RTK technology-based carbon emission monitoring system and method

Abstract

The invention provides a carbon emission monitoring system and method based on an RTK technology. The system comprises a data acquisition terminal, a data transmission module and a monitoring visualization terminal. The data acquisition terminal is used for acquiring monitoring data by establishing a GIS grid area, acquiring an encrypted ciphertext based on the monitoring data, and sending the encrypted ciphertext to the data transmission module and the monitoring visualization terminal; the data transmission module is used for verifying the working state of the monitoring equipment and carrying out localized storage on the encrypted ciphertext; and the monitoring visualization terminal is used for obtaining an action relationship between the movement track of the working equipment and the carbon emission concentration based on the encrypted ciphertext, and performing display and alarm based on the action relationship. Through deep fusion of RTK high-precision positioning, an intelligent filtering algorithm, dynamic modeling and edge calculation, the problems of data islands and extensive modeling in traditional monitoring are solved, and dynamic association between the working path of construction equipment and the carbon emission concentration is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon emission monitoring in the construction environment, and particularly relates to a carbon emission monitoring system and method based on RTK technology. Background Art

[0002] With the increasingly severe global environmental carbon emission governance, high-carbon projects have been gradually included in the key objects in urban development. As an important engine to promote the urbanization process, infrastructure construction plays an irreplaceable role in coping with high-carbon challenges. In recent years, governments and institutions in various countries have been accelerating infrastructure upgrading and promoting the practice and exploration of green and low-carbon development models, and have gradually realized the importance of carbon emission monitoring and control in the pre-construction and post-maintenance processes of infrastructure, and the main source of carbon emissions is the carbon emissions generated during the operation of engineering machinery and equipment.

[0003] High building density leads to serious satellite signal occlusion (such as the urban canyon effect), and the traditional GPS positioning error is large (>5m); the carbon emission diffusion is significantly affected by local wind direction and temperature gradient, and dynamic modeling is required. It is difficult to conduct real-time monitoring of carbon concentration in the construction area within the urban environment with high building density, unable to track the carbon emission diffusion trajectory of the project, and difficult to evaluate the impact of the carbon emissions of infrastructure projects on the urban environment, restricting the low-carbon ecological and environmental protection work of the city.

[0004] Existing carbon emission monitoring systems and methods usually can only realize real-time data collection in local areas during the construction process, unable to track the real-time position of equipment, static monitoring cannot reflect the dynamic diffusion of carbon emissions, the monitoring data cannot dynamically display the carbon emissions of the entire project, and the experimental results cannot conduct in-depth control and carbon emission research on the project.

[0005] In the face of problems in the prior art such as "signal occlusion caused by the urban canyon effect", "weak application level of data security mechanism", "unable to track dynamic diffusion", and "poor system migration adaptability", therefore, the present invention proposes a carbon emission monitoring system and method based on RTK technology to solve the problems existing in the prior art. Summary of the Invention

[0006] To solve the problems existing in the prior art, the present invention provides a carbon emission monitoring system and method based on RTK technology, aiming to overcome the problems of data islands and rough modeling in traditional monitoring through the deep integration of RTK high-precision positioning, intelligent filtering algorithm, dynamic modeling and edge computing, and realize the dynamic association between the working path of construction equipment and the carbon emission concentration.

[0007] To achieve the above object, the present invention provides the following solution: A carbon emission monitoring system based on RTK technology, the system includes: a data acquisition terminal, a data transmission module and a monitoring visualization terminal;

[0008] The data acquisition terminal is used to obtain monitoring data by establishing a GIS grid area, obtain an encrypted ciphertext based on the monitoring data, and send the encrypted ciphertext to the data transmission module and the monitoring visualization terminal;

[0009] The data transmission module is used to verify the working status of the monitoring device and locally store the encrypted ciphertext;

[0010] The monitoring visualization terminal is used to obtain the relationship between the movement trajectory of the engineering working equipment and the carbon emission concentration based on the encrypted ciphertext, and perform display and alarm based on the relationship.

[0011] Preferably, the data acquisition terminal includes: a carbon concentration monitor, a wind direction and wind force detector, a temperature and humidity detector, a vehicle-mounted RTK positioning module, a differential reference station, and a data preprocessing module;

[0012] The carbon concentration monitor is used to collect carbon concentration data of the construction site;

[0013] The wind direction and wind force detector is used to collect wind direction and wind force data of the construction site;

[0014] The temperature and humidity detector is used to collect temperature and humidity data of the construction site;

[0015] The vehicle-mounted RTK positioning module is used to obtain carrier phase data of the engineering transportation equipment and the construction operation equipment;

[0016] The differential reference station is used to set a spatial reference origin for the construction site and obtain origin coordinate position data;

[0017] The data preprocessing module is used to preprocess the monitoring data to obtain the encrypted ciphertext;

[0018] The monitoring data includes: the carbon concentration data, the wind direction and wind force data, the temperature and humidity data, the carrier phase data, and the origin coordinate position data.

[0019] Preferably, the vehicle-mounted RTK positioning module includes: a receiving antenna for receiving satellite signals and a transmitting antenna for transmitting the carrier phase data.

[0020] Preferably, the data preprocessing module includes: a data filtering unit, a data packaging unit, and a data sending unit;

[0021] The data filtering unit is used to filter the monitoring data;

[0022] The data packaging unit is used to encrypt the AES symmetric key using the RSA asymmetric public key, and encrypt the filtered monitoring data with the AES symmetric key to obtain the encrypted ciphertext;

[0023] The data sending unit is used to send the encrypted ciphertext.

[0024] Preferably, the data filtering unit processes the monitoring data by using a moving average filtering algorithm, a median filtering algorithm, and an average value filtering algorithm:

[0025] The result of this filtering = [the median of the monitoring data segment + the average value of the monitoring data segment] / 2;

[0026] After the filtering is completed, a carbon concentration data optimization algorithm is performed:

[0027] The optimized carbon concentration data = the project stage factor * the carbon concentration data + the equipment status factor * the carbon concentration data + the temperature and humidity factor * the carbon concentration data;

[0028] Among them, the project stage factor + the equipment status factor + the temperature and humidity factor = 1.

[0029] Preferably, the data transmission module includes: a connection verification module and a local storage module;

[0030] The connection verification module is used to verify the working status of the monitoring device;

[0031] The local storage module is used to locally store the encrypted ciphertext.

[0032] Preferably, the monitoring visualization terminal includes a data receiving module, a data parsing module, a data post-processing module, an edge cloud computing module, a data display module, and a construction alarm module;

[0033] The data receiving module is used to receive the encrypted ciphertext;

[0034] The data parsing module is used to parse the encrypted ciphertext to obtain the monitoring data;

[0035] The data post-processing module is used to perform post-processing on the parsed monitoring data;

[0036] The edge cloud computing module is used to perform data fusion on the post-processed monitoring data to obtain the relationship between the moving trajectory of the working equipment and the carbon emission concentration;

[0037] The data display module is used to display the relationship;

[0038] The construction alarm module is used to monitor for alarms.

[0039] Preferably, the data post-processing module performing post-processing on the parsed monitoring data includes:

[0040] Performing iterative Kalman filtering analysis on the parsed monitoring data, and then performing differential solution coordinate calculation.

[0041] Preferably, the edge cloud computing module fuses the post-processed data to obtain the functional relationship between the moving trajectory of the working device and the carbon emission concentration, including:

[0042] Adopt spatio-temporal alignment, Kriging interpolation and Gaussian plume model dynamic calibration, and combine with the Pearson correlation coefficient to analyze the correlation between the moving trajectory of the working device and the carbon emission concentration.

[0043] The present invention also provides a carbon emission monitoring method based on RTK technology, which is realized by applying the aforementioned carbon emission monitoring system based on RTK technology. The method includes:

[0044] S1. Establish a GIS grid area to obtain monitoring data, obtain encrypted ciphertext based on the monitoring data, and send the encrypted ciphertext to the data transmission module and the monitoring visualization terminal;

[0045] S2. Perform device working verification on the monitoring device, and store the encrypted ciphertext together with the corresponding timestamp in the local disk;

[0046] S3. Receive, parse, post-process, and data-fuse the encrypted ciphertext to obtain the functional relationship between the moving trajectory of the engineering working device and the carbon emission concentration, and perform display and alarm based on the functional relationship.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] 1. Spatial grid modeling: Use GIS technology to divide the construction area into multiple identical grid areas, fuse wind direction parameters to build a dynamic grid area, lay a model foundation for exploring the working path of construction equipment and the change of carbon emission concentration in the construction area, assist the edge cloud computing module to build a global carbon emission prediction model for the construction area, and dynamically adjust the downwind grid area when the wind direction and wind force change to improve the grid monitoring coverage rate.

[0049] 2. High-precision RTK positioning: The present invention introduces a high-precision RTK positioning module and uses the mathematical calculation method of RTK coordinate solution to achieve centimeter-level RTK path positioning, breaking through the traditional GPS meter-level precision positioning method, avoiding the influence of the ionosphere, troposphere, multipath effect, etc., and improving the reliability and positioning accuracy; at the same time, compared with the traditional vehicle-mounted RTK positioning module, the vehicle-mounted RTK positioning module used in the present invention can form a working state feedback channel through communication between the transmitting antenna and the connection verification module, and trigger the feedback channel when the vehicle-mounted RTK positioning module has a power supply failure or falls off, which can timely remind the user to replace or check the work.

[0050] 3. Hybrid Encryption and Efficient Transmission: The realization of the data security mechanism's hierarchical encryption and anti-attack design adopts a double-layer encryption mechanism combining RSA asymmetric encryption and AES symmetric encryption. Combined with wireless communication technology, it ensures data security and real-time performance, strengthens the protection of the database, and enhances the efficiency of the system in processing data by processing data sequentially.

[0051] 4. Edge Computing and Intelligent Early Warning: Through the edge cloud computing module, data alignment, trend fitting, and correlation analysis are performed on the data to construct a global carbon emission prediction model for the construction area, real-time control the carbon emission dissipation effect of the project, and achieve real-time early warning of carbon emission over-standard or equipment anomalies.

[0052] In view of the monitoring requirements in typical scenarios such as construction in urban dense areas and construction in ecologically fragile areas, through scenario-based designs such as multi-frequency RTK anti-occlusion positioning, dynamic plume model, and wide-area edge node networking, centimeter-level device tracking, high-precision diffusion prediction, and full-area coverage are achieved. Through the deep integration of RTK high-precision positioning, intelligent filtering algorithms, dynamic modeling, and edge computing, this invention overcomes the problems of data islands and rough modeling in traditional monitoring, realizes the dynamic association between the working paths of construction equipment and carbon emission concentrations, and provides an efficient and secure solution for green construction, smart cities, and carbon emission reduction management, with both technological foresight and engineering feasibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0054] Figure 1 It is a schematic structural diagram of the carbon emission monitoring system based on RTK technology in Embodiment 1 of the present invention;

[0055] Figure 2 It is a monitoring data flow chart of the carbon emission monitoring system based on RTK technology in Embodiment 1 of the present invention;

[0056] Figure 3 It is a schematic installation position diagram of the carbon concentration monitor in the construction site in Embodiment 1 of the present invention;

[0057] Figure 4 It is a schematic diagram of the AES key encryption and monitoring data encryption process in Embodiment 1 of the present invention;

[0058] Figure 5 It is a schematic diagram of the data security mechanism in Embodiment 1 of the present invention;

[0059] Figure 6Schematic diagram of the data transmission module in the first embodiment of the present invention. Detailed implementation manners

[0060] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0061] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0062] Embodiment 1

[0063] As Figure 1 shown, the present invention provides a carbon emission monitoring system based on RTK technology, including: a data acquisition terminal, a data transmission module, and a monitoring visualization terminal, where the interaction relationship between the modules and the transmission process of the monitoring data are as Figure 2 shown.

[0064] The data acquisition terminal is used to assist in installing monitoring devices by establishing a GIS grid area, obtain monitoring data through the monitoring devices, obtain encrypted ciphertext based on the monitoring data, and send the encrypted ciphertext to the data transmission module and the monitoring visualization terminal.

[0065] The data acquisition terminal includes: a carbon concentration monitor, a wind direction and wind force detector, a temperature and humidity detector, a vehicle-mounted RTK positioning module, a differential reference station, and a data preprocessing module.

[0066] Specifically, the grid area provides installation positions for the monitoring devices in the construction site by considering the working characteristics of the engineering equipment and the equipment installation requirements under the condition of determining the construction site area, and assists in generating the carbon emission trajectory during the project construction process. According to the coverage area of the construction site, the construction area is initially divided into basic grids of 50m×50m according to the grid of the GIS area map. Ensure that the construction area is within the coverage range of 5G wireless communication signals, and all data transmission methods in the system adopt 5G wireless communication technology to improve the rapidity of data transmission and equipment verification.

[0067] Using GIS technology to divide the construction area into multiple identical grid areas, integrating wind direction parameters to build a dynamic grid area, laying a model foundation for exploring the working path of construction equipment and the change of carbon emission concentration in the construction area, assisting the edge cloud computing module to construct a global carbon emission prediction model for the construction area, and dynamically adjusting the downwind grid area when the wind direction and wind force change to improve the grid monitoring coverage rate.

[0068] During the installation of the carbon concentration monitor in the grid area, the installation position of the carbon concentration monitor is as follows Figure 3 shown. First, install the carbon concentration monitor independently in the grid area where special engineering equipment (engineering equipment that is fixed and does not move or moves with a low frequency) is located. Then, install the carbon concentration monitor in the remaining grid areas in a way that is not adjacent to the grid areas where the carbon concentration monitors have already been installed. Finally, complete the installation layout of the carbon concentration monitors in the construction area.

[0069] The carbon concentration monitor uses a carbon dioxide monitor with a non-infrared interference working principle, and the monitoring data can replace the carbon dioxide concentration level in the grid area. According to the diffusion characteristics of carbon dioxide in the air, the carbon concentration monitor can obtain the monitoring data of the carbon emission concentration in the corresponding grid area.

[0070] The temperature and humidity monitor and the wind speed and direction monitor are installed at positions where the monitoring data can equivalently replace the temperature, humidity, wind speed, and direction data in the construction area. When the wind speed changes, in order to ensure the effectiveness of the carbon emission monitoring data in the grid area, the area of the grid area is expanded by 10%-20% towards the adjacent grid in the wind direction.

[0071] Count the types of engineering equipment that generate carbon emissions in the engineering project, such as excavators, concrete pavers, concrete mixer trucks, etc. When the engineering equipment enters a certain grid, divide the grid into 10m×10m. Install the vehicle-mounted RTK positioning module on the engineering equipment that generates moving displacement, and obtain the real-time carrier phase data of the engineering mobile equipment through the satellite signal receiving antenna. The vehicle-mounted RTK positioning module includes a receiving antenna for receiving satellite signals and a transmitting antenna for transmitting position data. Among them, both the vehicle-mounted RTK positioning module and the differential reference station communicate with four satellites to obtain high-precision carrier phase data. The vehicle-mounted RTK positioning module uses the receiving antenna to obtain the current carrier phase data of the mobile working equipment, and sends the carrier phase data of the working equipment to the data preprocessing module through the transmitting antenna, completing the monitoring work of the carrier phase data of the engineering mobile equipment. Compared with the traditional vehicle-mounted RTK positioning module, the vehicle-mounted RTK positioning module used in this system can communicate with the connection verification module through the transmitting antenna to form a working status feedback channel, and trigger the feedback channel when the vehicle-mounted RTK positioning module has a power supply failure or falls off, timely reminding the user to do replacement or inspection work.

[0072] Install the differential reference station at the origin position of the GIS construction area grid (assuming the construction site command center is the origin position of the construction area). Obtain real-time data through the internal satellite signal receiving antenna, and send the carrier phase data and coordinate position data of the differential reference station to the data preprocessing module through 5G wireless communication. The differential reference station obtains the carrier phase data and coordinate position data of the differential reference station after connecting and communicating with the satellite through the receiving antenna inside the differential reference station, and sends the carrier phase data and coordinate position data to the data preprocessing module through the transmitting antenna. The set working frequency updates the position data regularly to complete the acquisition and update of the carrier phase data and coordinate position data of the differential reference station.

[0073] The data preprocessing module includes a data filtering unit for filtering each monitoring data, a data packaging unit for encrypting the filtered monitoring data with RSA-2048 public key and AES-256 key and AES-256 key encryption, and a data sending unit for wirelessly sending the encrypted ciphertext (the data form presented after encryption).

[0074] Specifically, the data filtering unit uses the moving average filtering algorithm, median filtering algorithm, and average value filtering algorithm to perform preliminary filtering and noise reduction on the monitoring data. Determine the range of the filtered and noise-reduced monitoring data through the moving average filtering algorithm, that is: create a one-dimensional array with the array length of n, and let this array be N[n], which means a one-dimensional array containing n monitoring data. The update process of the moving average filtering algorithm is as follows:

[0075] Step 1, add new data. The number of monitoring data is n, where N[n - 1] is the current monitoring data, N[n - 2] is the monitoring data 1 time before (2 - 1 = 1), and similarly, N[n - n] is the monitoring data (n - 1) times before.

[0076] Step 2, filtering and noise reduction. The filtering calculation formulas of the median filtering algorithm and the average value filtering algorithm are as follows:

[0077] The result of this filtering = [the median of the monitoring data segment + the average value of the monitoring data segment] / 2.

[0078] Especially for the carbon concentration data, after completing the initial filtering, perform the carbon concentration data optimization algorithm, and the algorithm formula is as follows:

[0079] Optimized carbon concentration data = project stage factor * carbon concentration data + equipment status factor * carbon concentration data + temperature and humidity factor * carbon concentration data.

[0080] Among them, the project stage factor + the equipment status factor + the temperature and humidity factor = 1.

[0081] Step 3: Iterate data. Let N[n - n] = 0, then let N[n - n] = N[n - (n - 1)], and then let N[n - (n - 1)] = N[n - (n - 2)]... until N[n - 2] = N[n - 1], and wait for the next data filtering and noise reduction.

[0082] The data packaging unit uses RSA-2048 public key to encrypt the AES-256 key to generate the key ciphertext and uses the AES-256 key to encrypt the monitoring data to generate the monitoring ciphertext. The data sending unit sends the above two ciphertexts to the data transmission module and the monitoring visualization terminal through wireless communication technology.

[0083] The data sending unit uses wireless communication technology to realize two-way data sending with the data transmission module and the monitoring visualization terminal.

[0084] To ensure the security of data transmission, as Figure 4 shown, the data receiving module under the monitoring visualization terminal sends the RSA-2048 encryption public key to the data packaging unit under the data preprocessing module. The data packaging unit uses the RSA-2048 public key to encrypt the AES-256 key and sends the ciphertext to the data transmission module and the data receiving module through the data sending unit. At the same time, the data packaging module uses the AES-256 key to encrypt the monitoring data and sends the ciphertext to the data transmission module and the data receiving module through the data sending unit. In the above process of key transmission ciphertext and monitoring data ciphertext transmission, data security mechanisms are used for encryption protection.

[0085] The data security mechanism is realized through hierarchical encryption and anti-attack design. The design steps are as Figure 5 shown and specifically include:

[0086] Step 1: Key management. In the data receiving module, the RSA-2048 key pair is protected by HSM. In the data packaging unit, it depends on the comparison between the system time and the monitoring data timestamp. When the system time and the monitoring data timestamp reach 0:00 every day, the data packaging unit automatically completes the update of the AES-256 key and distributes it through the RSA-OAEP encryption method;

[0087] Step 2: Integrity protection. The encrypted AES-256 key ciphertext and monitoring data ciphertext are transmitted after attaching the SHA-256 signature and CRC32 check code. Timestamps and serial numbers are added to the ciphertext to reject duplicate or expired transmitted data, and SHA-256 is used in the data parsing module to verify data integrity;

[0088] Step 3: Transmission security. The TLS1.3 protocol realizes the encryption from the data sending unit to the data receiving module (end-to-end). Compared with TLS1.2, it can reduce the 1-RTT handshake delay, and ECDHE-RSA ensures forward secrecy;

[0089] Step 4: Storage security. For the data stored in the local storage module, the data is encrypted using AES-XTS full disk encryption, combined with RBAC permission control and two-factor authentication. Threshold signature can be used to fragment and store the RSA-2048 private key, and the risk of single-point leakage is theoretically reduced to 1 / n (n is the number of fragments).

[0090] The implementation of the data security mechanism's hierarchical encryption and anti-attack design adopts a double-layer encryption mechanism combining RSA-2048 encryption and AES-256 encryption. Combined with wireless communication technology, it ensures the security and real-time nature of the data, strengthens the protection of the database, and enhances the efficiency of the system in processing data by processing the data in sequence.

[0091] The data transmission module includes a connection verification module for verifying the working status of the monitoring device and a local storage module for locally storing the key ciphertext and monitoring ciphertext. The connection verification module includes verifying the working status of the monitoring device and wireless communication connection verification to determine the coverage of wireless transmission, enabling data to be transmitted between each device module using wireless communication technology to ensure the timeliness of data acquisition at the data acquisition terminal. The local storage module is used to store the key ciphertext and monitoring ciphertext encrypted by the data packaging unit in the local mobile storage space to increase the portability and storage security of the data.

[0092] As Figure 6 shown, after detecting the data transmission flag information, the connection verification module in the data transmission module will perform the function of verifying the working connection status of the monitoring devices under the data acquisition terminal through 5G wireless communication. Specifically, under 5G communication coverage, various monitoring devices can actively send device working status data (such as 2 - normal operation, 1 - low battery, 0 - device not working or unable to work due to an accident) and use the MQTT communication protocol to send the working status data to the connection verification module; the monitoring devices can also be passively verified. For example, the connection verification module uses the MQTT protocol to send verification instructions to various monitoring devices, and the monitoring devices will return the working status data to the connection verification module after receiving the verification instructions. By verifying the working connection status of the monitoring devices through the connection verification module, the validity of the transmitted data is ensured, and the local storage module will save the key ciphertext and monitoring data ciphertext sent by the data sending unit in the local mobile storage space.

[0093] The monitoring visualization terminal is connected to the data acquisition terminal and the data transmission module respectively through wireless communication technology. The monitoring visualization terminal includes a data receiving module for receiving ciphertext, a data parsing module for parsing ciphertext, a data post-processing module for iterative Kalman filtering and differential solution to calculate RTK coordinates, an edge cloud computing module for fusing monitoring data and analyzing and processing the fusion results, a data display module for displaying construction site carbon emission monitoring data and analysis results, and a construction alarm module for monitoring alarms.

[0094] The data receiving module receives the encrypted ciphertext sent by the data sending unit and can send the RSA-2048 public key to the data packaging unit. The data parsing module includes two links: obtaining the AES-256 key by decrypting the RSA-2048 private key ciphertext of the key and decrypting the monitoring ciphertext with the AES-256 key to obtain monitoring data. The data post-processing module performs iterative Kalman filtering analysis on the decrypted monitoring data to obtain the optimal estimated monitoring value of the monitoring data. After completing the iterative Kalman filtering analysis, differential solution coordinate calculation is performed on the vehicle-mounted RTK carrier phase data to obtain the actual spatial coordinates of the vehicle-mounted RTK positioning module, and then compared with the actual spatial coordinates of the differential reference station to obtain the relative spatial coordinates of the vehicle-mounted RTK positioning module in the grid area.

[0095] As Figure 4 shown, the data receiving module sends the received data to the data parsing module. The data parsing module decrypts the received AES-256 key ciphertext with the RSA-2048 private key to obtain the AES-256 key. The AES-256 key is used to decrypt the received monitoring data ciphertext to obtain the monitoring data of the data acquisition terminal. The data parsing module transmits the parsed monitoring data to the data post-processing module, and the data post-processing module performs iterative Kalman filtering and differential solution coordinate calculation of the carrier phase data successively.

[0096] The data post-processing module first filters each item of monitoring data by using the iterative Kalman filtering algorithm. Taking the steps of performing iterative Kalman filtering on the carbon emission concentration monitoring data as an example, the iterative Kalman filtering algorithm has the following steps:

[0097] Step 1, initialize the filtering parameters. This process refers to the setting of some hardware parameters of the carbon concentration monitor to obtain the initialized carbon concentration parameters. The calculation function is as follows:

[0098] (X, P) = diag(C meas , Δt, δ a , δ s )

[0099] Among them, C meas is the initial carbon emission concentration (CO2) monitoring value (ppm), Δt is the sampling time interval (seconds), δa is the standard deviation of the process noise acceleration (ppm / s 2 ), δ s is the standard deviation of the sensor measurement noise (ppm), P is the initial covariance second-order matrix, X is the initial state vector, and X = [concentration, rate of change of concentration].

[0100] Step 2, prediction stage. Perform the current prediction through the previous result parameters, and the calculation function is as follows,

[0101] (X pred , P pred ) = diag(X prev , P prev , Δt, δ a )

[0102] where X prev is the state vector at the previous moment, P prev is the covariance matrix at the previous moment, X pred is the predicted state vector, and P pred is the predicted covariance matrix.

[0103] Step 3, iterative update stage. Through the above calculated parameters, the optimal estimation parameters can be calculated, and the calculation function is as follows,

[0104] (X est , P est ) = diag(X pred , P pred , C meas , δ s , m t , eps)

[0105] where m t is the maximum number of iterations, eps is the convergence threshold, P est is the updated covariance matrix, X est is the optimal estimation state vector, and X est = [optimal estimated carbon concentration, rate of change of carbon concentration], and the optimal estimated carbon concentration is obtained.

[0106] Considering the real-time rapidity of system data processing, the iteration termination condition is that the convergence threshold eps = 10 ppm or the maximum number of iterations m t = 5 times. Through the above data processing design, the interference of the environment on the carbon emission concentration monitoring data is effectively suppressed.

[0107] The optimal estimated values of real-time monitoring data of various items are output by the iterative Kalman filter. The data post-processing module then performs differential calculation of coordinates by subtracting the carrier phase data and coordinate position data of the differential reference station from the vehicle-mounted RTK carrier phase data to obtain the actual spatial coordinates of the vehicle-mounted RTK engineering equipment that generates carbon emissions, and transmits the filtered data and calculated data to the edge cloud computing module.

[0108] The vehicle-mounted RTK coordinate solution algorithm eliminates common errors through double-difference carrier phase observations, establishes a linearized equation of geometric distance, and uses the LAMBDA method to fix the integer ambiguity. To solve the problem that traditional fixing may lead to premature convergence or oscillation in dynamic scenarios, a dynamic threshold that can be dynamically adjusted according to the driving speed of the engineering equipment is adopted to improve the reliability and effectiveness of carbon emission monitoring data. The specific process is as follows:

[0109] 1. Construct a double-difference observation equation. The geometric matrix (design matrix) is determined by the relative positions (direction cosines, etc.) of the satellites and the vehicle-mounted RTK positioning module. The calculation formulas for the geometric matrix A and the observation vector y are as follows:

[0110] (A, y) = G(δ s , δ R , Φ b , Φ R )

[0111] Among them, δ s is the known satellite spatial coordinate data; δ R is the initial spatial coordinate data of the vehicle-mounted RTK positioning module (when the initial spatial coordinates of the vehicle-mounted RTK positioning module are uncertain, the spatial coordinate data of the differential reference station is used as a substitute); Φ b is the carrier phase data of the differential reference station; Φ R is the carrier phase data of the vehicle-mounted RTK positioning module.

[0112] 2. Float solution and ambiguity fixing. Estimate the coordinate correction The float solution of the ambiguity The calculation formula is as follows.

[0113]

[0114] Among them, the weight matrix W depends on the quality of the observation data of the satellite itself.

[0115] Then use the decorrelation transformation of the LAMBDA method to optimize the search to obtain the optimal integer ambiguity solution. The calculation formula is as follows:

[0116]

[0117] Among them, is the ambiguity covariance matrix, B is the ambiguity coefficient matrix (usually the identity matrix of the same order as A). When N obtains the optimal solution, N = N opt , N opt is the optimal integer solution.

[0118] 3. Iterative convergence. When the coordinate correction is less than the dynamic threshold η, it is used as a sign that the actual spatial coordinate calculation of the vehicle-mounted RTK positioning module is completed. The calculation formula of the dynamic threshold η is as follows,

[0119]

[0120] where k is an empirical coefficient (depending on the number of iterations), v i is the residual of each time, and v = G3(y, A, δ R , N).

[0121] When the coordinate correction is not less than the dynamic threshold η, the updated coordinates at this time are obtained where Then an assignment is made, making Return to the first step to reconstruct the double-difference observation equation for a new round of coordinate calculation until the iterative convergence condition is met.

[0122] In the urban road construction scenario, combined with the multipath suppression technology, the system can converge to centimeter-level accuracy (horizontal ≤ 1 cm, elevation ≤ 2 cm) within 5 iterations, meeting the dynamic monitoring requirements of the engineering carbon emission trajectory.

[0123] The present invention realizes centimeter-level RTK path positioning by introducing a high-precision RTK positioning module and using the mathematical calculation method of RTK coordinate calculation, breaks through the traditional GPS meter-level accuracy positioning method, avoids being affected by the ionosphere, troposphere, multipath effect, etc., and improves the reliability and positioning accuracy.

[0124] The edge cloud computing module adopts a distributed node deployment strategy, with 3 - 5 nodes deployed per square kilometer. The communication nodes use the MQTT protocol to transmit data, and the subscription / publish mode realizes low-latency (<50 ms) communication; it synchronizes metadata with the cloud through the HTTP / 2 protocol, and the bandwidth occupancy rate ≤ 10%. The data fusion process includes spatio-temporal alignment, Kriging interpolation, and dynamic calibration of the Gaussian plume model, combines the Pearson correlation coefficient to analyze the correlation between the device trajectory and the carbon emission concentration, and directly performs model calculation and generation in the edge cloud computing module by calling functions in the block diagram, similar to the data packaging and function call of a black box. The system manages node resources through the Kubernetes cluster, uses the Raft algorithm to ensure data consistency, and completes task migration within 5 seconds when a node fails, ensuring data security.

[0125] The edge cloud computing module uses edge cloud computing technology to perform correlation calculations and matching analyses between the vehicle-mounted RTK relative spatial coordinates calculated by the data post-processing module and the carbon emission monitoring data. Combining with GIS technology, it calculates the carbon emission plume data of the engineering grid area, and conducts data correlation feature analysis on the movement trajectories of working equipment in each link of the construction process and the changing trend of carbon emission plume concentration, obtaining the functional relationship between the movement trajectories of working equipment and carbon emission concentration. At the same time, it transmits the data processing and analysis results to the data display module to achieve the monitoring of carbon emission concentration data at the construction site.

[0126] The edge cloud computing module takes the actual spatial coordinate position of the differential reference station as the reference origin coordinate of the construction site, places the reference origin coordinate in the dynamic grid area constructed by GIS technology, and establishes a mapping grid area model of the construction site, that is, a global construction area carbon emission prediction model, by aligning the RTK positioning data (frequency 10Hz) with the carbon emission monitoring data (frequency 1Hz) (here the frequency is the data alignment speed) through timestamps, and using linear interpolation to fill the time difference; maps the engineering equipment coordinates (vehicle-mounted RTK relative spatial coordinates) to the GIS grid, and uses the Kriging interpolation method to calculate the carbon emission concentration of unmonitored points in the grid. Combining with wind direction, wind force, temperature and humidity data, it trains the environmental impact factors, and determines the carbon emission concentration in the grid by combining the environmental impact factors when matching the grid and inserting the carbon emission concentration, and trains to obtain the global construction area carbon emission prediction model.

[0127] Furthermore, the edge cloud computing module can establish a local carbon emission prediction model for the short-term and local emission characteristics of the construction scenario. Select the working path of the engineering equipment, and select the surrounding grid area under the construction path. Compared with the engineering carbon emission prediction model, the local prediction model reduces the monitoring data samples and the workload of developing a local Gaussian mixture model for edge computing, and adapts to local needs.

[0128] The steps to establish a local carbon emission prediction model are as follows:

[0129] Step 1: Select the working path of the engineering equipment. The edge computing node calculates the surrounding grid area affected by this working path according to the engineering carbon emission prediction model, and takes the 2 - 3 layers of 10×10 adjacent grids closest to the path grid.

[0130] Step 2: The edge cloud computing module conducts model training on the monitoring data, marks the surrounding equipment paths of the target path in the engineering prediction model, and the edge computing node calculates the influence factors of the surrounding equipment paths on the carbon emission monitoring data of the target path.

[0131] Step 3: The carbon emissions caused by the target path follow a Gaussian model distribution. The numerical value of the carbon emissions of the target path in the surrounding grid concentration should be:

[0132] Carbon emission concentration = Project carbon emission concentration - Peripheral path 1 × Influence factor 1 - Peripheral path 2 × Influence factor 2 - ······ - Peripheral path n × Influence factor n.

[0133] Step 4: Introduce the influence of wind direction, wind force, temperature and humidity into the generated local prediction model, and add engineering equipment movement prediction data to form a local carbon prediction model of the target path under GIS.

[0134] The data display module includes a data conversion unit and a data display unit. The data conversion unit converts the monitoring data sorted out by the data post - processing module into visual charts, and the generated charts are visually presented through the data display unit to complete the data display work, and the calculation results and correlation analysis conclusions of the edge cloud computing module are displayed on the data display unit.

[0135] The data conversion unit fuses and translates the data calculated by the edge cloud computing module, and combines GIS technology to dynamically display the dynamic response model of engineering working equipment and carbon emission changes in the form of grids to users on the data display unit, showing the dynamic relationship between the carbon emissions of the construction area and the urban environment of the surrounding construction area.

[0136] The data display unit is a computer liquid crystal display screen, which displays the monitoring and prediction trends and analysis results on the data display unit.

[0137] The construction alarm module is used to monitor equipment abnormal operation alarms and carbon emission concentration exceeding - standard alarms. When it is found that the monitoring equipment is operating abnormally or the carbon concentration in the grid area exceeds the standard, the alarm of the construction alarm module is called to remind the user to check the working conditions of the corresponding monitoring equipment, or to adjust the actual working conditions in the grid area.

[0138] Through the edge cloud computing module, data alignment, trend fitting and correlation analysis are carried out on the data to construct a global construction area carbon emission prediction model, and the carbon emission dissipation effect of the project is grasped in real - time, realizing real - time early warning of carbon emission exceeding the standard or equipment abnormality.

[0139] In summary, in response to the monitoring requirements of typical scenarios such as construction in urban dense areas and construction in ecologically fragile areas, through scenario - based designs such as multi - frequency RTK anti - occlusion positioning, dynamic plume models, and wide - area edge node networking, centimeter - level equipment tracking, high - precision diffusion prediction and full - area coverage are achieved. For example, in urban road construction, the system positioning error ≤ 2cm, accurately obtaining the target spatial state; in construction in ecologically fragile areas, the high - precision positioning of the system realizes accurate tracking of human activity footprints and explores the impact of carbon emissions on fragile ecological zones.

[0140] This invention overcomes the difficulties of data silos and extensive modeling in traditional monitoring through the deep integration of RTK high-precision positioning, intelligent filtering algorithms, dynamic modeling and edge computing, and realizes the dynamic correlation between the working path of construction equipment and carbon emission concentration. It provides an efficient and safe solution for green construction, smart cities and carbon emission reduction management, and has both technological foresight and engineering feasibility.

[0141] Example 2

[0142] The present invention also provides a carbon emission monitoring method based on RTK technology, which is implemented using the system described in Example 1 and includes the following steps:

[0143] S1. Establishing a GIS grid area through a data acquisition terminal to acquire monitoring data, preprocessing the monitoring data to obtain encrypted ciphertext, and sending the encrypted ciphertext to a data transmission module and a monitoring visualization terminal;

[0144] S2. Verify the operation of the monitoring device through the data transmission module, and store the encrypted ciphertext together with the corresponding timestamp in the local disk;

[0145] S3. Decrypt, parse, post-process, and fuse the encrypted ciphertext through a monitoring visualization terminal to obtain the relationship between the movement trajectory of the working equipment and the carbon emission concentration, and display and alarm based on the relationship.

[0146] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. A carbon emission monitoring system based on RTK technology, characterized in that, The system includes: a data acquisition terminal, a data transmission module, and a monitoring visualization terminal; The data acquisition terminal is used to obtain monitoring data by establishing a GIS grid area, obtain an encrypted ciphertext based on the monitoring data, and send the encrypted ciphertext to the data transmission module and the monitoring visualization terminal; The data transmission module is used to verify the working status of the monitoring device and locally store the encrypted ciphertext; The monitoring visualization terminal is used to obtain the action relationship between the moving trajectory of the engineering working equipment and the carbon emission concentration based on the encrypted ciphertext, and perform display and alarm based on the action relationship.

2. The carbon emission monitoring system based on RTK technology according to claim 1, characterized in that, The data acquisition terminal includes: a carbon concentration monitor, a wind direction and wind force detector, a temperature and humidity detector, a vehicle-mounted RTK positioning module, a differential reference station, and a data preprocessing module; The carbon concentration monitor is used to collect carbon concentration data of the construction site; The wind direction and wind force detector is used to collect wind direction and wind force data of the construction site; The temperature and humidity detector is used to collect temperature and humidity data of the construction site; The vehicle-mounted RTK positioning module is used to obtain carrier phase data of the engineering transportation equipment and the construction operation equipment; The differential reference station is used to set a spatial reference origin for the construction site and obtain origin coordinate position data; The data preprocessing module is used to preprocess the monitoring data to obtain the encrypted ciphertext; The monitoring data includes: the carbon concentration data, the wind direction and wind force data, the temperature and humidity data, the carrier phase data, and the origin coordinate position data.

3. The carbon emission monitoring system based on RTK technology according to claim 2, characterized in that, The vehicle-mounted RTK positioning module includes: a receiving antenna for receiving satellite signals and a transmitting antenna for transmitting the carrier phase data.

4. The carbon emission monitoring system based on RTK technology according to claim 2, characterized in that, The data preprocessing module includes: a data filtering unit, a data packaging unit, and a data sending unit; The data filtering unit is used to filter the monitoring data; The data packaging unit is used to encrypt the AES symmetric key using the RSA asymmetric public key and encrypt the filtered monitoring data with the AES symmetric key to obtain the encrypted ciphertext; The data sending unit is used to send the encrypted ciphertext.

5. The carbon emission monitoring system based on RTK technology according to claim 4, wherein The data filtering unit processes the monitoring data using a sliding average filtering algorithm, a median filtering algorithm, and an average filtering algorithm: The current filtering result = [median of the monitoring data segment + average of the monitoring data segment] / 2; After completing the filtering, a carbon concentration data optimization algorithm is performed: Optimized carbon concentration data = project phase factor * carbon concentration data + equipment status factor * carbon concentration data + temperature and humidity factor * carbon concentration data; Among them, the project phase factor + the equipment status factor + the temperature and humidity factor = 1.

6. The carbon emission monitoring system based on RTK technology according to claim 1, characterized in that, The data transmission module includes: a connection verification module and a local storage module; The connection verification module is used to verify the working status of the monitoring device; The local storage module is used to locally store the encrypted ciphertext.

7. The carbon emission monitoring system based on RTK technology according to claim 1, characterized in that The monitoring visualization terminal includes a data receiving module, a data parsing module, a data post-processing module, an edge cloud computing module, a data display module, and a construction alarm module; The data receiving module is used to receive the encrypted ciphertext; The data parsing module is used to parse the encrypted ciphertext to obtain the monitoring data; The said data post - processing module is used for post - processing the parsed monitoring data; The said edge cloud computing module is used for data fusion of the post - processed monitoring data to obtain the functional relationship between the moving trajectory of the working equipment and the carbon emission concentration; The said data display module is used for displaying the said functional relationship; The said construction alarm module is used for monitoring alarms.

8. The carbon emission monitoring system based on RTK technology according to claim 7, characterized in that, The post - processing of the parsed monitoring data by the said data post - processing module includes: Performing iterative Kalman filter analysis on the parsed monitoring data, and then performing differential solution coordinate calculation.

9. The carbon emission monitoring system based on RTK technology according to claim 7, characterized in that, The fusion of the post - processed data by the said edge cloud computing module to obtain the functional relationship between the moving trajectory of the working equipment and the carbon emission concentration includes: Adopting spatio - temporal alignment, Kriging interpolation and Gaussian plume model dynamic calibration, and combining with the Pearson correlation coefficient to analyze the correlation between the moving trajectory of the working equipment and the carbon emission concentration.

10. A carbon emission monitoring method based on RTK technology, characterized in that, Implemented by using the carbon emission monitoring system based on RTK technology according to any one of claims 1 - 9, the method includes: S1. Establish a GIS grid area to obtain monitoring data, obtain an encrypted ciphertext based on the monitoring data, and send the encrypted ciphertext to the data transmission module and the monitoring visualization terminal; S2. Perform equipment working verification on the monitoring equipment, and store the encrypted ciphertext together with the corresponding timestamp in the local disk; S3. Receive, parse, post - process, and perform data fusion on the encrypted ciphertext to obtain the functional relationship between the moving trajectory of the engineering working equipment and the carbon emission concentration, and perform display and alarm based on the said functional relationship.

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