Data scheduling optimization method and system for electric carbon meter data transmission

Abstract

The invention is suitable for the technical field of data processing, and relates to a data scheduling optimization method and system for power carbon meter data transmission, and the method comprises the steps: S10, a data scheduling center obtains a data set needing to be scheduled by a power carbon meter; s20, the data scheduling center obtains path parameters of data transmission and calculates path performance; s30, the data scheduling center calculates different transmission requirements according to the data of the plurality of electric carbon meters, and divides the data into a plurality of data sets according to the transmission requirements; s40, acquiring a cloud platform data requirement, and matching a corresponding path by the data scheduling center according to the transmission requirement of the electric carbon meter data and the cloud platform data requirement; s50, the data scheduling center compresses the data of the electricity meter and the carbon meter; s60, the data scheduling center confirms the priority of the data and transmits the data in batches according to the priority; and S70, the data scheduling center confirms that the data transmitted to the cloud platform is received. The method is simple in process and convenient to operate, the metering efficiency of the electric carbon meter is improved, and the accuracy of electric carbon emission accounting is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and particularly relates to a data scheduling optimization method and system for electric carbon meter data transmission. Background Art

[0002] With the global emphasis on environmental protection and sustainable development, accurate and comprehensive power carbon emission measurement methods have become the technical foundation. At present, the research and application of power system carbon emission measurement theories, methods, standards, and equipment at home and abroad are still in their infancy. Traditional power carbon emission accounting methods cannot accurately reflect the spatio-temporal differences of user electricity consumption carbon emission factors, and it is difficult to characterize the indirect carbon emissions caused by the evolution of the power grid form and related transmission losses. Compared with traditional methods, an electric carbon meter can accurately, quickly, and in real-time measure the carbon emissions of each degree of electricity, providing a basis for enterprises to formulate a more green and low-carbon production model.

[0003] The patent application with the publication number CN118195636A provides a power system carbon emission measurement and uncertainty calculation method, including determining the power carbon emissions caused by the power generation behavior of all generating units in the regional power grid within the current time range, and the non-power carbon emissions from other sources and carbon emissions outside the accounting period caused by all generating units in the regional power grid under the same conditions; calculating the total carbon emissions generated by all generating units in the regional power grid for power production within the current time range based on the above power carbon emissions and non-power carbon emissions; obtaining the total power generation of all generating units in the regional power grid within the current time range, and calculating the power carbon emission factor within the current time range; finally calculating the uncertainty of the power carbon emission factor within the current time range. In this patent application, the measurement of carbon emissions also uses traditional calculation methods and does not apply an electric carbon meter, still suffering from the drawbacks of traditional technologies.

[0004] Therefore, how to ensure that the electric carbon meter accurately and quickly measures the carbon emissions of user electricity consumption is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the purpose of the present invention is to provide a data scheduling optimization method for electric carbon meter data transmission to solve the problems of low measurement accuracy and efficiency of traditional power carbon emission accounting methods; in addition, the present invention also provides a data scheduling optimization system for electric carbon meter data transmission.

[0006] To solve the above technical problems, the present invention adopts the following technical solutions:

[0007] In the first aspect, the present invention provides a data scheduling optimization method for electric carbon meter data transmission, including the following steps:

[0008] S10. The data scheduling center obtains the data set that needs to be scheduled by the electricity-carbon meter;

[0009] S20. The data scheduling center obtains the path parameters of data transmission and calculates the path performance;

[0010] S30. The data scheduling center calculates different transmission requirements based on the data of multiple electricity-carbon meters, and divides the data into multiple data sets according to the transmission requirements;

[0011] S40. Obtain the data requirements of the cloud platform, and the data scheduling center matches the corresponding path according to the transmission requirements of the electricity-carbon meter data and the data requirements of the cloud platform;

[0012] S50. The data scheduling center compresses the electricity-carbon meter data;

[0013] S60. The data scheduling center confirms the data priority and transmits the data in batches according to the priority;

[0014] S70. The data scheduling center confirms that the data transmitted to the cloud platform is received.

[0015] Further, the specific steps of step S20 are as follows:

[0016] S201. The data scheduling center obtains the path parameters of data transmission;

[0017] S202. The data scheduling center calculates the path performance according to the path parameters.

[0018] Further, the specific steps of dividing the data into multiple data sets in step S30 are as follows:

[0019] S301. Initialize multiple clustering centers;

[0020] S302. Assign each data point to the nearest clustering center;

[0021] S303. Recalculate the center of each cluster.

[0022] Further, the specific steps of step S40 are as follows:

[0023] S401. Obtain the data reception requirements of the cloud platform;

[0024] S402. Calculate the influence factors of basic data, transmission distance, and network status on the transmission process;

[0025] S403. Match the corresponding path according to the transmission requirements of the electricity-carbon meter data and the data requirements of the cloud platform.

[0026] Further, the calculation of the influence factors in step S402 includes:

[0027] The size of the data set affects the transmission, and the influence value is calculated by calculating the transmission time.

[0028] The transmission distance affects the data transmission, and the influence value is calculated by calculating the influence degree of the transmission distance on the radio electromagnetic wave.

[0029] The network condition affects the efficiency of transmitting data, and the influence value is calculated by calculating the data transmission rate within a certain time.

[0030] Further, the specific steps of step S50 are as follows:

[0031] S501. Wavelet threshold denoising: Set the soft threshold. For the sub-signals in some frequency bands, if their absolute value is less than a specific threshold, set them to zero; if the absolute value is greater than the threshold, keep them.

[0032] S502. Signal detection and classification: Detect the signal type through binary wavelet transform.

[0033] S503. FFT compression: For steady-state or steady-state disturbance signals, use FFT for compression to obtain the corresponding spectrum and record the information of different frequency components in the original signal.

[0034] S504. Wavelet packet transform: For transient disturbance signals, use wavelet packet transform for compression and perform threshold processing on the decomposition coefficients of each layer of the wavelet packet, keep the points related to the signal singularity, and ignore some points unrelated to the signal singularity.

[0035] S505. LZW coding: Use LZW coding to perform lossless compression on the data stored after lossy compression.

[0036] Further, the specific steps of step S70 are as follows:

[0037] S701. Acknowledgment confirmation: The data scheduling center receives the response frame sent by the cloud platform and reads the execution result of the request. If the execution result shows success, it is confirmed that the data transmitted to the cloud platform has been received.

[0038] S702. Data retransmission mechanism: If the data scheduling center fails to receive a valid response frame within the specified time or due to a checksum error, the data scheduling center determines that the data has not been successfully received and attempts to resend it.

[0039] Further, in step S10, the data set includes current data, voltage data, resistance data, and cumulative power consumption data.

[0040] Further, the path parameters include the source node, the destination node, the bandwidth, delay, packet loss rate, throughput, and security of each node on the path.

[0041] Second aspect, the present invention also provides a data scheduling optimization system for the data transmission of an electric carbon meter, including:

[0042] An analysis module, configured to analyze the data set, transmission distance, and network condition when the electric carbon meter terminal sends data;

[0043] A path management module, configured to obtain the parameter information of the path passed through during the data transmission of the electric carbon meter, and calculate the performance of the path according to the parameter information;

[0044] A matching module, configured to count the transmission requirements of the electric carbon meter data, obtain the requirements of the data required by the cloud platform, and match the corresponding transmission path according to the transmission requirements of the electric carbon meter data and the requirements of the data required by the cloud platform;

[0045] An electric carbon meter management module, configured to manage the data of multiple electric carbon meters and compress the data to reduce the storage space required for the data;

[0046] A transmission module, configured to transmit the electric carbon meter data to the cloud platform for reception according to the matching result of the matching module;

[0047] The analysis module, path management module, matching module, electric carbon meter management module, and transmission module are sequentially communicatively connected, and the transmission module is communicatively connected to the analysis module.

[0048] Compared with the prior art, the data scheduling optimization method and system for the data transmission of the electric carbon meter provided by the present invention has at least the following beneficial effects:

[0049] Traditional methods for calculating power carbon emissions cannot accurately reflect the spatio-temporal differences in the carbon emission factors of user electricity consumption, and it is difficult to characterize the indirect carbon emissions generated by the evolution of the power grid form and related transmission losses. Compared with traditional methods, the electric carbon meter can accurately, quickly, and in real-time measure the carbon emissions of each degree of electricity, providing a basis for enterprises to formulate a more green and low-carbon production model. The process of the present invention is simple and easy to operate. By matching the corresponding transmission path with the data, the transmission speed during the data transmission process of the electric carbon meter is increased, the measurement efficiency of the electric carbon meter is improved, and the accuracy of power carbon emission accounting is ensured, which is of great significance in promoting the recording and analysis of the carbon footprint of the entire power system process. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the solutions of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0051] Figure 1Flow chart of a data scheduling optimization method for electric carbon meter data transmission provided by an embodiment of the present invention;

[0052] Figure 2 Block diagram of a data scheduling optimization system for electric carbon meter data transmission provided by an embodiment of the present invention. Detailed implementation manners

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this invention belongs; the terms used herein in the specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For example, the terms such as "length", "width", "upper", "lower", "left", "right", "front", "rear", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. indicate the orientation or position based on the orientation or position shown in the drawings, which are only for convenience of description and cannot be construed as a limitation to the technical solution of the present invention.

[0054] The terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above drawings are intended to cover non-exclusive inclusion; the terms "first", "second", etc. in the specification and claims of the present invention or the above drawings are used to distinguish different objects and not to describe a specific order. In the specification and claims of the present invention and the above drawings, when an element is referred to as being "fixed to" or "mounted on" or "disposed on" or "connected to" another element, it can be directly or indirectly located on the other element. For example, when an element is referred to as being "connected to" another element, it can be directly or indirectly connected to the other element.

[0055] In addition, referring to "embodiment" herein means that a specific feature, structure or characteristic described in connection with the embodiment can be included in at least one embodiment of the present invention. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0056] The present invention provides a data scheduling optimization method for electric carbon meter data transmission, which is applied to the metering process of the carbon emissions of user electricity consumption. The data scheduling optimization method for electric carbon meter data transmission includes the following steps:

[0057] S10. The data scheduling center obtains the data set that needs to be scheduled by the electric carbon meter;

[0058] S20. The data scheduling center obtains the path parameters of data transmission and calculates the path performance;

[0059] S30. The data scheduling center calculates different transmission requirements based on the data of multiple electricity-carbon meters, and divides the data into multiple data sets according to the transmission requirements.

[0060] S40. Obtain the data requirements of the cloud platform. The data scheduling center matches the corresponding path according to the transmission requirements of the electricity-carbon meter data and the data requirements of the cloud platform.

[0061] S50. The data scheduling center compresses the electricity-carbon meter data.

[0062] S60. The data scheduling center confirms the data priority and transmits the data in batches according to the priority.

[0063] S70. The data scheduling center confirms that the data transmitted to the cloud platform is received.

[0064] The process of the present invention is simple and convenient to operate, improves the metering efficiency of the electricity-carbon meter, and ensures the accuracy of power carbon emission accounting.

[0065] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0066] The present invention provides a data scheduling optimization method for electricity-carbon meter data transmission, which is applied to the metering process of the carbon emissions of user electricity consumption, such as Figure 1 As shown, in this embodiment, the data scheduling optimization method for electricity-carbon meter data transmission includes the following steps:

[0067] S10. The data scheduling center obtains the data set that needs to be scheduled by the electricity-carbon meter.

[0068] Specifically, the data scheduling center includes an electricity-carbon meter management unit, a path management unit, a matching unit, a transmission unit, and an analysis unit. The electricity-carbon meter management unit can manage the data of multiple electricity-carbon meters and compress the data to reduce the storage space required for the data; the path management unit obtains the parameter information of the path through which the electricity-carbon meter data is transmitted during the transmission process, and calculates the performance of the path according to the parameter information; the matching unit counts the transmission requirements of the electricity-carbon meter data, obtains the requirements of the data required by the cloud platform, and matches the corresponding transmission path according to the transmission requirements of the electricity-carbon meter data and the requirements of the data required by the cloud platform; the transmission unit transmits the electricity-carbon meter data to the cloud platform for reception according to the matching result of the matching unit. The analysis unit analyzes the data set, transmission distance, and network status when the data is sent from the electricity-carbon meter end.

[0069] The cloud platform is a comprehensive management platform that integrates data reception, processing, transmission, and storage. It receives the data measured by the electricity-carbon meter. Based on the different nodes of power production, transmission, and consumption where the electricity-carbon meter is installed, the cloud platform can obtain the corresponding carbon emission factors, and then accurately calculate the carbon emissions through the electricity consumption and carbon emission factors. The data collection and measurement mode of the cloud platform is more scientific and the data is more accurate.

[0070] In this embodiment, the electricity-carbon meters include types such as gateway electricity-carbon meters, three-phase electricity-carbon meters, and single-phase electricity-carbon meters.

[0071] In this embodiment, the data set that the electricity-carbon meter needs to schedule refers to the data set of electrical parameters such as current, voltage, resistance, and cumulative electricity consumption.

[0072] S20. The data scheduling center obtains the path parameters of data transmission and calculates the path performance;

[0073] Specifically, step S20 includes the following steps:

[0074] S201. The data scheduling center obtains the path parameters of data transmission, including the source node, target node, bandwidth, delay, packet loss rate, throughput, and security of each node on the path.

[0075] Source node: The network node where the information source sends the original data packet.

[0076] Target node: The network node where the information source receives the original data packet.

[0077] Bandwidth: Monitor the port traffic by sending a PortStatsRequest message to the switch through a software-defined network (SDN) controller and receiving a PortStatsReply message. The calculation formula is where K is the bandwidth, X is the data transmission rate, and Y is the number of transmitted bits.

[0078] Delay: Calculate the link delay by sending a data packet with a timestamp and measuring the round-trip time (RTT).

[0079] Packet loss rate: The calculation formula is where the packet loss rate is K, the input packet is I, the output packet is L, and the packet loss rate is related to the data packet length and packet sending frequency. Usually, the packet loss rate of a gigabit network card is less than five ten-thousandths when the traffic is greater than 200 Mbps; the packet loss rate of a 100 Mbps network card is less than one ten-thousandth when the traffic is greater than 60 Mbps.

[0080] Throughput: It refers to the maximum rate at which data is transmitted from one end to the other in a network. The calculation formula is C = Blog2(1 + S / N), where C is the maximum information transmission rate of the channel (in bits per second bps), B is the bandwidth of the channel (in Hertz Hz), S is the average power of the signal (in Watts W), and N is the noise power (in W).

[0081] Security: The transmission path uses the HTTPS protocol. HTTPS encrypts data at the transport layer, effectively preventing data from being intercepted and tampered with during transmission, thus protecting the confidentiality and integrity of the data.

[0082] S202. Calculate the performance metrics of the path, including throughput, total bandwidth of the path, total delay, total packet loss rate, security, etc. The formula for calculating the weight of each parameter is where p represents the weight, u is an arbitrary constant, and m i represents the error in the observation, including systematic deviation error, random error, and human error. The proportional relationship between each weight value is Regardless of the value of u, the proportional relationship between this set of weights remains unchanged. The performance calculation formula is S n represents the path performance. r n represents the influence factor on the network stability value, p n represents the weights of each path parameter, and p1 + p2 +... + p n = 1. K n represents the path parameter, that is, the factor that can affect and represent the path performance. n takes values from 1 to N, and N represents the total number of path parameters, with a value of 5.

[0083] S30. The data scheduling center calculates different transmission requirements based on the data of multiple electric carbon meters, and divides the data into multiple data sets according to the transmission requirements;

[0084] Specifically, step S30 includes the following steps:

[0085] S301. Initialization: Use the K-means++ algorithm to select K initial cluster centers. Randomly select a sample point from the data set as the first initial cluster center, and then calculate the shortest distance between each sample point and the existing cluster centers, and determine the selection probability of the next cluster center according to this distance. The farther the sample point is from the existing cluster centers, the greater the probability of becoming the next cluster center;

[0086] S302. Assignment: Assign each data point to the nearest cluster center. Use the Euclidean distance as the distance metric, calculate the distance between the data point and each cluster center, and assign it to the nearest cluster center;

[0087] S303. Update: Recalculate the center of each cluster. For each cluster, take the average of all data points within the cluster as the new cluster center.

[0088] Since the data is divided into K clusters, the data within each cluster has a high similarity, while the data between different clusters has a low similarity. This algorithm can be used for data classification and clustering. Each cluster is a set of data with the same or similar transmission requirements, and the different requirements of different sets are G i .

[0089] S40. Obtain the data requirements of the cloud platform. The data scheduling center matches the corresponding path according to the transmission requirements of the electro-carbon meter data and the data requirements of the cloud platform;

[0090] Specifically, step S40 includes the following steps:

[0091] S401. Obtain the data reception requirements of the cloud platform;

[0092] Specifically, obtain the data reception requirements G y , and receive according to the data sending area, data storage, data security requirements, etc.;

[0093] S402. Calculate the transmission requirements of the electro-carbon meter data;

[0094] Specifically, calculate the influence factors of the data set, transmission distance, and network condition on the transmission process;

[0095] The size of the data set affects the transmission. Calculate the influence value by calculating the transmission time; the transmission distance affects the data transmission. Calculate the influence value by calculating the influence degree of the transmission distance on the radio electromagnetic wave; the network condition affects the efficiency of transmitting data. Calculate the influence value by calculating the data transmission rate within a certain time. Therefore, the size of the data set, transmission distance, and network condition are the influence factors of the transmission process.

[0096] Furthermore, use the formula where λ i represents the influence factor, ρ1 represents the influence weight of the data set size, ρ2 represents the influence weight of the transmission distance, ρ3 represents the influence weight of the network condition, is the type of data to be transmitted, i is the subscript of different data, taking values from 1 to n, ω1, ω2, ω3 represent the weights of the factors affecting the transmission time, represents the transmission rate specified in the transmission protocol, represents the bandwidth in the transmission path, represents the storage capacity of the network device, A s represents the transmission path, P r represents the received power, Pt represents the transmission power, G t and G r are the gains of the transmitting and receiving antennas respectively, γ is the wavelength of the signal; C miss represents the data lost during network transmission, C m represents the total data transmitted.

[0097] S403. Match the corresponding path according to the transmission requirements of the electro-carbon meter data and the data requirements of the cloud platform.

[0098] Specifically, the transmission requirement of the electro-carbon meter data is G i , the data requirement of the cloud platform is G y , and the preset target value of the transmission requirement is calculated as G j , where is the weight affecting the requirement.

[0099] The performance value of a certain aspect of the path is G r = p i * S n * λ i , where the path requirement weight p i , the path performance s n , λ i is the influencing factor.

[0100] If the performance G r of the path is greater than or equal to G j , it indicates that the path matches the transmission requirements of the electro-carbon meter data and the data requirements of the cloud platform.

[0101] S50. The data scheduling center compresses the electro-carbon meter data;

[0102] Specifically, step S50 includes the following steps:

[0103] S501. Wavelet threshold denoising: Set the soft threshold. For the sub-signals of some frequency bands, if their absolute value is less than a specific threshold, set them to zero; if the absolute value is greater than the threshold, keep them. The soft threshold can effectively suppress smaller noises;

[0104] S502. Signal detection and classification: Detect the signal type through binary wavelet transform. If the detected signal is a steady state or a steady state disturbance, use FFT to compress the signal; if the detected signal is a transient disturbance, use wavelet packet transform for compression;

[0105] S503. FFT compression: For steady state or steady state disturbance signals, use FFT for compression to obtain the corresponding spectrum and record the information of different frequency components in the original signal;

[0106] S504, Wavelet Packet Transform: For transient disturbance signals, wavelet packet transform is used for compression, and threshold processing is performed on the decomposition coefficients of each layer of the wavelet packet to retain the points related to the signal singularity and ignore some points unrelated to the signal singularity.

[0107] S505, LZW Coding: Use LZW coding to further losslessly compress the data stored after lossy compression to achieve a more effective compression effect. Through compression, not only the storage and transmission problems of large-scale data are solved, but also the high availability and analysis value of the data are maintained.

[0108] S60, The data scheduling center confirms the data priority and transmits it in batches according to the priority.

[0109] Specifically, the data scheduling center uses the proportional fairness algorithm to confirm the data priority, achieving a compromise between maximizing the throughput of the entire path and maximizing the fairness between data sets. In the proportional fairness scheduling algorithm, subcarriers are the basic units of resource allocation, and each subcarrier may multiplex multiple data sets. Therefore, PF also needs to select the data set accordingly. For this purpose, the scheduler selects the data set with the largest PF metric on the subcarrier in a given time slot.

[0110] According to the proportional fairness scheduling criterion, maximize the data set on subcarrier S The metric values are as follows: Among them, U represents a single data set, and each element k in this U set represents a data in the set. R s.k (t) represents the instantaneous rate that data k can achieve on subcarrier s at time t, and T k (t) represents the average throughput of data k up to time t. The priority ranking of data sets is performed by comparing the ratio of the instantaneous rate to the average throughput of each data set in a given time slot.

[0111] The proportional fairness scheduling algorithm realizes the compromise between system throughput and fairness between data sets through the weights of the numerator and denominator. When the channel condition of a data set is good, the real-time rate of the data is high. At this time, the weight of the numerator is large, and the total proportional fairness metric value is large, having a higher priority, reflecting the efficiency of the proportional fairness criterion. When a data set cannot be called for a period of time due to its poor channel condition, its accumulated average rate over a period of time becomes smaller, and the weight of its denominator becomes smaller. Similarly, the total proportional fairness metric value becomes larger, giving the data the right to be called, reflecting the proportional fairness criterion.

[0112] Furthermore, transmitting in batches according to the priority specifically includes:

[0113] Address field matching: The data scheduling center sends a read instruction to read the unique communication address possessed by each data set, and the data sets with matching addresses respond to the read instruction of the data scheduling center.

[0114] Code recognition: The data scheduling center performs code recognition on the data sets with matching addresses. The control code indicates the type of communication frame and the required operation. A control code of 11 indicates reading data, and the control code in the data frame returned by the electro-carbon meter will correspondingly increase by 80, indicating a correct response to the read request.

[0115] Checksum verification: The data scheduling center performs checksum verification on the data sets after code recognition. The checksum is the sum of all bytes from the frame start delimiter to before the checksum modulo 256, and is used to detect whether data has errors during transmission. If the checksum calculated by the cloud platform is consistent with that of the data scheduling center, it indicates that the data has not been corrupted.

[0116] End delimiter check: Confirm that each data frame ends with a specific end delimiter (16H), which is an important identifier when parsing data.

[0117] Send data according to the matching path. When sending, perform a 33H addition operation byte by byte, and when receiving, perform a 33H subtraction operation byte by byte to obtain the true data value.

[0118] Furthermore, in this embodiment, other settings that should also be performed:

[0119] Baud rate setting: The baud rate of the serial port assistant must be set to match that of the electro-carbon meter, otherwise correct communication cannot be achieved.

[0120] Data bits and parity: Configure the data bits and parity method according to the requirements of the DLT645-2007 communication protocol to ensure accurate encoding and decoding of data.

[0121] S70. The data scheduling center confirms that the data transmitted to the cloud platform has been received.

[0122] Specifically, step S70 includes the following steps:

[0123] S701. Acknowledgment confirmation: The data scheduling center receives the response frame sent by the cloud platform and reads the execution result of the request. If the execution result shows success, it confirms that the data transmitted to the cloud platform has been received. The electro-carbon meter sends a response frame for each legal request, and this response frame contains the execution result of the request, such as the successfully read data or the status of the command execution.

[0124] S702. Data retransmission mechanism: If the data scheduling center fails to receive a valid response frame within the specified time or due to a checksum error, the data scheduling center can determine that the data has not been successfully received and attempt to retransmit.

[0125] The present invention also provides a data scheduling optimization system for the data transmission of an electric carbon meter, including:

[0126] An analysis module, configured to analyze the data set, transmission distance, and network condition when the electric carbon meter terminal sends data;

[0127] A path management module, configured to obtain the parameter information of the path passed during the data transmission of the electric carbon meter, and calculate the performance of the path according to the parameter information;

[0128] A matching module, configured to count the transmission requirements of the electric carbon meter data, obtain the requirements of the data required by the cloud platform, and match the corresponding transmission path according to the transmission requirements of the electric carbon meter data and the requirements of the data required by the cloud platform;

[0129] An electric carbon meter management module, configured to manage the data of multiple electric carbon meters and compress the data to reduce the storage space required for the data;

[0130] A transmission module, configured to transmit the electric carbon meter data to the cloud platform for reception according to the matching result of the matching module;

[0131] The analysis module, the path management module, the matching module, the electric carbon meter management module, and the transmission module are sequentially communicatively connected, and the transmission module is communicatively connected to the analysis module.

[0132] The data scheduling optimization method and system for the data transmission of an electric energy meter described in the above embodiment, compared with the prior art, the traditional method for calculating electric power carbon emissions cannot accurately reflect the spatio-temporal differences of the carbon emission factors of user electricity consumption, and it is difficult to characterize the indirect carbon emissions generated by the evolution of the power grid form and related transmission losses. Compared with the traditional method, the electric carbon meter can accurately and quickly measure the carbon emissions of each degree of electricity in real time, providing a basis for enterprises to formulate a more green and low-carbon production model. The process of the present invention is simple and the operation is convenient. By matching the corresponding transmission path with the data, the transmission speed during the data transmission process of the electric carbon meter is improved, the metering efficiency of the electric carbon meter is improved, the accuracy of the electric power carbon emission accounting is ensured, and it has important significance in promoting the recording and analysis of the whole-process carbon footprint of the power system.

[0133] Obviously, the embodiments described above are only the preferred embodiments of the present invention, rather than all embodiments. The preferred embodiments of the present invention are given in the accompanying drawings, but they do not limit the patent scope of the present invention. The present invention can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosed content of the present invention more thorough and comprehensive. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions described in the foregoing specific embodiments, or perform equivalent replacements on some of the technical features. Any equivalent structure that makes use of the content of the specification and drawings of the present invention, directly or indirectly applied in other related technical fields, is similarly within the scope of the patent protection of the present invention.

Claims

1. A data scheduling optimization method for electric carbon meter data transmission, characterized in that: The following steps are involved: S10, the data dispatching center obtains the data set that needs to be dispatched by the electric carbon meter; S20, the data dispatching center obtains the path parameters of the data transmission and calculates the path performance; S30, the data dispatching center calculates different transmission requirements based on the data of multiple electricity and carbon meters, and divides the data into multiple data sets according to the transmission requirements; S40, obtaining the cloud platform data requirements, and the data dispatching center matches the corresponding path according to the transmission requirements of the electric carbon meter data and the cloud platform data requirements; S50, the data dispatching center compresses the electric carbon meter data; S60, the data dispatch center confirms the data priority and transmits the data in batches according to the priority; S70: The data dispatch center confirms that the data transmitted to the cloud platform is received.

2. A data scheduling optimization method for electric carbon meter data transmission according to claim 1, characterized in that: The specific steps of step S20 are as follows: S201, the data dispatching center obtains the path parameters of data transmission; S202: The data dispatching center calculates the path performance according to the path parameters.

3. The data scheduling optimization method for electric carbon meter data transmission according to claim 1 is characterized in that: The specific steps of dividing the data into multiple data sets in step S30 are as follows: S301, initializing multiple cluster centers; S302, assigning each data point to the nearest cluster center; S303: Recalculate the center of each cluster.

4. The data scheduling optimization method for electric carbon meter data transmission according to claim 1 is characterized in that: The specific steps of step S40 are as follows: S401, obtaining cloud platform data receiving requirements; S402, calculating the influence factors of basic data, transmission distance and network status on the transmission process; S403. Match the corresponding path according to the transmission requirements of the electricity carbon meter data and the cloud platform data requirements.

5. A data scheduling optimization method for electric carbon meter data transmission according to claim 4, characterized in that: The calculation of the impact factor in step S402 includes: The size of the data set affects the transmission, and the impact value is calculated by calculating the transmission time; The transmission distance affects the data transmission, and the impact value is calculated by calculating the degree of influence of the transmission distance on the radio electromagnetic waves; The network condition affects the efficiency of data transmission. The impact value is calculated by calculating the data transmission rate within a certain period of time.

6. A data scheduling optimization method for electric carbon meter data transmission according to claim 1, characterized in that: The specific steps of step S50 are as follows: S501, wavelet threshold denoising: set a soft threshold, for some frequency band sub-signals, if their absolute value is less than a specific threshold, they are set to zero; if their absolute value is greater than the threshold, they are retained; S502, signal detection and classification: detecting the signal type by binary wavelet transform; S503, FFT compression: for steady-state or steady-state disturbance signals, use FFT to compress them, obtain the corresponding spectrum, and record the information of different frequency components in the original signal; S504, wavelet packet transform: for transient disturbance signals, wavelet packet transform is used for compression, and threshold processing is performed on the decomposition coefficients of each layer of the wavelet packet, points related to the signal singularity are retained, and some points irrelevant to the signal singularity are ignored; S505, LZW encoding: Use LZW encoding to perform lossless compression on the data stored after lossy compression.

7. The data scheduling optimization method for electric carbon meter data transmission according to claim 1 is characterized in that: The specific steps of step S70 are as follows: S701, response confirmation: the data dispatch center receives the response frame sent by the cloud platform and reads the execution result of the request. If the execution result shows success, it confirms that the data transmitted to the cloud platform is received; S702, data retransmission mechanism: If the data scheduling center fails to receive a valid response frame within the specified time or due to a checksum error, the data scheduling center determines that the data has not been successfully received and attempts to resend it.

8. The data scheduling optimization method for electric carbon meter data transmission according to claim 1 is characterized in that: In step S10, the data set includes current data, voltage data, resistance data, and accumulated power consumption data.

9. A data scheduling optimization method for electric carbon meter data transmission according to claim 2, characterized in that: Path parameters include the source node, the destination node, the bandwidth of each node on the path, delay and packet loss rate, throughput, and security.

10. A system using the method according to any one of claims 1 to 9, characterized in that: include: The analysis module is used to analyze the data set, transmission distance, and network status when the electric carbon meter sends data; The path management module is used to obtain the parameter information of the path through which the data of the electric carbon meter is transmitted, and calculate the performance of the path according to the parameter information; The matching module is used to count the transmission requirements of the electric carbon meter data, obtain the data requirements required by the cloud platform, and match the corresponding transmission path according to the transmission requirements of the electric carbon meter data and the data requirements required by the cloud platform; The electric carbon meter management module is used to manage the data of multiple electric carbon meters and compress the data to reduce the storage space required for the data; A transmission module, used for transmitting the electric carbon meter data to a cloud platform for receiving according to the matching result of the matching module; The analysis module, the path management module, the matching module, the electric carbon meter management module, and the transmission module are communicatively connected in sequence, and the transmission module is communicatively connected with the analysis module.

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