiotemporal anchoring operators; To unify the spatiotemporal reference parameters, where t is the global timestamp of the corresponding acquisition window and p is the three-dimensional spatial coordinate of the corresponding emission point; is the anchoring coupling operator; C is the set of carbon emission characteristic parameters collected synchronously; P is the set of air pollutant emission characteristic parameters collected synchronously within the same acquisition window; This is a concatenation operator for parameters from the same source.

[0011] Furthermore, the spatiotemporal anchoring operator generating function is: ; In the formula: It is an N-order identity matrix, where N is the total number of dimensions of the concatenated carbon emission characteristic parameters and air pollutant characteristic parameters; This is the preset global time reference point; This is the preset minimum scale unit for the global time base; For standard natural index calculations; This is the standard Kronecker product operation; Let T be the 2-norm of the spatial coordinate column vector p; the superscript T is for vector transpose.

[0012] Furthermore, the extraction module performs a second alignment of the coupled emission parameters in the time dimension based on unified spatiotemporal reference parameters to eliminate micro-time-series deviations generated during the acquisition process. The time-aligned emission parameters are then decomposed to obtain the co-source emission correlation features, time-series evolution features, and point emission intensity features of carbon emissions and air pollutants. Finally, the decomposed multi-dimensional features are sequentially checked for spatiotemporal consistency, co-source correlation, and emission stability. Features that pass all checks are retained as co-source emission coupling features for emission source location and attribute identification. Among them, the spatiotemporal consistency verification determines whether the spatiotemporal reference to which the feature belongs matches the unified spatiotemporal reference; the source correlation verification determines whether the carbon emission feature and the air pollutant feature originate from the same emission source; and the emission stability verification determines whether the fluctuation range of the feature within the preset time window is within the preset range.

[0013] Furthermore, the positioning identification module, based on the extracted same-source emission coupling features, corrects the preset spatial coordinates of the emission points to obtain precise spatial positioning parameters of the emission source. Based on the same-source emission coupling features, it calculates the quantified parameters of coordinated emissions for the corresponding emission points. These quantified parameters are then matched with preset attribute judgment criteria. Based on the matching results, the emission points are assigned corresponding same-source coordinated emission attributes, non-coordinated emission attributes, or weakly coordinated emission attributes, thus completing the unique assignment of the coordinated emission attributes for each emission point. The calculation of the quantified parameters of coordinated emissions follows the following rules: ; In the formula: For the quantification coefficient of coordinated emissions; This is the time-series rate of change vector of the carbon emission characteristic parameter set within the corresponding time window; This is the time-series rate of change vector of the characteristic parameters of atmospheric pollutant emissions within the same time window; For Pearson vector correlation degree calculation; Let be the rank of the fluctuation covariance matrix of the carbon emission characteristic parameter set and the air pollutant characteristic parameter set within the corresponding time window; The positioning identification module generates a globally unique identifier for the corresponding emission source data based on the precise spatial positioning parameters of the emission point, the coordinated emission attributes, and the unified spatiotemporal reference parameters.

[0014] Furthermore, when correcting the preset spatial coordinates of the emission point, the following applies: ; In the formula: The precise spatial positioning parameters of the emission source, i.e., the corrected three-dimensional spatial coordinates, are denoted as... ; The preset original spatial coordinates of the emission point are denoted as follows: ; This is a spatial offset correction vector generated based on the coupling characteristics of emissions from the same source, and its components are... The values ​​are determined by the point emission intensity deviation, temporal evolution correlation, and pollutant distribution coupling degree in the same source emission coupling characteristics.

[0015] On the other hand, a method for monitoring industrial carbon emissions includes: Using a global time reference as the core, a unified spatiotemporal reference grid is constructed by combining the spatial coordinates of each emission point. Synchronous acquisition trigger control signals corresponding to emission points are generated according to the time sequence of grid nodes. The synchronous acquisition trigger signals are received and the acquisition window is locked. Carbon emission characteristic parameters and air pollutant emission characteristic parameters of emission points are synchronously acquired within the same window. The corresponding unified spatiotemporal reference parameters are extracted to generate spatiotemporal anchoring operators, and the two types of synchronously acquired parameters are irreversibly coupled and bound to the spatiotemporal reference parameters. The coupled and bound parameters are time-series aligned again, multi-dimensional emission characteristics are extracted, and after triple verification, the effective same-source emission coupling characteristics are retained. The spatial coordinates of emission points are corrected according to the coupling characteristics, the coordinated emission quantification parameters are calculated and the emission attributes are assigned, and a globally unique identifier for the emission source is generated. The identified emission source data and the full-link spatiotemporal matching data are classified and stored, and then distributed to the preset receiving end.

[0016] Compared with the known prior art, the technical solution provided by this invention has the following beneficial effects: In this invention, the system establishes a unified spatiotemporal benchmark covering all fixed emission points, enabling the synchronous collection of carbon emission and atmospheric pollutant emission characteristic parameters. This avoids data timing deviations and spatial misalignments from the source, ensuring the homogeneity and consistency of the collected data. At the same time, by irreversibly binding the synchronously collected data in a spatiotemporal coupling, it ensures that the data is tamper-proof and accurately verifiable throughout the entire process. Based on multi-dimensional feature decomposition and multiple compliance checks, it accurately extracts effective homogeneous emission coupling features for the precise correction and positioning of emission source spatial coordinates. Meanwhile, it can also quantify the intensity of synergistic emissions of carbon emissions and air pollutants, assign emission attributes and globally unique identifiers, effectively improve the accuracy and reliability of synergistic monitoring of industrial carbon emissions and pollutants, simplify data traceability and accounting processes, reduce the risk of data distortion, and provide support for accurate accounting of industrial carbon emissions, efficient management and control of pollutants, and dual-control supervision. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0018] Figure 1 This is a schematic diagram of an industrial carbon emission monitoring system. Figure 2 This is a flowchart illustrating a method for monitoring industrial carbon emissions. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] The present invention will be further described below with reference to embodiments.

[0021] Example: This embodiment provides an industrial carbon emission monitoring system, such as... Figure 1 As shown, it includes: The preset module is used to preset a unified spatiotemporal reference logic for the collection of carbon emission and air pollutant emission data at industrial fixed emission points, and generate trigger control signals for synchronous collection based on the logic. During the operation phase of the preset module, a unified spatiotemporal reference grid covering all fixed emission points is constructed by taking the preset global time reference as the core and combining the spatial coordinate parameters of each emission point. Based on the node timing of the unified spatiotemporal reference grid, a synchronous acquisition trigger control signal corresponding to each emission point is generated. Each grid node within the unified spatiotemporal reference grid corresponds to a unique spatiotemporal reference parameter, which includes a global timestamp and spatial coordinates of the point. The triggering sequence of the trigger control signal is aligned with the global timestamp of the corresponding grid node. The data acquisition module is used to respond to the synchronous acquisition trigger control signal and synchronously acquire carbon emission characteristic parameters and air pollutant emission characteristic parameters at the corresponding emission points. The acquisition module consists of an infrared flue gas analyzer, which is integrated with the existing CEMS continuous flue gas monitoring system. The acquisition module receives a synchronous acquisition trigger control signal, locks the acquisition window corresponding to the global timestamp, and synchronously issues an acquisition execution command. Based on the command, it acquires carbon emission characteristic parameters of the emission point within the locked acquisition window, and synchronously acquires air pollutant emission characteristic parameters of the same emission point within the same locked acquisition window. Among them, carbon emission characteristic parameters include carbon dioxide volume concentration, methane volume concentration, flue gas flow rate, flue gas temperature, and flue gas pressure; air pollutant emission characteristic parameters include nitrogen oxide mass concentration, sulfur dioxide mass concentration, particulate matter mass concentration, and volatile organic compound mass concentration. The binding module is used to couple and bind the synchronously collected carbon emission characteristic parameters and air pollutant emission characteristic parameters with the corresponding unified spatiotemporal reference parameters one by one. The binding module extracts the unified spatiotemporal reference parameters corresponding to the synchronous collection, generates a spatiotemporal anchoring operator that uniquely corresponds to the set of collected data, and irreversibly couples and binds the synchronously collected carbon emission characteristic parameters and air pollutant emission characteristic parameters with the unified spatiotemporal reference parameters based on the spatiotemporal anchoring operator. The binding technology uses SM3 hash encryption + timestamp digital signature + consortium blockchain notarization. After the spatiotemporal anchoring operator is generated, the hash value is uploaded to the blockchain. Any modification to the data will cause the hash verification to fail and the anchoring identifier to become invalid. The computational logic of the coupled system follows: ; In the formula: This is the coupled and bound collaborative emission dataset; Generates functions for spatiotemporal anchoring operators, used to convert unified spatiotemporal reference parameters into irreversible unique anchoring identifiers; To unify the spatiotemporal reference parameters, where t is the global timestamp of the corresponding acquisition window and p is the three-dimensional spatial coordinate of the corresponding emission point; is the anchoring coupling operator, used to bind the spatiotemporal anchoring identifier to the emission parameter set globally, so that any change in any parameter within the bound dataset will trigger the invalidation of the spatiotemporal anchoring identifier; C is the set of carbon emission characteristic parameters collected synchronously; P is the set of atmospheric pollutant emission characteristic parameters collected synchronously within the same acquisition window; This is a concatenation operator for parameters from the same source, used to concatenate carbon emission parameters and air pollutant parameters from the same location and time according to their source dimension, generating an N-dimensional column vector of emission parameters from the same source. The above formula transforms the unified spatiotemporal reference parameters into irreversible and unique spatiotemporal anchoring identifiers. Then, carbon emissions and air pollutant parameters at the same location and time are aligned and stitched together according to the same source dimension. Finally, the spatiotemporal anchoring identifiers are bound to the stitched emission parameter set across the entire domain. Once the data is modified, the spatiotemporal anchoring identifiers will become invalid, thereby achieving precise and irreversible coupling between emission data and spatiotemporal reference, ensuring that the data is traceable and authentic. The spatiotemporal anchoring operator generating function is: ; In the formula: It is an N-order identity matrix, where N is the total number of dimensions of the concatenated carbon emission characteristic parameters and air pollutant characteristic parameters; This is the preset global time reference point; The preset minimum scale unit for the global time base is 1 second; For standard natural index calculations; This is the standard Kronecker product operation; Let T be the 2-norm of the spatial coordinate column vector p; the superscript T indicates the vector transpose operation. in, The optional fixed value is UTC+8XXXX (year)-XX (month)-XX (day) 00:00:00; The preset size of the spatiotemporal reference grid is 1m×1m in the plane and 0.5m in the vertical direction; The above formula uses the global time reference point and the minimum scale as a basis, constructs the unique identification feature of the time dimension using the natural exponent operation, forms the anchoring feature of the spatial dimension through the Kronecker product and the 2-norm operation of the spatial coordinates, and combines the N-order identity matrix to adapt the multi-dimensional emission parameters to generate an anchoring operator with both time and space uniqueness, thus eliminating the problem of duplication or deviation in multi-parameter spatiotemporal anchoring. The extraction module is used to decompose and extract the carbon emission and air pollutant emission parameters that have been coupled and bound together, based on a unified spatiotemporal benchmark, the same source emission coupling characteristics (i.e., carbon-pollution same source synergistic emission characteristics). The extraction module performs a second alignment of the time-series dimension on the coupled emission parameters based on unified spatiotemporal reference parameters to eliminate micro-time-series deviations generated during the acquisition process. The time-series aligned emission parameters are then decomposed to obtain the co-source emission correlation characteristics, time-series evolution characteristics, and point emission intensity characteristics of carbon emissions and air pollutants. Finally, the decomposed multi-dimensional features are sequentially checked for spatiotemporal consistency, co-source correlation, and emission stability. Features that pass all checks are retained as co-source emission coupling features for emission source location and attribute identification. Among them, the spatiotemporal consistency verification determines whether the spatiotemporal reference to which the feature belongs matches the unified spatiotemporal reference; the same source correlation verification determines whether the carbon emission feature and the air pollutant feature originate from the same emission source; and the emission stability verification determines whether the fluctuation range of the feature within the preset time window is within the preset range, for example. Spatiotemporal consistency check: if the time deviation is ≤ ±0.5 seconds and the spatial deviation is ≤ ±0.5 m, it is considered a match; Source correlation verification: If the Pearson correlation coefficient between carbon emissions and pollutant parameters is ≥0.85, they are determined to be from the same source; Emission stability verification: If the data fluctuation range is ≤±5% within a 1-minute time window, it is considered stable; The location identification module is used to perform spatial location of the corresponding emission point, assignment of coordinated emission attributes and unique identification processing based on the extracted same-source emission coupling characteristics. The location identification module, based on the extracted same-source emission coupling characteristics, corrects the preset spatial coordinates of the emission points to obtain precise spatial location parameters of the emission source. Based on the same-source emission coupling characteristics, it calculates the quantification parameters of the corresponding emission points. These quantification parameters are then matched with preset attribute judgment criteria. Based on the matching results, the emission points are assigned corresponding same-source quantification attributes (co-emission, non-co-emission, or weakly quantification), thus completing the unique assignment of the quantification parameters to the emission points. The calculation of the quantification parameters follows the following rules: ; In the formula: It is a synergistic emission quantification coefficient used to characterize the intensity of co-source synergistic emissions of carbon emissions and air pollutants; This is the time-series rate of change vector of the carbon emission characteristic parameter set within the corresponding time window; This is the time-series rate of change vector of the characteristic parameters of atmospheric pollutant emissions within the same time window; This is the Pearson vector correlation operation, used to calculate the degree of correlation between the same-direction changes of two time-series rate of change vectors; The rank of the fluctuation covariance matrix of the carbon emission characteristic parameter set and the air pollutant characteristic parameter set within the corresponding time window is used to characterize the stability of the co-origin fluctuations of the two sets of parameters. The initial emission attributes are as follows: ≥0.7 indicates co-emission from the same source; 0.3≤ <0.7 indicates a weak co-emission attribute; <0.3 indicates non-cooperative emission attribute The formula first calculates the degree of co-correlation between carbon emissions and the time-series change rate of air pollutant parameters, then uses the rank of the covariance matrix of the two sets of parameters to measure the stability of the co-source fluctuation, and obtains the co-emission quantification coefficient (i.e. carbon-pollution co-emission intensity coefficient) through ratio calculation, thereby reflecting the intensity of co-emission from the same source and providing support for subsequent emission attribute assignment. The location identification module generates a globally unique identifier for the corresponding emission source data based on the precise spatial location parameters of the emission point, the coordinated emission attributes, and the unified spatiotemporal reference parameters. When correcting the preset spatial coordinates of the emission point, the following rules apply: ; In the formula: The precise spatial positioning parameters of the emission source, i.e., the corrected three-dimensional spatial coordinates, are denoted as... ; The preset original spatial coordinates of the emission point are denoted as follows: ; This is a spatial offset correction vector generated based on the coupling characteristics of emissions from the same source, and its components are... The parameters are determined by the point emission intensity deviation, temporal evolution correlation, and pollutant distribution coupling degree in the same source emission coupling characteristics, respectively: This formula is based on the original spatial coordinates of the emission point, and the spatial offset correction vector generated by the coupling characteristics of the same source emission is superimposed to complete the positioning correction. Each axial correction component is calculated by combining the emission intensity deviation, temporal evolution correlation, pollutant distribution coupling degree and the corresponding axial scale coefficient to adapt to the emission and spatial characteristics of different points. ; In the formula: This refers to the point emission intensity deviation in the same-source emission coupling characteristics; The preset spatial scale coefficient is set in the x-axis direction; The temporal evolution correlation degree in the coupling characteristics of emissions from the same source; The preset spatial scale coefficient is set in the y-axis direction; This refers to the pollutant distribution coupling degree in the coupling characteristics of emissions from the same source. The preset spatial scale coefficient is set in the z-axis direction; Under the same spatiotemporal reference, the absolute difference between the measured carbon emission intensity and the preset reference emission intensity at that point is taken; Take the absolute value of the Pearson correlation coefficient between carbon emission characteristic parameters and air pollutant characteristic parameters within the same time window; The spatial concentration overlap between carbon emission components and atmospheric pollutant components in the flue gas is taken, with a value ranging from 0 to 1. ∈[0.1,2], when there are significant spatial distribution differences in the emission points in the x-axis direction and it is necessary to enhance the positioning accuracy in this direction, the larger the value is, and vice versa; ∈[0.1,2], when the time-series emission characteristics of the emission point fluctuate significantly and the positioning deviation in the y-axis direction needs to be corrected synchronously, the value is larger, and vice versa; ∈[0.1,2], when there are multiple levels of emission structure in the vertical direction and the positioning error in the z-axis direction needs to be accurately corrected, the value is larger, and vice versa; The output module is used to perform operations such as distinguishing and storing the identified coordinated emission source data and the coupled and bound full-link spatiotemporal matching data, and distributing them to the preset receiving end. The preset module is connected to the acquisition module via a wireless network, the binding module is connected to the extraction module via a wireless network, the positioning tag module is connected to the output module via a wireless network, the positioning tag module and the output module are connected to the extraction module via a wireless network, and the acquisition module is connected to the binding module via a wireless network.

[0022] During system operation, the preset module runs a unified spatiotemporal reference logic for collecting carbon emission and air pollutant emission data at fixed industrial emission points. Based on this logic, it generates a synchronous acquisition trigger control signal. The acquisition module responds synchronously to the synchronous acquisition trigger control signal and simultaneously collects carbon emission characteristic parameters and air pollutant emission characteristic parameters at the corresponding emission points. The binding module runs in the background and couples the synchronously collected carbon emission characteristic parameters and air pollutant emission characteristic parameters with the corresponding unified spatiotemporal reference parameters one by one. The extraction module further performs decomposition and extraction of co-source emission coupling features based on the unified spatiotemporal reference for the coupled carbon emission and air pollutant emission parameters. Then, the positioning and identification module performs spatial positioning, collaborative emission attribute assignment, and unique identification processing for the corresponding emission points based on the extracted co-source emission coupling features. Finally, the output module is used to perform differentiated storage and distribution to the preset receiving end for the identified collaborative emission source data and coupled full-link spatiotemporal matching data.

[0023] In the above embodiments, the system can establish a unified spatiotemporal benchmark for industrial emission monitoring, realize the synchronous collection of carbon emission and air pollutant data, effectively eliminate temporal misalignment and spatial deviation, ensure the authenticity and traceability of data through irreversible coupling and binding, accurately correct the emission source location, quantify the coordinated emission intensity and complete attribute identification after multi-dimensional verification, making the monitoring data more accurate and reliable, simplifying the on-site accounting and source tracing process, improving the efficiency of industrial emission supervision, and providing reliable assistance for accurate carbon emission accounting and efficient pollutant control.

[0024] Referring to the system in the above embodiments, an example demonstrates an application of this system: A steel company uses this system to simultaneously monitor, trace, and control carbon emissions and air pollutants at fixed flue gas emission points in its sintering workshop.

[0025] The system first uses a globally unified time as the core, and combines it with the three-dimensional spatial coordinates of the emission point to build a unified spatiotemporal reference grid covering the point. It then matches a unique global timestamp and spatial coordinates to each grid node, and generates corresponding synchronous acquisition trigger control signals according to the node time sequence.

[0026] After receiving the trigger signal, the system locks the acquisition window and simultaneously acquires carbon emission characteristic parameters such as carbon dioxide and methane volume concentrations, flue gas flow rate, temperature, and pressure at the same acquisition time period, as well as air pollutant emission characteristic parameters such as nitrogen oxides, sulfur dioxide, particulate matter, and volatile organic compounds mass concentrations at the same location, thus completing the synchronous acquisition of data from the same source.

[0027] The system then extracts the unified spatiotemporal reference parameters corresponding to this collection, generates a dedicated spatiotemporal anchoring operator, and irreversibly couples and binds the two types of parameters collected synchronously with the spatiotemporal reference parameters to form a complete collaborative emission dataset. Any change to any parameter in the dataset will directly cause the spatiotemporal anchoring identifier to become invalid.

[0028] The system performs time-series secondary alignment on the bound data based on a unified spatiotemporal reference to eliminate micro-time-series deviations during the acquisition process. It decomposes and extracts three types of features: emission correlation from the same source, time-series evolution, and emission intensity at specific locations. After undergoing triple verification of spatiotemporal consistency, emission correlation from the same source, and emission stability, qualified emission coupling features from the same source are selected.

[0029] Based on this coupling characteristic, the system corrects the original spatial coordinates of the emission point to obtain accurate three-dimensional positioning parameters; at the same time, it calculates the quantification coefficient of the coordinated emission, determines the point to be a source of coordinated emission based on the coefficient matching, and then combines the accurate positioning results, coordinated emission attributes and spatiotemporal reference parameters to generate a globally unique identifier for the emission source.

[0030] Finally, the system will classify and store the identified collaborative emission source data and the spatiotemporal matching data of the entire chain, and simultaneously distribute them to the enterprise's environmental management platform and the receiving end of the local ecological and environmental monitoring department.

[0031] Example 2:

[0032] At the implementation level, based on Example 1, this example refers to... Figure 2 A further detailed description of an industrial carbon emission monitoring system in Example 1 is provided below: A method for monitoring industrial carbon emissions, comprising: With a global time reference as the core, a unified spatiotemporal reference grid is built by combining the spatial coordinates of each emission point, and synchronous acquisition trigger control signals corresponding to the emission points are generated according to the time sequence of grid nodes. Receive the synchronous acquisition trigger signal and lock the acquisition window, and synchronously acquire the carbon emission characteristic parameters and air pollutant emission characteristic parameters of the emission points within the same window; Extract the corresponding unified spatiotemporal reference parameters to generate a spatiotemporal anchoring operator, and irreversibly couple and bind the two types of synchronously collected parameters with the spatiotemporal reference parameters. Perform time-series secondary alignment on the parameters after coupling and binding, decompose and extract multi-dimensional emission features, and retain effective homogeneous emission coupling features after triple verification. Based on the coupling characteristics, the spatial coordinates of the emission points are corrected, the quantitative parameters of coordinated emissions are calculated and the emission attributes are assigned, and a globally unique identifier for the emission source is generated. The identified emission source data and the spatiotemporal matching data of the entire chain are classified and stored, and then distributed to the preset receiving end.

[0033] In summary, the system in the above embodiments establishes a unified spatiotemporal benchmark covering all fixed emission points, enabling the synchronous collection of carbon emission and air pollutant emission characteristic parameters. This avoids data timing deviations and spatial misalignments from the source, ensuring the homogeneity and consistency of the collected data. Furthermore, by irreversibly binding the synchronously collected data in a spatiotemporal manner, it ensures that the data is tamper-proof and accurately verifiable throughout the entire process. Based on multi-dimensional feature decomposition and multiple compliance checks, it accurately extracts effective homogeneous emission coupling features for precise correction and location of emission source spatial coordinates. Simultaneously, it can quantify and determine the synergistic emission intensity of carbon emissions and air pollutants, assign emission attributes and globally unique identifiers, effectively improving the accuracy and reliability of industrial carbon emission and pollutant synergistic monitoring, simplifying data traceability and accounting processes, reducing the risk of monitoring data distortion, and providing support for accurate industrial carbon emission accounting, efficient pollutant management, and dual-control supervision.

[0034] The above embodiments are only used to illustrate 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. Such modifications or substitutions will 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.

Claims

1. An industrial carbon emission monitoring system, characterized in that, include: The preset module is used to preset a unified spatiotemporal reference logic for the collection of carbon emission and air pollutant emission data at industrial fixed emission points, and generate trigger control signals for synchronous collection based on the logic. The data acquisition module is used to respond to the synchronous acquisition trigger control signal and synchronously acquire carbon emission characteristic parameters and air pollutant emission characteristic parameters at the corresponding emission points. The binding module is used to couple and bind the synchronously collected carbon emission characteristic parameters and air pollutant emission characteristic parameters with the corresponding unified spatiotemporal reference parameters one by one. The extraction module is used to perform decomposition and extraction of co-source emission coupling features based on a unified spatiotemporal benchmark on the carbon emission and air pollutant emission parameters that have been coupled and bound. The location identification module is used to perform spatial location of the corresponding emission point, assignment of coordinated emission attributes and unique identification processing based on the extracted same-source emission coupling characteristics. The output module is used to perform operations such as distinguishing and storing the identified coordinated emission source data and the coupled and bound full-link spatiotemporal matching data, and distributing them to the preset receiving end.

2. The industrial carbon emission monitoring system according to claim 1, characterized in that, The preset module operation phase takes a preset global time reference as the core, combines the spatial coordinate parameters of each emission point, constructs a unified spatiotemporal reference grid covering all fixed emission points, and generates a synchronous acquisition trigger control signal corresponding to each emission point based on the node timing of the unified spatiotemporal reference grid. Each grid node within the unified spatiotemporal reference grid corresponds to a unique spatiotemporal reference parameter, which includes a global timestamp and spatial coordinates of the point. The triggering sequence of the trigger control signal is aligned with the global timestamp of the corresponding grid node.

3. The industrial carbon emission monitoring system according to claim 1, characterized in that, The acquisition module receives a synchronous acquisition trigger control signal, locks the acquisition window corresponding to the global timestamp, and synchronously issues an acquisition execution command. Based on the command, it acquires carbon emission characteristic parameters of the emission point within the locked acquisition window, and synchronously acquires air pollutant emission characteristic parameters of the same emission point within the same locked acquisition window. Among them, carbon emission characteristic parameters include carbon dioxide volume concentration, methane volume concentration, flue gas flow rate, flue gas temperature, and flue gas pressure; atmospheric pollutant emission characteristic parameters include nitrogen oxide mass concentration, sulfur dioxide mass concentration, particulate matter mass concentration, and volatile organic compound mass concentration.

4. The industrial carbon emission monitoring system according to claim 1, characterized in that, The binding module extracts the unified spatiotemporal reference parameters corresponding to the synchronous acquisition, generates a spatiotemporal anchoring operator that uniquely corresponds to the group of acquired data, and irreversibly couples and binds the synchronously acquired carbon emission characteristic parameters and air pollutant emission characteristic parameters with the unified spatiotemporal reference parameters based on the spatiotemporal anchoring operator.

5. The industrial carbon emission monitoring system according to claim 1, characterized in that, The extraction module performs a second alignment of the time-series dimension on the coupled emission parameters based on unified spatiotemporal reference parameters to eliminate micro-time-series deviations generated during the acquisition process. The time-series aligned emission parameters are then decomposed to obtain the co-source emission correlation characteristics, time-series evolution characteristics, and point emission intensity characteristics of carbon emissions and air pollutants. Finally, the decomposed multi-dimensional features are sequentially checked for spatiotemporal consistency, co-source correlation, and emission stability. Features that pass all checks are retained as co-source emission coupling features for emission source location and attribute identification. Among them, the spatiotemporal consistency verification determines whether the spatiotemporal reference to which the feature belongs matches the unified spatiotemporal reference; the source correlation verification determines whether the carbon emission feature and the air pollutant feature originate from the same emission source; and the emission stability verification determines whether the fluctuation range of the feature within the preset time window is within the preset range.

6. The industrial carbon emission monitoring system according to claim 1, characterized in that, The positioning and identification module corrects the preset spatial coordinates of emission points based on the extracted same-source emission coupling features to obtain precise spatial positioning parameters of the emission source. Based on the same-source emission coupling features, it calculates the quantified parameters of coordinated emissions for the corresponding emission points. These quantified parameters are then matched with preset attribute judgment criteria. Based on the matching results, the emission points are assigned corresponding same-source coordinated emission attributes, non-coordinated emission attributes, or weakly coordinated emission attributes, thus completing the unique assignment of the coordinated emission attributes for each emission point. The calculation of the quantified parameters of coordinated emissions follows the following rules: ； In the formula: For the quantification coefficient of coordinated emissions; This is the time-series rate of change vector of the carbon emission characteristic parameter set within the corresponding time window; This is the time-series rate of change vector of the characteristic parameters of atmospheric pollutant emissions within the same time window; For Pearson vector correlation degree calculation; Let be the rank of the fluctuation covariance matrix of the carbon emission characteristic parameter set and the air pollutant characteristic parameter set within the corresponding time window; The positioning identification module generates a globally unique identifier for the corresponding emission source data based on the precise spatial positioning parameters of the emission point, the coordinated emission attributes, and the unified spatiotemporal reference parameters.

7. The industrial carbon emission monitoring system according to claim 6, characterized in that, When correcting the preset spatial coordinates of the emission point, the following rules apply: ； In the formula: The precise spatial positioning parameters of the emission source, i.e., the corrected three-dimensional spatial coordinates, are denoted as... ; The preset original spatial coordinates of the emission point are denoted as follows: ; This is a spatial offset correction vector generated based on the coupling characteristics of emissions from the same source, and its components are... The values ​​are determined by the point emission intensity deviation, temporal evolution correlation, and pollutant distribution coupling degree in the same source emission coupling characteristics.

8. The industrial carbon emission monitoring system according to claim 1, characterized in that, The preset module is interconnected with the acquisition module via a wireless network, the binding module is interconnected with the extraction module via a wireless network, the positioning identifier module is interconnected with the output module via a wireless network, the positioning identifier module and the output module are interconnected with the extraction module via a wireless network, and the acquisition module is interconnected with the binding module via a wireless network.

9. A method for monitoring industrial carbon emissions, wherein the method is an implementation method of an industrial carbon emission monitoring system as described in any one of claims 1-8, characterized in that, include: With a global time reference as the core, a unified spatiotemporal reference grid is built by combining the spatial coordinates of each emission point, and synchronous acquisition trigger control signals corresponding to the emission points are generated according to the time sequence of grid nodes. Receive the synchronous acquisition trigger signal and lock the acquisition window, and synchronously acquire the carbon emission characteristic parameters and air pollutant emission characteristic parameters of the emission points within the same window; Extract the corresponding unified spatiotemporal reference parameters to generate a spatiotemporal anchoring operator, and irreversibly couple and bind the two types of synchronously collected parameters with the spatiotemporal reference parameters. Perform time-series secondary alignment on the parameters after coupling and binding, decompose and extract multi-dimensional emission features, and retain effective homogeneous emission coupling features after triple verification. Based on the coupling characteristics, the spatial coordinates of the emission points are corrected, the quantitative parameters of coordinated emissions are calculated and the emission attributes are assigned, and a globally unique identifier for the emission source is generated. The identified emission source data and the spatiotemporal matching data of the entire chain are classified and stored, and then distributed to the preset receiving end.

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