Intelligent scheduling and optimizing system for vertical transportation of wharf house building materials based on Internet of Things

Abstract

The invention relates to the technical field of building construction and the Internet of Things, and discloses an intelligent scheduling and optimizing system for vertical transportation of wharf house building materials based on the Internet of Things, and the system comprises a data collection unit which is used for obtaining a to-do task, a tower crane state and material arrival time in real time; the task attractor generation unit is used for converting the to-do task into a task attractor in the decision model; the instantaneous comprehensive attraction calculation unit is used for calculating an instantaneous comprehensive attraction value between the tower crane and each task attractor; the scheduling instruction decision-making unit is used for selecting the task with the maximum attraction value as a target task; and the instruction issuing unit is used for issuing the target task to the corresponding tower crane. The attractive force value is calculated by constructing a dynamic decision model for balancing the driving force and the impedance force, and the model integrates the task urgency degree, the transfer cost, the predicted arrival time of materials in transit and the mutual exclusion effect among the multiple tower cranes, so that instant collaborative scheduling and autonomous safe avoidance among the multiple tower cranes are realized.

Description

Technical Field

[0001] The present invention relates to the field of building construction and the technical field of the Internet of Things, and specifically to an intelligent scheduling and optimization system for vertical transportation of building construction materials at a terminal based on the Internet of Things. Background Art

[0002] The development of IoT technology has driven digital transformation in the construction sector. In large-scale projects like wharf construction, vertical material transportation is a critical process, primarily accomplished by tower cranes. Traditional scheduling models rely heavily on manual experience or pre-defined construction plans. However, the construction site is a dynamic system, with factors such as task requirements, equipment status, and material supply fluctuating frequently. Fixed scheduling sequences are highly fragile in this environment, making it difficult to maximize operational efficiency.

[0003] In existing technology, on-site equipment scheduling and upstream supply chain management are typically two independent systems. Scheduling decisions are primarily based on the on-site to-do list. These decisions rarely or completely ignore the real-time in-transit status of the materials required for these tasks. This leads to a common scenario: the scheduling system issues a lifting order to a tower crane. However, the corresponding material transport fleet is still en route and has not yet arrived at the site. Ultimately, the tower crane is left waiting in vain at its designated location, resulting in significant waste of equipment, manpower, and time.

[0004] For large-scale port construction sites with multiple tower cranes, coordination and conflict avoidance between equipment present another major technical challenge. Existing scheduling methods often plan tasks only for a single piece of equipment and lack consideration for interaction between multiple intelligent agents. The responsibility for safe avoidance falls almost entirely on frontline operators, who coordinate through walkie-talkie communication and visual observation. This approach relies heavily on human experience and immediate reaction ability, making it not only inefficient but also posing significant safety risks when visibility is limited or communication is poor. The system fails to provide an inherent, proactive avoidance mechanism, resulting in an ineffective guarantee of the safety and smoothness of multi-machine joint operations. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides an intelligent scheduling and optimization system for vertical transportation of terminal building construction materials based on the Internet of Things, which solves the problems that traditional scheduling methods cannot adapt to dynamic changes on site, equipment waiting ineffectively due to poor material supply information, and the lack of autonomous safety avoidance mechanism when multiple tower cranes work together.

[0006] To achieve the above objectives, the present invention provides a system, and its specific technical solutions are as follows: The IoT-based intelligent scheduling and optimization system for vertical transportation of building construction materials at the terminal includes: The data acquisition unit is responsible for acquiring multi-source heterogeneous data in real time. The acquired data includes at least: pending task information, which includes task priority, material type, starting point and target point coordinates; tower crane status data, which includes the precise position of the tower crane, hook height, rotation angle and load; and the estimated arrival time of materials in transit.

[0007] The task attractor generation unit is connected to the data acquisition unit. This unit receives the pending task information and creates corresponding task attractors for one or more pending tasks. The task attractor is the digital representation of the pending task in the decision model. Its data structure is defined as: ; Where, The data structure for the task attractor; is the unique identifier of the task; is the starting three-dimensional space coordinate vector of the task; is the target three-dimensional space coordinate vector of the task; The timestamp of when the task was created; is the static priority value of the task; The identifier of the material required for the task.

[0008] The instantaneous comprehensive attraction calculation unit is connected to the data acquisition unit and the task attractor generation unit. This unit receives real-time tower crane status data and the estimated arrival time of in-transit materials. Combining this with the generated task attractors, it periodically calculates the instantaneous comprehensive attraction values ​​corresponding to multiple task attractors for a given tower crane.

[0009] The dispatch instruction decision unit is connected to the instantaneous comprehensive attraction calculation unit. This unit receives multiple instantaneous comprehensive attraction values ​​output by the instantaneous comprehensive attraction calculation unit, determines the instantaneous comprehensive attraction value with the largest value through comparison operation, and identifies the task attractor corresponding to the maximum value as the target task.

[0010] The instruction issuing unit is connected to the dispatch instruction decision unit. This unit receives the determined target task and transmits it as a dispatch instruction to the human-machine interface of the tower crane corresponding to the target task.

[0011] In a specific embodiment of the present invention, the instantaneous comprehensive attraction calculation unit determines the instantaneous comprehensive attraction value by quantifying the balance between the driving force representing the intrinsic scheduling value of the task and the resistance force representing the cost and risk required to execute the task. The calculation model is as follows: ; Where, For tower crane Task In time The instantaneous comprehensive attractiveness value; is the index of the tower crane; is the index of the task; is the current time; The urgency of the task; is the static priority of the task; is the supply chain damping factor; For transfer costs; Environmental risks; is the multi-agent mutual exclusion factor; are the weight coefficients of task urgency, task priority, transfer cost, environmental risk and multi-agent mutual exclusion factor respectively.

[0012] The driving force includes task urgency and task priorities Task urgency The calculation method is: ; Where, For the task In time The urgency value of is the current system time; For the task The creation timestamp of the .

[0013] The resistance includes the transfer cost and environmental risks . Transfer costs It is the quantification of the comprehensive cost required for the tower crane to transform from its current physical state to the starting state of the task, which is calculated through the preset cost function accomplish: ; in, For tower crane Execute the task In time The transfer cost; cost function Used to calculate the comprehensive cost, which is obtained by weighted summation of four independent cost components; is the horizontal movement cost component, whose value depends on the tower crane In time Current location and mission The spatial distance between the starting positions; is the vertical lifting cost component, and its value depends on the tower crane In time Hook height and mission The absolute value of the difference between the starting heights of is the cost component of the slewing motion, and its value depends on the tower crane Execute the task The rotation angle required to be adjusted; is the cost component of the spreader replacement, which is determined by obtaining the spreader type required by the task attractor and the spreader type currently installed on the tower crane. If the two are inconsistent, The value is the preset significant penalty value, otherwise it is zero.

[0014] In another specific embodiment of the present invention, the driving force of the instantaneous comprehensive attractiveness calculation model includes a supply chain damping factor The unit obtains the estimated arrival time of the in-transit materials associated with the task attractor through the data acquisition unit. , and based on this calculation ; ; in, For the task In time Supply chain damping factor; is the coefficient that controls the steepness of the function curve; is the current system time; For the task The estimated arrival time of the required materials.

[0015] In another specific embodiment of the present invention, when there are multiple tower cranes, the impedance term of the instantaneous comprehensive attraction calculation model includes a multi-agent mutual exclusion factor . It is calculated as follows: ; in, For tower crane Targeted tasks due to the presence of other tower cranes Mutually exclusive factors; is the total number of tower cranes; To remove Index of other tower cranes besides ; is the repulsive force constant; For tower crane In time The coordinate vector of For tower crane In time The coordinate vector of is the path interference function; For tower crane Execute the task The estimated motion trajectory of For tower crane In time The current working area of ​​​​the path interference function In judging and When there is potential interference, an additional significant path conflict penalty value is output, otherwise the output value is zero.

[0016] The present invention provides an intelligent scheduling and optimization system for vertical transportation of building materials at a terminal based on the Internet of Things. It has the following beneficial effects: 1. This invention constructs a dynamic decision-making model based on instantaneous comprehensive attraction values, quantitatively balancing multiple dimensions, such as task urgency and priority, with the transfer costs and environmental risks of executing the task. The system responds to changing operating conditions in real time, autonomously selecting the optimal task for each tower crane within each decision cycle. This mechanism replaces rigid, pre-set scheduling schemes, significantly improving equipment utilization and adaptability to dynamic site changes, achieving global optimization of the scheduling process.

[0017] 2. This invention integrates the estimated arrival time of in-transit materials from the upstream supply chain into the scheduling decision model. By applying dynamic, proactive suppression to tasks involving materials that have not yet arrived, it effectively avoids unnecessary waiting for tower cranes due to delayed material arrival. This design tightly couples on-site equipment scheduling with material supply status, ensuring the executability of scheduling instructions, reducing resource waste caused by information silos, and improving the consistency and smoothness of the overall operational process.

[0018] 3. This invention incorporates a mutual repulsion model between multiple tower cranes into the calculation of the instantaneous integrated attraction value. By quantifying the repulsive force of physical distance between cranes and the interference penalty of their estimated motion trajectories, this model inherently incorporates safety avoidance logic into scheduling decisions. This enables multiple tower cranes to achieve autonomous coordination and conflict avoidance without the need for mandatory intervention from a dispatch center, reducing operational risks and enhancing the safety and stability of multi-equipment joint operations in complex scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a system framework diagram of the present invention; Figure 2 Schematic diagram of the internal structure and data flow of the data acquisition module of the present invention; Figure 3 A schematic diagram of the task attractor generation process of the present invention; Figure 4 This is a schematic diagram of the instantaneous comprehensive attraction calculation process of the present invention; Figure 5 Schematic diagram of the scheduling instruction decision process of the present invention; Figure 6 This is a flowchart of the instruction issuance process of the present invention.

[0020] Among them, 10, data acquisition unit; 20, task attractor generation unit; 30, instantaneous comprehensive attraction calculation unit; 40, scheduling instruction decision unit; 50, instruction issuing unit. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0022] Refer to the attached Figure 1 , Figure 1 This is a schematic diagram of the system architecture according to one embodiment of the present invention. The IoT-based intelligent scheduling and optimization system for vertical transportation of building materials at terminals is physically deployed on a server or cloud platform and communicates with on-site devices via a network. The system includes a data acquisition unit 10, a task attractor generation unit 20, an instantaneous comprehensive attraction calculation unit 30, a scheduling instruction decision unit 40, and an instruction issuance unit 50.

[0023] The overall workflow of the system follows a cyclical, closed-loop process from data input to instruction output.

[0024] First, the data acquisition unit 10 serves as the system's sensor interface, acquiring multi-source heterogeneous data in real time. This data includes pending task information, including task priority, material type, and starting and destination coordinates; tower crane status data, including precise crane position, hook height, slew angle, and load; and the estimated arrival time of in-transit materials. The data acquisition unit 10 preprocesses and formats the collected data and distributes it to subsequent units.

[0025] The task attractor generation unit 20 is connected to the data acquisition unit 10. This unit receives pending task information transmitted from the data acquisition unit 10. Its function is to convert each piece of raw pending task information into a standardized data object identifiable within the decision model, namely a task attractor. The generated task attractor is then transmitted to the instantaneous comprehensive attraction calculation unit 30.

[0026] The instantaneous comprehensive attraction calculation unit 30 has its data input terminals connected to the data acquisition unit 10 and the task attractor generation unit 20. This unit is the core computing component of the system. During each preset decision cycle, it receives real-time tower crane status data and the estimated arrival time of in-transit materials from the data acquisition unit 10 for a specific tower crane. It then combines this data with the multiple task attractors received from the task attractor generation unit 20 to calculate the instantaneous comprehensive attraction value.

[0027] The instantaneous comprehensive attraction calculation unit 30 determines the instantaneous comprehensive attraction value by quantifying and balancing the driving force representing the intrinsic scheduling value of the task and the resistance force representing the cost and risk required to execute the task.

[0028] The calculation model is as follows: ; Where, For tower crane Task In time The instantaneous comprehensive attractiveness value; is the index of the tower crane; is the index of the task; is the current time; The urgency of the task; is the static priority of the task; is the supply chain damping factor; For transfer costs; Environmental risks; is the multi-agent mutual exclusion factor; They are the weight coefficients of task urgency, task priority, transfer cost, environmental risk and multi-agent mutual exclusion factor. Specifically, the calculation and adjustment mechanism of each weight coefficient is as follows: and task priority weights , its value is closely coupled with the macro progress of the project. The system calculates a dynamic progress deviation rate by continuously comparing the actual construction period consumption with the planned construction period, the completed project volume with the total project volume in the construction management platform. When the deviation rate shows that the project is at risk of delay, the system will use a preset gain function to adjust the progress deviation rate. and Perform nonlinear amplification so that scheduling decisions tend to serve tasks on the critical path. The adjustment is based on the operating efficiency data of the tower crane group. For example, by collecting and analyzing the integral value of the motor current (i.e. energy consumption) of each tower crane per unit time and the standby time of the non-operating state, when the comprehensive operating cost index exceeds the preset economic range, the cost will be increased accordingly. The value of is used to guide the system to give priority to the lifting sequence with lower energy consumption and shorter moving distance. Environmental risk weight The determination of the risk is directly related to the environmental parameters collected by the IoT sensors. For example, the system can substitute the wind speed, visibility and other data monitored in real time into a preset risk assessment model in the form of a piecewise function or lookup table. When the environmental conditions (such as wind speed) exceed the safety threshold, The value of will increase exponentially or stepwise, thus forcing the safety consideration to be increased in decision making. The calculation is based on the real-time geometric analysis of the tower crane operating space. The system solves the three-dimensional spatial coordinates of each tower crane boom and predicts its motion trajectory, dynamically calculating the volume or probability of the potential interference area. The higher the value, the better. The larger the value is, the stronger the safety avoidance between devices will be.

[0029] After the calculation is completed, the instantaneous comprehensive attraction calculation unit 30 outputs a set of instantaneous comprehensive attraction values ​​calculated for the designated tower crane to the dispatch instruction decision unit 40 .

[0030] The dispatch instruction decision unit 40 is connected to the instantaneous comprehensive attraction calculation unit 30. This unit receives the multiple instantaneous comprehensive attraction values ​​output by the instantaneous comprehensive attraction calculation unit 30. Its internal decision-making mechanism traverses and compares the received value set to determine the instantaneous comprehensive attraction value with the largest value. The unit then identifies the task attractor corresponding to this maximum value as the optimal target task for the current decision cycle and transmits this target task to the instruction issuing unit 50.

[0031] The instruction issuing unit 50 is connected to the dispatch instruction decision unit 40. This unit receives the determined target task and encapsulates it into a standardized dispatch instruction. This instruction contains execution information such as the task number, material information, and the coordinates of the starting and target points. It is then transmitted via the network to the human-machine interface of the tower crane corresponding to the target task, such as the display terminal in the cab. This completes a complete dispatch decision and execution cycle.

[0032] Refer to the attached Figure 2 , Figure 2 Figure 1 is a schematic diagram of the internal structure and data flow of data acquisition unit 10 according to one embodiment of the present invention. Data acquisition unit 10 is the system's perception foundation, serving as a data interface between the system and the external physical world and information systems, providing real-time, accurate data input for subsequent calculation and decision-making units.

[0033] The data acquisition unit 10 connects to a construction project management system or a Building Information Model (BIM) platform via a pre-defined API to retrieve pending task information from these external systems. The data structure of each pending task includes a unique task identifier, a pre-defined static priority value, the type and specifications of the required materials, and the starting and ending 3D coordinates of the task.

[0034] Simultaneously, the data acquisition unit 10 acquires real-time physical status data from multiple IoT sensors deployed on each tower crane. These sensors include a Global Positioning System (GPS) unit or ultra-wideband (UWB) positioning tag for obtaining the precise three-dimensional coordinates of the crane's boom end; a height encoder for measuring the vertical height of the hook relative to the boom; an angle encoder for measuring the boom's rotation angle; and a load sensor mounted on the hook assembly to measure the weight of the currently hoisted load. These sensors transmit data to the data acquisition unit 10 via wireless communication protocols such as 4G / 5G or LoRa.

[0035] Furthermore, the data collection unit 10 establishes a communication connection with a supply chain management (SCM) system or a third-party logistics information platform via an API. From these platforms, it obtains the estimated time of arrival (ETA) for in-transit materials associated with pending tasks. This ETA is a specific timestamp used to support scheduling decisions for subsequent units.

[0036] After receiving the above three types of data, the data acquisition unit 10 performs data preprocessing operations, including data cleaning to remove outliers, data formatting to unify the data structure, and adding accurate acquisition timestamps to all data.

[0037] The processed structured data is transmitted in real time to the task attractor generation unit 20 and the instantaneous comprehensive attraction calculation unit 30 respectively through the internal data bus or message queue, providing unified and reliable data input for subsequent calculations of the entire system.

[0038] Refer to the attached Figure 3 , Figure 3 Figure 2 is a schematic diagram of the task attractor generation process according to one embodiment of the present invention. Task attractor generation unit 20 has its data input connected to data acquisition unit 10. Its core function is to convert the raw to-do task information received from data acquisition unit 10 into standardized data objects, namely, task attractors, that can be recognized and processed by subsequent computational decision units.

[0039] When the data collection unit 10 captures new pending task information, the information is transmitted to the task attractor generation unit 20. The task attractor generation unit 20 instantiates a unique task attractor data object corresponding to each pending task information received within the system.

[0040] This task attractor is a digital representation of the to-do task in the scheduling decision model. It has a specific data structure, which is defined as: ; Where, The data structure for the task attractor; is the unique identifier of the task; is the starting three-dimensional space coordinate vector of the task; is the target three-dimensional space coordinate vector of the task; The timestamp of when the task was created; is the static priority value of the task; The identifier of the material required for the task.

[0041] After completing the instantiation of the task attractor and filling in the data fields, the task attractor generation unit 20 transmits the generated one or more task attractor objects to the instantaneous comprehensive attraction calculation unit 30 through the internal data interface for subsequent attraction calculation.

[0042] Refer to the attached Figure 4 , Figure 4 The figure below is a schematic diagram of the instantaneous integrated attraction calculation process according to one embodiment of the present invention. The instantaneous integrated attraction calculation unit 30 is the core computing component of the system, with its data input terminals connected to the data acquisition unit 10 and the task attractor generation unit 20. Within a preset calculation cycle, this unit integrates the real-time status data acquired from the data acquisition unit 10 and the multiple task attractors obtained from the task attractor generation unit 20 for each tower crane on site. It then calculates a quantified instantaneous integrated attraction value (ICAV) for each pairing between the tower crane and each task attractor.

[0043] This module determines the instantaneous comprehensive attractiveness value by quantifying the balance between the driving force representing the intrinsic scheduling value of the task and the resistance force representing the cost and risk required to execute the task. The calculation model is as follows: ; Where, For tower crane Task In time The instantaneous comprehensive attractiveness value; is the index of the tower crane; is the index of the task; is the current time; The urgency of the task; is the static priority of the task; is the supply chain damping factor; For transfer costs; Environmental risks; is the multi-agent mutual exclusion factor; They are the weight coefficients of task urgency, task priority, transfer cost, environmental risk and multi-agent mutual exclusion factor. Specifically, the calculation and adjustment mechanism of each weight coefficient is as follows: and task priority weights , its value is closely coupled with the macro progress of the project. The system calculates a dynamic progress deviation rate by continuously comparing the actual construction period consumption with the planned construction period, the completed project volume with the total project volume in the construction management platform. When the deviation rate shows that the project is at risk of delay, the system will use a preset gain function to adjust the progress deviation rate. and Perform nonlinear amplification so that scheduling decisions tend to serve tasks on the critical path. The adjustment is based on the operating efficiency data of the tower crane group. For example, by collecting and analyzing the integral value of the motor current (i.e. energy consumption) of each tower crane per unit time and the standby time of the non-operating state, when the comprehensive operating cost index exceeds the preset economic range, the cost will be increased accordingly. The value of is used to guide the system to give priority to the lifting sequence with lower energy consumption and shorter moving distance. Environmental risk weight The determination of the risk is directly related to the environmental parameters collected by the IoT sensors. For example, the system can substitute the wind speed, visibility and other data monitored in real time into a preset risk assessment model in the form of a piecewise function or lookup table. When the environmental conditions (such as wind speed) exceed the safety threshold, The value of will increase exponentially or stepwise, thus forcing the safety consideration to be increased in decision making. The calculation is based on the real-time geometric analysis of the tower crane operating space. The system solves the three-dimensional spatial coordinates of each tower crane boom and predicts its motion trajectory, dynamically calculating the volume or probability of the potential interference area. The higher the value, the better. The larger the value is, the stronger the safety avoidance between devices will be.

[0044] The driving force term, the numerator of the formula, is determined by the urgency of the task. and task priorities Task urgency The calculation method is: ; Where, For the task In time The urgency value of is the current system time; For the task The creation timestamp of the .

[0045] The resistance term, the denominator of the formula, mainly includes the transfer cost and environmental risks . Transfer costs It is the quantification of the comprehensive cost required for the tower crane to transform from its current physical state to the starting state of the task, which is calculated through the preset cost function accomplish: ; in, For tower crane Execute the task In time The transfer cost; cost function Used to calculate the comprehensive cost, which is obtained by weighted summation of four independent cost components; is the horizontal movement cost component, whose value depends on the tower crane In time Current location and mission The spatial distance between the starting positions; is the vertical lifting cost component, and its value depends on the tower crane In time Hook height and mission The absolute value of the difference between the starting heights of is the cost component of the slewing motion, and its value depends on the tower crane Execute the task The rotation angle required to be adjusted; is the cost component of the spreader replacement, which is determined by obtaining the spreader type required by the task attractor and the spreader type currently installed on the tower crane. If the two are inconsistent, The value is the preset significant penalty value, otherwise it is zero.

[0046] The calculation model includes the supply chain damping factor The unit obtains the estimated arrival time of the in-transit materials associated with the task attractor through the data acquisition unit. , and based on this calculation ; ; in, For the task In time Supply chain damping factor; is the coefficient that controls the steepness of the function curve; is the current system time; For the task The estimated arrival time of the required materials.

[0047] When there are multiple tower cranes on site, the impedance term also includes the multi-agent mutual exclusion factor . It is calculated as follows: ; in, For tower crane Targeted tasks due to the presence of other tower cranes Mutually exclusive factors; is the total number of tower cranes; To remove Index of other tower cranes besides ; is the repulsive force constant; For tower crane In time The coordinate vector of For tower crane In time The coordinate vector of is the path interference function; For tower crane Execute the task The estimated motion trajectory of For tower crane In time The current working area of ​​​​the path interference function In judging and When there is potential interference, an additional significant path conflict penalty value is output, otherwise the output value is zero.

[0048] After completing the calculation of all to-be-done task attractors, the instantaneous comprehensive attraction calculation unit 30 outputs a set of instantaneous comprehensive attraction values ​​generated for the designated tower crane to the dispatch instruction decision unit 40 as a whole.

[0049] Refer to the attached Figure 5 , Figure 5 Figure 4 is a schematic diagram of a dispatch instruction decision process according to one embodiment of the present invention. The dispatch instruction decision unit 40, whose data input is connected to the instantaneous comprehensive attraction calculation unit 30, functions as the system's decision-making core, converting the quantitative evaluation results output by the previous unit into a unique, deterministic dispatch action.

[0050] This unit receives a set of instantaneous comprehensive attraction values ​​for a specific tower crane, output by the instantaneous comprehensive attraction calculation unit 30. Each value in this set uniquely corresponds to a pending task attractor and comprehensively quantifies the scheduling value of the task itself against the cost and risk of executing it. Therefore, this value directly represents the tower crane's overall suitability for performing the specific task at the current moment.

[0051] The decision-making mechanism within the dispatch instruction decision unit 40 is a deterministic, optimal selection process. The unit's configured comparator performs a traversal operation on the received numerical set. During this traversal, the unit compares all instantaneous integrated attraction values ​​within the set and identifies the maximum value. This process is a purely numerical operation, without any heuristics or fuzzy judgments, ensuring the objectivity and reproducibility of the decision results.

[0052] The unit determines the task attractor corresponding to this maximum value as the target task for the tower crane within the current decision cycle. The essence of this selection process is to integrate the complex calculation results of the instantaneous comprehensive attraction calculation unit, which integrates task urgency, static priority, transfer costs, supply chain status, and multi-machine coordination risks, into a single, clear scheduling instruction. By selecting the task with the highest attractiveness, the system achieves optimal resource allocation under the current working conditions.

[0053] After determining the target task, the scheduling instruction decision unit 40 outputs the task attractor data object containing all the information of the target task as a decision result to the instruction issuing unit 50 through the internal data interface for subsequent instruction packaging and issuance.

[0054] Refer to the attached Figure 6 , Figure 6 The instruction issuing unit 50 has a data input terminal connected to the scheduling instruction decision unit 40 and is the final output interface connecting the system decision result with the on-site physical execution.

[0055] The instruction issuing unit 50 receives, processes, and transmits scheduling instructions. It first receives the task attractor data object identified as the target task from the scheduling instruction decision unit 40. This data object contains all the core information required to execute the task, but its data structure is designed for internal system calculations.

[0056] Therefore, upon receiving the task attractor, the unit performs an instruction encapsulation operation. During this operation, the unit extracts key fields from the task attractor data object and converts it into a standardized, human-computer interaction-oriented scheduling instruction data structure. This scheduling instruction data structure contains clear, operator-directed fields, such as the task's unique identifier, the name and specifications of the material to be hoisted, the 3D coordinates of the material's starting loading point, the 3D coordinates of the material's target unloading point, and the type of spreader required to execute the task.

[0057] After completing the command packaging, the command issuing unit 50 transmits the structured dispatch instruction data to the designated tower crane's onboard human-machine interface terminal via a pre-defined, secure network communication protocol. This terminal is typically an industrial-grade tablet computer or dedicated display installed in the tower crane's cab. The dispatch instruction is displayed on the display in the form of a graphical interface or a clear text list, allowing the crane operator to review and execute the lifting operation accordingly.

[0058] After a successful command is issued, the command issuing unit 50 also records the issuance event in the system log. This log includes the unique identifier of the task being issued, the target tower crane number, and the precise timestamp of the command issuance. This log provides data support for subsequent job tracing, workload statistics, and system performance analysis.

[0059] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. The intelligent scheduling and optimization system for vertical transportation of building construction materials at the terminal based on the Internet of Things is characterized by: include: Data collection unit, used to obtain real-time information on pending tasks, tower crane status data, and estimated arrival times of in-transit materials; a task attractor generating unit connected to the data acquisition unit and configured to create corresponding task attractors for one or more to-be-done tasks based on the to-be-done task information acquired from the data acquisition unit; an instantaneous comprehensive attraction calculation unit connected to the data acquisition unit and the task attractor generation unit, and configured to combine the tower crane state data with the task attractors and, for a specified tower crane, periodically calculate the instantaneous comprehensive attraction values ​​corresponding to the plurality of task attractors; a scheduling instruction decision unit connected to the instantaneous comprehensive attraction calculation unit, configured to receive a plurality of the instantaneous comprehensive attraction values ​​and determine the task attractor corresponding to the instantaneous comprehensive attraction value with the largest value as the target task; An instruction issuing unit is connected to the scheduling instruction decision unit and is used to receive the target task and issue it as a scheduling instruction to the corresponding tower crane.

2. The intelligent scheduling and optimization system for vertical transportation of building construction materials at a terminal based on the Internet of Things according to claim 1 is characterized in that: The instantaneous comprehensive attractiveness calculation unit is configured to determine the instantaneous comprehensive attractiveness value by quantitatively balancing the driving force representing the intrinsic scheduling value of the task and the impedance force representing the cost and risk required to perform the task; wherein the driving force includes task urgency and task priority, and the impedance force includes transfer cost and environmental risk.

3. The intelligent scheduling and optimization system for vertical transportation of building construction materials at a terminal based on the Internet of Things according to claim 2 is characterized in that: When evaluating the transfer cost in the impedance force, the instantaneous comprehensive attraction calculation unit is configured to quantify the comprehensive cost required to convert the tower crane from the current physical state to the starting state of the task attractor. The comprehensive cost is determined by integrating four factors: horizontal moving distance, vertical lifting height, rotation angle, and whether the sling needs to be replaced.

4. The intelligent scheduling and optimization system for vertical transportation of building construction materials at a terminal based on the Internet of Things according to claim 3 is characterized in that: When determining the comprehensive cost of whether the spreader needs to be replaced, the instantaneous comprehensive attractiveness calculation unit is configured to: obtain the spreader type required by the task attractor and the spreader type currently installed on the tower crane, and when it is determined that the two are inconsistent, include a preset significant penalty value in the transfer cost to reduce the attractiveness of the task that requires the spreader to be replaced.

5. The intelligent scheduling and optimization system for vertical transportation of building construction materials at a terminal based on the Internet of Things according to claim 2 is characterized in that: When evaluating the task urgency in the driving force, the instantaneous comprehensive attraction calculation unit is configured to use the accumulated waiting time of the task attractor since its creation as the quantitative value of the task urgency, so that its attraction is dynamically enhanced over time.

6. The intelligent scheduling and optimization system for vertical transportation of building construction materials at a terminal based on the Internet of Things according to claim 1 is characterized in that: The instantaneous comprehensive attraction calculation unit is further configured to: obtain the estimated arrival time of the in-transit materials associated with the task attractor through the data acquisition unit, and apply a dynamic advance inhibition effect to the instantaneous comprehensive attraction value based on the estimated arrival time to manage the scheduling feasibility of tasks for materials that have not yet arrived.

7. The intelligent scheduling and optimization system for vertical transportation of building construction materials at a terminal based on the Internet of Things according to claim 6 is characterized in that: The dynamic suppression effect applied by the instantaneous comprehensive attraction calculation unit is configured such that its suppression intensity is positively correlated with the remaining duration of the estimated arrival time; when the remaining duration is longer, the suppression intensity is the largest, and as the remaining duration shortens, the suppression intensity decreases smoothly until it is completely lifted when the material arrives.

8. The intelligent scheduling and optimization system for vertical transportation of building construction materials at a terminal based on the Internet of Things according to claim 1 is characterized in that: The instantaneous comprehensive attraction calculation unit is further configured to: when there are multiple tower cranes, obtain real-time status data of other tower cranes, and adjust the instantaneous comprehensive attraction value by introducing a mutual exclusion model to achieve autonomous coordination and safe avoidance between tower cranes.

9. The intelligent scheduling and optimization system for vertical transportation of building construction materials at a terminal based on the Internet of Things according to claim 8 is characterized in that: When implementing the mutual repulsion model, the instantaneous comprehensive attraction calculation unit is configured to take the physical distance between the tower cranes as an inverse function, where the closer the distance, the stronger the repulsive force generated; and include the repulsive force as a penalty item in the calculation of the instantaneous comprehensive attraction value.

10. The intelligent scheduling and optimization system for vertical transportation of building construction materials at a terminal based on the Internet of Things according to claim 9 is characterized in that: When implementing the mutual exclusion model, the instantaneous comprehensive attraction calculation unit is further configured to: perform interference evaluation on the estimated motion trajectory of the designated tower crane and the current operating area of ​​another tower crane, and impose an additional significant path conflict penalty value when it is determined that potential interference exists.

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