Automatic management system for ground control of tower crane

Abstract

The invention provides an automatic management system for tower crane ground control, and relates to the technical field of tower crane ground control management. A dynamic multi-dimensional cost model is constructed by collecting spatial obstacle data, environmental risk indexes and time efficiency parameters, and an environmental protection reference value is generated and transmitted to a credit evaluation unit. The credit evaluation unit calculates an equipment health green credit score based on an environmental protection reference value and real-time operation data, and dynamically adjusts a green weighting factor of task allocation. And the task allocation unit constructs an economic decision matrix in combination with the cost parameter and the credit evaluation result, generates an optimal task allocation scheme through priority ranking, and triggers path optimization. And 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 thermodynamic diagram and supports manual intervention. According to the system, resource optimization configuration and dynamic risk management and control of tower crane operation are achieved, and the construction efficiency and economical efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of tower crane ground control management, and specifically to an automated management system for tower crane ground control. Background Art

[0002] As a key piece of equipment in construction, the ground control management of tower cranes mainly relies on manual experience and fixed rules in traditional operations. Conventional management methods usually perform task scheduling by presetting task allocation schemes and combining the load capacity of the tower crane and the difficulty of the operation. To a certain extent, this method can meet the construction requirements, but it mainly depends on the skill level of the operator and the subjective judgment of the on-site environment.

[0003] In the field of tower crane ground control management, the existing technologies generally have insufficient adaptability to complex dynamic scenarios; traditional systems are difficult to effectively integrate multi-dimensional interference factors in the construction environment, resulting in a mismatch between resource allocation and actual needs. In addition, the existing systems' ability to evaluate the health status of equipment makes the maintenance strategy lag behind the actual wear and tear of the equipment, increasing the probability of unplanned downtime. Moreover, the potential for energy recovery and the cost of environmental risks are not incorporated into the dynamic decision-making framework, resulting in high energy consumption during operation and difficulty in ensuring environmental compliance, greatly increasing the usage cost of tower cranes. Summary of the Invention

[0004] To achieve the above objectives, the present invention is realized through the following technical solutions: An automated management system for tower crane ground control, comprising: Dynamic cost calculation unit: Real-time collects spatial obstacle data, environmental risk indicators, and time efficiency parameters, constructs a multi-dimensional cost model including spatial obstacle premium, environmental risk coefficient, and time cost weight; generates an environmental protection benchmark value and transmits it to the credit evaluation unit, and at the same time outputs dynamic cost parameters to the task allocation unit; Credit evaluation unit: Calculates the equipment health green credit score based on the environmental protection benchmark value, and at the same time continuously optimizes the equipment health green credit score by integrating real-time collected data, generates a credit evaluation result, and feeds it back to the task allocation unit; Task allocation unit: Constructs an economic decision matrix through the cost parameters of the dynamic cost calculation unit and the credit evaluation result. This matrix includes an equipment capacity factor determined by the load capacity and operation efficiency of the tower crane, and a health weighting factor dynamically adjusted by the credit evaluation result; generates a task allocation scheme through priority ranking and triggers economic constraint conditions; Task constraint unit: Converts the economic constraint conditions into path optimization objectives, and at the same time feeds back the energy recovery benefits generated during the implementation process to the multi-dimensional cost model; Visual interaction unit: Integrate the tower crane operation data and the task assignment plan, display the dynamic cost heat map in real time through the ground control terminal, and support the operators to intervene and adjust the operation priority; Feed back the manual intervention instructions to the task assignment unit to coordinate its economic constraint conditions with the actual needs of ground control.

[0005] When the dynamic cost calculation unit constructs a multi-dimensional cost model, the spatial obstacle data is collected by fusing lidar and cameras, accurately identifying the three-dimensional coordinates, volume of the obstacles and their minimum distance from the tower crane boom; When the distance of the obstacle is less than the boom safety threshold, the system triggers the calculation of the spatial obstacle premium. Among them, the premium coefficient has a non-linear inverse relationship with the obstacle distance, specifically 0.2×(safety threshold distance - actual distance). For example, for every 1-meter reduction in the obstacle distance, the premium coefficient increases by 0.2, so as to quantify the direct impact of spatial obstacles on the operation cost; The environmental risk indicators obtain the wind speed, humidity and ground settlement data in real time through meteorological sensors and vibration sensors, and dynamically compare them with the industry safety standard values. For example, when the wind speed exceeds 12 meters per second, the environmental risk coefficient is increased to 1.5, and when it is lower than 8 meters per second, the coefficient is adjusted to 0.8, and the quantified indicators map the environmental risk level; The time efficiency parameter is generated based on the task deadline and historical operation efficiency data. For example, the time cost weight of an urgent task with a remaining time of less than 2 hours is set to 0.6, and the weight of an ordinary task is 0.3, reflecting the differential impact of task urgency on the cost model; The weights of the multi-dimensional cost model are determined by a hybrid mode. The initial weights are based on the statistical analysis of historical operation data. For example, the spatial obstacle premium accounts for 40%, the environmental risk coefficient 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 weight of the environmental risk coefficient by 5% to 10%, and correspondingly reduces the time cost weight; During the weighted summation process, each dimension parameter is integrated into a comprehensive cost parameter according to the real-time weight. For example, at a certain moment, the weights are 0.45 for the spatial obstacle premium, 0.4 for the environmental risk coefficient, and 0.15 for the time cost weight. Then the comprehensive cost calculation formula is the obstacle premium multiplied by 0.45, plus the risk coefficient multiplied by 0.4, plus the time cost multiplied by 0.15; The system uses the product of the environmental risk coefficient and the time cost weight as the core evaluation indicator. For example, when the product is less than 0.3, it is determined that the environmental protection benchmark value is excellent, and the credit evaluation unit is triggered to make a positive correction to the healthy green credit score of the equipment.

[0006] The generation process of the environmental protection benchmark value follows the dynamic calculation logic of the multi-dimensional cost model, and real-time collects environmental risk indicators through the sensing devices deployed at the tower crane operation site; the system uses a laser dust monitor to collect PM2.5 concentration data, and collects noise decibel values through a sound level meter, with a range of 30-130 dB; these measured data are compared with the industry standard data built into the system, where the noise limit is set at 70 dB during the day and 55 dB at night, and the PM2.5 limit is 75 μg / m³. When the measured value exceeds the standard, the system automatically calculates the single-item risk coefficient, and the calculation formula is: risk coefficient = 1 + (measured value - standard value) / standard value × 0.5; the weight calculation adopts a dynamic adjustment mechanism, with the basic weight set at 60% for environmental risk indicators and 40% for time efficiency parameters. When the task urgency coefficient exceeds 0.8, the time efficiency weight is increased by 40% + urgency coefficient × 10%, and at the same time the environmental risk weight is correspondingly reduced, and the adjustment range is controlled within ±15%; the final generation of the environmental protection benchmark value adopts a composite calculation formula: ∑(single-item 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 operation time - remaining time) / standard operation time × 0.2; the system sets a dual update trigger mechanism, including periodic updates every 5 minutes and event-triggered updates when the change rate of any risk indicator exceeds 20% / min, and at the same time sends a signal to the visual interaction unit.

[0007] The credit evaluation unit calculates the equipment health green credit score based on the environmental protection benchmark value. First, it obtains the actual operation cost data of the tower crane through real-time data collection, including parameters such as energy consumption value, maintenance cost, and operation time, and compares it 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 analytic hierarchy process evaluation link, the system constructs an evaluation system including 2 first-level indicators, including the cost deviation rate accounting for 60% and the energy efficiency accounting for 40%; 3 second-level indicators, including short-term cost fluctuation accounting for 30%, long-term maintenance trend accounting for 40%, and real-time energy consumption change accounting for 30%. Among them, the short term is defined as hourly data, such as cost fluctuation within 1 hour, and the long term is based on the daily average maintenance cost trend. The system normalizes each indicator and then performs weighted summation according to the 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 the hourly cost volatility of a certain tower crane is 5%, the 7-day maintenance cost trend is -2%, and the real-time energy consumption is 8% lower than the benchmark, the calculation process of its green credit score is the accumulation of the weighted values of each indicator. The system updates the green credit score every 15 minutes and monitors the change rate of the green credit score through trend analysis. When the change rate of three consecutive updates exceeds 5%, it triggers a re-evaluation of the credit rating. The finally generated credit evaluation results are divided into A ≥ 85 points, 84 ≥ B ≥ 70, and C < 70 points, and are fed back to the task allocation unit in real time.

[0008] When the task allocation unit constructs the economic decision matrix, the equipment capacity factor is determined through standardized performance tests. The test conditions include 100% of the rated load, 0% of the rated load, and extreme working condition wind speed ≥ 15 m / s. Combining with the actual operation efficiency in the recent 30 days in the historical operation data, such as the average lifting speed and the deviation rate of the slewing angle, a comprehensive evaluation is carried out. For example, if a tower crane has a lifting speed of 1.2 m / s and a slewing angle deviation ≤ 2° in the full-load test, and the average operation efficiency of the historical data reaches 92%, then its equipment capacity factor is calculated as 0.92. The adjustment logic of the green weighting factor is defined by a piecewise function. When the green credit score ≥ 85, the weighting factor is increased to 0.25; when it is 70 - 84, the reference value of 0.15 is maintained; when it is lower than 70, it is reduced to 0.05, and the threshold division is based on the industry credit rating specification. The economic decision matrix uses the equipment capacity factor as the X-axis and the green weighting 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 times the X-axis coordinate point + 0.3 times the Y-axis coordinate point, where the weight coefficient is determined through regression analysis of the historical task success rate. For example, if a tower crane has an ability factor of 0.9 and a green credit score of 88 corresponding to Y = 0.25, then the comprehensive score = 0.9×0.7 + 0.25×0.3 = 0.705. The system preferentially selects the equipment with the highest score to execute the task. The task urgency is introduced as a hidden dimension in the decision space. When the remaining task time < 1 hour, the X weight in the comprehensive score formula is automatically increased to 0.8 to ensure that urgent tasks are preferentially matched with high-capability equipment.

[0009] When the task assignment unit generates a task assignment 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 that the longest time for a single task ≤ 2 hours and the obstacle avoidance distance of the hoisting path ≥ 3 meters. For example, for a hoisting task that needs to be completed within two hours, the system calculates the feasible paths of each tower crane through the state transition equation and selects the plan with a total time of 85 minutes and an energy consumption of 120 kW·h as the optimal solution. The reinforcement learning mechanism designs a reward function with the historical task data of the past 90 days as the training set. The reward for successfully completing a task is increased by 1, the reward is increased by 0.1 for each minute ahead of schedule, and the reward is increased by 0.5 for each 10 kW·h reduction in energy consumption. The penalty for overtime or failure is reduced by 2. When the energy recovery potential is used as an additional weight, its priority is realized 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 from the predicted value of the regenerative braking energy. For example, when the capacity factor of a tower crane is 0.9, the green weighting factor is 0.25, and the energy recovery coefficient is 0.8, its comprehensive score is 0.9 × 0.5 + 0.25 × 0.3 + 0.8 × 0.2 = 0.665, and high-scoring equipment is preferentially matched. When a sudden situation triggers re-planning, the system completes the constraint refresh and path re-calculation within 5 seconds and preferentially assigns tasks with high recovery potential to high-performance equipment to ensure the real-time performance and safety of the plan.

[0010] When the task constraint unit transforms the economic constraint conditions into path optimization objectives, the core parameters of the ant colony optimization algorithm are set as the initial pheromone concentration of 0.5, the evaporation coefficient of 0.3, and the heuristic factor of 2.5. The pheromone update rule adopts the elite ant strategy. The pheromone increment Δτ on the optimal path = 1 / (1 + path energy consumption), and the path weight is iterated every 10 milliseconds. For example, when the tower crane needs to transport goods from point A to point B, the system dynamically calculates the transfer probability P of each node through simulating the path exploration behavior of 200 ants, P = (τ^α)(η^β) / Σ(τ^α)(η^β), where τ is the pheromone concentration, η = 1 / path length, α = 1, β = 2, and finally generates the optimal path with the lowest energy consumption and an obstacle avoidance distance ≥ 3 meters. 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 multi-dimensional cost model. For example, when 50 kW·h of energy is recovered, the spatial obstacle premium is reduced by 2.5%. The system sets a dynamic convergence detection mechanism. When the difference in path length for 5 consecutive iterations < 1%, the optimization result is immediately output to ensure that the path re-planning is completed within 100 milliseconds. When a sudden obstacle causes the original path to fail, the system achieves fast convergence through incremental pheromone update, and the measured response time ≤ 150 milliseconds.

[0011] The visual interaction unit integrates the real-time operation data of the tower crane and the task assignment scheme to construct a dynamic 3D visual interface. The system uses the Unity engine to develop a 3D scene, rendering the position of the tower crane, the boom angle, and the load status in real time. Through OpenCV, parameters such as the spatial obstacle premium and environmental risk factor output by the multi-dimensional cost model are mapped into a heat map layer. For example, when the spatial premium in a certain area rises to 0.9 due to a sudden obstacle, a high-brightness red flashing effect immediately appears in the 3D model in this area; the multi-gesture control interface supports six industrial-level gestures. Swiping horizontally with two fingers adjusts the task priority, pinching with three fingers selects a specific tower crane, and long-pressing for 2 seconds triggers path replanning; the replanned task path is immediately projected onto the 3D scene, and the cost change is intuitively shown 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.

[0012] The present invention provides a ground control automation management system for tower cranes, which has the following beneficial effects: 1. Through the dynamic multi-dimensional data fusion and intelligent decision-making mechanism, the present invention significantly improves the resource utilization efficiency and economy of the ground control management system for tower cranes.

[0013] 2. Through the dynamic credit evaluation mechanism and energy recovery, the present invention realizes the precise balance between resource consumption and environmental protection benefits; based on the dynamic comparison between the real-time health data of the equipment and the environmental benchmark value, a credit evaluation result is generated, and high-credit equipment is preferentially allocated to execute key tasks, effectively reducing the probability of energy consumption redundancy and environmental risk exposure caused by equipment performance decline.

[0014] 3. Through the coordination of 3D visual interaction and adaptive decision-making, the present invention greatly enhances the real-time response ability under complex working conditions. The system supports the connection between manual intervention instructions and automated decisions, significantly improving construction safety and management flexibility, while reducing the risk of equipment loss caused by human operation delays. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic flow chart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments; based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0017] When a tower crane ground control automation management system starts, the dynamic cost calculation unit begins to collect operation environment data through a high-precision sensor network; the lidar constructs three-dimensional data at a scanning frequency of 20 times per second, detects a steel member stacking area with a height of 2.8 meters in the northeast corner of the construction area, and after measurement, the minimum distance from the tower crane jib is 2.3 meters, triggering the calculation of spatial obstacle premium; according to the rule that the premium coefficient corresponding to each meter of distance is 0.2, 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 an 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 transmitted back by the construction management interface shows that the remaining time for the steel structure hoisting task on the critical path is 87 minutes, and the system sets the time cost weight to 0.52 accordingly.

[0018] The multi-dimensional cost model processes these input data in real time. Since the environmental risk coefficient has exceeded the threshold of 1.2 for 10 minutes, the system automatically increases the environmental risk weight from the base value of 35% to 38%, and correspondingly reduces the time cost weight to 22%; through 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.

[0019] The generation of the environmental protection baseline value is carried out synchronously with the 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, among which PM2.5 exceeds the limit of 75 micrograms per cubic meter, and the noise is lower than the standard of 70 decibels during the day; through the risk coefficient calculation formula, the single-item evaluation 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 protection baseline value of 1.134 and transmits it to the credit evaluation unit in real time through the data bus.

[0020] After receiving the environmental protection baseline value, the credit evaluation unit immediately starts the equipment health assessment process; the system retrieves the operation data from the tower crane control system, including the energy consumption of 138 kWh in the recent 1 hour, the motor temperature of 68 degrees Celsius, and the gearbox vibration value of 2.8 millimeters per second; by comparing with the baseline value of 125 kWh, the cost deviation rate of 10.4% is calculated; the analytic hierarchy process is adopted in the evaluation process, comprehensively considering three dimensions of short-term fluctuations, long-term trends and real-time states, and integrating them according to the weight ratio of 3:4:3, and finally a green credit score of 82 points is obtained, corresponding to a B-level rating, and the system sets a green weighting factor of 0.16 accordingly.

[0021] When the task assignment unit processes the second batch of tower crane hoisting tasks, it conducts task feature analysis. Among them, the hoisting of the T205 steel beam is marked as an urgent task because it involves high-altitude welding and there is only 75 minutes left. The system retrieves the performance data of each tower crane. The equipment capacity factor shown by this tower crane in the recent test is 0.93. Combining the 0.25 green weighting factor corresponding to its current green credit score of 88 points, the initial comprehensive score is 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 adjusts the scoring weights, increasing the proportion of the equipment capacity factor to 0.8 and reducing the weight of the green weighting factor from 0.3 to 0.2, making it the optimal choice.

[0022] In the path planning stage, the task constraint unit uses the ant colony optimization algorithm for path calculation. The initial parameters of the algorithm are set as 300 ants, the initial pheromone concentration is 0.5, the evaporation coefficient is 0.3, and the heuristic factors are taken as 1 and 2 respectively. After 85 milliseconds of iterative calculation, the system outputs the optimal path with a total length of 62 meters, an estimated time consumption of 14 minutes, an energy consumption of 89 kWh, and a minimum obstacle avoidance distance of 3.2 meters throughout the process. The system identifies the descending section that generates 22 kWh of regenerative energy and presets an energy recovery coefficient of 0.85.

[0023] The visual interaction unit presents the three-dimensional operation scenario on the large screen of the command center in real time. The planned path of the tower crane is highlighted in blue, and the heat map color changes according to the cost parameters in different areas. When the operator retrieves the path details through specific gesture operations, the system displays the complete cost analysis within 1.8 seconds, where the time cost accounts for 35%, the energy consumption cost is 41%, and the risk cost is 24%. When a temporary equipment restricted area is added, the system completes the path replanning within 3.2 seconds. The new plan increases the detour distance by 5 meters but completely avoids the risk area.

[0024] During the execution process, the system monitors that the vibration value of the tower crane suddenly increases to 4.1 mm per second, exceeding the threshold of 3.5. The credit evaluation unit immediately reduces the green credit score of the equipment from 88 points to 72 points, and adjusts the green weighting factor to 0.08. The task assignment unit automatically transfers two precision hoisting tasks, and at the same time, a red alarm pops up on the visual interface. The entire response process only takes 5.3 seconds. The system accumulatively recovers 153 kWh of regenerative energy on that day. Through the mechanism of reducing the space obstacle premium by 1% for every 10 kWh, the total operating cost is reduced by 4.7%.

[0025] This system has significantly improved the utilization rate of tower cranes, reduced energy consumption, and greatly improved the emergency response speed. From data collection to intelligent decision-making, and then to path optimization and human-machine interaction, each unit works together to achieve a significant improvement in resource utilization efficiency and economy.

[0026] Although embodiments of the present invention have been shown and described, those of ordinary skill in the art will appreciate that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present 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, builds a multidimensional cost model including spatial obstacle premium, environmental risk coefficient and time cost weight; generates environmental benchmark value and transmits it to the credit evaluation unit, and outputs dynamic cost parameters to the task allocation unit; Credit evaluation unit: Calculates the equipment health green credit score based on the environmental benchmark value, integrates the real-time collected data to continuously optimize the equipment health green credit score, generates credit evaluation results and feeds them back to the task allocation unit; Task allocation unit: constructs an economic decision matrix through the cost parameters and credit evaluation results of the dynamic cost calculation unit. The matrix includes the equipment capacity factor determined by the load capacity and operating efficiency of the tower crane, and the health weighting factor dynamically adjusted by the credit evaluation results; generates a task allocation plan through priority sorting and triggers economic constraints; Task constraint unit: converts economic constraints into path optimization goals, and feeds back the energy recovery benefits generated during the implementation process to the multidimensional cost model in real time; Visualization 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 to adjust job priorities; feeds back manual intervention instructions to the task allocation unit to coordinate its economic constraints 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 collects environmental risk indicators and time efficiency parameters at the same time; Based on the collected data, the spatial barrier premium, environmental risk factor 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 is characterized by: 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 is characterized by: When the credit evaluation unit calculates the equipment health green credit score based on the environmental protection benchmark value, the actual operating cost of the tower crane is compared with the environmental protection 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 the degree of compliance with environmental protection requirements; the credit evaluation unit integrates the equipment operation data collected in real time, 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 by 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.

5. The tower crane ground control automation management system according to claim 4 is characterized by: The task allocation unit constructs an economic decision matrix, calculates the equipment capacity factor according to the load capacity and operating efficiency of the tower crane, and the equipment capacity factor is determined by performance testing. The performance test is based on the rated load, lifting speed and rotation angle of the tower crane, combined with the actual performance in the 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 according to the credit evaluation result 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 below the threshold, the green weighting factor decreases accordingly; The economic decision matrix integrates the equipment capability factor and the green weighting factor through matrix operation to form a multi-dimensional decision space; In the decision space, each task allocation scheme corresponds to a decision point, whose coordinates are determined by the equipment capability factor and the green weighting factor; the task allocation unit searches for the optimal decision point in the decision space by priority sorting.

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

7. The tower crane ground control automation management system according to claim 1 is characterized by: When converting the economic constraint conditions into path optimization targets, the task constraint unit analyzes the economic constraint conditions triggered by the task allocation unit, and maps the economic constraint conditions 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 adjusts the path planning parameters so that the tower crane can complete the operation task under the premise of satisfying the economic constraint conditions; 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.

8. The tower crane ground control automation management system according to claim 1, characterized in that: The visualization interaction unit integrates the real-time operation data of the tower crane, including position information, load status and operation progress, and 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, and displays the cost parameters on the three-dimensional model of the tower crane operation scene with changes in color and intensity; the visualization interaction unit provides a multi-gesture control interface, and the multi-gesture control interface 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 recognizable by the task allocation unit, triggering the re-planning of the task allocation plan.

Images