Carbon footprint monitoring method, device, computer equipment, readable storage medium and program product

Abstract

This application relates to a carbon footprint monitoring method, apparatus, computer equipment, readable storage medium, and program product. The method includes: acquiring the current surface temperature and current magnetic flux density of electrical equipment; acquiring the current sensitivity and current zero magnetic field offset voltage of a microsensor array based on the current surface temperature; correcting the current magnetic flux density based on the current sensitivity and current zero magnetic field offset voltage, and acquiring the current current value flowing through the electrical equipment based on the corrected magnetic flux density; acquiring the total carbon emissions of the electrical equipment based on the current surface temperature and current value, and monitoring the carbon footprint of the electrical equipment based on the total carbon emissions. The method provided in this application makes the monitoring results of the carbon footprint of electrical equipment more accurate.

Description

Technical Field

[0001] This application relates to the field of carbon emission assessment technology, and in particular to a carbon footprint monitoring method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Technology

[0002] Under the "dual carbon" goal, the power industry is transforming from simply replacing clean energy to decarbonizing the entire industrial chain. As the "capillaries" of the power grid, the full life-cycle carbon footprint (PCF) accounting of power equipment (such as high-voltage cables and switchgear) has become a necessity.

[0003] Currently, carbon accounting for power equipment is mainly conducted by reviewing the supplier's ISO (International Organization for Standardization) 14001 certificate and factory test reports, etc. However, this accounting method does not incorporate the actual operating performance of the power equipment after it is connected to the grid into the evaluation system, resulting in inaccurate carbon accounting results. Summary of the Invention

[0004] Therefore, it is necessary to provide a carbon footprint monitoring method, device, computer equipment, computer-readable storage medium, and computer program product that can improve the accuracy of carbon accounting results for power equipment, addressing the aforementioned technical problems.

[0005] In a first aspect, this application provides a carbon footprint monitoring method, the method comprising:

[0006] The current surface temperature and current magnetic flux density of any power equipment in the power grid at the current data acquisition time are obtained; wherein the current surface temperature and the current magnetic flux density are acquired by a micro-sensor array based on the giant magnetoresistive effect;

[0007] Based on the current surface temperature, obtain the current sensitivity of the microsensor array and the current zero magnetic field offset voltage of the microsensor array at the current surface temperature;

[0008] Based on the current sensitivity and the current zero magnetic field offset voltage, the current magnetic flux density is corrected to obtain the corrected magnetic flux density, and based on the corrected magnetic flux density, the current current value flowing through the power equipment is obtained.

[0009] For the preset time period in which the current data acquisition time is located, based on the current surface temperature and the current current value, the total carbon emissions of the power equipment during the preset time period are obtained, and based on the total carbon emissions, the carbon footprint of the power equipment is monitored.

[0010] In one embodiment, obtaining the current sensitivity of the microsensor array and the current zero magnetic field offset voltage of the microsensor array at the current surface temperature based on the current surface temperature includes:

[0011] The reference zero magnetic field offset voltage of the microsensor array at a reference temperature, and the temperature difference between the current surface temperature and the reference temperature are obtained.

[0012] Based on the reference zero magnetic field offset voltage and the temperature difference, the current sensitivity of the microsensor array and the current zero magnetic field offset voltage of the microsensor array at the current surface temperature are obtained.

[0013] In one embodiment, the step of correcting the current magnetic flux density based on the current sensitivity and the current zero magnetic field offset voltage to obtain a corrected magnetic flux density, and obtaining the current current value flowing through the power equipment based on the corrected magnetic flux density, includes:

[0014] The current voltage of the power equipment is obtained based on the current magnetic induction intensity;

[0015] Based on the current voltage, the current sensitivity, and the current zero magnetic field offset voltage, the current magnetic flux density is corrected to obtain the corrected magnetic flux density.

[0016] Obtain the size parameters of the microsensor array and the number of giant magnetoresistive probes in the microsensor array;

[0017] Based on the corrected magnetic flux density, the size parameters, and the number of probes, the current value flowing through the power equipment is obtained.

[0018] In one embodiment, obtaining the total carbon emissions of the power equipment within the preset time period based on the current surface temperature and the current current value includes:

[0019] Obtain the reference resistance value of the power equipment at a reference temperature;

[0020] Based on the temperature difference between the current surface temperature and the reference temperature, and the reference resistance value, the current dynamic resistance value of the power equipment is obtained;

[0021] Based on the current current value and the current dynamic resistance value, obtain the current ohmic loss power;

[0022] Based on the current ohmic loss power, the total carbon emissions of the power equipment during the preset time period are obtained.

[0023] In one embodiment, obtaining the total carbon emissions of the power equipment within the preset time period based on the current ohmic loss power includes:

[0024] Obtain the marginal emission factor of the power grid, and obtain the total carbon emissions based on the product of the marginal emission factor and the current ohmic loss power.

[0025] In one embodiment, monitoring the carbon footprint of the power equipment based on the total carbon emissions includes:

[0026] A hash calculation is performed on the data combination formed by the current surface temperature, the current current value, and the total carbon emissions;

[0027] The hash value is digitally signed, and the data combination and the digital signature are uploaded to the blockchain node;

[0028] If the digital signatures of the corresponding data combinations at all data collection times within the preset time period are obtained, all digital signatures in the blockchain node are decrypted, and if all digital signatures are successfully decrypted, the carbon footprint of the power equipment is monitored based on the data combinations in the blockchain node.

[0029] Secondly, this application also provides a carbon footprint monitoring device, the device comprising:

[0030] The first acquisition module is used to acquire the current surface temperature and current magnetic induction intensity of any power equipment in the power grid at the current data acquisition time; wherein, the current surface temperature and the current magnetic induction intensity are acquired by a micro-sensor array based on the giant magnetoresistive effect;

[0031] The second acquisition module is used to acquire, based on the current surface temperature, the current sensitivity of the microsensor array and the current zero magnetic field offset voltage of the microsensor array at the current surface temperature;

[0032] The correction module is used to correct the current magnetic flux density based on the current sensitivity and the current zero magnetic field offset voltage to obtain the corrected magnetic flux density, and to obtain the current current value flowing through the power equipment based on the corrected magnetic flux density.

[0033] The third acquisition module is used to acquire the total carbon emissions of the power equipment within the preset time period in which the current data acquisition time is located, based on the current surface temperature and the current current value, and to monitor the carbon footprint of the power equipment based on the total carbon emissions.

[0034] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the methods in any of the above embodiments.

[0035] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the methods in any of the above embodiments.

[0036] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the methods in any of the above embodiments.

[0037] The aforementioned carbon footprint monitoring method, device, computer equipment, computer-readable storage medium, and computer program product acquire the current surface temperature and current magnetic flux density of any power equipment in the power grid at the current data acquisition time. The current surface temperature and current magnetic flux density are acquired by a micro-sensor array based on the giant magnetoresistive effect. Based on the current surface temperature, the current sensitivity of the micro-sensor array and the current zero magnetic field offset voltage of the micro-sensor array at the current surface temperature are acquired. Based on the current sensitivity and the current zero magnetic field offset voltage, the current magnetic flux density is corrected to obtain the corrected magnetic flux density, and based on the corrected magnetic flux density, the current current value flowing through the power equipment is acquired. For a preset time period in which the current data acquisition time is located, based on the current surface temperature and the current current value, the total carbon emissions of the power equipment within the preset time period are acquired, and based on the total carbon emissions, the carbon footprint of the power equipment is monitored. The method provided in this application obtains the total carbon emissions of power equipment within a preset time period based on the surface temperature and magnetic induction intensity of the power equipment collected at multiple data acquisition times within the preset time period. The total carbon emissions calculated in this way combine the actual operating performance of the power equipment after it is connected to the grid, making the monitoring results of the carbon footprint of the power equipment more accurate. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a flowchart illustrating a carbon footprint monitoring method in one embodiment;

[0040] Figure 2This is a flowchart illustrating the current zero magnetic field offset voltage acquisition step in one embodiment;

[0041] Figure 3 Here is a system architecture diagram of the carbon footprint monitoring method in another embodiment;

[0042] Figure 4 This is a structural block diagram of a carbon footprint monitoring device in one embodiment;

[0043] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0045] In one embodiment, such as Figure 1 As shown, a carbon footprint monitoring method is provided. This embodiment illustrates the method's application to a terminal. It is understood that this method can also be applied to a server, or to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0046] S102. Obtain the current surface temperature and current magnetic flux density of any power equipment in the power grid at the current data acquisition time; wherein the current surface temperature and current magnetic flux density are acquired by a micro-sensor array based on the giant magnetoresistive effect.

[0047] Optionally, the power equipment may be, but is not limited to, high-voltage cables or switchgear. If the power equipment is a high-voltage cable, the micro-sensor array may be deployed on the cable joint; if the power equipment is a switchgear, the micro-sensor array may be deployed on the switchgear busbar. Microsensors based on the giant magnetoresistance (GMR) effect have the characteristics of high sensitivity, wide frequency response, and small size, making them suitable for mounting on the conductor surface. Multiple GMR units are evenly distributed in a ring on a flexible PCB (Printed Circuit Board) strip and wrapped around the conductor under test. The conductor under test may be, but is not limited to, a cable joint or a switchgear busbar. The micro-sensor array integrates a MEMS (Micro-Electro-Mechanical Systems) temperature and humidity sensor, which measures the real-time temperature in close contact with the conductor surface.

[0048] S104. Based on the current surface temperature, obtain the current sensitivity of the microsensor array and the current zero magnetic field offset voltage of the microsensor array at the current surface temperature.

[0049] Sensitivity refers to the ratio of the change in the sensor's output signal to the change in the input measured physical quantity. For GMR sensors, the core expression of sensitivity is the ratio of the change in output voltage to the change in magnetic induction intensity B. The higher the sensitivity, the stronger the sensor's ability to sense changes in weak magnetic fields. Zero magnetic field offset voltage refers to the base voltage value output by the sensor when no external magnetic field acts on it.

[0050] Optionally, the current surface temperature can be input into a preset fitting function to output the current sensitivity and the current zero magnetic field offset voltage.

[0051] S106. Based on the current sensitivity and the current zero magnetic field offset voltage, the current magnetic induction intensity is corrected to obtain the corrected magnetic induction intensity, and based on the corrected magnetic induction intensity, the current current value flowing through the power equipment is obtained.

[0052] Alternatively, since the GMR sensitivity drifts with temperature, the magnetic flux density can be corrected using cubic spline interpolation and polynomial regression.

[0053] S108. Based on the current surface temperature and current value, obtain the total carbon emissions of the power equipment within the preset time period when the current data acquisition time is located, and monitor the carbon footprint of the power equipment based on the total carbon emissions.

[0054] Optionally, while monitoring the carbon footprint of power equipment, the comprehensive green level of power equipment suppliers can be evaluated using the Fuzzy AHP-TOPSIS (Fuzzy Analytic Hierarchy Process - Technique for Order Preference by Similarity to Ideal Solution) hybrid evaluation model. The green level is a comprehensive quantitative assessment of the environmental friendliness, energy efficiency, and data reliability of power equipment suppliers and their products throughout their entire lifecycle. Its core is to measure their degree of low carbon emissions, high efficiency, and reliability. The Fuzzy AHP-TOPSIS hybrid evaluation model is a hybrid multi-attribute decision-making algorithm combining Fuzzy AHP and TOPSIS. Its core is to solve the subjectivity and fuzziness issues in the evaluation indicators by "fuzzifying subjective weights + quantifying distance ranking," ultimately accurately quantifying the comprehensive level of the evaluated object.

[0055] Optionally, when evaluating the overall green performance of suppliers producing power equipment, the supplier's TOPSIS distance is first calculated. TOPSIS distance is a core indicator that measures how close the evaluated object (such as a power equipment supplier) is to the "ideal solution." The overall performance of the evaluated object is quantified by calculating the Euclidean distance; the specific formula is shown below:

[0056]

[0057]

[0058] In the formula, Let be the Euclidean distance between the i-th supplier and the ideal solution (optimal solution). Let be the Euclidean distance between the i-th supplier and the negative ideal solution (worst solution); The weighted normalized value of the i-th supplier on the j-th indicator, which may be, but is not limited to, the carbon emissions, surface temperature, dynamic resistance value, or current flowing through the power equipment produced by the i-th supplier. The j-th indicator represents either the maximum (benefit-oriented) or minimum (cost-oriented) value among all suppliers. Let j be the minimum (benefit-oriented) or maximum (cost-oriented) value of the j-th indicator among all suppliers. Among them, the carbon emissions, surface temperature, dynamic resistance value, or current flowing through the power equipment are all cost-oriented indicators. Benefit-oriented indicators may include, but are not limited to, the product qualification rate, recycled material utilization rate, and clean energy usage ratio of the supplier's products.

[0059] Optionally, a proximity value can be used to characterize a supplier's overall greenness level; a higher proximity value indicates a higher overall greenness level. The proximity value can be calculated using the following formula:

[0060]

[0061] In the formula, Let be the proximity value of the i-th supplier.

[0062] Optionally, after obtaining the carbon emissions of power equipment and the overall green level of suppliers, a supplier profile page, a real-time monitoring page, and a ranking dashboard can be displayed on the interface. The supplier profile page uses a radar chart to show a supplier's scores in three dimensions: "production greenness," "operational energy efficiency," and "data transparency." The real-time monitoring page displays real-time current and temperature heatmaps for each equipment node, as well as the cumulative value of "today's carbon emissions." The ranking dashboard dynamically displays the supplier's green level (A / B / C / D), and the ranking is updated daily as operational data accumulates.

[0063] In the aforementioned carbon footprint monitoring method, the current surface temperature and current magnetic flux density of any power equipment in the power grid at the current data acquisition time are obtained. The current surface temperature and current magnetic flux density are collected by a micro-sensor array based on the giant magnetoresistive effect. Based on the current surface temperature, the current sensitivity of the micro-sensor array and the current zero magnetic field offset voltage of the micro-sensor array at the current surface temperature are obtained. Based on the current sensitivity and the current zero magnetic field offset voltage, the current magnetic flux density is corrected to obtain the corrected magnetic flux density, and based on the corrected magnetic flux density, the current current value flowing through the power equipment is obtained. For a preset time period in which the current data acquisition time is located, the total carbon emissions of the power equipment within the preset time period are obtained based on the current surface temperature and current value, and the carbon footprint of the power equipment is monitored based on the total carbon emissions. The method provided in this application obtains the total carbon emissions of the power equipment within the preset time period based on the surface temperature and magnetic flux density collected at multiple data acquisition times within the preset time period. This calculation of the total carbon emissions combines the actual operating performance of the power equipment after its connection to the grid, making the monitoring results of the carbon footprint of the power equipment more accurate.

[0064] In some embodiments, such as Figure 2 As shown, based on the current surface temperature, the current sensitivity of the microsensor array and the current zero magnetic field offset voltage of the microsensor array at the current surface temperature are obtained, including:

[0065] S202, Obtain the reference zero magnetic field offset voltage of the microsensor array at the reference temperature, and the temperature difference between the current surface temperature and the reference temperature.

[0066] S204. Based on the reference zero magnetic field offset voltage and temperature difference, obtain the current sensitivity of the micro-sensor array and the current zero magnetic field offset voltage of the micro-sensor array at the current surface temperature.

[0067] Alternatively, the current sensitivity and the current zero magnetic field offset voltage can be calculated using the following formula:

[0068]

[0069] In the formula, The current sensitivity is expressed in volts per tesla (V / T), where T is the current surface temperature, and 25 indicates that the reference temperature is 25°C. The nominal sensitivity at the reference temperature (obtained from factory calibration). , , All of these are sensitivity temperature coefficients, which are constants that can be obtained through high and low temperature chamber experiments. ,Right now This represents the temperature difference between the current surface temperature and the reference temperature. This is the current zero magnetic field offset voltage. The reference zero magnetic field offset voltage, , , All of these are zero-drift temperature coefficients, which are constants that can be obtained through experimental calibration.

[0070] In this embodiment, by introducing a reference temperature parameter and a temperature difference, dynamic calibration of sensitivity and zero magnetic field offset voltage is achieved, which counteracts the impact of temperature changes on sensor performance and solves the problem of current and carbon emission calculation errors caused by temperature drift.

[0071] In some embodiments, the current magnetic flux density is corrected based on the current sensitivity and the current zero magnetic field offset voltage to obtain a corrected magnetic flux density, and the current current value flowing through the power equipment is obtained based on the corrected magnetic flux density, including: obtaining the current voltage of the power equipment based on the current magnetic flux density; correcting the current magnetic flux density based on the current voltage, current sensitivity, and current zero magnetic field offset voltage to obtain a corrected magnetic flux density; obtaining the size parameters of the microsensor array and the number of giant magnetoresistive probes in the microsensor array; and obtaining the current current value flowing through the power equipment based on the corrected magnetic flux density, size parameters, and number of probes.

[0072] Alternatively, the correction process for the current magnetic flux density can be represented by the following equation:

[0073]

[0074] In the formula, The current voltage of the electrical equipment at the current surface temperature. B is the reference voltage of the power equipment at the reference temperature, and B is the corrected magnetic flux density.

[0075] Alternatively, the current value flowing through the electrical equipment can be calculated using the following formula:

[0076]

[0077] In the formula, The current value flowing through the power equipment is denoted as r; the radius of the ring formed by the micro-sensor array is denoted as r, which is the size parameter of the micro-sensor array and is determined by the size of the mounting fixture; N is the number of giant magnetoresistive probes in the micro-sensor array. The more probes there are, the stronger the anti-interference capability. The value of free permeability (a constant) is approximately ; The tangential magnetic flux density measured by the i-th sensor at time t is the corrected magnetic flux density.

[0078] In this embodiment, by combining sensitivity and zero magnetic field offset voltage to correct the magnetic induction intensity, the systematic error of the sensor itself can be offset, and the interference of factors such as zero drift and temperature drift on the detection results can be reduced, making the corrected magnetic induction intensity closer to the true value. The size parameters of the micro-sensor array and the number of probes are introduced into the current value calculation, making full use of the multi-probe data fusion advantage of array-type sensing, reducing the influence of random errors of individual probes, and improving the stability and reliability of current detection results of power equipment.

[0079] In some embodiments, obtaining the total carbon emissions of the power equipment within a preset time period based on the current surface temperature and the current current value includes: obtaining the reference resistance value of the power equipment at a reference temperature; obtaining the current dynamic resistance value of the power equipment based on the temperature difference between the current surface temperature and the reference temperature, and the reference resistance value; obtaining the current ohmic loss power based on the current current value and the current dynamic resistance value; and obtaining the total carbon emissions of the power equipment within a preset time period based on the current ohmic loss power.

[0080] Optionally, the formula for calculating the current dynamic resistance value is as follows:

[0081]

[0082] In the formula, This is the current dynamic resistance value. The reference resistance value, Temperature coefficient of resistance for conductor materials in electrical equipment; The skin effect coefficient (dimensionless, usually greater than 1) reflects the phenomenon that alternating current tends to flow towards the surface of the conductor, resulting in a reduction in the effective cross-section. It is related to the frequency and the diameter of the conductor.

[0083] Optionally, the formula for calculating the current ohmic loss power is as follows:

[0084]

[0085] In this embodiment, a temperature-resistance correlation mechanism is introduced to correct the dynamic resistance value of the power equipment based on the current surface temperature. This overcomes the error of traditional calculation of ohmic loss using fixed resistance, making the power loss calculation more consistent with the actual operating state of the equipment, thereby ensuring the accuracy of carbon emission accounting. Relying on real-time collected current values ​​and surface temperature data, the ohmic loss and carbon emissions of different operating stages can be dynamically calculated, getting rid of the limitations of relying on theoretical estimation or static parameters, and meeting the needs of carbon emission monitoring of power equipment under all operating conditions.

[0086] In some embodiments, obtaining the total carbon emissions of power equipment within a preset time period based on the current ohmic loss power includes: obtaining the marginal emission factor of the power grid, and obtaining the total carbon emissions based on the product between the marginal emission factor and the current ohmic loss power.

[0087]

[0088] Optionally, Total carbon emissions For preset time periods; The marginal emission factor (MEF) at time t (unit: kgCO2e / kWh) is different from the annual average emission factor. The MEF reflects the carbon emission intensity of the marginal units called upon for every additional 1 kWh of electricity load at the current time, and can more accurately measure the environmental benefits of energy-saving behavior.

[0089] In this embodiment, the marginal emission factor of the power grid is introduced as the calculation basis instead of a fixed emission coefficient. This fully considers the dynamic changes in the real-time power generation structure of the power grid, making the calculation of carbon emissions corresponding to the ohmic losses of power equipment more consistent with the actual operation of the power grid. The total carbon emissions are calculated by the direct product relationship of "marginal emission factor × ohmic loss power", which eliminates the need for complex intermediate conversion steps. While ensuring accuracy, it reduces the computational complexity and facilitates rapid deployment and application in real-time monitoring systems.

[0090] In some embodiments, monitoring the carbon footprint of power equipment based on total carbon emissions includes: performing a hash calculation on a data combination formed by the current surface temperature, current current value, and total carbon emissions; digitally signing the hash value and uploading the data combination and digital signature to a blockchain node; decrypting all digital signatures in the blockchain node when digital signatures of the corresponding data combinations at all data collection times within a preset time period are obtained; and monitoring the carbon footprint of power equipment based on the data combination in the blockchain node when all digital signatures are successfully decrypted.

[0091] Optionally, to prevent data falsification (e.g., suppliers tampering with energy consumption data for rating purposes), a hardware root of trust is introduced, the specific process of which is as follows: (1) The SE (Security Element) chip in the sensing terminal combines the data. Perform digital signature: , where I is the current current value, T is the current surface temperature, and CF is the total carbon emissions; (2) The gateway uploads the data packet and signature to the blockchain node; (3) The blockchain smart contract verifies the signature and records the data fingerprint (Hash) in the block to generate a unique transaction ID.

[0092] In this embodiment, a data fingerprint is generated through hash calculation, combined with the SE chip digital signature and blockchain notarization, to lock the data combination (temperature, current, total carbon emissions) from the source of collection, thereby preventing the possibility of suppliers tampering with energy consumption data and beautifying the carbon footprint, and ensuring the authenticity of the data.

[0093] In one exemplary embodiment, another carbon footprint monitoring method is provided, the system architecture of which is shown in the figure below. Figure 3 As shown; the layers are as follows: Trusted Sensing Layer (Edge Side): Deployed at key nodes of power equipment (such as cable joints and switchgear busbars), it uses GMR arrays to collect current and temperature, signs the raw data through SE chips, and transmits it wirelessly via LoRa (Long Range Radio) / NB-IoT (Narrowband Internet of Things); Edge Computing Layer (Side Side): Deployed in smart gateways in substations or distribution rooms, it is responsible for aggregating sensor data, executing temperature compensation algorithms to eliminate sensor temperature drift, and calculating real-time carbon emissions based on locally deployed carbon integral models, reducing cloud communication pressure; Trusted Evidence Storage Layer (Chain Side): Based on a consortium blockchain network (such as Hyperledger Fabric), the gateway uploads the calculated carbon emission data hash to the chain, ensuring that the data is tamper-proof once generated, solving the trust problem; Application Evaluation Layer (Cloud Side): Deployed on cloud servers, it obtains trusted operational data from the blockchain, combines it with LCA (Life Cycle Assessment) static data, and uses multi-attribute decision algorithms to output dynamic scores and ratings for suppliers.

[0094] Optionally, the method includes two parts: edge-side dynamic carbon accounting logic and cloud-based TOPSIS evaluation logic.

[0095] The edge-side dynamic carbon accounting logic includes the following:

[0096] (1) Initialization: Read device parameters from the configuration file: (20-degree resistance), α (temperature drift coefficient). Establish a blockchain connection client.

[0097] (2) Main loop (executes every second):

[0098] Data Acquisition: Reading the current from the sensor and temperature .

[0099] Temperature compensation: The temperature compensation function of the giant magnetoresistive (GMR) sensor is called to correct the current value using spline interpolation parameters.

[0100] Get Factor: Request the power grid API to obtain the marginal emission factor for the current hour. Calculate the resistance: .

[0101] Calculate losses: .

[0102] Calculate carbon emissions: .

[0103] Accumulation: .

[0104] On-chain: If The increase in (total carbon emissions) exceeded the threshold, and the data was packaged. The data is sent to the blockchain node, where ID is the identification information of the power equipment, Timestamp is the data collection timestamp, Carbon is the carbon emission data, and Signature is the digital signature.

[0105] The cloud-based TOPSIS evaluation logic includes the following:

[0106] (1) Input: A set of metrics for all suppliers .

[0107] (2) Update dynamic metrics: Traverse each supplier and query the blockchain for the latest information on the equipment they supply. and average (Temperature rise). Update the corresponding column in the decision matrix X.

[0108] (3) Standardization: Take the reciprocal or linear transformation of cost indicators (such as carbon emissions and resistance) to make them consistent in direction (the larger the value, the better).

[0109] (4) Weighting: Multiply the standardized matrix by the weight vector W calculated by AHP.

[0110] (5) Sorting: Calculate the distance between each supplier and the "perfect supplier" (a virtual supplier where all metrics are optimal). Output a new score list. And reclassify the levels.

[0111] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0112] Based on the same inventive concept, this application also provides a carbon footprint monitoring device for implementing the carbon footprint monitoring method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more carbon footprint monitoring device embodiments provided below can be found in the limitations of the carbon footprint monitoring method described above, and will not be repeated here.

[0113] In one exemplary embodiment, such as Figure 4 As shown, a carbon footprint monitoring device is provided, comprising: a first acquisition module 401, a second acquisition module 402, a correction module 403, and a third acquisition module 404, wherein:

[0114] The first acquisition module 401 is used to acquire the current surface temperature and current magnetic induction intensity of any power equipment in the power grid at the current data acquisition time; wherein the current surface temperature and the current magnetic induction intensity are acquired by a micro-sensor array based on the giant magnetoresistive effect.

[0115] The second acquisition module 402 is used to acquire the current sensitivity of the microsensor array and the current zero magnetic field offset voltage of the microsensor array at the current surface temperature based on the current surface temperature.

[0116] The correction module 403 is used to correct the current magnetic induction intensity based on the current sensitivity and the current zero magnetic field offset voltage to obtain the corrected magnetic induction intensity, and to obtain the current current value flowing through the power equipment based on the corrected magnetic induction intensity.

[0117] The third acquisition module 404 is used to acquire the total carbon emissions of the power equipment within the preset time period in which the current data acquisition time is located, based on the current surface temperature and the current current value, and to monitor the carbon footprint of the power equipment based on the total carbon emissions.

[0118] In some embodiments, the second acquisition module 402 is further configured to acquire the reference zero magnetic field offset voltage of the microsensor array at a reference temperature, and the temperature difference between the current surface temperature and the reference temperature; and based on the reference zero magnetic field offset voltage and the temperature difference, acquire the current sensitivity of the microsensor array and the current zero magnetic field offset voltage of the microsensor array at the current surface temperature.

[0119] In some embodiments, the correction module 403 is further configured to: obtain the current voltage of the power equipment based on the current magnetic induction intensity; correct the current magnetic induction intensity based on the current voltage, the current sensitivity, and the current zero magnetic field offset voltage to obtain a corrected magnetic induction intensity; obtain the size parameters of the micro-sensor array and the number of giant magnetoresistive probes in the micro-sensor array; and obtain the current current value flowing through the power equipment based on the corrected magnetic induction intensity, the size parameters, and the number of probes.

[0120] In some embodiments, the third acquisition module 404 is further configured to acquire the reference resistance value of the power equipment at a reference temperature; acquire the current dynamic resistance value of the power equipment based on the temperature difference between the current surface temperature and the reference temperature and the reference resistance value; acquire the current ohmic loss power based on the current current value and the current dynamic resistance value; and acquire the total carbon emissions of the power equipment within the preset time period based on the current ohmic loss power.

[0121] In some embodiments, the third acquisition module 404 is further configured to acquire the marginal emission factor of the power grid and acquire the total carbon emissions based on the product between the marginal emission factor and the current ohmic loss power.

[0122] In some embodiments, the third acquisition module 404 is further configured to perform a hash calculation on the data combination formed by the current surface temperature, the current current value, and the total carbon emissions; digitally sign the hash value and upload the data combination and the digital signature to the blockchain node; when the digital signatures of the corresponding data combinations at all data collection times in the preset time period are obtained, decrypt all the digital signatures in the blockchain node, and when all the digital signatures are successfully decrypted, monitor the carbon footprint of the power equipment based on the data combination in the blockchain node.

[0123] Each module in the aforementioned carbon footprint monitoring device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0124] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a carbon footprint monitoring method.

[0125] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0126] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0127] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0128] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0129] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0130] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0131] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0132] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for monitoring carbon footprint, characterized in that, The method includes: The current surface temperature and current magnetic flux density of any power equipment in the power grid at the current data acquisition time are obtained; wherein the current surface temperature and the current magnetic flux density are acquired by a micro-sensor array based on the giant magnetoresistive effect; Based on the current surface temperature, obtain the current sensitivity of the microsensor array and the current zero magnetic field offset voltage of the microsensor array at the current surface temperature; Based on the current sensitivity and the current zero magnetic field offset voltage, the current magnetic flux density is corrected to obtain the corrected magnetic flux density, and based on the corrected magnetic flux density, the current current value flowing through the power equipment is obtained. For the preset time period in which the current data acquisition time is located, based on the current surface temperature and the current current value, the total carbon emissions of the power equipment during the preset time period are obtained, and based on the total carbon emissions, the carbon footprint of the power equipment is monitored.

2. The method according to claim 1, characterized in that, The step of obtaining the current sensitivity of the microsensor array and the current zero magnetic field offset voltage of the microsensor array at the current surface temperature based on the current surface temperature includes: The reference zero magnetic field offset voltage of the microsensor array at a reference temperature, and the temperature difference between the current surface temperature and the reference temperature are obtained. Based on the reference zero magnetic field offset voltage and the temperature difference, the current sensitivity of the microsensor array and the current zero magnetic field offset voltage of the microsensor array at the current surface temperature are obtained.

3. The method according to claim 1, characterized in that, The process of correcting the current magnetic flux density based on the current sensitivity and the current zero magnetic field offset voltage to obtain a corrected magnetic flux density, and obtaining the current current value flowing through the power equipment based on the corrected magnetic flux density, includes: The current voltage of the power equipment is obtained based on the current magnetic induction intensity; Based on the current voltage, the current sensitivity, and the current zero magnetic field offset voltage, the current magnetic flux density is corrected to obtain the corrected magnetic flux density. Obtain the size parameters of the microsensor array and the number of giant magnetoresistive probes in the microsensor array; Based on the corrected magnetic flux density, the size parameters, and the number of probes, the current value flowing through the power equipment is obtained.

4. The method according to claim 1, characterized in that, The step of obtaining the total carbon emissions of the power equipment within the preset time period based on the current surface temperature and the current current value includes: Obtain the reference resistance value of the power equipment at a reference temperature; Based on the temperature difference between the current surface temperature and the reference temperature, and the reference resistance value, the current dynamic resistance value of the power equipment is obtained; Based on the current current value and the current dynamic resistance value, obtain the current ohmic loss power; Based on the current ohmic loss power, the total carbon emissions of the power equipment during the preset time period are obtained.

5. The method according to claim 4, characterized in that, The step of obtaining the total carbon emissions of the power equipment within the preset time period based on the current ohmic loss power includes: Obtain the marginal emission factor of the power grid, and obtain the total carbon emissions based on the product of the marginal emission factor and the current ohmic loss power.

6. The method according to claim 1, characterized in that, The monitoring of the carbon footprint of the power equipment based on the total carbon emissions includes: A hash calculation is performed on the data combination formed by the current surface temperature, the current current value, and the total carbon emissions; The hash value is digitally signed, and the data combination and the digital signature are uploaded to the blockchain node; If the digital signatures of the corresponding data combinations at all data collection times within the preset time period are obtained, all digital signatures in the blockchain node are decrypted, and if all digital signatures are successfully decrypted, the carbon footprint of the power equipment is monitored based on the data combinations in the blockchain node.

7. A carbon footprint monitoring device, characterized in that, The device includes: The first acquisition module is used to acquire the current surface temperature and current magnetic induction intensity of any power equipment in the power grid at the current data acquisition time; wherein, the current surface temperature and the current magnetic induction intensity are acquired by a micro-sensor array based on the giant magnetoresistive effect; The second acquisition module is used to acquire, based on the current surface temperature, the current sensitivity of the microsensor array and the current zero magnetic field offset voltage of the microsensor array at the current surface temperature; The correction module is used to correct the current magnetic flux density based on the current sensitivity and the current zero magnetic field offset voltage to obtain the corrected magnetic flux density, and to obtain the current current value flowing through the power equipment based on the corrected magnetic flux density. The third acquisition module is used to acquire the total carbon emissions of the power equipment within the preset time period in which the current data acquisition time is located, based on the current surface temperature and the current current value, and to monitor the carbon footprint of the power equipment based on the total carbon emissions.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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