Carbon emission monitoring method and system based on tower crane operation characteristics

Abstract

The invention provides a carbon emission monitoring method and system based on tower crane operation characteristics, and the method comprises the following steps: obtaining the operation data of a tower crane in real time, including the load weight data of a lifting hook in the vertical direction, the vertical displacement data of the lifting hook, the rotation angle data of an upper structure of the tower crane, and the horizontal displacement data of a tower crane trolley in the tower arm direction; based on the operation data, an operation time sequence set is obtained, the operation time sequence set comprises four standard data sequences, and each standard data sequence comprises a plurality of collection moments and standard operation data corresponding to the operation data at each collection moment; acquiring a target acquisition time period, and positioning a plurality of target acquisition moments in the target acquisition time period, thereby obtaining a first operation sequence subset, and obtaining a total estimated value of carbon emission of the tower crane in the target acquisition time period; the method breaks through the extensive limitation that a traditional electric meter only monitors the total electric quantity, and carbon emission contributions of different action units such as lifting, rotation and amplitude variation are accurately quantified.

Description

Technical Field

[0001] The present application relates to the technical field of construction site equipment monitoring, and specifically to a carbon emission monitoring method and system based on tower crane operation characteristics. Background Art

[0002] As global requirements for carbon emissions control continue to increase, carbon emissions monitoring in the construction industry has become a crucial component of reducing energy consumption and achieving green construction. Tower cranes are a common piece of large-scale equipment used on construction sites, and their energy consumption and carbon emissions during operation have a significant impact on the project's overall carbon emissions.

[0003] At present, traditional tower crane carbon emission monitoring methods mostly rely on smart meter monitoring (estimation through total electricity meter electricity measurement data). However, since tower cranes involve multi-modal complex actions such as lifting, rotation, and boom length adjustment during operation, the energy consumption characteristics of different operation stages vary greatly. The method of obtaining overall electricity consumption data only through the total electricity meter does not meet the requirements of refined monitoring. It is impossible to distinguish the energy consumption contribution of specific operation links, and it is difficult to understand the carbon emissions of various parts of the tower crane. It is difficult to provide effective support for construction site management and decision-making, and it is even more difficult to know how to reduce the carbon emissions of tower crane equipment. Summary of the Invention

[0004] In view of the above-mentioned defects or deficiencies in the prior art, this application aims to provide a carbon emission monitoring method and system based on the operation characteristics of a tower crane, so as to realize the refined dynamic monitoring of carbon emissions of a tower crane; In the first aspect, the present application proposes a carbon emission monitoring method based on tower crane operation characteristics, comprising the following steps: Acquire the operation data of the tower crane in real time, including the load weight data in the vertical direction of the hook, the vertical displacement data of the hook, the rotation angle data of the tower crane superstructure, and the horizontal displacement data of the tower crane trolley along the tower arm direction; Based on the operating data, a running sequence set is obtained, where the running sequence set includes four standard data sequences, each of the standard data sequences includes multiple collection moments and standard operating data corresponding to the operating data at each collection moment; Obtaining a target collection period and locating a plurality of target collection moments within the target collection period, thereby obtaining a first operation sequence subset; the target collection moment is a collection moment within the target collection period, and the first operation sequence subset is a set of standard operation data corresponding to all target collection moments within the target collection period, intercepted from the operation sequence set; According to the first operation sequence subset corresponding to the target collection period, an estimated total carbon emission value of the tower crane in the target collection period is obtained.

[0005] According to the technical solution provided by the present application, obtaining the estimated total carbon emissions of the tower crane during the target collection period based on the first operation sequence subset corresponding to the target collection period includes the following steps: Obtaining a first carbon emission estimation value of the lifting unit based on the standard operating data corresponding to the load weight data and the standard operating data corresponding to the vertical displacement data at each target collection time within the target collection period; Obtaining a second carbon emission estimation value of the rotary unit based on the standard operating data corresponding to the load weight data and the standard operating data corresponding to the rotation angle data at each target collection time within the target collection period; Obtaining a third carbon emission estimation value of the luffing unit based on the standard operating data corresponding to the load weight data and the standard operating data corresponding to the horizontal displacement data at each target collection time within the target collection period; The first carbon emission estimate value, the second carbon emission estimate value, and the third carbon emission estimate value within the target collection period are superimposed to obtain a total carbon emission estimate value of the tower crane within the target collection period.

[0006] According to the technical solution provided in this application, obtaining the runtime sequence set based on the runtime data includes the following steps: Performing data noise reduction and standardization on the load weight data, the vertical displacement data, the rotation angle data, and the horizontal displacement data to obtain corresponding standard operation data; The standard operation data at the same collection time are aligned, and the collection times are arranged in chronological order to obtain an operation sequence set.

[0007] According to the technical solution provided in this application, obtaining the first carbon emission estimated value, the second carbon emission estimated value, and the third carbon emission estimated value includes the following steps: Establish a lifting regression model, a rotation regression model, and a variable amplitude regression model. The input of each regression model is connected to the operating characteristics of the corresponding standard operating data, and the output is connected to the power-carbon emission converter. The input of the lifting regression model includes the interactive characteristics of load weight change and displacement change, and the output is a first carbon emission estimation value; The rotation regression model includes an input of an interaction characteristic between a rotation angle change and a load torque, and outputs a second carbon emission estimate. The input of the amplitude regression model includes the interaction characteristics of horizontal displacement change and load weight, and the output is the third carbon emission estimation value.

[0008] According to the technical solution provided in this application, the input of the lifting regression model also includes the sinusoidal compensation factor of the load swing angle, the input of the rotation regression model also includes the environmental disturbance coefficient of the wind speed sensor, and the input of the amplitude variation regression model also includes the cosine correction of the track inclination of the tower crane trolley.

[0009] According to the technical solution provided by the present application, after obtaining the runtime sequence set based on the runtime data, the following steps are further included: Convert the standard operating data corresponding to the rotation angle data into the azimuth of the polar coordinate system, convert the standard operating data corresponding to the horizontal displacement data into the polar diameter, and convert the standard operating data corresponding to the vertical displacement data into the height, to obtain the operating point information at each acquisition moment; Dividing the collection into a plurality of segments, and obtaining the tower crane operation trajectory corresponding to each collection segment according to the operation point information at each collection moment within each collection segment; Obtaining an action scheduling combination of the tower crane and a total amount of carbon emission segments within each of the collection segments, wherein the action scheduling combination includes at least one unit action of a slewing unit, a hoisting unit, and / or a luffing unit; and the total amount of carbon emission segments is obtained from a second operation sequence subset corresponding to the collection segment; The tower crane operation trajectory, the action scheduling combination and the total carbon emission fragment of the tower crane in the collection segment are stored in the traceability database; the traceability database includes multiple tower crane operation trajectories, and multiple action scheduling combinations corresponding to each tower crane operation trajectory, and each action scheduling combination corresponds to a total carbon emission fragment.

[0010] According to the technical solution provided by this application, the method further includes the following steps: receiving a target operation task of the tower crane, and obtaining a target operation trajectory according to the target operation task; Retrieving and traversing the traceability database, obtaining an action call combination corresponding to the minimum total amount of carbon emission fragments among the multiple action scheduling combinations corresponding to the tower crane operation trajectory corresponding to the target operation trajectory, and using it as an alternative action call combination; Based on the alternative action call combination, a target action call combination is obtained, and the tower crane is controlled to complete the target operation task with the target action call combination.

[0011] According to the technical solution provided by this application, obtaining a target action calling combination based on the candidate action calling combination includes the following steps: Obtain the current dynamic environmental parameters of the construction site where the tower crane is located, including the real-time wind speed and the density of obstacles around the tower crane; Based on the dynamic environment parameters, determining whether the candidate action call combination has a collision risk or a safety risk; If not, the candidate action calling combination is used as the target action calling combination.

[0012] According to the technical solution provided by this application, after obtaining the carbon emission sequence in each operation segment, the following steps are also included: When it is detected that the growth rate of the total amount of carbon emission fragments in N consecutive target collection periods exceeds a set threshold, an alarm mechanism is triggered.

[0013] In a second aspect, the present application proposes a carbon emission monitoring system based on tower crane operation characteristics, which is used to implement the carbon emission monitoring method based on tower crane operation characteristics as described above; comprising: An acquisition module configured to acquire the operation data of the tower crane in real time, wherein the operation data includes the load weight data in the vertical direction of the hook, the vertical displacement data of the hook, the rotation angle data of the tower crane superstructure, and the horizontal displacement data of the tower crane trolley along the tower arm direction; a processing module configured to obtain a running sequence set based on the running data, the running sequence set comprising four consecutive job segments and a standard data sequence group under each job segment, the standard data sequence group comprising a plurality of standard data sequences, each of the standard data sequences comprising a plurality of collection moments and standard running data corresponding to the running data at each collection moment; an analysis module configured to obtain a target collection period and locate a plurality of target collection moments within the target collection period, thereby obtaining a first operation sequence subset; the target collection moment is a collection moment within the target collection period, and the first operation sequence subset is a set of standard operation data corresponding to all target collection moments within the target collection period, intercepted from the operation sequence set; The analysis module is further configured to obtain an estimated total carbon emission value of the tower crane within the target collection period based on the first operation sequence subset corresponding to the target collection period.

[0014] Compared with the existing technology, the beneficial effect of the present application is that: the present application breaks through the extensive limitation of traditional electricity meters that only monitor total electricity consumption by dividing the target collection period and associating the operating parameters at each moment of the target collection period, and accurately quantifies the contribution of different action units of lifting, rotation, and amplitude variation to the overall carbon emissions within the target collection period. For example, it can clearly identify the carbon emission difference between "heavy load and high speed rotation" and "light load and low speed amplitude variation", provide data support for targeted optimization, and improve the refined carbon emission traceability capability; in addition, the multi-dimensional standard data sequence of the operating time sequence set (such as load-displacement correlation time sequence) can dynamically map the energy consumption characteristics of the tower crane's compound action, and combined with the timestamp mark of the carbon emission sequence, directly locate the high-carbon emission operation stage (such as frequent start and stop, overload swing and other abnormal working conditions), and guide the operator to adjust the operation rhythm or equipment parameters, thereby reducing the carbon emissions of the tower crane equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flowchart of the steps of the carbon emission monitoring method based on tower crane operation characteristics provided in an embodiment of the present application; Figure 2 A schematic structural diagram of a carbon emission monitoring system based on tower crane operation characteristics provided in an embodiment of the present application.

[0016] The text annotations in the figure represent: 201. Lifting monitoring unit; 202. Load monitoring unit; 203. Rotation monitoring unit; 204. Luffing monitoring unit; 205. Data processing unit; 206. Lifting regression model; 207. Rotation regression model; 208. Luffing regression model. DETAILED DESCRIPTION

[0017] The present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the invention are shown in the accompanying drawings.

[0018] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0019] Example 1 As mentioned in the background technology, in order to solve the problems in the existing technology, this application proposes a carbon emission monitoring method based on the operation characteristics of the tower crane, such as Figure 1 As shown, the following steps are included: S1. Acquire the operation data of the tower crane in real time, wherein the operation data includes the load weight data in the vertical direction of the hook, the vertical displacement data of the hook, the rotation angle data of the tower crane superstructure, and the horizontal displacement data of the tower crane trolley along the tower arm direction; Specifically, the load weight data of the hook in the vertical direction is collected in real time by the force sensor installed on the lifting mechanism of the tower crane, the vertical displacement data of the hook is collected in real time by the displacement sensor installed on the lifting mechanism of the tower crane, and the horizontal displacement data of the tower crane trolley along the tower arm direction is collected in real time by the displacement sensor installed on the tower crane luffing mechanism. Among them, the force sensor includes but is not limited to strain gauge force sensor, resistance strain sensor or piezoelectric force sensor; the displacement sensor includes but is not limited to wire displacement sensor, laser displacement sensor or ultrasonic displacement sensor; the angle sensor includes but is not limited to MEMS inclination sensor, capacitive inclination sensor or gyroscope.

[0020] S2. Obtaining an operation sequence set based on the operation data, where the operation sequence set includes four standard data sequences, each of which includes multiple collection moments and standard operation data corresponding to the operation data at each collection moment; Specifically, the running sequence set includes four standard data sequences, namely, a standard data sequence corresponding to load weight data, a standard data sequence corresponding to vertical displacement data, a standard data sequence corresponding to rotation angle data, and a standard data sequence corresponding to horizontal displacement data. Each standard data sequence includes multiple collection moments and standard running data corresponding to the data at each collection moment. For example, the standard data sequence corresponding to load weight data includes multiple collection moments and standard running data corresponding to the load weight data at each collection moment.

[0021] In a preferred embodiment, obtaining the runtime sequence set based on the runtime data includes the following steps: Performing data noise reduction and standardization on the load weight data, the vertical displacement data, the rotation angle data, and the horizontal displacement data to obtain corresponding standard operation data; Aligning the standard operating data at the same collection time, and arranging the collection times in chronological order to obtain an operating sequence set; Specifically, a digital filtering algorithm is used to reduce the noise of the collected operating data to remove noise interference; then, normalization and other methods are used to standardize the data and map the data to a specific range (such as [0, 1]) to obtain the standard operating data corresponding to the load weight data, vertical displacement data, rotation angle data, and horizontal displacement data. Then, the standard operating data corresponding to the load weight data, the standard operating data corresponding to the vertical displacement data, the standard operating data corresponding to the rotation angle data, and the standard operating data corresponding to the horizontal displacement data at the same acquisition time are aligned to ensure the temporal consistency of the data, thereby forming an operating time series set.

[0022] S3. Obtain a target collection period, and locate several target collection moments within the target collection period, thereby obtaining a first operation sequence subset; the target collection moment is a collection moment within the target collection period, and the first operation sequence subset is a set of standard operation data corresponding to all target collection moments within the target collection period, intercepted from the operation sequence set; S4. Obtain an estimated total carbon emission value of the tower crane during the target collection period based on the first operation sequence subset corresponding to the target collection period.

[0023] Furthermore, obtaining the estimated total carbon emissions of the tower crane during the target collection period based on the first operation sequence subset corresponding to the target collection period includes the following steps: Obtaining a first carbon emission estimation value of the lifting unit based on the standard operating data corresponding to the load weight data and the standard operating data corresponding to the vertical displacement data at each target collection time within the target collection period; Obtaining a second carbon emission estimation value of the rotary unit based on the standard operating data corresponding to the load weight data and the standard operating data corresponding to the rotation angle data at each target collection time within the target collection period; Obtaining a third carbon emission estimation value of the luffing unit based on the standard operating data corresponding to the load weight data and the standard operating data corresponding to the horizontal displacement data at each target collection time within the target collection period; The first carbon emission estimate value, the second carbon emission estimate value, and the third carbon emission estimate value within the target collection period are superimposed to obtain a total carbon emission estimate value of the tower crane within the target collection period.

[0024] Specifically, the lifting unit is the part of the tower crane responsible for the vertical lifting and lowering of the hook, and its operation is related to the load weight and vertical displacement. The first carbon emission estimation value is the carbon emission of the unit during the target collection period estimated based on the operating data of the lifting unit. The slewing unit is the part of the tower crane responsible for the rotation of the superstructure, and its operation is related to the load weight and rotation angle. The second carbon emission estimation value is the carbon emission of the unit during the target collection period estimated based on the operating data of the slewing unit. The boom unit is the part of the tower crane responsible for the horizontal movement of the tower crane trolley along the tower arm direction, and its operation is related to the load weight and horizontal displacement. The third carbon emission estimation value is the carbon emission of the unit during the target collection period estimated based on the operating data of the boom unit. The total carbon emission estimation value is the sum of the carbon emission estimation values ​​of the lifting unit, slewing unit and boom unit during the target collection period, which represents the total carbon emissions of the tower crane during that period.

[0025] Furthermore, obtaining the first carbon emission estimated value, the second carbon emission estimated value, and the third carbon emission estimated value includes the following steps: Establish a lifting regression model 206, a rotation regression model 207 and a variable amplitude regression model 208, the input end of each regression model is connected to the operating characteristics of the corresponding standard operating data, and the output end is connected to the power-carbon emission converter; The input of the lifting regression model 206 includes the interactive characteristics of load weight and displacement change, and the output is the first carbon emission estimation value; The input of the rotation regression model 207 includes the interactive characteristics of the rotation angle change and the load torque, and the output is the second carbon emission estimation value; The input of the amplitude regression model 208 includes the interactive characteristics of the horizontal displacement change and the load weight, and the output is the third carbon emission estimation value.

[0026] Specifically, the electricity-carbon emission converter converts the estimated electricity consumption into carbon emissions based on the carbon emission factor of the State Grid (such as 0.5 kg CO2 / kWh); the standard data sequence corresponding to the load weight data and the standard data sequence corresponding to the vertical displacement data within the target collection period are input into the lifting regression model 206, and the lifting regression model 206 outputs a first carbon emission estimation value; the standard data sequence corresponding to the rotation angle data and the standard data sequence corresponding to the load weight data within the target collection period are input into the rotation regression model 207, and the rotation regression model 207 outputs a second carbon emission estimation value; the standard data sequence corresponding to the horizontal displacement data and the standard data sequence corresponding to the load weight data within the target collection period are input into the variable amplitude regression model 208, and the variable amplitude regression model 208 outputs a third carbon emission estimation value.

[0027] Furthermore, the input of the lifting regression model 206 also includes the sine compensation factor of the load swing angle, the input of the rotation regression model 207 also includes the environmental disturbance coefficient of the wind speed sensor, and the input of the amplitude variation regression model 208 also includes the cosine correction of the track inclination of the tower crane trolley.

[0028] Specifically, the hoisting regression model utilizes a regression model (including but not limited to a neural network model, a gradient boosting tree model, or a support vector machine model) trained based on load weight, displacement acceleration, and measured energy consumption values ​​from historical operation data, and cross-validation is used to ensure model accuracy. The sine compensation factor for the load swing angle: Considering that the hook may swing during the lifting process, this factor is used to correct the calculation results of the hoisting regression model 206 to more accurately reflect actual energy consumption and carbon emissions. Its value is related to the sine value of the load swing angle. The environmental disturbance coefficient of the wind speed sensor: This coefficient is converted from the wind speed data detected by the wind speed sensor and is used to correct the slewing regression model 207, as wind speed can affect the rotation of the tower crane superstructure, thereby affecting energy consumption and carbon emissions. The cosine correction factor for the tower crane trolley track inclination: Since the tower crane trolley track may have a certain inclination, this correction factor is used to adjust the amplitude regression model 208 to make the calculation results more consistent with actual conditions. Its value is related to the cosine value of the track inclination.

[0029] Optionally, the training process of the lifting regression model includes the following steps: collecting historical operation data, including the load weight change rate, displacement acceleration and corresponding power consumption value; normalizing the data; fitting the regression equation using the least squares method to obtain model parameters; and evaluating the model accuracy through cross-validation (R²≥0.85).

[0030] Specifically, during tower crane operation, the load swing angle data of the hook is acquired through an angle sensor or visual monitoring equipment. Its sine value is calculated as a compensation factor and input into the lifting regression model 206 along with data such as load weight change and displacement. A wind speed sensor is installed to monitor the wind speed of the tower crane's operating environment in real time. Based on a pre-set conversion relationship between wind speed and environmental disturbance coefficient, the wind speed data is converted into an environmental disturbance coefficient and input into the rotation regression model 207 along with data such as the rotation angle and load torque. An angle measuring instrument is used to measure the inclination of the tower crane's trolley track, and its cosine value is calculated as a correction factor. This correction factor, along with data such as horizontal displacement and load weight, is input into the amplitude regression model 208.

[0031] Based on the lifting, rotation and amplitude variation regression model 208, this embodiment takes into account the impact of environmental factors such as load swing, wind speed, track inclination, etc. on the energy consumption and carbon emissions of the tower crane during actual operation. By introducing corresponding compensation factors, disturbance coefficients and correction amounts, the input of the regression model is adjusted to make the calculation results of the regression model closer to the actual situation, thereby improving the accuracy of carbon emission estimation.

[0032] In a preferred embodiment, after obtaining the runtime sequence set based on the runtime data, the method further includes the following steps: Convert the standard operating data corresponding to the rotation angle data into the azimuth of the polar coordinate system, convert the standard operating data corresponding to the horizontal displacement data into the polar diameter, and convert the standard operating data corresponding to the vertical displacement data into the height, to obtain the operating point information at each acquisition moment; Specifically, the operating point information is composed of the polar coordinate azimuth angle converted from the crane's rotation angle data at each acquisition moment, the polar diameter converted from the horizontal displacement data, and the height converted from the vertical displacement data. This information is used to determine the crane's specific position in space. The crane's operating trajectory is formed by connecting the various operating point information in chronological order during the crane's operation, reflecting the crane's movement path. Using a coordinate conversion algorithm, the collected crane rotation angle data is converted into azimuth angles in a polar coordinate system, the horizontal displacement data into polar diameters, and the vertical displacement data into heights. By recording the operating point information at each acquisition moment and connecting these points in chronological order, the crane's operating trajectory is generated. For example, at a certain acquisition moment, the crane's rotation angle is 45°, which converts to a 45° azimuth angle in the polar coordinate system; the horizontal displacement is 10 meters, which is a 10-meter polar diameter; and the vertical displacement is 8 meters, which is a 8-meter height. This information constitutes the crane's operating point at that moment.

[0033] Obtaining an action scheduling combination of the tower crane and a total carbon emission segment within each collection segment, wherein the action scheduling combination includes at least one unit action of a slewing unit, a hoisting unit, and / or a luffing unit; the total carbon emission segment is obtained from a second operation sequence subset corresponding to the collection segment; the second operation sequence subset is a set of standard operation data corresponding to all target collection moments within the collection segment, intercepted from the operation sequence set; Specifically, within a single acquisition segment, the action scheduling combinations include independent scheduling of the crane's slewing, hoisting, and luffing mechanisms, simultaneous operation of hoisting and luffing, simultaneous operation of hoisting and slewing, and parallel operation of all three mechanisms. These represent the crane's operating modes. By analyzing the operational data of each crane unit (hoisting, slewing, and luffing) within the acquisition segment, the action scheduling combinations are determined to determine which units are in operation. For example, if the hoisting and slewing units are operating but the luffing unit is not, the action scheduling combination for that segment is for the hoisting and slewing units.

[0034] The tower crane operation trajectory, the action scheduling combination and the total carbon emission fragment of the tower crane in the collection segment are stored in the traceability database; the traceability database includes multiple tower crane operation trajectories, and multiple action scheduling combinations corresponding to each tower crane operation trajectory, and each action scheduling combination corresponds to a total carbon emission fragment.

[0035] Specifically, the total carbon emission segment is the sum of the corresponding carbon emission estimates for each crane unit (hoisting unit, slewing unit, and luffing unit) within a collection segment, reflecting the crane's total carbon emissions within that collection segment. The traceability database stores crane operation trajectories, motion scheduling combinations, and corresponding carbon emission segment totals for subsequent query and analysis. It supports systematic storage, effective management and backup, and supports data query, traceability, and export. The acquired crane operation trajectories, motion scheduling combinations, and carbon emission segment totals are stored in the traceability database in a specific data format for easy query and analysis.

[0036] This implementation method obtains the tower crane operation trajectory and action scheduling combination by performing coordinate conversion and analysis on the tower crane operation data, calculates the total amount of carbon emission fragments based on the obtained carbon emission sequence, and stores this information in the traceability database to achieve comprehensive recording and data traceability of the tower crane operation process, providing data support for subsequent optimization of tower crane operations and carbon emission management.

[0037] In a preferred embodiment, the method further comprises the following steps: receiving a target operation task of the tower crane, and obtaining a target operation trajectory according to the target operation task; Specifically, a target task is a specific task assigned to a crane by the user or the construction management system, such as lifting a batch of materials to a specific floor and location. Construction managers send the target task instructions to the crane through the crane control system or related management software. The system then plans the crane's target trajectory based on the task requirements and the construction site layout. For example, if materials are to be lifted from point A to point B, the system will plan a reasonable target trajectory based on the locations of the two points and the crane's operating range.

[0038] Retrieving and traversing the traceability database, obtaining an action call combination corresponding to the minimum total amount of carbon emission fragments among the multiple action scheduling combinations corresponding to the tower crane operation trajectory corresponding to the target operation trajectory, and using it as an alternative action call combination; Specifically, the system retrieves tower crane operation trajectories similar to the target operation trajectory from the traceability database, obtains all action scheduling combinations corresponding to these trajectories and their total carbon emission fragments, and through comparison, selects the action scheduling combination with the smallest total carbon emission fragments as the alternative action call combination.

[0039] Based on the alternative action call combination, a target action call combination is obtained, and the tower crane is controlled to complete the target operation task with the target action call combination.

[0040] Furthermore, obtaining a target action call combination based on the candidate action call combination includes the following steps: Obtain the current dynamic environmental parameters of the construction site where the tower crane is located, including the real-time wind speed and the density of obstacles around the tower crane; Based on the dynamic environment parameters, determining whether the candidate action call combination has a collision risk or a safety risk; Specifically, the acquired dynamic environmental parameters are fed into a risk assessment regression model. Combined with a selection of alternative actions, the model analyzes the predicted trajectory and state of the crane when executing that combination. For example, based on the real-time wind speed and the crane's rotational motion, it determines whether the wind will cause the crane to sway excessively and collide with surrounding obstacles. The weight and height of the hoisted object can also be used to assess the risk of the object falling or colliding.

[0041] If not, the candidate action calling combination is used as the target action calling combination.

[0042] Specifically, the system obtains the current dynamic environmental parameters of the crane's construction site and uses these parameters to determine whether the proposed action combinations pose a collision or safety risk. If no risk exists, the proposed action combination is used as the target action combination, and the crane is controlled to complete the target task according to that combination. If a risk exists, the action scheduling combinations are readjusted or screened until a safe and low-carbon target action combination is determined.

[0043] In a preferred embodiment, after obtaining the carbon emission sequence in each operation segment, the following steps are further included: When it is detected that the growth rate of the total amount of carbon emission fragments in N consecutive target collection periods exceeds a set threshold, an alarm mechanism is triggered.

[0044] Specifically, the system monitors the total estimated carbon emissions over N consecutive target collection periods in real time and calculates the growth rate between adjacent target collection periods. For example, if N = 3, when the growth rate of the total carbon emissions fragments in the first, second, and third target collection periods continuously exceeds the set threshold, the system immediately activates an alarm mechanism, emitting an audible and visual alarm through the alarm device at the construction site and simultaneously sending an alert message to the construction manager's mobile phone or management platform, promptly identifying any abnormal increase in carbon emissions during tower crane operation so that relevant personnel can take measures to adjust and optimize.

[0045] Example 2 On the basis of Example 1, this embodiment proposes a carbon emission monitoring system based on tower crane operation characteristics, which is used to implement the carbon emission monitoring method based on tower crane operation characteristics as described in Example 1; Figure 2 As shown, including: An acquisition module configured to acquire in real time operating data of the tower crane, the operating data including load weight data in the vertical direction of the hook, vertical displacement data of the hook, rotation angle data of the tower crane superstructure, and horizontal displacement data of the tower crane trolley along the tower arm direction; Specifically, the acquisition module includes a load monitoring unit 202, which collects the load weight data in the vertical direction of the hook in real time through a force sensor installed on the tower crane lifting mechanism; The lifting monitoring unit 201 collects the vertical displacement data of the hook in real time through the displacement sensor installed on the tower crane lifting mechanism; The rotation monitoring unit 203 obtains the rotation angle data of the tower crane superstructure in real time through the angle sensor installed on the tower crane rotation mechanism; The luffing monitoring unit 204 collects the horizontal displacement data of the tower crane trolley along the tower arm direction in real time through the displacement sensor installed on the tower crane luffing mechanism; The data processing unit 205 imports the pre-processed data into the artificial intelligence regression model for training. After the training is completed, the real-time collected data is input to achieve real-time prediction of carbon emissions during the tower crane operation; a processing module configured to obtain an operation sequence set based on the operation data, the operation sequence set including four standard data sequences, each of the standard data sequences including multiple collection moments and standard operation data corresponding to the operation data at each collection moment; The processing module includes a data processing unit 205, which records the operating data of the above four sensors at a set sampling frequency, performs data preprocessing, forms a specific time series, and transmits it to the analysis module; an analysis module configured to obtain a target collection period and locate a plurality of target collection moments within the target collection period, thereby obtaining a first operation sequence subset; the target collection moment is a collection moment within the target collection period, and the first operation sequence subset is a set of standard operation data corresponding to all target collection moments within the target collection period, intercepted from the operation sequence set; The analysis module is further configured to obtain an estimated total carbon emission value of the tower crane within the target collection period based on the first operation sequence subset corresponding to the target collection period.

[0046] The analysis module includes a lifting regression model 206 , a rotation regression model 207 and an amplitude regression model 208 .

[0047] Specifically, the system also includes a data storage unit, which is used to systematically store, effectively manage, and back up the collected sensor raw data, carbon emission prediction data, and operation status information, and supports data query, traceability, and export functions. The storage medium of the data storage unit includes but is not limited to a database system. It also includes a data display unit that receives processed data and displays the tower crane operation status and carbon emission estimates in an intuitive manner. The data display unit includes a real-time monitoring and visualization module, which is used to present the tower crane carbon emission estimation results in real time on a web page or mobile terminal, and supports various visualization forms such as charts and curves. The data display unit also includes an abnormal alarm module, which promptly sends an alarm message to the management terminal or mobile device when the predicted carbon emission value exceeds the set threshold.

[0048] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. The above is only the preferred implementation method of this application. It should be pointed out that due to the limitations of textual expression, there are objectively infinite specific structures. For ordinary technicians in this technical field, without departing from the principles of the present invention, they can also make several improvements, modifications or changes, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the inventive concept and technical solution to other occasions without improvement, should be regarded as the scope of protection of this application.

Claims

1. A carbon emission monitoring method based on tower crane operation characteristics, characterized in that: The following steps are involved: Acquire the operation data of the tower crane in real time, including the load weight data in the vertical direction of the hook, the vertical displacement data of the hook, the rotation angle data of the tower crane superstructure, and the horizontal displacement data of the tower crane trolley along the tower arm direction; Based on the operating data, a running sequence set is obtained, where the running sequence set includes four standard data sequences, each of the standard data sequences includes multiple collection moments and standard operating data corresponding to the operating data at each collection moment; Acquiring a target acquisition period, and locating a plurality of target acquisition moments within the target acquisition period, thereby obtaining a first running sequence subset; The target collection moment is a collection moment within the target collection period, and the first operation sequence subset is a standard operation data set intercepted from the operation sequence set and corresponding to all target collection moments within the target collection period; According to the first operation sequence subset corresponding to the target collection period, an estimated total carbon emission value of the tower crane in the target collection period is obtained.

2. The carbon emission monitoring method based on tower crane operation characteristics according to claim 1 is characterized in that: Obtaining an estimated total carbon emission value of the tower crane during the target collection period based on the first operation sequence subset corresponding to the target collection period includes the following steps: Obtaining a first carbon emission estimation value of the lifting unit based on the standard operating data corresponding to the load weight data and the standard operating data corresponding to the vertical displacement data at each target collection time within the target collection period; Obtaining a second carbon emission estimation value of the rotary unit based on the standard operating data corresponding to the load weight data and the standard operating data corresponding to the rotation angle data at each target collection time within the target collection period; Obtaining a third carbon emission estimation value of the luffing unit based on the standard operating data corresponding to the load weight data and the standard operating data corresponding to the horizontal displacement data at each target collection time within the target collection period; The first carbon emission estimate value, the second carbon emission estimate value, and the third carbon emission estimate value within the target collection period are superimposed to obtain a total carbon emission estimate value of the tower crane within the target collection period.

3. The carbon emission monitoring method based on tower crane operation characteristics according to claim 1 is characterized in that: The step of obtaining a running sequence set based on the running data includes the following steps: Performing data noise reduction and standardization on the load weight data, the vertical displacement data, the rotation angle data, and the horizontal displacement data to obtain corresponding standard operation data; The standard operation data at the same collection time are aligned, and the collection times are arranged in chronological order to obtain an operation sequence set.

4. The carbon emission monitoring method based on tower crane operation characteristics according to claim 2 is characterized in that: Acquiring the first carbon emission estimated value, the second carbon emission estimated value, and the third carbon emission estimated value comprises the following steps: Establishing a lifting regression model (206), a rotation regression model (207) and a variable amplitude regression model (208), wherein the input end of each regression model is connected to the operation characteristics of the corresponding standard operation data, and the output end is connected to the electric energy-carbon emission converter; The input of the lifting regression model (206) includes the interactive characteristics of load weight and displacement change, and the output is a first carbon emission estimation value; The input of the rotation regression model (207) includes the interactive characteristics of the rotation angle change and the load torque, and the output is a second carbon emission estimation value; The input of the amplitude regression model (208) includes the interaction characteristics of horizontal displacement change and load weight, and the output is the third carbon emission estimation value.

5. The carbon emission monitoring method based on tower crane operation characteristics according to claim 4 is characterized in that: The input of the lifting regression model (206) also includes a sine compensation factor of the load swing angle, the input of the rotation regression model (207) also includes an environmental disturbance coefficient of the wind speed sensor, and the input of the amplitude regression model (208) also includes a cosine correction value of the track inclination angle of the tower crane trolley.

6. The carbon emission monitoring method based on tower crane operation characteristics according to claim 1 is characterized in that: After obtaining the runtime sequence set based on the runtime data, the following steps are further included: Convert the standard operating data corresponding to the rotation angle data into the azimuth of the polar coordinate system, convert the standard operating data corresponding to the horizontal displacement data into the polar diameter, and convert the standard operating data corresponding to the vertical displacement data into the height, to obtain the operating point information at each acquisition moment; Dividing the collection into a plurality of segments, and obtaining the tower crane operation trajectory corresponding to each collection segment according to the operation point information at each collection moment within each collection segment; Obtaining an action scheduling combination of the tower crane and a total amount of carbon emission segments within each of the collection segments, wherein the action scheduling combination includes at least one unit action of a slewing unit, a hoisting unit, and / or a luffing unit; and the total amount of carbon emission segments is obtained from a second operation sequence subset corresponding to the collection segment; The tower crane operation trajectory, the action scheduling combination and the total carbon emission fragment of the tower crane in the collection segment are stored in the traceability database; the traceability database includes multiple tower crane operation trajectories, and multiple action scheduling combinations corresponding to each tower crane operation trajectory, and each action scheduling combination corresponds to a total carbon emission fragment.

7. The carbon emission monitoring method based on tower crane operation characteristics according to claim 6 is characterized in that: The method further comprises the following steps: receiving a target operation task of the tower crane, and obtaining a target operation trajectory according to the target operation task; Retrieving and traversing the traceability database, obtaining an action call combination corresponding to the minimum total amount of carbon emission fragments among the multiple action scheduling combinations corresponding to the tower crane operation trajectory corresponding to the target operation trajectory, and using it as an alternative action call combination; Based on the alternative action call combination, a target action call combination is obtained, and the tower crane is controlled to complete the target operation task with the target action call combination.

8. The carbon emission monitoring method based on tower crane operation characteristics according to claim 7 is characterized in that: The step of obtaining a target action call combination based on the candidate action call combination includes the following steps: Obtain the current dynamic environmental parameters of the construction site where the tower crane is located, including the real-time wind speed and the density of obstacles around the tower crane; Based on the dynamic environment parameters, determining whether the candidate action call combination has a collision or safety risk; If not, the candidate action calling combination is used as the target action calling combination.

9. The carbon emission monitoring method based on tower crane operation characteristics according to claim 1 is characterized in that: After obtaining the carbon emission sequence in each operation segment, the following steps are also included: When it is detected that the growth rate of the total amount of carbon emission fragments in N consecutive target collection periods exceeds a set threshold, an alarm mechanism is triggered.

10. A carbon emission monitoring system based on tower crane operation characteristics, used to implement the carbon emission monitoring method based on tower crane operation characteristics according to any one of claims 1 to 9; characterized in that: include: An acquisition module configured to acquire the operation data of the tower crane in real time, wherein the operation data includes the load weight data in the vertical direction of the hook, the vertical displacement data of the hook, the rotation angle data of the tower crane superstructure, and the horizontal displacement data of the tower crane trolley along the tower arm direction; a processing module configured to obtain an operation sequence set based on the operation data, the operation sequence set including four standard data sequences, each of the standard data sequences including multiple collection moments and standard operation data corresponding to the operation data at each collection moment; an analysis module configured to obtain a target acquisition period and locate a plurality of target acquisition moments within the target acquisition period, thereby obtaining a first running sequence subset; The target collection moment is a collection moment within the target collection period, and the first operation sequence subset is a standard operation data set intercepted from the operation sequence set and corresponding to all target collection moments within the target collection period; The analysis module is further configured to obtain an estimated total carbon emission value of the tower crane within the target collection period based on the first operation sequence subset corresponding to the target collection period.

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