An automated management system for ground control of tower cranes

Abstract

The present invention provides an automated management system for ground control of tower cranes, which relates to the technical field of ground control management of tower cranes. By collecting spatial obstacle data, environmental risk indicators and time efficiency parameters, a dynamic multi-dimensional cost model is constructed, and an environmental benchmark value is generated and transmitted to a credit evaluation unit. The credit evaluation unit calculates the equipment health green credit score based on the environmental benchmark value and real-time operation data, and dynamically adjusts the green weighting factor of task allocation. The task allocation unit constructs an economic decision matrix based on cost parameters and credit evaluation results, generates an optimal task allocation plan through priority sorting, and triggers path optimization. The task constraint unit optimizes the operation path and feeds back the energy recovery benefit to the cost model in real time. The visual interaction unit controls and manages in real time through a three-dimensional thermal map, and supports manual intervention. The system realizes the optimal allocation of resources and dynamic risk control of tower crane operations, thereby improving construction efficiency and economy.

Description

Technical Field

[0001] The present invention relates to the technical field of tower crane ground control management, in particular to an automatic ground control management system for tower cranes. Background Art

[0002] Tower cranes, as critical equipment in construction, have traditionally relied on manual experience and fixed rules for ground control and management. Conventional management typically uses pre-set task allocation plans, taking into account the crane's load capacity and the difficulty of the operation. While this approach can meet construction needs to a certain extent, it relies heavily on the operator's skill level and subjective judgment of the site environment.

[0003] In the field of tower crane ground control and management, existing technologies generally lack adaptability to complex and dynamic scenarios. Traditional systems struggle to effectively integrate the multi-dimensional interference factors in the construction environment, resulting in a mismatch between resource allocation and actual needs. Furthermore, existing systems' inability to assess equipment health status causes maintenance strategies to lag behind actual equipment wear and tear, increasing the probability of unplanned downtime. Furthermore, they fail to incorporate energy recovery potential and environmental risk costs into dynamic decision-making frameworks, resulting in high energy consumption during operations and difficulties in ensuring environmental compliance, significantly increasing the cost of tower crane operation. Summary of the Invention

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: a tower crane ground control automation management system, comprising:

[0005] Dynamic cost calculation unit: collects spatial obstacle data, environmental risk indicators, and time efficiency parameters in real time, constructs a multidimensional cost model that includes spatial obstacle premium, environmental risk coefficient, and time cost weight; generates environmental benchmark values ​​and transmits them to the credit evaluation unit, while outputting dynamic cost parameters to the task allocation unit;

[0006] Credit evaluation unit: Calculates equipment health and green credit scores based on environmental benchmark values, integrates real-time collected data to continuously optimize equipment health and green credit scores, generates credit evaluation results, and feeds them back to the task allocation unit;

[0007] Task allocation unit: This unit uses the cost parameters and credit evaluation results of the dynamic cost calculation unit to construct an economic decision matrix. This matrix includes the equipment capacity factor determined by the tower crane's load capacity and operating efficiency, and the health weighting factor dynamically adjusted by the credit evaluation results. It generates a task allocation plan through priority sorting and triggers economic constraints.

[0008] Task constraint unit: This unit converts economic constraints into path optimization objectives and simultaneously feeds back the energy recovery benefits generated during the implementation process into the multidimensional cost model in real time;

[0009] Visual interaction unit: Integrates tower crane operation data and task allocation plans, displays dynamic cost heat maps in real time through ground control terminals, and supports operators to intervene and adjust work priorities; feeds back manual intervention instructions to the task allocation unit, so that its economic constraints are coordinated with the actual needs of ground control.

[0010] When the dynamic cost calculation unit constructs a multidimensional cost model, spatial obstacle data is collected by fusion of lidar and camera to accurately identify the three-dimensional coordinates, volume and minimum distance of the obstacle from the tower crane boom; when the obstacle distance is less than the boom safety threshold, the system triggers the calculation of the spatial obstacle premium, where the premium coefficient is nonlinearly inversely proportional to the obstacle distance, specifically 0.2×(safety threshold distance-actual distance). For example, for every 1 meter reduction in obstacle distance, the premium coefficient increases by 0.2, thereby quantifying the direct impact of spatial obstacles on operating costs; environmental risk indicators obtain wind speed, humidity and ground subsidence data in real time through meteorological sensors and vibration sensors, and dynamically compare them with industry safety standard values. For example, when the wind speed exceeds 12 meters per second, the environmental risk coefficient increases to 1.5, and when it is less than 8 meters per second, the coefficient is adjusted to 0.8. The quantitative indicators map the environmental risk level; the time efficiency parameter is generated based on the task deadline and historical operation efficiency numbers. For example, the time cost weight of an emergency task with a remaining time of less than 2 hours is set to 0.6, and the time cost weight of an ordinary task with a remaining time of less than 2 hours is set to 0.6 The weight of each task is 0.3, reflecting the differentiated impact of task urgency on the cost model. The weights of the multidimensional cost model are determined through a hybrid model, with initial weights based on statistical analysis of historical operation data. For example, the spatial barrier premium accounts for 40%, the environmental risk factor accounts for 35%, and the time cost weight accounts for 25%. When the environmental risk indicator exceeds the safety threshold for 10 consecutive minutes, the system automatically increases the environmental risk factor weight by 5% to 10% and correspondingly reduces the time cost weight. During the weighted summation process, the parameters of each dimension are integrated into a comprehensive cost parameter based on real-time weights. For example, if the weights at a certain moment are 0.45 for the spatial barrier premium, 0.4 for the environmental risk factor, and 0.15 for the time cost weight, the comprehensive cost calculation formula is the barrier premium multiplied by 0.45, the risk factor multiplied by 0.4, and the time cost multiplied by 0.15. The system uses the product of the environmental risk factor and the time cost weight as the core evaluation indicator. For example, when the product is less than 0.3, the environmental baseline value is determined to be excellent, triggering a positive correction of the equipment health green credit score by the credit assessment unit.

[0011] The generation process of environmental benchmark values ​​follows the dynamic calculation logic of the multi-dimensional cost model, and environmental risk indicators are collected in real time through sensor equipment deployed at the tower crane operation site. The system uses a laser dust monitor to collect PM2.5 concentration data and a sound level meter to collect noise decibel values ​​with a range of 30-130dB. These measured data are compared with the industry standard data built into the system, where the noise limit is set at 70dB during the day and 55dB at night, and the PM2.5 limit is 75μg / m³. When the measured value exceeds the standard, the system automatically calculates the individual risk factor using the following formula: risk factor = 1 + (measured value - standard value) / standard value × 0.5; the weight calculation adopts a dynamic adjustment mechanism, and the basic weight is set to the environmental risk index 6 0%, time efficiency parameter 40%. When the task urgency coefficient exceeds 0.8, the time efficiency weight is increased by 40% + urgency coefficient × 10%, and the environmental risk weight is reduced accordingly, and the adjustment range is controlled within the range of ±15%. The final generation of the environmental benchmark value adopts a composite calculation formula: ∑ (single risk coefficient × dynamic weight) × time efficiency correction coefficient, where the time efficiency correction coefficient is dynamically calculated according to the remaining time of the task, and the formula is 1 + (standard operating time - remaining time) / standard operating time × 0.2. The system sets a dual update trigger mechanism, including periodic updates every 5 minutes and event trigger updates when the change rate of any risk indicator exceeds 20% / min, and sends a signal to the visual interaction unit at the same time.

[0012] The credit evaluation unit calculates the equipment health green credit score based on the environmental protection benchmark value. First, the actual operating cost data of the tower crane is obtained through real-time data collection, including parameters such as energy consumption, maintenance costs and operating time, and compared with the environmental protection benchmark value to calculate the cost deviation rate; the cost deviation rate uses the formula: (actual cost-environmental protection benchmark value) / environmental protection benchmark value×100%; in the hierarchical analysis evaluation link, the system construction includes 2 first-level indicators, including cost deviation rate accounting for 60% and energy efficiency accounting for 40%; 3 second-level indicators, including short-term cost fluctuations accounting for 30%, long-term maintenance trends accounting for 40%, and real-time energy consumption changes accounting for 30%. The short-term is defined as hourly data, such as cost fluctuations within 1 hour, and the long-term is based on the daily average maintenance cost trend. The system normalizes each indicator and then performs a weighted summation based on its weights: green credit score = cost deviation rate × 0.6 × (short-term fluctuation × 0.3 + long-term trend × 0.4) + energy efficiency × 0.4 × real-time energy consumption × 0.3. For example, when a tower crane has a 5% hourly cost fluctuation rate, a -2% 7-day maintenance cost trend, and a real-time energy consumption 8% lower than the benchmark, its green credit score is calculated by adding up the weighted values ​​of each indicator. The system updates the green credit score every 15 minutes and monitors the rate of change through trend analysis. A credit rating reassessment is triggered when the rate of change exceeds 5% for three consecutive updates. The resulting credit rating is scored as A ≥ 85, 84 ≥ B ≥ 70, and C < 70, which is fed back to the task allocation unit in real time.

[0013] When the task allocation unit constructs the economic decision matrix, the equipment capability factor is determined through standardized performance tests. The test conditions include 100% of the rated load, 0% of the rated load, and extreme working conditions with a wind speed ≥15m / s. The actual operating efficiency of the past 30 days in the historical operating data, such as the average lifting speed and the rotation angle deviation rate, is comprehensively evaluated. For example, the lifting speed of a tower crane in the full-load test is 1.2m / s, the rotation angle deviation is ≤2°, and the average operating efficiency of the historical data is 92%. The equipment capability factor is calculated to be 0.92. The adjustment logic of the green weighting factor is defined by a piecewise function. When the green credit score is ≥85, the weighting factor is increased to 0.25; when the green credit score is 70-84, the benchmark value is maintained at 0.15; when it is below 70, it is reduced to 0.05. The threshold division is based on the industry credit rating. The economic decision matrix uses the equipment capability factor as the X-axis and the green plus Z weighted factor as the Y-axis to form a two-dimensional decision space. Each task allocation plan corresponds to a coordinate point (X, Y). The comprehensive score calculation formula is: comprehensive score = 0.7 multiplied by the X-axis coordinate point + 0.3 multiplied by the Y-axis coordinate point. The weight coefficient is determined by regression analysis of historical task success rates. For example, a tower crane with a capability factor of 0.9 and a green credit score of 88 corresponds to Y=0.25. The comprehensive score = 0.9×0.7+0.25×0.3=0.705. The system prioritizes the equipment with the highest score to perform the task. The decision space introduces task urgency as a hidden dimension. When the remaining time of the task is less than 1 hour, the X weight in the comprehensive score formula is automatically increased to 0.8, ensuring that urgent tasks are preferentially matched with high-capability equipment.

[0014] When the task allocation unit generates a task allocation plan, the state variables of the dynamic programming algorithm are defined as the task time window, the current position of the tower crane and the remaining load capacity. The objective function is to minimize the weighted sum of the total operation time and energy consumption. The constraints include the maximum time of a single task ≤ 2 hours and the obstacle avoidance distance of the hoisting path ≥ 3 meters. For example, a hoisting task needs to be completed within two hours. The system calculates the feasible paths of each tower crane through the state transfer equation and selects the solution with a total time of 85 minutes and an energy consumption of 120kW·h as the optimal solution; the reinforcement learning mechanism uses the historical task data of the past 90 days as the training set to design the reward function. The reward for successfully completing the task is 1, the reward is 0.1 for every 1 minute ahead of schedule, the reward is 0.5 for every 10kW·h reduction in energy consumption, and the reward is 0.5 for every timeout or failure. The penalty is reduced by 2. When energy recovery potential is used as an additional weight, its priority is achieved through a composite scoring formula: the final score = equipment capacity factor × 0.5 + green weighting factor × 0.3 + energy recovery coefficient × 0.2, where the energy recovery coefficient is dynamically calculated based on the predicted value of regenerative braking energy. For example, when a tower crane has a capacity factor of 0.9, a green weighting factor of 0.25, and an energy recovery coefficient of 0.8, its comprehensive score is 0.9 × 0.5 + 0.25 × 0.3 + 0.8 × 0.2 = 0.665, giving priority to matching with high-scoring equipment. When an emergency triggers replanning, the system completes constraint refresh and path recalculation within 5 seconds, and prioritizes assigning tasks with high recovery potential to high-efficiency equipment to ensure the real-time and security of the solution.

[0015] When the task constraint unit converts economic constraints into path optimization objectives, the core parameters of the ant colony optimization algorithm are set to 0.5 initial pheromone concentration, 0.3 volatility coefficient, and 2.5 heuristic factor. The pheromone update rule adopts the elite ant strategy, the pheromone increment on the optimal path is Δτ=1 / (1+path energy consumption), and the path weight is iterated every 10 milliseconds. For example, when a tower crane needs to transport goods from point A to point B, the system simulates the path exploration behavior of 200 ants and dynamically calculates the transfer probability of each node P=(τ^α)(η^β) / Σ(τ^α)(η^β), where τ is the pheromone concentration, η=1 / path length, α=1, β=2, Ultimately, the optimal path with the lowest energy consumption and an obstacle avoidance distance of ≥3 meters is generated; the energy recovery benefit value is collected in real time by the regenerative braking energy sensor and converted into a cost adjustment coefficient K=1-0.05×recovered energy (kW·h), which directly acts on the spatial obstacle premium parameter of the multidimensional cost model. For example, when 50kW·h of energy is recovered, the spatial obstacle premium is reduced by 2.5%. The system sets up a dynamic convergence detection mechanism, and immediately outputs the optimization result when the difference in path length is less than 1% for five consecutive iterations, ensuring that path replanning is completed within 100 milliseconds; when a sudden obstacle causes the original path to fail, the system achieves rapid convergence through incremental pheromone updates, and the measured response time is ≤150 milliseconds.

[0016] The visualization interaction unit integrates the real-time operation data and task allocation plan of the tower crane to build a dynamic three-dimensional visualization interface. The system uses the Unity engine to develop three-dimensional scenes, and renders the tower crane position, boom angle and load status in real time. It also uses OpenCV to map the spatial obstacle premium, environmental risk coefficient and other parameters output by the multi-dimensional cost model into a heat map layer. For example, when the spatial premium of a certain area rises to 0.9 due to sudden obstacles, the area immediately appears in a bright red flashing effect in the three-dimensional model; the multi-gesture control interface supports six industrial-grade gestures, two-finger horizontal swipe to adjust task priority, three-finger pinch to select a specific tower crane, and long press for 2 seconds to trigger path replanning; the re-planned task path is instantly projected onto the three-dimensional scene, and the cost changes are intuitively displayed through the migration of heat map color blocks. For example, after the new path avoids the high-risk area, the original red area gradually turns yellow.

[0017] The present invention provides a tower crane ground control automation management system, which has the following beneficial effects:

[0018] 1. The present invention significantly improves the resource utilization efficiency and economy of the tower crane ground control management system through dynamic multi-dimensional data fusion and intelligent decision-making mechanism.

[0019] 2. This invention achieves a precise balance between resource consumption and environmental benefits through a dynamic credit evaluation mechanism and energy recovery. Based on a dynamic comparison of real-time equipment health data and environmental benchmark values, it generates credit evaluation results, prioritizing the allocation of high-credit equipment to perform critical tasks, effectively reducing energy consumption redundancy and the probability of environmental risk exposure caused by equipment performance degradation.

[0020] 3. The present invention significantly enhances the real-time response capability under complex working conditions through the coordination of three-dimensional visualization interaction and adaptive decision-making. The system supports the connection between manual intervention instructions and automated decision-making, significantly improving construction safety and management flexibility, while reducing the risk of equipment loss due to human operation delays. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0023] When an automated management system for ground control of a tower crane is started, the dynamic cost calculation unit begins to collect working environment data through a high-precision sensor network; the laser radar constructs three-dimensional data at a scanning frequency of 20 times per second, and detects a steel component stacking area with a height of 2.8 meters in the northeast corner of the construction area. The minimum distance from the tower crane boom is measured to be 2.3 meters, triggering the calculation of a spatial obstacle premium; according to the preset rule of 0.2 premium coefficient per meter of distance, if the safety threshold distance is 3 meters, the premium coefficient of this area is determined to be 0.14; at the same time, the meteorological sensor monitors the instantaneous wind speed of 10.2 meters per second, exceeding the threshold of 8 meters per second, and the environmental risk coefficient is automatically adjusted to 1.28; the progress data returned by the construction management interface shows that the remaining time for the steel structure lifting task on the critical path is 87 minutes, and the system sets the time cost weight to 0.52 accordingly.

[0024] The multidimensional cost model processes these input data in real time. Since the environmental risk coefficient exceeded the threshold of 1.2 for 10 minutes, the system automatically increased the environmental risk weight from the basic value of 35% to 38%, and correspondingly reduced the time cost weight to 22%; after weighted calculation, the current comprehensive cost parameter is 0.54 multiplied by 0.4 plus 1.28 multiplied by 0.38 plus 0.52 multiplied by 0.22, and the final result is 0.732.

[0025] The generation of environmental benchmark values ​​is carried out simultaneously with cost calculation. The PM2.5 sensor detects the current concentration of 83 micrograms per cubic meter, and the noise sensor measures 67 decibels. The system compares the measured values ​​with the preset standard values, where PM2.5 exceeds the limit of 75 micrograms per cubic meter and the noise is lower than the daytime standard of 70 decibels. Through the risk coefficient calculation formula, individual assessment results of 1.053 and 0.957 are obtained respectively. Combined with the time efficiency correction coefficient of 1.12 corresponding to the current task urgency, the system generates an environmental benchmark value of 1.134 and transmits it to the credit evaluation unit in real time through the data bus.

[0026] After receiving the environmental benchmark value, the credit evaluation unit immediately started the equipment health assessment process; the system retrieved the operating data from the tower crane control system, including the energy consumption of 138 kWh in the last hour, the motor temperature of 68 degrees Celsius, and the gearbox vibration value of 2.8 mm per second; by comparing with the benchmark value of 125 kWh, a cost deviation rate of 10.4% was calculated; the evaluation process adopted the hierarchical analysis method, comprehensively considering the three dimensions of short-term fluctuations, long-term trends and real-time status, and integrating them according to a weight ratio of 3:4:3, and finally obtained a green credit score of 82 points, corresponding to a B rating. The system set a green weighting factor of 0.16 accordingly.

[0027] When handling the second batch of tower crane hoisting tasks, the task allocation unit conducted a task characteristic analysis. Among them, the hoisting of the T205 steel beam was marked as an urgent task because it involved high-altitude welding and had only 75 minutes remaining time. The system retrieved the performance data of each tower crane. The equipment capability factor of this tower crane in the recent test was 0.93. Combined with the green weighting factor of 0.25 corresponding to its current green credit score of 88 points, the initial comprehensive score was 0.93 multiplied by 0.7 plus 0.25 multiplied by 0.3, which equals 0.726. Due to the urgency of the task, the system automatically adjusted the scoring weight, increasing the proportion of the equipment capability factor to 0.8 and reducing the weight of the green weighting factor from 0.3 to 0.2, making it the optimal choice.

[0028] During the path planning phase, the task constraint unit uses an ant colony optimization algorithm to calculate the path. The algorithm initialization parameters are set to 300 ants, an initial pheromone concentration of 0.5, a volatility coefficient of 0.3, and heuristic factors of 1 and 2, respectively. After 85 milliseconds of iterative calculation, the system outputs an optimal path with a total length of 62 meters, an estimated time of 14 minutes, an energy consumption of 89 kWh, and a minimum obstacle avoidance distance of 3.2 meters throughout the entire process. The system identifies a descending segment that generates 22 kWh of regenerative energy and presets an energy recovery coefficient of 0.85.

[0029] The visual interaction unit presents the three-dimensional operation scene in real time on the large screen of the command center; the planned path of the tower crane is highlighted in blue, and different areas show heat map color changes according to cost parameters; when the operator calls up the path details through specific gestures, the system displays a complete cost analysis within 1.8 seconds, of which time cost accounts for 35%, energy consumption cost 41%, and risk cost 24%; when the equipment restricted area is temporarily added, the system completes the path re-planning within 3.2 seconds. The new plan increases the detour distance by 5 meters but completely avoids the risk area.

[0030] During the execution process, the system detected that the vibration value of the tower crane suddenly increased to 4.1 mm per second, exceeding the threshold of 3.5; the credit evaluation unit immediately reduced the equipment's green credit score from 88 to 72, and the green weighting factor was adjusted to 0.08; the task allocation unit automatically transferred two precision lifting tasks, and a red alarm popped up on the visual interface; the entire response process took only 5.3 seconds; on that day, the system recovered a total of 153 kWh of renewable energy, and through the mechanism of reducing the spatial barrier premium by 1% for every 10 kWh, the total operating cost was reduced by 4.7%.

[0031] The system significantly improves tower crane utilization, reduces energy consumption, and greatly speeds up emergency response. From data collection to intelligent decision-making, and then to path optimization and human-computer interaction, the collaborative work of various units has achieved a significant improvement in resource utilization efficiency and economy.

[0032] 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. A tower crane ground control automation management system, It is characterized by including: Dynamic cost calculation unit: collects spatial obstacle data, environmental risk indicators, and time efficiency parameters in real time, constructs a multidimensional cost model that includes spatial obstacle premium, environmental risk coefficient, and time cost weight; generates environmental benchmark values ​​and transmits them to the credit evaluation unit, while outputting dynamic cost parameters to the task allocation unit; Credit evaluation unit: Calculates the equipment health green credit score based on the environmental benchmark value, and integrates the real-time collected data to continuously optimize the equipment health green credit score, generates a credit evaluation result and feeds it back to the task allocation unit; the credit evaluation unit compares the actual operating cost of the tower crane with the environmental benchmark value to calculate the cost deviation rate; the cost deviation rate reflects the cost control level of the tower crane in the current operating environment and its compliance with environmental protection requirements; the credit evaluation unit integrates the real-time collected equipment operation data, and comprehensively evaluates the green credit score of the equipment through hierarchical analysis, taking the cost deviation rate and energy efficiency as the first-level indicators, and further subdividing them into second-level indicators, including short-term cost fluctuations, long-term maintenance trends and real-time energy consumption changes; each indicator is assigned a different weight, and the initial green credit score of the equipment is calculated through weighted average; at the same time, the credit evaluation unit generates a credit evaluation result based on the changing trend of the green credit score; The task allocation unit constructs an economic decision matrix based on the cost parameters and credit evaluation results of the dynamic cost calculation unit. This matrix includes the equipment capacity factor determined by the tower crane's load capacity and operating efficiency, and the health weighting factor dynamically adjusted by the credit evaluation results. Task allocation plans are generated through priority sorting and economic constraints are triggered. The economic decision matrix integrates the equipment capacity factor and the green weighting factor through matrix operations to form a multi-dimensional decision space. In the decision space, each task allocation plan corresponds to a decision point, whose coordinates are determined by the equipment capacity factor and the green weighting factor. The task allocation unit searches for the optimal decision point in the decision space through priority sorting. Task constraint unit: This unit converts economic constraints into path optimization objectives and simultaneously feeds back the energy recovery benefits generated during the implementation process into the multidimensional cost model in real time; Visual interaction unit: Integrates tower crane operation data and task allocation plans, displays dynamic cost heat maps in real time through ground control terminals, and supports operators to intervene and adjust work priorities; feeds back manual intervention instructions to the task allocation unit, so that its economic constraints are coordinated with the actual needs of ground control.

2. The tower crane ground control automation management system according to claim 1, characterized in that: The dynamic cost calculation unit constructs a multi-dimensional cost model, collects spatial obstacle data in the tower crane operating environment in real time through sensors, and simultaneously collects environmental risk indicators and time efficiency parameters; Based on the collected data, the spatial barrier premium, environmental risk coefficient and time cost weight are constructed; The multidimensional cost model integrates the spatial barrier premium, environmental risk coefficient and time cost weight by weighted summation to generate comprehensive cost parameters, and generates environmental benchmark values ​​through environmental performance evaluation.

3. The tower crane ground control automation management system according to claim 2, characterized in that: The process of generating environmental benchmark values ​​strictly follows the dynamic calculation logic of the multidimensional cost model: the dynamic cost calculation unit collects environmental risk indicators in real time, compares the actual measured values ​​of each indicator with the industry safety standard threshold, and calculates the environmental risk coefficient of each indicator; at the same time, the system combines the time efficiency parameters of the current task and performs weighted calculation according to the preset weight ratio to generate the initial environmental benchmark value.

4. The tower crane ground control automation management system according to claim 3, characterized in that: The task allocation unit constructs an economic decision matrix and calculates the equipment capacity factor based on the load capacity and operating efficiency of the tower crane. The equipment capacity factor is determined through performance testing. The performance test is based on the rated load, lifting speed and slewing angle of the tower crane, combined with the actual performance in historical operation data, to evaluate the operating capacity of the tower crane under different working conditions; at the same time, the task allocation unit dynamically adjusts the green weighting factor based on the credit evaluation results generated by the credit evaluation unit; the green weighting factor reflects the importance of the green credit score of the equipment in the task allocation decision, and its adjustment logic is based on the piecewise function of the green credit score. When the green credit score is higher than the preset threshold, the green weighting factor increases; when the green credit score is lower than the threshold, the green weighting factor decreases accordingly.

5. The tower crane ground control automation management system according to claim 4, characterized in that: When generating a task allocation plan, the task allocation unit classifies the tasks to be assigned according to urgency and job type, and establishes a task priority queue. Based on the distribution of decision points in the economic decision matrix, the most suitable tower crane equipment is matched to each task. The matching process calculates the optimal operation path and time schedule through a dynamic programming algorithm. At the same time, the task allocation unit introduces a reinforcement learning mechanism to continuously optimize the parameters of the matching algorithm based on the success rate and operation efficiency of historical task allocations. When encountering emergencies or changes in the operating environment, the task allocation unit will replan the task allocation plan. The task allocation unit considers the expected value of energy recovery benefits and uses energy recovery potential as an additional weight for task allocation, so that tasks with high recovery potential are preferentially allocated to high-efficiency equipment.

6. The tower crane ground control automation management system according to claim 1, characterized in that: When converting the economic constraints into path optimization targets, the task constraint unit analyzes the economic constraints triggered by the task allocation unit and maps the economic constraints into path planning parameters. During the path optimization process, the task constraint unit uses an ant colony optimization algorithm to simulate the behavior of ants in finding the shortest path and dynamically adjust the path planning parameters so that the tower crane can complete the task while satisfying the economic constraints. The task constraint unit monitors the energy recovery generated during the operation in real time, collects the regenerated energy data of the tower crane during the operation, converts it into energy recovery benefit value, and feeds it back to the multidimensional cost model; The multidimensional cost model dynamically adjusts cost parameters according to the feedback of energy recovery benefit value.

7. The tower crane ground control automation management system according to claim 1, characterized in that: The visualization interaction unit integrates the real-time operating data of the tower crane, including location information, load status and operation progress, as well as the task allocation plan generated by the task allocation unit; the visualization interaction unit uses a three-dimensional visualization engine to build a dynamic cost heat map, displaying the cost parameters in the form of changes in color and intensity on the three-dimensional model of the tower crane operation scene; the visualization interaction unit provides a multi-gesture control interface, which establishes two-way communication with the task allocation unit. When the operator issues a manual intervention command, the visualization interaction unit immediately converts the command into an adjustment signal that can be recognized by the task allocation unit, triggering the replanning of the task allocation plan.

Images