Intelligent flow machine dynamic collaborative operation management platform based on digital storage yard

Abstract

The invention relates to the technical field of information management, in particular to an intelligent flow machine dynamic collaborative operation management platform based on a digital storage yard. Comprising a digital storage yard information acquisition module, an intelligent scheduling and collaborative decision-making module, an operation progress monitoring and feedback module, a data visualization and human-computer interaction interface, a dynamic path optimization and prediction module, a streaming machine collaborative operation scheduling module and a data management and analysis module. According to the invention, through intelligent task allocation and flow machine cooperative scheduling, the empty running time and operation conflicts of the flow machines are reduced, and the overall operation efficiency of the logistics storage yard is significantly improved; through cooperative operation of multiple devices and reasonable allocation of resources in the storage yard, resource waste is avoided, the utilization rate of the storage yard space and the devices is improved, the system can adapt to different storage yard environments and operation requirements, a scheduling strategy is optimized according to historical data and real-time data, and the intelligent level of the system is improved.

Description

Technical Field

[0001] The present invention relates to the field of information management technology, and in particular to a digital yard-based intelligent flow machine dynamic collaborative operation management platform. Background Art

[0002] With the rapid development of the modern logistics industry, yard management plays an important role in container stacking, flow machine scheduling, cargo handling and other links. The traditional yard management method relies on manual scheduling and experience judgment, and there are problems such as uneven resource allocation, low operation efficiency, and untimely scheduling response. With the continuous development of technologies such as the Internet of Things, big data, and artificial intelligence, traditional operation management methods can no longer meet the needs of modernization and efficiency. Existing operation management systems often have problems such as information islands, uneven resource allocation, and low operation efficiency, which leads to longer cargo turnover time and increased costs. How to use these emerging technologies to improve the automation level, scheduling accuracy and operation efficiency of the yard and build an efficient, intelligent and highly collaborative operation management platform has become an urgent problem to be solved. Summary of the invention

[0003] In view of the above-mentioned shortcomings of the prior art, the present invention provides a dynamic collaborative operation management platform for intelligent flow machines based on a digital yard, which can effectively solve the problems of information islands, uneven resource allocation and low operation efficiency that often exist in the prior art operation management system.

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: The present invention provides a digital yard-based intelligent flow machine dynamic collaborative operation management platform, including a digital yard information collection module, an intelligent scheduling and collaborative decision-making module, an operation progress monitoring and feedback module, a data visualization and human-computer interaction interface, a dynamic path optimization and prediction module, a flow machine collaborative operation scheduling module, and a data management and analysis module; The digital yard information acquisition module includes a variety of sensor integration; The intelligent scheduling and collaborative decision-making module includes real-time data analysis, intelligent scheduling algorithm, and efficient collaboration; The operation progress monitoring and feedback module includes real-time progress monitoring, key indicator analysis, feedback and optimization; The data visualization and human-computer interaction interface includes real-time visualization interface, human-computer interaction design, custom dashboards, reports and decision support tools.

[0005] According to the above-mentioned intelligent flow machine dynamic collaborative operation management platform based on digital yard, the multiple sensors integrated include RFID (radio frequency identification), GPS (global positioning system), and camera monitoring.

[0006] According to the above-mentioned intelligent flow machine dynamic collaborative operation management platform based on digital yard, the RFID (radio frequency identification) is used to install RFID tags on goods and equipment to realize automatic identification and tracking of their location information and status. The GPS (global positioning system) is used to track the real-time location information of transport vehicles, provide basic data support for dynamic scheduling, and ensure the best driving path of vehicles. Camera monitoring uses high-resolution cameras to monitor the working status in the yard in real time and identify different types of equipment and goods.

[0007] According to the above-mentioned digital yard-based intelligent flow machine dynamic collaborative operation management platform, the intelligent scheduling algorithm is based on real-time data and uses machine learning and optimization algorithms to dynamically schedule equipment. The efficient collaboration rules for collaborative operations between equipment can be dynamically adjusted according to actual conditions.

[0008] According to the above-mentioned intelligent flow machine dynamic collaborative operation management platform based on digital yard, the key indicator analysis is used to collect key indicators in the operation process (such as no-load rate, equipment failure rate, etc.), and the feedback and optimization will promptly feed back the analysis results to the operator to help him understand the current operation status and adjust the operation plan according to the feedback information.

[0009] According to the above-mentioned intelligent flow machine dynamic collaborative operation management platform based on digital yard, the real-time visual interface adopts dynamic update technology to ensure the real-time nature of interface information, so that users can grasp the operation status of the yard at any time. The human-computer interaction design provides a variety of interaction methods, including touch screen, mouse operation and voice command, to enhance the user's operating experience.

[0010] According to the above-mentioned intelligent mobile machine dynamic collaborative operation management platform based on digital yard, the dynamic path optimization and prediction module uses an algorithm to dynamically optimize the mobile machine operation path.

[0011] According to the above-mentioned intelligent flow machine dynamic collaborative operation management platform based on digital yard, the flow machine collaborative operation scheduling module is responsible for coordinating the operation sequence and path planning between the flow machines when multiple flow machines are operating at the same time, and avoiding operation conflicts between the flow machines, such as collisions, competition for cargo spaces, etc., by real-time monitoring of the operating status and operation progress of each flow machine.

[0012] According to the above-mentioned intelligent mobile machinery dynamic collaborative operation management platform based on digital yard, the data management and analysis module is responsible for the storage, management and analysis of all data on the platform, mining and analyzing historical operation data, and extracting valuable information.

[0013] Compared with the known prior art, the technical solution provided by the present invention has the following beneficial effects: 1. The present invention reduces the idle time and operation conflicts of flow machines through intelligent task allocation and coordinated scheduling of flow machines, and significantly improves the overall operation efficiency of the logistics yard.

[0014] 2. The present invention collects and monitors the flow machine status in real time, realizes fault warning and timely maintenance, prolongs the service life of the flow machine, and reduces the equipment failure rate.

[0015] 3. The present invention is based on information collection and intelligent decision-making in the digital yard, which reduces manual intervention and improves the automation and accuracy of operations.

[0016] 4. The present invention reasonably allocates resources in the yard through the collaborative operation of multiple devices, avoids waste of resources, and improves the utilization rate of yard space and equipment. The system can adapt to different yard environments and operating requirements, optimize the scheduling strategy according to historical data and real-time data, and improve the intelligence level of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 It is a schematic diagram of the process structure of the present invention; Figure 2 for Figure 1 Flow chart of digital yard information collection module; Figure 3 for Figure 1 Flow chart of intelligent scheduling and collaborative decision-making module; Figure 4 for Figure 1 Flow chart of the operation progress monitoring and feedback module; Figure 5 for Figure 1 Schematic diagram of the process of data visualization and human-computer interaction interface.

[0019] Figure numerals: 1. Digital yard information acquisition module; 11. Integration of multiple sensors; 111. RFID (radio frequency identification); 112. GPS (global positioning system); 113. Camera monitoring; 12. Data transmission and storage; 13. Data integration and processing; 2. Intelligent scheduling and collaborative decision-making module; 21. Real-time data analysis; 22. Intelligent scheduling algorithm; 23. Efficient collaboration; 3. Operation progress monitoring and feedback module; 31. Real-time progress monitoring; 32. Key indicator analysis; 33. Feedback and optimization; 4. Data visualization and human-computer interaction interface; 41. Real-time visualization interface; 42. Human-computer interaction design; 43. Custom dashboard; 44. Report and decision support tool; 5. Dynamic path optimization and prediction module; 6. Flow-machine collaborative operation scheduling module; 7. Data management and analysis module. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention.

[0021] The present invention will be further described below in conjunction with the embodiments.

[0022] Example: Refer to Figures 1 to 5 The intelligent flow machine dynamic collaborative operation management platform based on the digital yard includes a digital yard information collection module 1. The digital yard information collection module 1 includes a variety of sensor integration 11. The multiple sensor integration 11 includes RFID (radio frequency identification) 111, GPS (global positioning system) 112, and camera monitoring 113. RFID (radio frequency identification) 111 is used to install RFID tags on goods and equipment to realize automatic identification and tracking of their location information and status. GPS (global positioning system) 112 is used to track the real-time location information of transport vehicles, provide basic data support for dynamic scheduling, and ensure the best driving path of vehicles. Camera monitoring 113 uses high-resolution cameras to monitor the working status in the yard in real time, identify different types of equipment and goods, and analyze the operation efficiency through image recognition technology.

[0023] As the information source of the entire intelligent flow machine dynamic collaborative operation management platform based on the digital yard, this module shoulders the important task of accurately and comprehensively collecting data. The massive data collected by these sensors, including the detailed location information of the equipment, the precise coordinates of the goods, the operating status of the vehicle, and the operating parameters of the equipment, will be quickly and reliably transmitted to the platform's data center through high-speed and stable wireless networks, such as Wi-Fi or 5G networks. In the data center, a powerful data processing system will preliminarily organize and classify these raw data, providing a solid data foundation for subsequent intelligent scheduling, collaborative decision-making and other functional modules, ensuring that the entire management platform can operate and function efficiently based on accurate and timely data information.

[0024] Intelligent scheduling and collaborative decision-making module 2, intelligent scheduling and collaborative decision-making module 2 includes real-time data analysis 21, intelligent scheduling algorithm 22, and efficient collaboration 23. Intelligent scheduling algorithm 22 is based on real-time data and uses machine learning and optimization algorithms to dynamically schedule equipment. Efficient collaboration 23 is used for collaborative operation rules between equipment. It can be dynamically adjusted according to actual conditions to ensure the smoothness and efficiency of the entire operation process. This module receives data from the digital yard information collection module in real time, including equipment status, operation progress, task priority and other information. Real-time data analysis 21 combines big data technology to conduct in-depth analysis of historical operation data and identify key factors that affect operation efficiency. This module receives data from the digital yard information collection module in real time, including equipment status, operation progress, task priority and other information.

[0025] This module relies on cutting-edge big data analysis technology and advanced artificial intelligence algorithms, and takes the real-time data transmitted by the digital yard information acquisition module as the core basis to build a highly intelligent decision-making model, so as to implement accurate and dynamic real-time scheduling of flow machines and other operating equipment. At the big data analysis level, the system deeply mines massive historical operation data and current real-time data to explore the hidden laws and patterns. For example, through detailed analysis of the frequency of in and out of warehouses of different types of goods, the operation flow in different time periods, and the operating efficiency of equipment under various working conditions, it provides rich reference information for intelligent decision-making. On this basis, artificial intelligence algorithms use machine learning, deep learning and other technical means to model and predict various complex operation scenarios.

[0026] The operation progress monitoring and feedback module 3 includes real-time progress monitoring 31, key indicator analysis 32, and feedback and optimization 33. The key indicator analysis 32 is used to collect key indicators in the operation process (such as no-load rate, equipment failure rate, etc.). The feedback and optimization 33 will promptly feed back the analysis results to the operator to help him understand the current operation status and adjust the operation plan according to the feedback information.

[0027] During the monitoring process, the system will focus on collecting and deeply analyzing a series of key indicators in the operation process. As an important basis for measuring operation efficiency, the operation time can intuitively reflect the time spent on each task from start to completion. By counting and comparing the operation time of different types of tasks, the bottleneck links in the operation process can be found, providing strong clues for optimizing the operation process. The idle rate reflects the proportion of time that the equipment runs without load during operation. A high idle rate means a waste of equipment resources. By monitoring and analyzing the idle rate, the task allocation and operation route planning of the equipment can be adjusted in a targeted manner to improve the utilization rate of the equipment. Monitoring the equipment failure rate is crucial to ensuring the continuity of the operation. Once the equipment fails, the system will immediately issue an alarm and record detailed information such as the time, location, and type of the failure, so that maintenance personnel can respond and handle it quickly.

[0028] Data visualization and human-computer interaction interface 4. Data visualization and human-computer interaction interface 4 includes real-time visualization interface 41, human-computer interaction design 42, custom dashboard 43, report and decision support tools 44. Real-time visualization interface 41 adopts dynamic update technology to ensure the real-time nature of interface information. Users can grasp the operation status of the yard at any time. Human-computer interaction design 42 provides a variety of interaction methods, including touch screen, mouse operation and voice command, to enhance the user's operating experience, so that users at different levels can use the system efficiently. Custom dashboard 43 can customize the dashboard according to the user's own needs, and choose to display specific key performance indicators (KPI) and real-time data views to better monitor and manage yard operations.

[0029] This module aims to provide users with an intuitive, convenient and multi-dimensional information display and operation platform. Through a carefully designed data visualization interface, complex yard data is converted into clear and easy-to-understand graphics and charts, which facilitates operators and managers to quickly obtain key information and make effective decisions. In terms of data visualization, the interface presents the yard layout in an intuitive graphical way, including cargo space distribution, channel settings, equipment parking positions, etc., so that operators can understand the overall structure and resource distribution of the yard at a glance. The equipment operation status is displayed through dynamic icons or real-time data panels. For example, different color indicator lights indicate the operation, stop, fault and other states of the equipment, and at the same time display the key operating parameters of the equipment, such as speed, load, power, etc., so that operators can grasp the working status of the equipment in real time. The task allocation is presented in a combination of task list and Gantt chart. The task list lists the name, priority, start time, expected completion time and other information of each task in detail. The Gantt chart uses the timeline as the dimension to intuitively display the execution progress and time arrangement of each task on different equipment, which is convenient for operators to fully control the entire task process.

[0030] Dynamic path optimization and prediction module 5: Dynamic path optimization and prediction module 5 uses algorithms to dynamically optimize the operation path of the flow machine, and intelligently adjusts the operation path according to factors such as real-time changes in the yard and equipment load to avoid operation conflicts and improve operation efficiency.

[0031] This module focuses on the intelligent optimization of the operation path of the flow machine and the forward-looking prediction of the operation process. By using advanced algorithm technology, the real-time change information of the yard and the operating load data of the equipment are deeply integrated to realize the dynamic adjustment of the operation path and the advance planning of the best operation strategy. In terms of dynamic path optimization, the algorithm is based on the yard data collected in real time, including the dynamic changes of the cargo stacking position, the real-time operation status of the equipment, and the traffic flow information. When the flow machine receives the operation task, the system will plan the initial operation path for it according to the actual situation of the current yard. However, during the operation process, the state of the yard is constantly changing. For example, new goods may be transported in or out, and the operation routes of other equipment may change. These factors may cause the original operation path to no longer be the best choice. At this time, the dynamic path optimization algorithm will start quickly, re-evaluate the real-time layout and traffic conditions of the yard, and comprehensively consider the load conditions of the equipment, such as the current load weight, remaining power or fuel, etc., to make intelligent adjustments to the operation path of the flow machine. By avoiding congested roads, circumventing dangerous areas during operation, and choosing the shortest and smoothest driving route, the operation time and energy consumption of the mobile machine can be effectively reduced, the operation efficiency can be improved, and the operation risk can be reduced.

[0032] The flow machine collaborative operation scheduling module 6 is responsible for coordinating the operation sequence and path planning between the flow machines when multiple flow machines are operating at the same time. It monitors the operating status and operation progress of each flow machine in real time to avoid operation conflicts between the flow machines, such as collisions, competition for cargo space, etc.

[0033] The flow machine collaborative operation scheduling module 6 aims to build an efficient, orderly and seamless multi-device operation system by carefully setting scientific and reasonable collaborative operation rules between flow machines and other types of equipment (such as stackers, conveyor belts, cranes, etc.), thereby minimizing equipment idle time and operation conflicts, and comprehensively improving overall operation efficiency. In the process of model construction, the key parameters such as the functional characteristics, operating range, operating speed and load capacity of each equipment are first sorted out and analyzed in detail. For example, stackers are good at stacking and picking up goods in the vertical direction, with high operating accuracy and stability; conveyor belts can realize rapid transportation of goods in the horizontal direction, which is suitable for the circulation of long-distance and large-volume goods; cranes have strong lifting capabilities and can be used to handle the loading and unloading of large and heavy goods. Based on these equipment characteristics, collaborative operation rules that match them are formulated.

[0034] Data management and analysis module 7 is responsible for the storage, management and analysis of the platform's existing data, mining and analysis of historical operation data, and extracting valuable information. Responsible for the storage, management and analysis of the platform's existing data. Mining and analysis of historical operation data, extracting valuable information, such as the frequency of entry and exit of different types of goods, the average operation efficiency of flow machines, and peak operation hours. These analysis results can provide a basis for subsequent operation planning, resource allocation optimization, and decision support. For example, optimize the layout of cargo spaces according to the frequency of goods entry and exit, and place goods that frequently enter and exit the warehouse in cargo spaces close to the entrances and exits to further improve operation efficiency.

[0035] The specific working principle of the present invention is as follows: First, RFID tags are installed on goods and equipment to automatically identify and track their location information and status. The massive amounts of data collected by these sensors, including detailed location information of equipment, precise coordinates of goods, vehicle operating status, and equipment operating parameters, will be quickly and reliably transmitted to the platform's data center for comprehensive data collection through a high-speed and stable wireless network, and the initial data will be entered into the data management and analysis module for storage; Real-time data analysis 21 combines big data technology to conduct in-depth analysis of historical operation data. Intelligent scheduling algorithm 22 uses machine learning and optimization algorithms based on real-time data to dynamically schedule equipment. Efficient collaboration 23 is used for collaborative operation rules between equipment, which can be dynamically adjusted according to actual conditions. Sensors and wireless communication equipment are installed on the flow machine to enable it to transmit the operating status information to the flow machine information collection and monitoring module in real time. The module processes and analyzes the received data, on the one hand, displays the flow machine status in real time on the monitoring interface, and on the other hand, pushes abnormal information to the maintenance personnel terminal in a timely manner; When there is a demand for goods in and out of the warehouse, the intelligent task allocation module uses intelligent algorithms to calculate the optimal task allocation plan based on the cargo information and cargo location information in the digital yard model and the real-time status of the flow machine in the flow machine information collection and monitoring module, and sends the task instructions to the corresponding flow machine; During the flow machine operation, the flow machine collaborative operation scheduling module continuously monitors the operation trajectory and operation progress of each flow machine. If a conflict risk is found, the flow machine's operation path or task sequence is immediately adjusted according to the dynamic time window algorithm or other conflict avoidance strategies, and the adjustment instructions are sent to the relevant flow machines; Finally, the data management and analysis module 7 is responsible for the storage, management and analysis of the platform's data, mining and analyzing historical operation data to extract valuable information.

[0036] The data management and analysis module continuously collects and stores new data throughout the operation process, and regularly analyzes and mines historical data. For example, it generates an operation efficiency report every week, and proposes suggestions for optimizing cargo space based on the frequency of goods entering and leaving the warehouse every month. These analysis results are fed back to the operation task intelligent allocation module and the digital yard modeling module to continuously optimize the operation plan and yard layout.

[0037] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the protection scope of the technical solutions of the embodiments of the present invention.

Claims

1. A digital yard-based intelligent mobile machine dynamic collaborative operation management platform, characterized by: include: Digital yard information collection module (1), intelligent scheduling and collaborative decision-making module (2), operation progress monitoring and feedback module (3), data visualization and human-computer interaction interface (4), dynamic path optimization and prediction module (5), flow-machine collaborative operation scheduling module (6), data management and analysis module (7); The digital yard information collection module (1) includes a plurality of sensor integrations (11); The intelligent scheduling and collaborative decision-making module (2) includes real-time data analysis (21), intelligent scheduling algorithm (22), and efficient collaboration (23); The operation progress monitoring and feedback module (3) includes real-time progress monitoring (31), key indicator analysis (32), feedback and optimization (33); The data visualization and human-computer interaction interface (4) includes a real-time visualization interface (41), a human-computer interaction design (42), a custom dashboard (43), and a report and decision support tool (44).

2. The digital yard-based intelligent flow machine dynamic collaborative operation management platform according to claim 1 is characterized in that: The multiple sensor integration (11) includes RFID (111), GPS (112), and camera monitoring (113).

3. The intelligent flow machine dynamic collaborative operation management platform based on digital yard according to claim 2 is characterized in that: The RFID (111) is used to install RFID tags on goods and equipment to realize automatic identification and tracking of their location information and status. The GPS (112) is used to track the real-time location information of the transport vehicle, provide basic data support for dynamic scheduling, and ensure the optimal driving path of the vehicle. The camera monitoring (113) uses a high-resolution camera to monitor the working status in the yard in real time and identify different types of equipment and goods.

4. The intelligent mobile machine dynamic collaborative operation management platform based on digital yard according to claim 1 is characterized in that: The intelligent scheduling algorithm (22) is based on real-time data and uses machine learning and optimization algorithms to dynamically schedule equipment. The efficient collaboration (23) is used to dynamically adjust the collaborative operation rules between equipment according to actual conditions.

5. The intelligent mobile machine dynamic collaborative operation management platform based on digital yard according to claim 1 is characterized in that: The key indicator analysis (32) is used to collect key indicators during the operation process, including no-load rate and equipment failure rate. The feedback and optimization (33) promptly feeds back the analysis results to the operator to help him understand the current operation status and adjust the operation plan based on the feedback information.

6. The intelligent flow machine dynamic collaborative operation management platform based on digital yard according to claim 1 is characterized in that: The real-time visual interface (41) uses dynamic update technology to ensure the real-time nature of the interface information, so that the user can grasp the operation status of the yard at any time. The human-computer interaction design (42) provides a variety of interaction methods, including touch screen, mouse operation and voice command, to enhance the user's operating experience.

7. The intelligent mobile machine dynamic collaborative operation management platform based on digital yard according to claim 1 is characterized in that: The dynamic path optimization and prediction module (5) uses an algorithm to dynamically optimize the machine operation path.

8. The intelligent mobile machine dynamic collaborative operation management platform based on digital yard according to claim 1 is characterized in that: The flow machine collaborative operation scheduling module (6) is responsible for coordinating the operation sequence and path planning between the flow machines when multiple flow machines are operating simultaneously, and avoiding operation conflicts between the flow machines, such as collisions and competition for cargo spaces, by real-time monitoring of the operating status and operation progress of each flow machine.

9. The intelligent mobile machine dynamic collaborative operation management platform based on digital yard according to claim 1 is characterized in that: The data management and analysis module (7) is responsible for the storage, management and analysis of all data on the platform, mining and analyzing historical operation data, and extracting valuable information.

Images