Building carbon emission real-time monitoring system and method based on power line carrier communication

Abstract

The invention provides a building carbon emission real-time monitoring system and method based on power line carrier communication, and relates to the technical field of building carbon emission monitoring, and the method comprises the steps: collecting multi-dimensional carbon emission data in a building region, carrying out the preprocessing, and packaging the data into a standardized data frame according to a communication protocol; modulating the data frame into a high-frequency carrier signal through an OFDM (Orthogonal Frequency Division Multiplexing) technology, injecting the high-frequency carrier signal into a power line carrier network through a power line coupler, receiving a power line carrier signal and demodulating the power line carrier signal into a carbon emission digital signal; respectively adopting a Kalman filtering algorithm, a neural network model and a carbon emission conversion model to carry out denoising, calibration and carbon emission calculation on the signal to generate a carbon emission chemical value and a trend chart; storing the data in a cloud server cluster, and integrating a differential upgrading algorithm engine to update an algorithm and a model; data are displayed in real time, and an acousto-optic early warning mechanism is triggered in time, so that the wiring cost can be reduced, the stability of signal transmission is enhanced, and the efficiency and accuracy of building carbon emission monitoring are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of building carbon emission monitoring, and particularly to a real-time building carbon emission monitoring system and method based on power line carrier communication. Background Art

[0002] With the global emphasis on environmental protection and sustainable development, the demand for real-time monitoring of carbon emissions in building management systems is increasing day by day.

[0003] In the prior art, wireless sensor networks or wired networks are often used for data transmission, but these methods have problems such as high installation costs and poor network stability. Most current building carbon emission monitoring systems collect carbon emission data from various regions through wireless sensor networks and then summarize and analyze them through a central server. However, wireless sensor networks are vulnerable to environmental factors. For example, walls and equipment in buildings can attenuate, reflect, and interfere with wireless signals, resulting in unstable data transmission and thus reducing the accuracy of monitoring data. Although wired networks are stable, a large amount of wiring work is required, which not only increases the economic burden of building intelligent transformation but also makes it extremely difficult to wire and transform in old buildings with complex building structures. At the same time, once the traditional monitoring system is built, it is very difficult to maintain in the later stage and it is difficult to adapt to the rapidly changing building environment requirements. For example, when the functional areas in the building are adjusted or the equipment is updated, the cost of rewiring and debugging the system is extremely high.

[0004] Therefore, it is necessary to provide a real-time building carbon emission monitoring system and method based on power line carrier communication to solve the above technical problems. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a real-time building carbon emission monitoring system and method based on power line carrier communication, which is used to solve the problems that the existing building carbon emission monitoring system is vulnerable to interference during signal transmission, resulting in a reduction in the accuracy of monitoring data; the wiring cost of the traditional monitoring system is high, increasing the economic burden of building intelligent transformation, and it is difficult to maintain and adapt to the rapidly changing building environment requirements; and the implementation cost of carbon monitoring in old buildings is large and the transformation is difficult.

[0006] The real-time building carbon emission monitoring system based on power line carrier communication provided by the present invention includes: A data acquisition and preprocessing module, which is used to acquire multi-dimensional carbon emission data in a target building area, preprocess the multi-dimensional carbon emission data, and package it into a standardized data frame according to a preset communication protocol for storage; A power line carrier communication module, which is used to modulate the standardized data frame into a high-frequency carrier signal through OFDM technology, inject it into the building power line carrier network in the target building area through a power line coupler, and receive the power line carrier signal in the building power line carrier network and demodulate it into a carbon emission digital signal; A central data processing and analysis module, which is used to denoise, calibrate and calculate carbon emissions for the carbon emission digital signal by using the Kalman filter algorithm, neural network model and carbon emission conversion model respectively, generate a carbon emission quantification value and a carbon emission trend chart and import them into the local database; A cloud service and remote management module, which is used to store the carbon emission quantification value and the carbon emission trend chart in a cloud server cluster, and integrate a differential upgrade algorithm engine to remotely update the Kalman filter algorithm, the neural network model and the carbon emission conversion model; A human-computer interaction and early warning module, which is used to display the carbon emission quantification value and the carbon emission trend chart in real time, and trigger a building sound and light early warning mechanism when the carbon emission quantification value is monitored to be abnormal.

[0007] Preferably, the data acquisition and preprocessing module is used to acquire multi-dimensional carbon emission data in the target building area, preprocess the multi-dimensional carbon emission data and package it into a standardized data frame according to a preset communication protocol for storage. Specifically, it includes: A distributed carbon emission sensor array unit, which is used to cover the target building area in a distributed deployment manner, and respectively collect the building CO2 concentration, building temperature and humidity, and building power parameters in the target building area through an integrated CO2 concentration sensor, temperature and humidity sensor, and power parameter sensor, and summarize them to generate the multi-dimensional carbon emission data; A microprocessor unit, which is used to adopt a microprocessor chip and integrate an analog-to-digital conversion module, perform filtering and denoising, signal amplification and analog-to-digital conversion processing on the multi-dimensional carbon emission data, and package the multi-dimensional carbon emission data into the standardized data frame according to the preset communication protocol; A local cache unit, which is used to construct a data cache space by using a non-volatile storage medium, temporarily store the packaged standardized data frame, and transmit the standardized data frame to the power line carrier communication module through an SPI interface.

[0008] Preferably, the power line carrier communication module is used to modulate the standardized data frame into a high-frequency carrier signal through OFDM technology, inject it into the building power line carrier network in the target building area through a power line coupler, and receive the power line carrier signal in the building power line carrier network and demodulate it into a carbon emission digital signal. Specifically, it includes: The OFDM modulation chip unit is used to support the adaptive adjustment of the carrier frequency within the frequency band range of 2 to 30 MHz, receive the standardized data frame, integrate the OFDM signal processing circuit through the OFDM technology, and modulate the standardized data frame into the high-frequency carrier signal; The power line coupler unit is used to inject the high-frequency carrier signal into the building power carrier network in the target building area through the power line coupler, and perform bidirectional coupling on the high-frequency carrier signal and the building power line by adopting a capacitive-inductive hybrid coupling topology structure to generate the power line carrier signal; The signal amplification and filtering circuit unit is used to perform real-time gain adjustment and noise filtering processing on the power line carrier signal through an automatic gain control mechanism and a band-pass filtering circuit; The OFDM demodulation chip unit is used to demodulate the power line carrier signal after real-time gain adjustment and noise filtering processing into the carbon emission digital signal by adopting the OFDM technology, and transmit the carbon emission digital signal to the central data processing and analysis module through a power line interface.

[0009] Preferably, the central data processing and analysis module is used to perform denoising, calibration, and carbon emission calculation on the carbon emission digital signal by respectively adopting a Kalman filtering algorithm, a neural network model, and a carbon emission conversion model, generate a carbon emission quantization value and a carbon emission trend chart, and import them into the local database. Specifically, it includes: The industrial-grade embedded processor unit is used to receive the carbon emission digital signal and coordinate the data parsing algorithm execution unit and the local database management unit by adopting a multi-core embedded processor architecture; The data parsing algorithm execution unit includes a Kalman filtering denoising sub-unit, a neural network calibration sub-unit, and a carbon emission conversion sub-unit; the Kalman filtering denoising sub-unit is used to perform dynamic noise estimation and filtering on the carbon emission digital signal based on the Kalman filtering algorithm to generate a first carbon emission digital signal; the neural network calibration sub-unit is used to perform non-linear calibration on the first carbon emission digital signal through the neural network model to generate a second carbon emission digital signal; the carbon emission conversion sub-unit is used to construct the carbon emission conversion model to convert the second carbon emission digital signal into the carbon emission quantization value, and draw the carbon emission trend chart according to the carbon emission quantization value; The local database management unit is used to import the carbon emission quantization value and the carbon emission trend chart into the local database by adopting a real-time database architecture, and interact with the cloud service and remote management module through an Ethernet interface. Among them, the local database supports the query, call, and local analysis of the carbon emission quantization value.

[0010] Preferably, the Kalman filtering denoising sub-unit is used to perform dynamic noise estimation and filtering on the carbon emission digital signal based on the Kalman filtering algorithm, and generate a first carbon emission digital signal. The corresponding calculation formula is as follows: In the formula, represents the state prediction value of the carbon emission digital signal at the k-th moment; represents the state transition matrix of the carbon emission digital signal at the k-th moment; represents the state estimation value of the updated carbon emission digital signal at the (k - 1)-th moment; represents the error covariance matrix of the updated carbon emission digital signal at the (k - 1)-th moment; represents the state transition matrix of the transposed matrix; represents the process noise covariance matrix of the carbon emission digital signal at the k-th moment; represents the Kalman gain matrix of the carbon emission digital signal at the k-th moment; represents the observation matrix of the carbon emission digital signal at the k-th moment; represents the observation matrix of the transposed matrix; represents the observation noise covariance matrix of the carbon emission digital signal at the k-th moment; represents the state estimation value of the updated carbon emission digital signal at the k-th moment, that is, the first carbon emission digital signal; represents the carbon emission digital signal at the k-th moment; I represents the identity matrix; represents the error covariance matrix of the updated carbon emission digital signal at the k-th moment.

[0011] Preferably, the neural network calibration sub-unit is used to perform non-linear calibration on the first carbon emission digital signal through the neural network model, and generate a second carbon emission digital signal. The corresponding calculation formula is as follows: In the formula, represents the sum of inputs of the j-th neuron in the hidden layer of the neural network model; represents the input weight of the j-th hidden neuron; represents the bias of the j-th neuron in the hidden layer; represents the output of the j-th neuron in the hidden layer; represents the activation function of the hidden layer; represents the sum of inputs of the output layer of the neural network model; n represents the number of neurons in the hidden layer; represents the weight from the j-th hidden neuron to the output; Represents the output layer bias; Represents the second carbon emission digital signal; Represents the output layer activation function.

[0012] Preferably, calculate the error gradients of the output layer and the hidden layer: In the formula, Represents the error gradient of the output layer; y represents the unbiased reference signal for training; Represents the error gradient of the j-th neuron in the hidden layer; Represents the derivative of the output layer activation function; Represents the derivative of the hidden layer activation function; Update the input weight of the j-th hidden neuron , the weight from the j-th hidden neuron to the output , the bias of the j-th neuron in the hidden layer and the output layer bias : In the formula, Represents the updated input weight of the j-th hidden neuron; Represents the learning rate; Represents the updated weight from the j-th hidden neuron to the output; Represents the updated bias of the j-th neuron in the hidden layer; Represents the updated output layer bias.

[0013] Preferably, the carbon emission conversion subunit is used to construct the carbon emission conversion model and convert the second carbon emission digital signal into the carbon emission quantization value, specifically including: In the formula, LHZ represents the carbon emission quantization value; m represents the number of the second carbon emission digital signals; Represents the i-th second carbon emission digital signal; Represents the carbon emission factor corresponding to the i-th second carbon emission digital signal.

[0014] Preferably, the human-computer interaction and early warning module is used to display the carbon emission quantization value and the carbon emission trend chart in real time, and trigger the building sound and light early warning mechanism when the carbon emission quantization value is monitored to be abnormal, specifically including: The touch display terminal unit is used to visually display the carbon emission quantization value and the carbon emission trend chart through a capacitive touch display screen, and provide a graphical user interface to support the touch interaction operation of the user terminal; An early warning logic module unit is used to build a threshold comparator and a rule engine inside, and compare the carbon emission quantification value with a preset carbon emission threshold in real time. When it is monitored that the carbon emission quantification value reaches the preset carbon emission threshold, the building acoustic and optical warning mechanism is triggered.

[0015] A real-time monitoring method for building carbon emissions based on power line carrier communication, the method includes: Collect multi-dimensional carbon emission data in the target building area, preprocess the multi-dimensional carbon emission data and package it into a standardized data frame according to a preset communication protocol for storage; Modulate the standardized data frame into a high-frequency carrier signal through OFDM technology, inject it into the building power line carrier network in the target building area through a power line coupler, and receive the power line carrier signal in the building power line carrier network and demodulate it into a carbon emission digital signal; Use the Kalman filter algorithm, neural network model and carbon emission conversion model respectively to denoise, calibrate and calculate carbon emissions for the carbon emission digital signal, generate a carbon emission quantification value and a carbon emission trend chart and import them into the local database; Store the carbon emission quantification value and the carbon emission trend chart in the cloud server cluster, and integrate a differential upgrade algorithm engine to remotely update the Kalman filter algorithm, the neural network model and the carbon emission conversion model; Display the carbon emission quantification value and the carbon emission trend chart in real time. When it is monitored that the carbon emission quantification value is abnormal, trigger the building acoustic and optical warning mechanism.

[0016] Compared with related technologies, the real-time monitoring system and method for building carbon emissions based on power line carrier communication provided by the present invention have the following beneficial effects: The present invention includes a data acquisition and preprocessing module, which is used to collect multi-dimensional carbon emission data in the target building area, preprocess the multi-dimensional carbon emission data, and package it into a standardized data frame according to a preset communication protocol for storage; a power line carrier communication module, which is used to modulate the standardized data frame into a high-frequency carrier signal through OFDM technology, inject it into the building power line carrier network in the target building area through a power line coupler, and receive the power line carrier signal in the building power line carrier network and demodulate it into a carbon emission digital signal; a central data processing and analysis module, which is used to denoise, calibrate and calculate carbon emissions for the carbon emission digital signal by using the Kalman filter algorithm, neural network model and carbon emission conversion model respectively, generate a carbon emission quantification value and a carbon emission trend chart and import them into the local database; a cloud service and remote management module, which is used to store the carbon emission quantification value and the carbon emission trend chart in the cloud server cluster, and integrate a differential upgrade algorithm engine to remotely update the Kalman filter algorithm, neural network model and carbon emission conversion model; a human-computer interaction and early warning module, which is used to display the carbon emission quantification value and the carbon emission trend chart in real time, and trigger a building sound and light warning mechanism when an abnormal carbon emission quantification value is detected, so as to reduce the wiring cost, enhance the stability of signal transmission, improve the efficiency and accuracy of building carbon emission monitoring, and further improve the energy utilization efficiency of the building, reduce carbon emissions, and better meet the requirements of the rapidly changing building environment.

[0017] The present invention adopts power line carrier communication technology and uses the existing power lines in the building as the transmission medium, without additional wiring, greatly reducing the wiring cost, especially suitable for the carbon monitoring transformation of old buildings, reducing the implementation cost and transformation difficulty of carbon monitoring in old buildings. At the same time, the power line transmission signal has high stability and is not easily affected by environmental factors, enhancing the stability of data transmission and improving the accuracy of monitoring data. The system of the present invention adopts a modular design, and each functional module is independent. When a certain module fails, only the corresponding module needs to be replaced, solving the problem of difficult maintenance of traditional systems. In addition, the system of the present invention supports the remote update function, and the system can be maintained and upgraded without the need for staff to go to the site, enhancing the scalability and adaptability of the system and being able to better meet the requirements of the rapidly changing building environment. In addition, the present invention adopts an efficient data parsing algorithm to denoise, calibrate and calculate carbon emissions for the received data, effectively improving the accuracy of carbon emission monitoring and providing reliable data support for building energy conservation and emission reduction. Managers can formulate reasonable energy conservation and emission reduction measures based on accurate carbon emission data, improve the energy utilization efficiency of the building, and reduce carbon emissions. Brief Description of the Drawings

[0018] Figure 1 It is a system block diagram of the building carbon emission real-time monitoring system based on power line carrier communication of the present invention; Figure 2Flowchart of the building carbon emission real-time monitoring method based on power line carrier communication of the present invention. Detailed implementation manners

[0019] The present invention will be further described below in conjunction with the accompanying drawings and implementation manners.

[0020] Embodiment 1

[0021] As Figure 1 shown, a building carbon emission real-time monitoring system based on power line carrier communication, the system includes: A data acquisition and preprocessing module, configured to acquire multi-dimensional carbon emission data in a target building area, preprocess the multi-dimensional carbon emission data, and package it into a standardized data frame according to a preset communication protocol for storage; The building carbon emission real-time monitoring system based on power line carrier communication is an intelligent building management system integrating power line communication technology and multi-dimensional data processing algorithms. The system uses the existing power lines in the building as the data transmission medium, and through a distributed sensor network and multi-level algorithm processing, realizes real-time monitoring, quantitative analysis and early warning management of building carbon emissions, and solves the problems of high wiring cost and poor signal stability of traditional monitoring systems.

[0022] Among them, the target building area refers to the actual building area where real-time carbon emission monitoring is required. The multi-dimensional carbon emission data refers to a data set containing multiple physical quantities such as CO2 concentration, environmental temperature and humidity, and power parameters. The preset communication protocol refers to the data transmission specification set in advance, which is used to standardize the format, verification method and transmission rules of the data frame to ensure the consistency and reliability of data interaction between modules. The binary data unit obtained by encapsulating the carbon emission data according to the preset communication protocol is the standardized data frame, which includes a timestamp, a regional address, data content and a check code, facilitating transmission and parsing in the power line carrier network.

[0023] In practical applications, the data acquisition and preprocessing module can obtain multi-dimensional carbon emission-related physical quantities such as CO2 concentration, environmental temperature and humidity, and power parameters in real time through the sensor array deployed in each area of the building. Then, this module can preprocess these carbon emission data, encapsulate them into standardized data frames containing timestamps, regional addresses, data content and check codes according to the preset communication protocol, and finally store these standardized data frames in the local cache unit for transmission.

[0024] A power line carrier communication module, configured to modulate the standardized data frame into a high-frequency carrier signal through OFDM technology, inject it into the building power line carrier network in the target building area through a power line coupler, and receive the power line carrier signal in the building power line carrier network and demodulate it into a carbon emission digital signal; It should be noted that through the OFDM (Orthogonal Frequency Division Multiplexing) technology, the standardized data frame can be decomposed into multiple orthogonal sub - carriers for parallel transmission, with strong anti - multipath interference ability. Thus, high - frequency carrier signal can be efficiently modulated within the frequency band of 2 to 30 MHz, improving the stability of power line communication. The high - frequency carrier signal refers to a sine wave signal with a frequency of 2 to 30 MHz, which can be transmitted on the power line and is compatible with the 50Hz power frequency signal. The power line coupler adopts a hybrid capacitance - inductance topology structure, which can achieve bidirectional coupling of the high - frequency carrier signal and the building power line, while isolating strong - electricity interference to ensure efficient signal injection and separation. The building power line carrier network can use the existing building power lines to construct a data transmission network without additional wiring, and interconnect each acquisition node and the central processing unit through the power line coupler to form a carrier communication link. The power line carrier signal contains carbon emission data and communication protocol information, and can be restored to a digital signal through an OFDM demodulation chip. The carbon emission digital signal refers to the digitized data demodulated by the power line carrier communication module.

[0025] It can be understood that the power line carrier communication module can adopt the OFDM (Orthogonal Frequency Division Multiplexing) technology to modulate the standardized data frame into a high - frequency carrier signal in the 2 - 30 MHz frequency band and inject it into the building power line carrier network through a power line coupler with a hybrid capacitance - inductance topology structure. This module also undertakes the signal demodulation function, which can separate the carrier signal from the power line and restore it to a carbon emission digital signal, realizing data transmission without additional wiring using the power line medium. In a reinforced concrete building environment, the communication bit error rate can be controlled within 10⁻ 6 below.

[0026] The central data processing and analysis module is used to denoise, calibrate and calculate carbon emissions for the carbon emission digital signal respectively by using the Kalman filter algorithm, neural network model and carbon emission conversion model, generate carbon emission quantification values and carbon emission trend charts and import them into the local database; It can be understood that the Kalman filtering algorithm can dynamically estimate the true value of the signal based on the state space model, suppress power line noise and sensor random errors through a recursive prediction and update process, and improve data accuracy. The neural network model can use the BP algorithm to construct a nonlinear mapping model, fit the deviation relationship between temperature, humidity and the sensor through forward propagation, and optimize the weights through backpropagation to compensate for measurement errors caused by device aging and environmental changes. The carbon emission conversion model can convert the calibrated data into standardized carbon emissions based on the carbon emission factor standards of the IPCC (Intergovernmental Panel on Climate Change). The carbon emission quantification value refers to the standardized carbon emission value generated after algorithm processing and is the core quantification index for building carbon management. The carbon emission trend chart is a curve or bar chart with the time axis as the horizontal axis and the carbon emission quantification value as the vertical axis. It supports trend analysis of real-time data and historical data and can assist managers in formulating emission reduction strategies. The local database uses the SQLite real-time database, which can store the processed carbon emission data and trend charts, support local quick query and call, and synchronize data with the cloud server at the same time.

[0027] The central data processing and analysis module constructs a three-level algorithm processing system: first, the Kalman filtering algorithm is used to dynamically estimate and filter the digital signal noise to suppress random interference in power line transmission; then, the neural network model is used to perform nonlinear calibration on the signal to compensate for measurement deviations caused by temperature, humidity changes and device aging, and improve the calibration accuracy; finally, based on the carbon emission conversion model of the IPCC (Intergovernmental Panel on Climate Change) standard, the calibrated data is converted into standardized carbon emissions, and a real-time trend chart is generated and stored in the local SQLite database.

[0028] The cloud service and remote management module is used to store the carbon emission quantification value and the carbon emission trend chart in the cloud server cluster, and integrate a differential upgrade algorithm engine to remotely update the Kalman filtering algorithm, the neural network model and the carbon emission conversion model; Among them, the cloud server cluster refers to a group of cloud servers with a distributed architecture.

[0029] The cloud service and remote management module can adopt a distributed server cluster architecture, store long-term monitoring data through a time series database, and provide a remote access interface based on the RESTful protocol. The integrated differential upgrade algorithm engine can automatically compare the software versions of each module, generate binary difference data packets and push them remotely, so that iterative updates of Kalman filter parameters, neural network models and carbon emission factors can be realized, and system maintenance can be completed without on-site manual intervention.

[0030] The human-computer interaction and warning module is used to display the carbon emission quantification value and the carbon emission trend chart in real time, and trigger the building sound and light warning mechanism when the carbon emission quantification value is monitored to be abnormal.

[0031] It can be understood that the human-computer interaction and warning module can visually display the carbon emission quantification value and the trend analysis result in real time through an industrial-grade touch display terminal, and can build a rule engine to compare the monitoring data with the preset threshold. When the carbon emission index is abnormal, it triggers the sound and light warning mechanism and synchronously links the building alarm system. And this module supports interactive operations such as parameter setting and historical data query.

[0032] In the specific implementation process, the data acquisition and preprocessing module is used to collect multi-dimensional carbon emission data in the target building area, preprocess the multi-dimensional carbon emission data and package it into a standardized data frame according to the preset communication protocol for storage. Specifically, it includes: The distributed carbon emission sensor array unit is used to cover the target building area in a distributed deployment manner, and collect the building CO2 concentration, building temperature and humidity, and building power parameters in the target building area through the integrated CO2 concentration sensor, temperature and humidity sensor, and power parameter sensor respectively, and summarize them to generate the multi-dimensional carbon emission data; The microprocessor unit is used to adopt a microprocessor chip and integrate an analog-to-digital conversion module to perform filtering and denoising, signal amplification, and analog-to-digital conversion processing on the multi-dimensional carbon emission data, and package the multi-dimensional carbon emission data into the standardized data frame according to the preset communication protocol; The local cache unit is used to build a data cache space using a non-volatile storage medium, temporarily store the packaged standardized data frame, and transmit the standardized data frame to the power line carrier communication module through the SPI interface.

[0033] Among them, the distributed carbon emission sensor array unit of the data acquisition and preprocessing module adopts a grid deployment strategy to achieve full coverage monitoring of the building area through the collaborative layout of multiple types of sensors. Among them, the CO2 concentration sensor is based on the non-dispersive infrared (NDIR) principle to perceive the CO2 concentration in each area of the building in real time; the temperature and humidity sensor uses a combination of a capacitive humidity-sensitive element and a thermistor to synchronously collect ambient temperature and humidity parameters; the power parameter sensor obtains power operation parameters such as current, voltage, and power factor in real time through the principle of electromagnetic induction or Hall effect. The three types of sensors work together to form a multi-dimensional carbon emission data set, which can not only directly reflect the indoor air quality status but also provide basic data support for subsequent carbon emission conversion.

[0034] The microprocessor unit serves as the signal processing core of this module. It adopts a low-power embedded microprocessor architecture and integrates a high-precision analog-to-digital conversion (ADC) module and a hardware filtering circuit. In the signal processing flow, first, the band-pass filtering circuit is used to suppress the interference of environmental noise on the original signal, and an instrumentation amplifier is utilized to achieve the gain adjustment of weak signals, ensuring that the analog signals output by the sensor meet the ADC input range requirements. The digital signals after analog-to-digital conversion are further encapsulated according to a preset communication protocol, which can be the Modbus protocol based on TCP / IP, and information such as timestamps, regional addresses, data content, and check codes are embedded in the data frame to form a standardized data frame format, laying a foundation for the reliable transmission of subsequent power line carrier communication.

[0035] The local cache unit is constructed with a non-volatile random access memory (NVRAM) to build a data buffer space. Its core function is to achieve the timing matching of data acquisition and carrier transmission. When the microprocessor completes the data frame encapsulation, it first temporarily stores the standardized data frame in the cache unit and establishes a data transmission link with the power line carrier communication module through the SPI (Serial Peripheral Interface) bus. This caching mechanism can effectively cope with the time-varying characteristics of the power line communication channel. When the transmission is interrupted due to the fluctuation of the channel quality, it ensures that the data frame is not lost and continues to be transmitted after the channel recovers, thereby enhancing the overall data integrity of the monitoring system.

[0036] The three functional units of this module achieve collaborative work through a standardized bus interface: the analog signals output by the sensor array are connected to the ADC channel of the microprocessor through the conditioning circuit, and the processed digital signals are transmitted to the cache unit through the internal data bus; the cache unit interacts with the baseband processor of the power line carrier module through the SPI bus to form a complete data link of acquisition, processing, caching, and transmission. This architecture design not only meets the synchronous acquisition requirements of multi-dimensional data in the building environment but also improves the transmission efficiency of data in the power line channel through preprocessing and standardized encapsulation, providing reliable data input for the high-precision algorithm operation of the subsequent central processing module.

[0037] In practical applications, this module can flexibly adjust the sensor deployment density according to the building function partition, reduce the system energy consumption through the dynamic power management technology of the microprocessor, and the non-volatile cache design ensures that the acquired data is not lost in the case of power failure and fully adapts to the complex operating environment of the building.

[0038] The power line carrier communication module is used to modulate the standardized data frame into a high-frequency carrier signal through OFDM technology, inject it into the building power line carrier network in the target building area through a power line coupler, receive the power line carrier signal in the building power line carrier network and demodulate it into a carbon emission digital signal, specifically including: The OFDM modulation chip unit is used to support the adaptive adjustment of the carrier frequency within the frequency band range of 2 to 30 MHz, receive the standardized data frame, integrate the OFDM signal processing circuit through the OFDM technology, and modulate the standardized data frame into the high-frequency carrier signal; The power line coupler unit is used to inject the high-frequency carrier signal into the building power carrier network in the target building area through the power line coupler, and perform bidirectional coupling on the high-frequency carrier signal and the building power line by adopting a capacitive-inductive hybrid coupling topology structure to generate the power line carrier signal; The signal amplification and filtering circuit unit is used to perform real-time gain adjustment and noise filtering processing on the power line carrier signal through an automatic gain control mechanism and a band-pass filter circuit; The OFDM demodulation chip unit is used to demodulate the power line carrier signal after real-time gain adjustment and noise filtering processing into the carbon emission digital signal by adopting the OFDM technology, and transmit the carbon emission digital signal to the central data processing and analysis module through a power line interface.

[0039] In practical applications, the OFDM modulation chip unit of the power line carrier communication module adopts a software-defined radio architecture, supports the adaptive adjustment of the carrier frequency within the frequency band range of 2 to 30 MHz, and realizes the efficient modulation of the data frame through the built-in OFDM signal processing circuit. In the signal transmission process, the chip receives the standardized data frame from the data acquisition module, decomposes the data stream into multiple orthogonal subcarriers for parallel transmission, and generates a high-frequency carrier signal through the inverse fast Fourier transform (IFFT). This multi-carrier modulation method can effectively resist multipath fading and harmonic interference in the power line channel. For example, a stable data transmission rate of 1.2 Mbps can be achieved in a reinforced concrete building environment.

[0040] The power line coupler unit adopts a capacitive-inductive hybrid topology structure. As the physical interface between the high-frequency carrier signal and the building power line, it can realize the bidirectional coupling transmission of the signal. This unit injects the high-frequency carrier signal into the power line through a coupling capacitor, and at the same time uses an inductive element to suppress the interference of the power frequency power to the carrier signal, forming a band-pass filtering effect. In the receiving direction, the power line coupler can separate the carrier signal from the power line, ensure the physical layer compatibility of the 50 Hz power frequency current and the 2 to 30 MHz carrier signal, avoid the interference of the strong power system to the communication signal, and ensure the reliability of the data transmission link.

[0041] The signal amplification and filtering circuit unit can integrate an automatic gain control (AGC) module and a band-pass filtering network to build a real-time conditioning mechanism for power line carrier signals. In the signal transmission path, the AGC module dynamically adjusts the amplitude of the carrier signal according to the channel attenuation to ensure that the transmission power of the signal in the power line meets the electromagnetic compatibility (EMC) standard; in the receiving path, the band-pass filter can suppress high-frequency noise in the power line, such as switching power supply interference, and low-frequency power frequency interference, and extract the effective carrier signal through the passband characteristics of 2 to 30 MHz, cooperate with the AGC to optimize the signal-to-noise ratio of the received signal, and control the communication bit error rate within 10⁻ 6 The following.

[0042] The OFDM demodulation process of this module is completed by the OFDM demodulation chip unit. The received power line carrier signal is converted to the frequency domain through the fast Fourier transform (FFT), the data streams on each subcarrier are separated, and the standardized data frame is restored through channel decoding and error control. The adaptive equalization algorithm built in the chip can compensate for the frequency-selective fading of the power line channel, estimate the channel characteristics in real time through the pilot signal, and dynamically adjust the demodulation parameters to ensure the signal demodulation accuracy when the building power load changes. And the demodulated carbon emission digital signal can be transmitted to the central data processing and analysis module through the power line interface.

[0043] Through the above method, the existing power lines in the building can be used to build a communication network without re-wiring, thus reducing the wiring cost and improving the signal transmission stability, which is especially suitable for the intelligent transformation of old buildings.

[0044] The central data processing and analysis module is used to denoise, calibrate and calculate carbon emissions for the carbon emission digital signal by using the Kalman filter algorithm, neural network model and carbon emission conversion model respectively, generate the carbon emission quantification value and the carbon emission trend chart and import them into the local database, specifically including: The industrial-grade embedded processor unit is used to receive the carbon emission digital signal and coordinate the data parsing algorithm execution unit and the local database management unit by using the multi-core embedded processor architecture; The data parsing algorithm execution unit includes a Kalman filter denoising sub-unit, a neural network calibration sub-unit and a carbon emission conversion sub-unit; the Kalman filter denoising sub-unit is used to perform dynamic noise estimation and filtering on the carbon emission digital signal based on the Kalman filter algorithm to generate the first carbon emission digital signal; the neural network calibration sub-unit is used to perform non-linear calibration on the first carbon emission digital signal through the neural network model to generate the second carbon emission digital signal; the carbon emission conversion sub-unit is used to build the carbon emission conversion model to convert the second carbon emission digital signal into the carbon emission quantification value and draw the carbon emission trend chart according to the carbon emission quantification value; A local database management unit is used to import the carbon emission quantification values and the carbon emission trend charts into the local database using a real-time database architecture, and interact with the cloud service and remote management module through an Ethernet interface. Among them, the local database supports the query, invocation, and local analysis of the carbon emission quantification values.

[0045] It can be understood that the industrial-grade embedded processor unit of the central data processing and analysis module adopts a multi-core embedded processor architecture to build a high-performance data processing platform. This unit receives the carbon emission digital signals transmitted by the power line carrier communication module through an internal bus and coordinates the parallel operation of the data parsing algorithm execution unit and the local database management unit.

[0046] The data parsing algorithm execution unit constructs a three-level processing link: The Kalman filter denoising sub-unit performs recursive prediction and update processing on the carbon emission digital signals based on a dynamic noise estimation model. This sub-unit estimates the true value of the signal and the noise covariance in real time by establishing a signal state space model, suppresses the random interference and sensor drift in the power line transmission, generates the first carbon emission digital signal after noise suppression, and reduces the data fluctuation amplitude. The neural network calibration sub-unit adopts a backpropagation neural network architecture with hidden layers to perform non-linear deviation compensation for the first carbon emission digital signal. This sub-unit fits the complex mapping relationship between factors such as temperature and humidity changes and device aging and measurement deviations through forward propagation, and iteratively optimizes the network weights using historical calibration data, and then can perform non-linear calibration on the first carbon emission digital signal to generate a high-precision second carbon emission digital signal, effectively compensating for the measurement errors caused by environmental interference. The carbon emission conversion sub-unit constructs a quantification model based on the IPCC standard to convert the second carbon emission digital signal into a standardized carbon emission quantification value. This sub-unit first calculates the energy consumption of the power parameter signal, combines the regional power grid carbon emission factor database, and generates the carbon emission quantification value through a weighted summation algorithm; then constructs a trend analysis model based on time series data, and uses a sliding window algorithm to calculate the hourly, daily, and monthly average carbon emission trends, and draws a dynamic trend chart to provide visual support for building energy consumption analysis.

[0047] The local database management unit adopts a real-time database architecture, stores the carbon emission quantification values and trend charts indexed by timestamp in the local database, and supports millisecond-level data query and call. At the same time, this unit establishes a communication link with the remote management module through the Ethernet interface and cloud service to achieve real-time synchronization of local data and the cloud server: on the one hand, uploads historical data to the cloud distributed storage cluster, and on the other hand, receives the cloud differential upgrade instruction to update the local algorithm model parameters. This database is built-in with a data compression engine, which can perform incremental compression on the frequently collected carbon emission data, saving a large amount of storage space, and at the same time supports the local offline analysis function, and historical data backtracking and trend prediction can still be carried out when the network is interrupted.

[0048] The Kalman filter denoising sub-unit is used to perform dynamic noise estimation and filtering on the carbon emission digital signal based on the Kalman filter algorithm to generate a first carbon emission digital signal, and the corresponding calculation formula is as follows: In the formula, represents the state prediction value of the carbon emission digital signal at the k-th moment; represents the state transition matrix of the carbon emission digital signal at the k-th moment; represents the state estimation value of the updated carbon emission digital signal at the (k - 1)-th moment; represents the error covariance matrix of the updated carbon emission digital signal at the (k - 1)-th moment; represents the state transition matrix of the transposed matrix; represents the process noise covariance matrix of the carbon emission digital signal at the k-th moment; represents the Kalman gain matrix of the carbon emission digital signal at the k-th moment; represents the observation matrix of the carbon emission digital signal at the k-th moment; represents the observation matrix of the transposed matrix; represents the observation noise covariance matrix of the carbon emission digital signal at the k-th moment; represents the state estimation value of the updated carbon emission digital signal at the k-th moment, that is, the first carbon emission digital signal; represents the carbon emission digital signal at the k-th moment; I represents the identity matrix; represents the error covariance matrix of the updated carbon emission digital signal at the k-th moment.

[0049] It is understandable that the Kalman filter denoising subunit performs real-time denoising on the carbon emission digital signal in power line carrier transmission through a recursive prediction and update mechanism, and can effectively suppress interferences such as random pulse noise and sensor drift error in the power line channel. Specifically, in the prediction stage, the subunit constructs a signal prediction model for the current moment based on the historical signal state, and evaluates the prediction uncertainty in combination with the characteristics of process noise; in the update stage, it adaptively fuses the predicted value and the actual observed value by dynamically adjusting the Kalman gain to form an optimal signal estimate. This mechanism reduces the signal noise amplitude by more than 80% and significantly narrows the data fluctuation range, providing highly reliable input for subsequent carbon emission calculations.

[0050] This subunit does not need to preset the noise statistical characteristics. It can track the dynamic changes of power line noise in real time through the iterative update of the error covariance matrix, such as the interference fluctuations caused by the start and stop of building equipment, and maintain the stability of the denoising effect in a complex electromagnetic environment. Measured data shows that this unit can increase the signal-to-noise ratio of the signal by 4 to 6 dB, and increase the effective data ratio of the carbon emission digital signal from 75% to more than 98%, solving the problem of data distortion caused by noise in power line carrier communication.

[0051] In addition, the computing efficiency of this subunit can adapt to the requirements of real-time monitoring, control the single-cycle processing delay within 10 ms, and thus meet the high-frequency acquisition requirements of building carbon emission data.

[0052] The neural network calibration subunit is used to perform nonlinear calibration on the first carbon emission digital signal through the neural network model to generate a second carbon emission digital signal. The corresponding calculation formula is as follows: In the formula, represents the sum of inputs of the j-th neuron in the hidden layer of the neural network model; represents the input weight of the j-th hidden neuron; represents the bias of the j-th neuron in the hidden layer; represents the output of the j-th neuron in the hidden layer; represents the activation function of the hidden layer; represents the sum of inputs of the output layer of the neural network model; n represents the number of neurons in the hidden layer; represents the weight from the j-th hidden neuron to the output; represents the bias of the output layer; represents the second carbon emission digital signal; represents the activation function of the output layer.

[0053] Calculate the error gradients of the output layer and the hidden layer: In the formula, represents the error gradient of the output layer; y represents the unbiased reference signal for training; represents the error gradient of the j-th neuron in the hidden layer; represents the derivative of the activation function of the output layer; represents the derivative of the activation function of the hidden layer; Update the input weight of the j-th hidden neuron , the weight from the j-th hidden neuron to the output , the bias of the j-th neuron in the hidden layer and the output layer bias : In the formula, represents the updated input weight of the j-th hidden neuron; represents the learning rate; represents the updated weight from the j-th hidden neuron to the output; represents the updated bias of the j-th neuron in the hidden layer; represents the updated output layer bias.

[0054] In practical applications, the neural network calibration subunit forms a dynamic calibration ability for non-linear deviations such as environmental interference and device aging through the collaborative mechanism of forward propagation and backward propagation. During the forward propagation process, the hidden layer maps the input signal to a non-linear space through the activation function, and the output layer generates a calibrated second carbon emission digital signal based on weighted summation, thereby being able to effectively fit the complex mapping relationship between temperature and humidity changes and sensor deviations. Measured data shows that this mechanism can reduce the 10% measurement deviation caused by temperature drift of the CO2 concentration sensor to less than 1%, and the non-linear error compensation rate of the power parameter sensor reaches more than 92%.

[0055] The backward propagation mechanism calculates the error gradients of the output layer and the hidden layer, and dynamically updates the network weights and biases in combination with the learning rate, enabling the neural network model to automatically learn the deviation law from historical calibration data. In the long-term operation scenario of a building, this mechanism can track the sensitivity attenuation caused by device aging in real time, such as a drift rate of 0.5% per year, and continuously optimize the calibration parameters without manual intervention, avoiding the lag of traditional hardware calibration methods.

[0056] In addition, the real-time processing delay of this subunit is controlled within 20 ms, which can adapt to the high-frequency data acquisition requirements above 10 Hz, providing high-precision data support for the real-time monitoring and early warning of building carbon emissions.

[0057] The carbon emission conversion subunit is used to construct the carbon emission conversion model and convert the second carbon emission digital signal into the carbon emission quantification value, and specifically includes: In the formula, LHZ represents the carbon emission quantification value; m represents the number of second carbon emission digital signals; represents the i-th second carbon emission digital signal; represents the carbon emission factor corresponding to the i-th second carbon emission digital signal.

[0058] It should be noted that the carbon emission conversion subunit can construct a carbon emission conversion model based on the IPCC standard, and convert the denoised and calibrated second carbon emission digital signal into a standardized carbon emission quantification value through a weighted summation mechanism.

[0059] This subunit supports parallel conversion of multi-type energy consumption data. By configuring the carbon emission factors corresponding to different energies, it can achieve unified quantification of multi-dimensional data such as electricity and natural gas, and control the error of the quantification result within 3%.

[0060] At the same time, this subunit can generate a carbon emission trend chart based on time series, providing visual support for building energy consumption analysis. Its calculation efficiency meets the processing requirements of 10Hz high-frequency data. And the quantification value output by this subunit can be directly connected to the local database and the cloud storage cluster to support real-time monitoring and remote management of data, providing a standardized data basis for building carbon footprint accounting, energy-saving strategy optimization, and anomaly warning.

[0061] The human-computer interaction and warning module is used to display the carbon emission quantification value and the carbon emission trend chart in real time. When an abnormality in the carbon emission quantification value is detected, it triggers the building sound and light warning mechanism, and specifically includes: The touch display terminal unit is used to visually display the carbon emission quantification value and the carbon emission trend chart through a capacitive touch display screen, and provide a graphical user interface to support the touch interaction operation of the user terminal; The warning logic module unit is used to internally install a threshold comparator and a rule engine, and compare the carbon emission quantification value with the preset carbon emission threshold in real time. When it is detected that the carbon emission quantification value reaches the preset carbon emission threshold, it triggers the building sound and light warning mechanism.

[0062] Among them, the touch display terminal unit uses an industrial-grade capacitive touch display screen to build a highly reliable graphical user interface. This unit drives the display screen through a dedicated graphics processing chip, and presents the carbon emission quantification value and historical trend chart in real time in the form of visualizations such as dynamic curves and bar charts, supporting high-definition display with a resolution of 1024×768. Users can realize functions such as data query, parameter setting, and system control through touch operations. Its touch response delay is controlled within 50ms, which can meet the requirements of real-time interaction. And the graphical user interface is built-in with a multi-level permission management mechanism, which can distinguish the operation permissions of administrators and ordinary users to ensure the security of system parameter settings.

[0063] The early warning logic module unit constructs an automated anomaly response mechanism by integrating a threshold comparator and a rule engine. This unit can read the carbon emission quantification value in the local database in real time, and dynamically compare it with the preset multi-level early warning thresholds. And the primary early warning threshold can be 120% of the normal level, and the secondary early warning threshold is 150% of the normal level.

[0064] When the monitored data reaches the threshold, the rule engine triggers the audible and visual early warning logic: on the one hand, it controls the buzzer and LED indicator in the building through a relay to form an audible, visual, and electrical combined early warning; on the other hand, it pushes the early warning information to the management terminal, and synchronously displays the abnormal area, the exceeded parameters, and the recommended handling measures. The early warning logic supports custom rule configuration, and can flexibly set different early warning thresholds according to the functional characteristics of the building, such as the office area, the computer room, etc., to improve the accuracy of anomaly identification.

[0065] In a commercial building area, through the human-computer interaction and early warning module, the refresh frequency of carbon emission data can be controlled to be 1 time per second, the early warning response time is less than 300ms, and 12 carbon emission sudden increase events caused by abnormal operation of the air-conditioning system have been successfully identified and handled within one year, effectively reducing the carbon emissions of the building.

[0066] Embodiment 2

[0067] As Figure 2 shown, a method for real-time monitoring of building carbon emissions based on power line carrier communication, the method includes: S1, collect multi-dimensional carbon emission data in the target building area, preprocess the multi-dimensional carbon emission data and package it into a standardized data frame according to a preset communication protocol for storage; S2, modulate the standardized data frame into a high-frequency carrier signal through OFDM technology, inject it into the building power line carrier network in the target building area through a power line coupler, and receive the power line carrier signal in the building power line carrier network and demodulate it into a carbon emission digital signal; S3. Respectively use the Kalman filter algorithm, neural network model, and carbon emission conversion model to denoise, calibrate, and calculate carbon emissions for the carbon emission digital signal, generate a carbon emission quantification value and a carbon emission trend chart, and import them into the local database; S4. Store the carbon emission quantification value and the carbon emission trend chart in the cloud server cluster, and integrate a differential upgrade algorithm engine to remotely update the Kalman filter algorithm, the neural network model, and the carbon emission conversion model; S5. Real-time display the carbon emission quantification value and the carbon emission trend chart. When an abnormal carbon emission quantification value is detected, trigger the building's acoustic and optical warning mechanism.

[0068] Through the introduction of the above embodiments, the present invention provides a real-time building carbon emission monitoring system and method based on power line carrier communication, including a data acquisition and preprocessing module for collecting multi-dimensional carbon emission data in the target building area, preprocessing the multi-dimensional carbon emission data, and packing it into a standardized data frame according to a preset communication protocol for storage; a power line carrier communication module for modulating the standardized data frame into a high-frequency carrier signal through OFDM technology, injecting it into the building power line carrier network in the target building area through a power line coupler, receiving the power line carrier signal in the building power line carrier network, and demodulating it into a carbon emission digital signal; a central data processing and analysis module for respectively using the Kalman filter algorithm, neural network model, and carbon emission conversion model to denoise, calibrate, and calculate carbon emissions for the carbon emission digital signal, generate a carbon emission quantification value and a carbon emission trend chart, and import them into the local database; a cloud service and remote management module for storing the carbon emission quantification value and the carbon emission trend chart in the cloud server cluster, and integrating a differential upgrade algorithm engine to remotely update the Kalman filter algorithm, neural network model, and carbon emission conversion model; a human-computer interaction and warning module for real-time displaying the carbon emission quantification value and the carbon emission trend chart, and triggering the building's acoustic and optical warning mechanism when an abnormal carbon emission quantification value is detected. Thereby, the wiring cost can be reduced, the stability of signal transmission can be enhanced, the efficiency and accuracy of building carbon emission monitoring can be improved, and further, the energy utilization efficiency of the building can be increased, carbon emissions can be reduced, and the rapidly changing building environment requirements can be better adapted.

[0069] The present invention adopts power line carrier communication technology and uses the existing power lines in the building as the transmission medium, eliminating the need for additional wiring, greatly reducing the wiring cost, and being particularly suitable for the carbon monitoring transformation of old buildings, reducing the implementation cost and transformation difficulty of carbon monitoring in old buildings. At the same time, the power line has high signal transmission stability and is not easily affected by environmental factors, enhancing the stability of data transmission and improving the accuracy of monitoring data. The system of the present invention adopts a modular design, and each functional module is independent. When a certain module fails, only the corresponding module needs to be replaced, solving the problem of difficult maintenance of traditional systems. In addition, the system of the present invention supports remote update function, and the system can be maintained and upgraded without the need for staff to go to the site, enhancing the scalability and adaptability of the system and being able to better adapt to the rapidly changing building environment requirements. In addition, the present invention adopts an efficient data parsing algorithm to denoise, calibrate and calculate carbon emissions for the received data, effectively improving the accuracy of carbon emission monitoring and providing reliable data support for building energy conservation and emission reduction. Managers can formulate reasonable energy conservation and emission reduction measures based on accurate carbon emission data, improve the energy utilization efficiency of the building, and reduce carbon emissions.

[0070] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0071] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. The storage medium includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc memories, magnetic disk memories, tape memories, or any other medium that can be used to carry or store data and is computer-readable.

[0072] It should also be noted that the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent in such process, method, commodity or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, commodity or device including the element.

Claims

1. A real-time building carbon emission monitoring system based on power line carrier communication, characterized in that, The system includes: A data acquisition and preprocessing module, which is used to acquire multi-dimensional carbon emission data in the target building area, preprocess the multi-dimensional carbon emission data, and package it into a standardized data frame according to a preset communication protocol for storage; A power line carrier communication module, which is used to modulate the standardized data frame into a high-frequency carrier signal through OFDM technology, inject it into the building power line carrier network in the target building area through a power line coupler, and receive the power line carrier signal in the building power line carrier network and demodulate it into a carbon emission digital signal; A central data processing and analysis module, which is used to denoise, calibrate and calculate carbon emissions for the carbon emission digital signal by using the Kalman filter algorithm, neural network model and carbon emission conversion model respectively, generate a carbon emission quantification value and a carbon emission trend chart and import them into the local database; A cloud service and remote management module, which is used to store the carbon emission quantification value and the carbon emission trend chart in the cloud server cluster, and integrate a differential upgrade algorithm engine to remotely update the Kalman filter algorithm, the neural network model and the carbon emission conversion model; A human-computer interaction and early warning module, which is used to display the carbon emission quantification value and the carbon emission trend chart in real time, and trigger a building sound and light early warning mechanism when the carbon emission quantification value is monitored to be abnormal.

2. The real-time building carbon emission monitoring system based on power line carrier communication according to claim 1, characterized in that, The data acquisition and preprocessing module, which is used to acquire multi-dimensional carbon emission data in the target building area, preprocess the multi-dimensional carbon emission data, and package it into a standardized data frame according to a preset communication protocol for storage, specifically includes: A distributed carbon emission sensor array unit, which is used to cover the target building area in a distributed deployment manner, and respectively acquire the building CO2 concentration, building temperature and humidity, and building power parameters in the target building area through an integrated CO2 concentration sensor, temperature and humidity sensor, and power parameter sensor, and summarize them to generate the multi-dimensional carbon emission data; A microprocessor unit, which is used to adopt a microprocessor chip and integrate an analog-to-digital conversion module, perform filtering and denoising, signal amplification and analog-to-digital conversion processing on the multi-dimensional carbon emission data, and package the multi-dimensional carbon emission data into the standardized data frame according to the preset communication protocol; A local cache unit, which is used to construct a data cache space by using a non-volatile storage medium, temporarily store the packaged standardized data frame, and transmit the standardized data frame to the power line carrier communication module through an SPI interface.

3. The real-time building carbon emission monitoring system based on power line carrier communication according to claim 1, characterized in that, The power line carrier communication module, which is used to modulate the standardized data frame into a high-frequency carrier signal through OFDM technology, inject it into the building power line carrier network in the target building area through a power line coupler, and receive the power line carrier signal in the building power line carrier network and demodulate it into a carbon emission digital signal, specifically includes: An OFDM modulation chip unit, which is used to support adaptive adjustment of the carrier frequency in the frequency band range of 2 to 30 MHz, receive the standardized data frame, integrate an OFDM signal processing circuit through the OFDM technology and modulate the standardized data frame into the high-frequency carrier signal; A power line coupler unit for injecting the high-frequency carrier signal into the building power carrier network of the target building area through the power line coupler, and performing bidirectional coupling on the high-frequency carrier signal and the building power line by using a capacitive-inductive hybrid coupling topology to generate the power line carrier signal; A signal amplification and filtering circuit unit for performing real-time gain adjustment and noise filtering processing on the power line carrier signal through an automatic gain control mechanism and a band-pass filter circuit; An OFDM demodulation chip unit for demodulating the power line carrier signal after real-time gain adjustment and noise filtering processing into the carbon emission digital signal by using the OFDM technology, and transmitting the carbon emission digital signal to the central data processing and analysis module through a power line interface; 4. The real-time building carbon emission monitoring system based on power line carrier communication according to claim 1, characterized in that, The central data processing and analysis module for denoising, calibrating, and calculating carbon emissions of the carbon emission digital signal by using a Kalman filter algorithm, a neural network model, and a carbon emission conversion model respectively, generating a carbon emission quantification value and a carbon emission trend chart and importing them into a local database, specifically including: An industrial-grade embedded processor unit for receiving the carbon emission digital signal and coordinating a data parsing algorithm execution unit and a local database management unit by using a multi-core embedded processor architecture; A data parsing algorithm execution unit, including a Kalman filter denoising sub-unit, a neural network calibration sub-unit, and a carbon emission conversion sub-unit; the Kalman filter denoising sub-unit is used for dynamically estimating and filtering the noise of the carbon emission digital signal based on the Kalman filter algorithm to generate a first carbon emission digital signal; the neural network calibration sub-unit is used for non-linearly calibrating the first carbon emission digital signal through the neural network model to generate a second carbon emission digital signal; the carbon emission conversion sub-unit is used for constructing the carbon emission conversion model to convert the second carbon emission digital signal into the carbon emission quantification value, and drawing the carbon emission trend chart according to the carbon emission quantification value; A local database management unit for importing the carbon emission quantification value and the carbon emission trend chart into the local database by using a real-time database architecture, and interacting with the cloud service and remote management module through an Ethernet interface, wherein the local database supports the query, call, and local analysis of the carbon emission quantification value; 5. The real-time building carbon emission monitoring system based on power line carrier communication according to claim 4, characterized in that, The Kalman filter denoising sub-unit is used for dynamically estimating and filtering the noise of the carbon emission digital signal based on the Kalman filter algorithm to generate a first carbon emission digital signal, and the corresponding calculation formula is as follows: Wherein, represents the state prediction value of the carbon emission digital signal at the k-th moment; represents the state transition matrix of the carbon emission digital signal at the k-th moment; represents the state estimation value of the updated carbon emission digital signal at the (k - 1)-th moment; represents the error covariance matrix of the updated carbon emission digital signal at the (k - 1)-th moment; represents the state transition matrix of the transposed matrix; represents the process noise covariance matrix of the carbon emission digital signal at the k-th moment; represents the Kalman gain matrix of the carbon emission digital signal at the k-th moment; represents the observation matrix of the carbon emission digital signal at the k-th moment; represents the observation matrix of the transposed matrix; represents the observation noise covariance matrix of the carbon emission digital signal at the k-th moment; represents the state estimation value of the updated carbon emission digital signal at the k-th moment, that is, the first carbon emission digital signal; represents the carbon emission digital signal at the k-th moment; I represents the identity matrix; represents the error covariance matrix of the updated carbon emission digital signal at the k-th moment.

6. The real-time building carbon emission monitoring system based on power line carrier communication according to claim 5, characterized in that The neural network calibration sub-unit is used for non-linearly calibrating the first carbon emission digital signal through the neural network model to generate a second carbon emission digital signal, and the corresponding calculation formula is as follows: In the formula, represents the sum of inputs of the j-th neuron in the hidden layer of the neural network model; represents the input weight of the j-th hidden neuron; represents the bias of the j-th neuron in the hidden layer; represents the output of the j-th neuron in the hidden layer; represents the activation function of the hidden layer; represents the sum of inputs of the output layer of the neural network model; n represents the number of neurons in the hidden layer; represents the weight from the j-th hidden neuron to the output; represents the bias of the output layer; represents the second carbon emission digital signal; represents the activation function of the output layer.

7. The real-time building carbon emission monitoring system based on power line carrier communication according to claim 6, wherein Calculate the error gradient of the output layer and the hidden layer: In the formula, represents the error gradient of the output layer; y represents the unbiased reference signal for training; represents the error gradient of the j-th neuron in the hidden layer; represents the derivative of the activation function of the output layer; represents the derivative of the activation function of the hidden layer; Update the input weights of the j-th hidden neuron , the weights from the j-th hidden neuron to the output , the bias of the j-th neuron in the hidden layer and the output layer bias : In the formula, represents the input weight of the j-th hidden neuron after update; represents the learning rate; represents the weight from the j-th hidden neuron to the output after update; represents the bias of the j-th neuron in the hidden layer after update; represents the bias of the output layer after update.

8. The real-time building carbon emission monitoring system based on power line carrier communication according to claim 7, characterized in that, The carbon emission conversion sub-unit is used for constructing the carbon emission conversion model and converting the second carbon emission digital signal into the carbon emission quantification value, specifically including: In the formula, LHZ represents the carbon emission quantification value; m represents the number of the second carbon emission digital signals; represents the i-th second carbon emission digital signal; represents the carbon emission factor corresponding to the i-th second carbon emission digital signal.

9. The real-time building carbon emission monitoring system based on power line carrier communication according to claim 1, wherein The human-computer interaction and early warning module is used to display the carbon emission quantification value and the carbon emission trend chart in real time. When the carbon emission quantification value is detected to be abnormal, it triggers the building sound and light warning mechanism, specifically including: The touch display terminal unit is used to visually display the carbon emission quantification value and the carbon emission trend chart through a capacitive touch display screen, and provide a graphical user interface to support the touch interaction operation of the user terminal; The early warning logic module unit is used to internally install a threshold comparator and a rule engine, and compare the carbon emission quantification value with the preset carbon emission threshold in real time. When the carbon emission quantification value is detected to reach the preset carbon emission threshold, it triggers the building sound and light warning mechanism.

10. A method for real-time monitoring of building carbon emissions based on power line carrier communication, applied to the real-time monitoring system of building carbon emissions based on power line carrier communication according to any one of claims 1-9, characterized in that, The method includes: Collect multi-dimensional carbon emission data in the target building area, preprocess the multi-dimensional carbon emission data, and package it into a standardized data frame according to the preset communication protocol for storage; Modulate the standardized data frame into a high-frequency carrier signal through OFDM technology, inject it into the building power carrier network in the target building area through a power line coupler, receive the power line carrier signal in the building power carrier network, and demodulate it into a carbon emission digital signal; Use the Kalman filter algorithm, neural network model, and carbon emission conversion model respectively to denoise, calibrate, and calculate carbon emissions for the carbon emission digital signal, generate the carbon emission quantification value and the carbon emission trend chart, and import them into the local database; Store the carbon emission quantification value and the carbon emission trend chart in the cloud server cluster, and integrate a differential upgrade algorithm engine to remotely update the Kalman filter algorithm, the neural network model, and the carbon emission conversion model; Display the carbon emission quantification value and the carbon emission trend chart in real time. When the carbon emission quantification value is detected to be abnormal, trigger the building sound and light warning mechanism.

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