Intelligent monitoring method and system for carbon emission during operation of green building

Abstract

The invention discloses an intelligent monitoring method and system for running carbon emission of a green building, and relates to the technical field of carbon emission monitoring, periodic data of monitoring sensors related to running carbon emission of the green building are defined as data trains, and each data train is equipped with a touring mechanism; distributing the data trains to a high-speed channel, a medium-speed channel or a low-speed channel for transmission based on the carbon emission monitoring demand related characteristics of the data trains; identifying delay data related to running carbon emission of the green building by using a cyclic mechanism, further updating establishment of a data train, and executing lane changing operation based on updated data train characteristics; and automatically updating the temporary parking space capacity, summarizing and packaging the data of all the data trains into a carbon emission monitoring data packet when identifying that all the data trains arrive at the corresponding temporary parking space, and performing carbon emission intelligent monitoring on green building operation by using the carbon emission monitoring data packet. The problems of transmission delay and fragmentation of carbon emission periodic sensor data are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission monitoring, and in particular to a method and system for intelligently monitoring carbon emissions from the operation of green buildings. Background Art

[0002] As a major carbon emitter, the construction industry urgently needs efficient carbon emission monitoring technology. Green building operations involve numerous sensors, complex data types, and data characteristics that change dynamically with the equipment's operating status.

[0003] Traditional monitoring systems lack a dynamic adjustment mechanism and are unable to flexibly allocate transmission channels based on data characteristics, resulting in delays in high-priority data or waste of bandwidth resources. Energy consumption data with high real-time requirements cannot be used for equipment control in a timely manner. Sensors may generate delayed data related to carbon emissions from green building operations during the data collection cycle, but traditional methods find it difficult to accurately identify and integrate this data, resulting in damage to the integrity and accuracy of periodic data, affecting the reliability of carbon emission assessments.

[0004] Therefore, in response to the above problems, there is an urgent need for an intelligent monitoring method and system for carbon emissions from green building operations. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a method and system for intelligent monitoring of carbon emissions from green building operations, which solves the transmission delay and fragmentation of carbon emission periodic sensor data, as well as the resource rigidity bottleneck problem of traditional fixed green building operation carbon emission monitoring data transmission bandwidth channel.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for intelligent monitoring of carbon emissions from green building operations, comprising the following steps: receiving periodic data from monitoring sensors related to carbon emissions from green building operations, defining the data collected by each monitoring sensor in each cycle as a data train, and equipping each data train with a patrol mechanism based on a bonding chain; allocating the data trains to high-speed, medium-speed or low-speed channels for transmission based on the carbon emission monitoring requirements of each data train; during the data train transmission process, using the patrol mechanism to identify delayed data related to carbon emissions from green building operations generated by the same monitoring sensor in the same cycle, updating the formation of the data train in combination with the delayed data related to carbon emissions from green building operations, and performing lane change operations through the patrol mechanism based on the updated data train characteristics; automatically updating the capacity of temporary parking spaces by utilizing the mapping between the patrol mechanism and temporary parking spaces, and when it is identified that all data trains have arrived at the corresponding temporary parking spaces, aggregating and packaging the data from all data trains into a carbon emission monitoring data packet, and using the carbon emission monitoring data packet to perform intelligent carbon emission monitoring of green building operations.

[0007] Furthermore, the patrol mechanism includes: an identification storage unit, a feature perception unit, an association matching unit, a decision control unit and an interactive execution unit; the patrol mechanism is specifically used to: use the identification storage unit to pre-write the unique identification of the data train, the unique identification information includes the monitoring sensor ID and the data generation cycle number; use the feature perception unit to capture the feature vector of the data train in real time during the transmission process, and feed the feature vector back to the decision control unit, the feature vector includes data volume, carbon emission data type identifier, and real-time transmission speed priority; use the association matching unit to compare the delayed data with the unique identification of the data train to determine the ownership relationship, and the interactive execution unit drives the delayed data to be integrated into the target data train; use the decision control unit to match the target channel and determine the lane change position based on the updated features, and control the patrol mechanism to separate from the data train through the interactive execution unit, and at the same time create a temporary virtual transmission tunnel; use the interactive execution unit to transmit the patrol mechanism carrying the concentrated features through the temporary virtual transmission tunnel to the target channel pre-position, complete the re-combination after the data train arrives, and destroy the temporary virtual transmission tunnel; use the association matching unit to map the temporary parking space through the unique identification.

[0008] Furthermore, the specific analysis of allocating data trains to high-speed, medium-speed or low-speed channels for transmission is: using the patrol mechanism to extract the carbon emission monitoring demand-related characteristics of each data train, the carbon emission monitoring demand-related characteristics include data real-time requirements, data volume and the weight of the impact of data on carbon emission assessment, and setting the transmission adaptation conditions of high-speed channels, medium-speed channels and low-speed channels based on the carbon emission monitoring demand-related characteristics, and transmitting each data train to a matching transmission channel.

[0009] Furthermore, the specific analysis of the lane changing operation performed by the patrol mechanism is as follows: using the delayed data related to carbon emissions from green building operations to update the data train, extracting the updated data train features, the updated data train features including the updated data volume, data real-time demand changes and impact weight adjustments; comparing the updated data train features with the transmission adaptation conditions of the current channel and the transmission adaptation conditions of other channels to match the target channel; using the patrol mechanism to predict the predicted lane changing position of the updated data train when switching from the original channel to the target channel; extracting the concentrated features of the updated data train, writing the concentrated features into the patrol mechanism, and then separating the patrol mechanism carrying the concentrated features from the updated data train by automatically triggering the breaking of the bonding chain, the concentrated features It includes data train identification, target channel information and lane change position information; the separated patrol mechanism is transmitted to a position where the target channel and the lane change position are consistent in transmission space distance, and a temporary virtual transmission tunnel is established at this position, and the patrol mechanism carrying the concentrated feature is transmitted to the target channel for waiting by using the virtual transmission tunnel, and the temporary virtual transmission tunnel connects the target channel with the original channel; when the updated data train passes through the temporary virtual transmission tunnel, the updated data train is transmitted to the target channel through the temporary virtual transmission tunnel, and the patrol mechanism in the target channel is matched by the concentrated feature, and the patrol mechanism is recombined with the updated data train through the bonding chain regeneration; when the instruction for the patrol mechanism to recombine with the updated data train is received, the temporary virtual transmission tunnel is automatically destroyed.

[0010] Furthermore, the specific analysis of updating the data train using the delayed data related to carbon emissions from the operation of green buildings is as follows: after the data train has entered the transmission channel, it is identified whether the monitoring sensor has generated new data in the same cycle, and the new data is marked as the delayed data of the data train, and the delayed data carries the monitoring sensor ID and the data generation cycle number; the patrol mechanism is used to scan the delayed data related to carbon emissions from the operation of green buildings outside the transmission channel in real time, and by comparing the monitoring sensor ID and the data generation cycle number carried by the delayed data related to carbon emissions from the operation of green buildings, the target data train belonging to the same cycle of the same monitoring sensor is identified; the patrol mechanism sends a target data train association instruction to the delayed data, and transmits the delayed data to the corresponding target data train in a directionally correct manner.

[0011] Furthermore, the specific prediction method of the predicted lane change position is: based on the current position of the data train in the original channel, the transmission speed difference between the original channel and the target channel, and the predetermined arrival synchronization requirement, the predicted lane change position of the data train is determined using a patrol mechanism.

[0012] Furthermore, the specific analysis of the automatic update of temporary parking space capacity is as follows: temporary parking spaces with the same number of monitoring sensors are set for the temporary data train parking garage, and each temporary parking space has a unique mapping relationship with the corresponding monitoring sensor in the patrol mechanism of the data train in a specific period, and the mapping relationship is associated through the monitoring sensor ID and the data generation cycle number in the patrol mechanism; the patrol mechanism is used to obtain the data volume of the data train in real time during the data train transmission process, and the data volume is transmitted to the corresponding temporary parking space; the temporary parking space capacity is automatically adjusted according to the received data volume, and data buffer space is reserved for each temporary parking space; when the data train is updated and formed, the patrol mechanism is used to send the updated data volume to the temporary parking space, and the parking space capacity is adjusted again according to the updated data volume.

[0013] Furthermore, the specific analysis of the use of carbon emission monitoring data packets to conduct intelligent carbon emission monitoring of green building operations is as follows: using the carbon emission monitoring data packets to extract the original data collected by each monitoring sensor in the same period, the original data including energy consumption data of building energy equipment, indoor environmental parameter data and building material carbon emission factor related data, using the carbon emission monitoring demand-related characteristics of each data train to match the impact weight of each original data on carbon emission assessment, combining the impact weight to identify the carbon emission status of green building operation in the period, and identifying the carbon emission change trend of green building operation based on the carbon emission monitoring data packets of historical periods.

[0014] A smart monitoring system for carbon emissions from green building operations, applying the above-mentioned smart monitoring method for carbon emissions from green building operations, comprises: a data receiving and defining module for receiving periodic data from monitoring sensors related to carbon emissions from green building operations, defining the data collected by each monitoring sensor in each cycle as a data train, and equipping each data train with a patrol mechanism based on a bonding chain; a channel allocation module for allocating data trains to high-speed, medium-speed, or low-speed channels for transmission based on the carbon emissions monitoring requirements of each data train; a lane change processing module for, during data train transmission, using the patrol mechanism to identify delayed data related to carbon emissions from green building operations generated by the same monitoring sensor in the same cycle, updating the data train composition based on the delayed data, and executing lane change operations through the patrol mechanism based on the updated data train characteristics; and a parking garage management and monitoring module for automatically updating the capacity of temporary parking spaces by mapping the patrol mechanism to temporary parking spaces. When all data trains are identified as arriving at the corresponding temporary parking spaces, the data from all data trains are aggregated and packaged into a carbon emissions monitoring data packet, and the carbon emissions monitoring data packet is used to perform smart carbon emissions monitoring of green building operations.

[0015] The present invention has the following beneficial effects: This method and system for intelligent monitoring of carbon emissions from green building operation defines the data collected by each monitoring sensor in each cycle as a data train, and equips each data train with a patrol mechanism. The refined data management method enables the data of each sensor and each cycle to be independently and effectively tracked and managed, which helps to more accurately grasp the data situation and provide a reliable basis for subsequent analysis; based on the carbon emission monitoring demand-related characteristics of each data train, the data train is allocated to a high-speed, medium-speed or low-speed channel for transmission; the layered transmission strategy can reasonably allocate network resources according to the importance and urgency of the data, ensure the rapid transmission of key data, improve the overall data transmission efficiency, avoid network congestion, and ensure the timeliness of carbon emission monitoring data; during the data train transmission process, the patrol mechanism is used to identify the delayed data related to the green building operation carbon emissions generated by the same monitoring sensor in the same cycle, and the formation of the data train is updated in combination with the delayed data related to the green building operation carbon emissions, which effectively solves the data The delay problem that may occur during the transmission process ensures that the data of each cycle is complete and accurate, avoids the deviation of monitoring results due to data missing or errors, and improves the reliability of carbon emission monitoring; based on the updated data train characteristics, the lane change operation is performed through the patrol mechanism, so that the data transmission can be dynamically adjusted according to the real-time situation, further optimize the transmission path, adapt to the changes in the network environment, enhance the flexibility and adaptability of the entire monitoring system, and ensure stable operation under different conditions; utilize the mapping between the patrol mechanism and the temporary parking space to automatically update the capacity of the temporary parking space, and the intelligent resource management method can dynamically adjust the temporary storage resources according to the actual situation of the data train, avoid resource waste or shortage, improve resource utilization efficiency, and reduce system operating costs; when all data trains are identified to arrive at the corresponding temporary parking space, the data of all data trains are automatically aggregated and packaged into a carbon emission monitoring data package, and the data package is used to perform intelligent carbon emission monitoring of green building operations, making carbon emission monitoring more scientific and efficient.

[0016] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of a method for intelligently monitoring carbon emissions from green building operations according to the present invention.

[0018] Figure 2 This is a schematic diagram of the lane-changing operation in a smart monitoring method for carbon emissions from green building operations according to the present invention.

[0019] Figure 3 This is a structural diagram of a green building operation carbon emission intelligent monitoring system according to the present invention. DETAILED DESCRIPTION

[0020] The embodiments of the present application adopt a method and system for intelligent monitoring of carbon emissions from green building operations. Through dynamic channel allocation, integration of delayed data related to carbon emissions from green building operations, and intelligent capacity management, they overcome the core defects of traditional monitoring systems in data transmission, storage, and analysis, and provide a systematic solution for real-time, accurate, and intelligent management of carbon emissions from green buildings.

[0021] The overall idea of ​​the embodiments of this application is: The data stream of carbon emission monitoring sensors in green building operations is abstracted into a data train, and the data collected in each cycle forms an independent train unit. A three-channel transmission system with high, medium and low speeds is established to achieve differentiated data transmission guarantees. By equipping the data train with a patrol mechanism, delayed data identification and compensation related to carbon emissions from green building operations, dynamic switching of transmission channels based on real-time characteristics, data integrity verification and temporary buffer design can be realized.

[0022] See also Figure 1 、 Figure 2 An embodiment of the present invention provides a technical solution: a method for intelligent monitoring of carbon emissions from green building operations, comprising the following steps: receiving periodic data from monitoring sensors related to carbon emissions from green building operations, defining the data collected by each monitoring sensor in each period as a data train, and equipping each data train with a patrol mechanism based on a bonding chain; allocating the data trains to high-speed, medium-speed, or low-speed channels for transmission based on the carbon emission monitoring requirements of each data train; during the data train transmission process, using the patrol mechanism to identify delayed data related to carbon emissions from green building operations generated by the same monitoring sensor in the same period, updating the formation of the data train based on the delayed data related to carbon emissions from green building operations, and performing lane change operations through the patrol mechanism based on the updated data train characteristics; automatically updating the capacity of temporary parking spaces by mapping the patrol mechanism to temporary parking spaces, and when it is identified that all data trains have arrived at the corresponding temporary parking spaces, aggregating and packaging the data from all data trains into carbon emission monitoring data packets, and using the carbon emission monitoring data packets to perform intelligent carbon emission monitoring of green building operations.

[0023] Specifically, the periodic data of monitoring sensors related to carbon emissions from the operation of green buildings include but are not limited to the following categories: indoor environmental parameter data: carbon dioxide concentration, temperature, humidity and other parameters in the air are collected through carbon dioxide concentration sensors and temperature and humidity sensors to reflect indoor air quality and user activity intensity; building energy equipment data: energy consumption information of air conditioners, lighting, elevators, water pumps and other equipment is collected through electricity metering, gas metering and other energy consumption sensors; data related to carbon emission factors of building materials: carbon emission factors of building materials in the use phase are collected through the integration of external databases or material identification, which are used to calculate embodied carbon emissions; external environment and weather data: outdoor temperature, humidity, wind speed, solar radiation and other factors are collected to assist in analyzing the relationship between changes in building energy consumption and carbon emissions.

[0024] A data train refers to the data set collected by each monitoring sensor within a cycle, which contains the original values ​​of all sampling points within the cycle and the necessary time stamps. It has characteristics such as data volume, importance, and real-time requirements.

[0025] The patrol mechanism is the core logical unit for the full lifecycle management of data trains. Its functions include: storing the unique identification information of data trains, which includes the monitoring sensor ID and data generation cycle number, and using this identification to associate and bind all data generated by the same monitoring sensor within the same cycle; collecting and updating the feature vectors of data trains in real time, including but not limited to data volume, carbon emission data type identifier, real-time transmission speed priority, updated data volume changes, and impact weight adjustments; scanning delayed data outside the transmission channel, identifying the target data train by comparing the monitoring sensor ID and cycle number carried by the delayed data, and transmitting the delayed data in a targeted manner to update the data train assembly; based on the updated data train characteristics, matching the target transmission channel, predicting lane change locations, controlling the separation of the patrol mechanism from the data train, pre-positioning and re-combining the target channel, and managing the creation and destruction of temporary virtual transmission tunnels; and by establishing a unique mapping relationship with temporary parking spaces, transmitting the data volume of the data train to the corresponding parking space in real time, driving dynamic adjustment of parking space capacity and reservation of buffer space.

[0026] In this embodiment, the patrol mechanism includes an identification storage unit, a feature perception unit, an association matching unit, a decision control unit and an interactive execution unit, wherein: the identification storage unit is used to solidify the unique identification information of the data train to form an association benchmark; the feature perception unit is used to collect and analyze the static and dynamic features of the data train in real time; the association matching unit is used to realize the directional association between the delayed data and the target data train based on the unique identification, and the mapping association between the patrol mechanism and the temporary parking space; the decision control unit is used to decide the channel matching, lane change position prediction and separation or generation timing of the data train based on the feature perception results; the interactive execution unit is used to execute the generation or separation operation with the data train, the creation or destruction instruction of the temporary virtual transmission tunnel, and the interaction with the capacity information of the parking space.

[0027] The patrol mechanism achieves the above functions through the coordinated operation of various units: the identification storage unit pre-writes the unique identification of the data train to provide a benchmark for association matching; the feature perception unit captures the feature changes of the data train during transmission in real time, such as the increase in data volume caused by the addition of delayed data, and feeds back the feature data to the decision control unit; the association matching unit determines the ownership relationship by comparing the delayed data with the unique identification of the data train, and the interactive execution unit drives the delayed data to be integrated into the target data train; the decision control unit matches the target channel and calculates the lane change position based on the updated features, and controls the patrol mechanism to separate from the data train through the interactive execution unit, and creates a temporary virtual transmission tunnel at the same time; the interactive execution unit transmits the patrol mechanism carrying concentrated features through the temporary virtual transmission tunnel to the target channel for pre-positioning, completes re-combination after the data train arrives, and destroys the temporary virtual transmission tunnel; the association matching unit maps the temporary parking space through the unique identification, and the feature perception unit transmits the data volume information to the parking space to drive dynamic capacity adjustment.

[0028] The roving mechanism is paired with the data train through a bonding chain. The bonding chain specifically adopts a distributed link structure with an encrypted identifier as the core and dynamic instructions as the control. The link core relies on a unique encrypted feature code to ensure the uniqueness and security of the binding, which is different from the static binding based on a single identifier in the existing technology. The breaking and regeneration of the bonding chain are triggered by logical instructions to realize the dynamic association between the roving mechanism and the data train, and adapt to the flexible switching of the data train between multiple channels.

[0029] The design of the patrol mechanism is different from the simple message queue tag in the existing technology. It not only records the basic identification of the data train, but also dynamically records the update characteristics of the data train during the transmission process. It realizes the lane changing, merging and matching functions of the data train through the logical connection of the patrol mechanism, thereby improving real-time performance and data integration efficiency.

[0030] Specifically, based on the carbon emission monitoring requirements of each data train, the data train is assigned to a high-speed, medium-speed, or low-speed channel for transmission. This is achieved by the following steps: Through the patrol mechanism, relevant features of carbon emission monitoring needs are extracted from each data train, including: real-time requirements: describing the timeliness requirements of the data train for carbon emission assessment. Real-time can be divided into three levels: high, medium and low. For example, sudden changes in indoor carbon dioxide concentration require rapid response, which is high real-time. Building energy statistical data can be moderately delayed, which is medium real-time. Building material factors are updated infrequently, which is low real-time. Data volume: the overall volume of the data train. The weight of the impact of data on carbon emission assessment: the importance of data in the data train to carbon emission assessment, such as carbon dioxide concentration and energy consumption have higher weights for emission calculations, while external environmental parameters have lower weights.

[0031] The adaptation conditions of each channel are pre-set based on system operation experience and requirements: high-speed channels are adapted to data trains with high real-time performance, small data volume, and large impact weight; medium-speed channels are adapted to data trains with medium real-time performance, medium data volume, and medium impact weight; low-speed channels are adapted to data trains with low real-time performance, large data volume, and small impact weight.

[0032] A multi-attribute matching algorithm, such as weighted scoring or decision matrix, is used to compare the data train characteristics with the three channel adaptation conditions, calculate the matching degree, and assign the channel with the highest matching degree to the data train. If the matching degree is within the boundary range, dynamic adjustment is made based on the load of the current channel.

[0033] In this implementation scheme, channel allocation comprehensively considers real-time performance, data volume, and impact weight, ensuring that important and urgent data is given priority through high-speed channels, while secondary data occupies medium and low-speed channels, thereby optimizing the allocation of network resources and avoiding the drawbacks of existing technologies that divide channels based solely on data volume, resulting in resource waste or information delays.

[0034] Specifically, see Figure 2The specific steps of performing lane change operations through the patrol mechanism are as follows: when the system receives delay data related to carbon emissions from green building operations belonging to the same sensor and the same period, the patrol mechanism merges the delay data related to carbon emissions from green building operations with the data train, and recalculates the updated data volume, real-time demand changes, and impact weight adjustments; compares the updated data train features with the transmission adaptation conditions of the current channel and other channels, calculates the matching degree of each channel, and selects the target channel with the highest matching degree. If the target channel is the same as the current channel, there is no need to change lanes; the patrol mechanism combines the current position of the data train in the original channel, the transmission speed difference between the original channel and the target channel, and the system synchronization requirements to calculate the predicted lane change position; extracts the updated concentrated features from the data train, including The data train identification, target channel information and lane change position information are written into the patrol mechanism, and then the patrol mechanism carrying the concentrated feature is separated from the updated data train by automatically triggering the breaking of the bonding chain; according to the predicted lane change position, a temporary virtual transmission tunnel is established between the original channel and the target channel, so that the two channels are logically connected at this position, and the patrol mechanism carrying the concentrated feature moves in advance along the temporary virtual transmission tunnel to the same position of the target channel to wait; when the data train runs to the predicted lane change position on the original channel, the data train is introduced into the target channel through the temporary virtual transmission tunnel, and the patrol mechanism is recombined with the updated data train through the bonding chain regeneration; when the patrol mechanism and the data train are recombined, the system automatically destroys the temporary virtual transmission tunnel and restores the isolation between the original channel and the target channel.

[0035] The lane-changing operation ensures that the data train switches to the appropriate channel in time according to feature changes during transmission, ensuring the real-time and synchronization of data. By pre-calculating the lane-changing position and establishing a temporary virtual transmission tunnel, it can avoid channel congestion and data loss. Compared with the delays and conflicts caused by simply jumping channels in existing technologies, it has significant advantages.

[0036] In this embodiment, a patrol mechanism is used to identify delayed data related to green building operation carbon emissions generated by the same monitoring sensor within the same cycle. The data train is then updated based on the delayed data. The following steps are specifically implemented: After the data train has entered the transmission channel, it continues to receive data from each sensor. Input data with the same sensor ID and cycle number that arrives later than the established data train is marked as delayed data related to green building operation carbon emissions. The patrol mechanism periodically scans the delayed data buffer related to green building operation carbon emissions and quickly finds the corresponding target data train by comparing the monitoring sensor ID and data generation cycle number carried by the delayed data. The patrol mechanism positioning avoids traversing all data trains and significantly improves matching efficiency. The patrol mechanism sends an association instruction to the delayed data related to green building operation carbon emissions, instructing it to transmit the data packet to the target data train. The delayed data related to green building operation carbon emissions is appended to the end of the data train according to the data protocol, and the data train characteristics are simultaneously updated. After merging the delayed data related to green building operation carbon emissions, if the real-time requirements, data volume, or impact weight of the data train change significantly, the lane change decision process is entered; otherwise, transmission continues along the original channel.

[0037] A mechanism for updating delayed data related to carbon emissions from green building operations ensures that all data generated by the same sensor in the same cycle is included in the same data train, preventing data fragmentation or loss. This itinerary mechanism quickly locates the target data train, addressing the inefficient processing of delayed data related to carbon emissions from green building operations in existing technologies. Dynamic feature updates also ensure accurate subsequent decision-making.

[0038] The following steps are used to predict lane change positions: The current position of the data train on the original channel, the transmission rate of the current channel, and the transmission rate of the target channel are acquired in real time, and the remaining channel distance and the synchronous arrival time of the data trains on each channel in the current cycle are predicted. The difference between the remaining channel distance and the current position of the data train on the original channel is obtained, and then the difference is taken with the transmission rate of the current channel to obtain the estimated arrival time of the data train remaining on the original channel. Based on the transmission rate of the target channel, the estimated arrival time of the data train for the entire transmission on the target channel is obtained. If the difference between the estimated arrival time of the data train remaining on the original channel and the estimated arrival time of the data train for the entire transmission on the target channel and the synchronous arrival time of the data trains on each channel in the current cycle is greater than a preset time deviation threshold, the switching position point is calculated. The channel safety interval and data train length constraints are set to obtain a switching interval that meets the safety interval. The switching position point within the switching interval is identified so that the arrival time of the data train to the target channel is as close as possible to the synchronous arrival time of the data trains on each channel in the current cycle. Since other data trains may change lanes at the same time, the switching position point calculation result is updated in real time to ensure that multiple lane change operations do not conflict.

[0039] By estimating the optimal switching position before changing lanes, the time that data trains spend traveling in the target channel can be precisely adjusted so that each data train arrives at the channel end almost simultaneously. This takes into account channel rate differences, current positions, and arrival time requirements. Compared with existing technologies with fixed lane change points, this significantly improves time synchronization accuracy and channel utilization efficiency.

[0040] The specific analysis of separating the patrol mechanism carrying concentrated features from the updated data train by automatically triggering the breaking of the glue chain is as follows: when a lane change operation is required, the patrol mechanism and the data train are separated by triggering the glue chain breaking instruction through the concentrated features. The specific steps are: using the decision control unit of the patrol mechanism to extract the concentrated features of the data train and write them into its own storage area, at this time generating a separation trigger signal; transmitting the separation trigger signal to the core link of the glue chain, which has built-in feature comparison logic. When it is detected that the patrol mechanism already carries concentrated features, the glue chain breaking instruction is automatically executed; after the glue chain is broken, the patrol mechanism is untied from the data train, and the patrol mechanism is driven by the interactive execution unit and transmitted to the pre-position of the target channel through a temporary virtual transmission tunnel. The data train continues to be transmitted along the original channel, and its metadata area only retains the glue chain breaking mark for subsequent combination verification.

[0041] The specific analysis of recombining the roving mechanism with the updated data train through glue chain regeneration is as follows: for the roving mechanism that has pre-positioned at the target channel, a combination request signal is released through the interactive execution unit, which carries the complete encrypted feature code; when the data train reaches the target channel through the temporary virtual transmission tunnel, the break mark in its metadata area, that is, the encrypted feature code fragment, is double-marked and compared with the complete encrypted feature code of the roving mechanism; if the comparison is consistent, the interactive execution unit of the roving mechanism generates a link regeneration instruction, which drives the core link of the glue chain to reconstruct the bidirectional binding link based on the original encrypted feature code; after the glue chain is regenerated, it automatically restores to the activated state, and the roving mechanism and the data train re-establish real-time data interaction to complete the combination; at this time, the temporary virtual transmission tunnel is automatically destroyed due to the triggering of the combination instruction.

[0042] Specifically, the automatic update of temporary parking space capacity is achieved by the following steps: during initialization, the temporary parking garage is set up with the same number of temporary parking spaces as the number of monitoring sensors, and a unique mapping relationship is established between each parking space and the patrol mechanism of the corresponding sensor. The mapping is achieved through the sensor ID and cycle number in the patrol mechanism, ensuring that the data train can stop correctly even after multiple lane changes; during the data train transmission process, the patrol mechanism sends the current data volume of the data train to the corresponding parking space manager in real time, and the parking space manager automatically adjusts the available capacity according to the data volume and reserves buffer space for different transmission stages; if the data volume of the data train changes due to delayed data integration related to carbon emissions from green building operations, the patrol mechanism will send the new data volume to the parking space manager, and the parking space capacity will be updated synchronously to avoid data overflow or waste of space when it arrives at the parking space; by comparing the total number of monitoring sensors with the number of parking spaces that have been parked, it is determined whether all data trains are in place; when all parking spaces have data trains temporarily stored, the data packaging process is triggered.

[0043] In this implementation, the capacity of temporary parking spaces is automatically updated to ensure that the parking space capacity matches the data volume of the data train, avoiding overflow or space waste caused by fixed buffers in the existing technology; in addition, the parking spaces are quickly located through mapping relationships and the capacity is updated in a timely manner to ensure packaging efficiency and data integrity after the data train is in place.

[0044] Specifically, the use of carbon emission monitoring data packets to conduct intelligent carbon emission monitoring of green building operations is implemented by the following steps: extracting the original data of each monitoring sensor in the same monitoring period from the carbon emission monitoring data packet, including energy consumption data of building energy-using equipment, indoor environmental parameter data and carbon emission factors of building materials; using the influence weights in the carbon emission monitoring demand-related characteristics of the aforementioned data trains to match the contribution of each original data to the carbon emission assessment, for example, the change in carbon dioxide concentration and energy consumption per unit time have higher weights in carbon emission accounting, and the weight of temperature and humidity data is relatively low; according to the current carbon emission accounting standards, such as the emission factor method, the energy consumption conversion coefficient method, etc., various types of data are calculated Calculate and obtain the building's operating carbon emissions during the cycle; at the same time, calculate indicators such as carbon emission intensity and carbon emissions per unit area based on data packets of historical cycles, and analyze the impact of different time periods and different equipment operations on carbon emissions; use statistical analysis or machine learning algorithms to conduct trend analysis on monitoring data packets of historical cycles, and identify the changing patterns of building operation carbon emissions, such as differences between holidays and weekdays, seasonal changes, and the impact of equipment maintenance; generate a carbon emission analysis report, which includes emissions, major emission sources, time series trends, and emission reduction recommendations, to provide decision-making support for building managers, and compare real-time monitoring results with preset standards to issue timely alarms or trigger energy-saving control strategies.

[0045] In this implementation plan, the use of carbon emission monitoring data packets for analysis can process multi-source heterogeneous data at one time, avoid errors caused by single data, provide comprehensive carbon emission assessment results, and combine historical data trend analysis to help building managers detect anomalies in a timely manner and formulate effective low-carbon operation strategies.

[0046] See also Figure 3A green building operation carbon emission intelligent monitoring system, which applies the above-mentioned green building operation carbon emission intelligent monitoring method, includes: a data receiving and defining module, configured to receive periodic data from monitoring sensors related to green building operation carbon emission, define the data collected by each monitoring sensor in each period as a data train, and equip each data train with a patrol mechanism based on a bonding chain; a channel allocation module, configured to allocate the data trains to high-speed, medium-speed, or low-speed channels for transmission based on the carbon emission monitoring requirements of each data train; a lane change processing module, configured to use the patrol mechanism to identify delayed data related to green building operation carbon emission generated by the same monitoring sensor in the same period during data train transmission, update the data train composition based on the delayed data, and perform lane change operations based on the updated data train characteristics through the patrol mechanism; and a parking garage management and monitoring module, configured to automatically update the temporary parking space capacity by mapping the patrol mechanism to temporary parking spaces. When all data trains are identified to have arrived at the corresponding temporary parking spaces, the data from all data trains are aggregated and packaged into a carbon emission monitoring data packet, and the carbon emission monitoring data packet is used to perform intelligent carbon emission monitoring of green building operation.

[0047] In summary, this application has at least the following effects: The periodic data of monitoring sensors related to carbon emissions from green building operations are defined as data trains equipped with a patrol mechanism to achieve refined management; transmission channels are allocated according to characteristics to optimize transmission efficiency and ensure timely delivery of key data for carbon emissions assessment; the patrol mechanism is used to process delayed data related to carbon emissions from green building operations during transmission to ensure integrity, and it can also dynamically change lanes to adapt to network changes; capacity is automatically updated with the help of patrol and temporary parking space mapping to improve resource utilization; when the data train is in place, it is automatically aggregated and packaged into monitoring packages for smart monitoring, reducing manual intervention and lowering error rates, making green building carbon emissions monitoring more scientific, efficient and economically feasible.

[0048] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods or systems. Therefore, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0049] The present invention is described with reference to the flowcharts and structure diagrams of the methods and systems according to the embodiments of the present invention. It should be understood that each process and combination of modules in the flowcharts and structure diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate the instructions for implementing the processes in the flowcharts. Figure 1 process or processes and structures Figure 1 A device that specifies functionality within a module or modules.

[0050] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 process or processes and structures Figure 1 Functionality specified in a module or modules.

[0051] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 process or processes and structures Figure 1 Steps for specifying functionality in a module or multiple modules.

[0052] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0053] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A green building operation carbon emission intelligent monitoring method, characterized by: The following steps are involved: Receive periodic data from monitoring sensors related to carbon emissions from green building operations, define the data collected by each monitoring sensor in each period as a data train, and equip each data train with a patrol mechanism based on the bonding chain; Based on the carbon emission monitoring requirements of each data train, the data train is assigned to a high-speed, medium-speed or low-speed channel for transmission; During the data train transmission process, a patrol mechanism is used to identify the delayed data related to green building operation carbon emissions generated by the same monitoring sensor in the same cycle. The data train is updated based on the delayed data related to green building operation carbon emissions, and the patrol mechanism is used to perform lane change operations based on the updated data train characteristics. By mapping the patrol mechanism with temporary parking spaces, the capacity of temporary parking spaces is automatically updated. When all data trains are identified to have arrived at the corresponding temporary parking spaces, the data of all data trains are aggregated and packaged into a carbon emission monitoring data package, which is then used to conduct intelligent carbon emission monitoring of green building operations.

2. A green building operation carbon emission intelligent monitoring method according to claim 1, characterized in that: The patrol mechanism includes: an identification storage unit, a feature perception unit, an association matching unit, a decision control unit and an interaction execution unit; The patrol mechanism is specifically used to: use the identification storage unit to pre-write the unique identification of the data train, the unique identification information includes the monitoring sensor ID and the data generation cycle number; The feature perception unit is used to capture the feature vectors of the data train in real time during transmission and feed the feature vectors back to the decision control unit. The feature vectors include data volume, carbon emission data type identifier, and real-time transmission speed priority. The association matching unit compares the unique identifier of the delayed data with the data train to determine the ownership relationship, and the interactive execution unit drives the delayed data to be integrated into the target data train; The decision control unit matches the target channel and determines the lane change position based on the updated features. The interactive execution unit controls the patrol mechanism to separate from the data train and creates a temporary virtual transmission tunnel. The interactive execution unit is used to transmit the patrol mechanism carrying the concentrated features to the target channel pre-position through the temporary virtual transmission tunnel, and the re-combination is completed after the data train arrives, and the temporary virtual transmission tunnel is destroyed; The temporary parking spaces are mapped using unique identifiers using an association matching unit.

3. A green building operation carbon emission intelligent monitoring method according to claim 1, characterized in that: The specific analysis of allocating data trains to high-speed, medium-speed or low-speed channels for transmission is as follows: The patrol mechanism is used to extract the carbon emission monitoring demand-related characteristics of each data train. The carbon emission monitoring demand-related characteristics include data real-time requirements, data volume, and the weight of the data's impact on carbon emission assessment. Based on the carbon emission monitoring demand-related characteristics, the transmission adaptation conditions of high-speed channels, medium-speed channels, and low-speed channels are set, and each data train is transmitted to the matching transmission channel.

4. The method for intelligently monitoring carbon emissions from green building operations according to claim 1 is characterized in that: The specific analysis of the lane change operation performed by the patrol mechanism is as follows: Using delayed data related to carbon emissions from green building operations to update a data train, extracting features of the updated data train, including updated data volume, changes in data real-time requirements, and impact weight adjustments; Compare the updated data train characteristics with the transmission adaptation conditions of the current channel and the transmission adaptation conditions of other channels to match the target channel; The updated data is used to predict the predicted lane change position of the train from the original channel to the target channel using the patrol mechanism; Extracting concentrated features of the updated data train, writing the concentrated features into the patrol mechanism, and then separating the patrol mechanism carrying the concentrated features from the updated data train by automatically triggering the breaking of the bonding chain, wherein the concentrated features include data train identification, target channel information, and lane change position information; The separated patrol mechanism is transmitted to a position where the target channel and the lane change position are at the same distance in the transmission space, and a temporary virtual transmission tunnel is established at this position. The patrol mechanism carrying the concentrated feature is transmitted to the target channel through the virtual transmission tunnel to wait. The temporary virtual transmission tunnel connects the target channel with the original channel. When the updated data train passes through the temporary virtual transmission tunnel, the updated data train is transmitted to the target channel through the temporary virtual transmission tunnel, and the roving mechanism in the target channel is matched by the concentrated features, and the roving mechanism is recombined with the updated data train through the bonding chain regeneration; When receiving the instruction for the patrol mechanism to rejoin with the updated data train, the temporary virtual transmission tunnel is automatically destroyed.

5. A green building operation carbon emission intelligent monitoring method according to claim 4, characterized in that: The specific analysis of updating the data train using delayed data related to carbon emissions from green building operations is as follows: When the data train has entered the transmission channel, it identifies whether the monitoring sensor has generated new data in the same cycle, and marks the new data as the delayed data of the data train. The delayed data carries the monitoring sensor ID and the data generation cycle number; The patrol mechanism is used to scan the delayed data related to carbon emissions from the operation of green buildings outside the transmission channel in real time. By comparing the ID of the monitoring sensor carrying the delayed data related to carbon emissions from the operation of green buildings and the data generation cycle number, the target data train belonging to the same monitoring sensor and the same cycle is identified; The target data train association instruction is sent to the delayed data through the patrol mechanism, and the delayed data is transmitted to the corresponding target data train.

6. A green building operation carbon emission intelligent monitoring method according to claim 4, characterized in that: The specific prediction method of the predicted lane change position is: based on the current position of the data train in the original channel, the transmission speed difference between the original channel and the target channel, and the predetermined arrival synchronization requirement, the predicted lane change position of the data train is determined using a patrol mechanism.

7. The method for intelligently monitoring carbon emissions from green building operations according to claim 2, characterized in that: The specific analysis of the automatic update of temporary parking space capacity is as follows: Set up temporary parking spaces for temporary data train parking garages with the same number of monitoring sensors. Each temporary parking space has a unique mapping relationship with the patrol mechanism of the data train corresponding to the monitoring sensor in a specific cycle. The mapping relationship is associated with the monitoring sensor ID and the data generation cycle number in the patrol mechanism. The patrol mechanism is used to obtain the data volume of the data train in real time during the data train transmission process, and transmit the data volume to the corresponding temporary parking space; Automatically adjust the capacity of temporary parking spaces based on the amount of data received, and reserve data buffer space in each temporary parking space; When the data train is updated and formed, the updated data volume is sent to the temporary parking space using the patrol mechanism, and the parking space capacity is adjusted again according to the updated data volume.

8. The method for intelligently monitoring carbon emissions from green building operations according to claim 3 is characterized in that: The specific analysis of the use of carbon emission monitoring data packets to conduct intelligent carbon emission monitoring of green building operations is as follows: the carbon emission monitoring data packets are used to extract the original data collected by each monitoring sensor in the same period, and the original data include energy consumption data of building energy equipment, indoor environmental parameter data and data related to the carbon emission factors of building materials. The carbon emission monitoring demand-related characteristics of each data train are used to match the impact weight of each original data on the carbon emission assessment, and the carbon emission status of the green building operation in the period is identified in combination with the impact weight. The carbon emission monitoring data packets based on the historical period are used to identify the carbon emission change trend of the green building operation.

9. A green building operation carbon emission intelligent monitoring system, applying a green building operation carbon emission intelligent monitoring method according to any one of claims 1 to 8, characterized in that: include: The data receiving and definition module is used to receive periodic data from monitoring sensors related to carbon emissions from green building operations, define the data collected by each monitoring sensor in each period as a data train, and equip each data train with a patrol mechanism based on the bonding chain; Channel allocation module, used to allocate data trains to high-speed, medium-speed or low-speed channels for transmission based on the carbon emission monitoring requirements of each data train; A lane change processing module is used to use a patrol mechanism to identify delayed data related to green building operation carbon emissions generated by the same monitoring sensor in the same cycle during data train transmission, update the formation of the data train based on the delayed data related to green building operation carbon emissions, and perform lane change operations through the patrol mechanism based on the updated data train characteristics; The parking garage management and monitoring module is used to automatically update the capacity of temporary parking spaces by utilizing the mapping between the patrol mechanism and temporary parking spaces. When all data trains are identified to have arrived at the corresponding temporary parking spaces, the data of all data trains are aggregated and packaged into a carbon emission monitoring data package, which is then used to conduct intelligent carbon emission monitoring of green building operations.

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