Logistics warehousing system based on information fusion and intelligent scheduling

Abstract

The invention belongs to the technical field of information, specifically, multiple sensor data are fused through multi-source information fusion and a deep learning algorithm, a specific fusion network and an optimization algorithm are applied, recognition errors and positioning deviation are greatly reduced, grabbing errors are reduced, and the cargo damage rate is remarkably reduced. For dynamic carrying path planning, a traditional preset map algorithm is easy to collide and is long in delay; a real-time map is constructed through reinforcement learning and a real-time environment perception algorithm, training is carried out according to a reward function, the collision frequency is sharply reduced, the carrying time is shortened, and energy consumption is reduced. When the storage space is utilized, the traditional random storage efficiency is low; the space optimization and intelligent storage strategy algorithm utilizes a genetic algorithm for modeling, the storage space utilization rate is improved, the goods searching time is shortened, and the damage rate is reduced.

Description

Technical Field

[0001] The present invention belongs to the field of information technology, and in particular relates to a logistics warehousing system based on information fusion and intelligent scheduling. Background Art

[0002] With the rapid development of e-commerce and modern logistics, the scale of the logistics and warehousing industry continues to expand, and the requirements for warehousing efficiency and management are becoming increasingly stringent. Industrial robots, as key equipment for achieving logistics and warehousing automation, play a vital role in cargo handling, storage, and sorting. However, the current application of industrial robots in the logistics and warehousing field still faces many challenges, which hinders the further development of the logistics and warehousing industry.

[0003] Inaccurate cargo identification and positioning: Logistics warehouses contain a wide variety of goods, with varying shapes, sizes, and packaging, often placed in disorganized locations. Traditional industrial robot cargo identification and positioning systems rely primarily on single-source visual recognition technology, which is susceptible to factors such as lighting conditions, obstructions from cargo, and reflective packaging, resulting in low recognition and positioning accuracy. In practice, robots often grab the wrong cargo or fail to accurately locate it, reducing efficiency and potentially damaging goods and disrupting warehouse order.

[0004] Irrational transport path planning: Logistics warehouses are complex environments, with numerous obstacles such as shelves, aisles, other equipment, and human activity. Existing industrial robot transport path planning methods are mostly based on pre-set maps and fixed algorithms, and are unable to perceive real-time environmental changes and the impact of dynamic obstacles. When encountering unexpected situations, robots struggle to quickly adjust their transport paths, resulting in inefficient transportation and even collisions.

[0005] Inadequate storage space utilization: In logistics warehousing, rational use of storage space can improve storage capacity and operational efficiency. Currently, industrial robots often lack effective space planning and management during the cargo storage process, and are unable to intelligently arrange storage based on factors such as cargo size, weight, and storage requirements. This often leads to problems such as wasted storage space and irrational cargo stacking, limiting warehouse storage capacity and operational efficiency. Summary of the Invention

[0006] The present invention provides a logistics warehousing system based on information fusion and intelligent scheduling, comprising:

[0007] The cargo accurate identification and positioning algorithm module based on multi-source information fusion and deep learning uses multiple sensors to collect multi-source cargo information in real time, and after fusion processing, combines deep learning to achieve accurate identification and positioning;

[0008] A dynamic transport path planning algorithm module based on reinforcement learning and real-time environmental perception builds a real-time environmental map and uses reinforcement learning to generate the optimal dynamic path;

[0009] The warehouse space efficient utilization system module based on space optimization and intelligent storage strategy establishes an optimization model based on warehouse and cargo information and formulates storage strategies;

[0010] An efficient cargo sorting production system module based on multi-robot collaboration and task allocation optimization, which rationally allocates sorting tasks, establishes a collaborative mechanism and has optimization functions;

[0011] The intelligent monitoring and management platform module of the logistics warehousing process realizes data interaction, collection, analysis and early warning functions; multi-source information fusion adopts a fusion network based on the attention mechanism, and the fusion formula is: Among them, xi is the sensor data, αi is the attention weight, which is dynamically calculated by a three-layer fully connected network with 256, 128, and 64 neurons respectively, and optimized by the backpropagation algorithm.

[0012] Furthermore, in the cargo precise identification and positioning algorithm module based on multi-source information fusion and deep learning, the multi-source sensors include a high-resolution visual sensor with a resolution of 0.1mm, a lidar with a ranging accuracy of ±5mm, an ultrasonic sensor with a detection distance accuracy of ±10mm, and a radio frequency identification reader with a reading distance of up to 3m. The deployment position is determined through simulation and actual testing.

[0013] Furthermore, in the dynamic transport path planning algorithm module based on reinforcement learning and real-time environment perception, the state space includes the robot position P(x, y), posture θ, surrounding obstacle information

[0014] O and the task goal T, the action space is the robot displacement Δd and rotation angle Δθ. The reward function is defined as:

[0015] Where d is the path length, c is the number of collisions, λ is the collision penalty coefficient (ranging from 0.8 to 1.2), t is the transport time, and μ is the time penalty coefficient (ranging from 0.01 to 0.05).

[0016] Furthermore, the Proximal Policy Optimization (PPO) algorithm is used for training, with an experience replay buffer capacity of 200,000 entries, a learning rate of 5e-5, 15 epochs per iteration, and the target network updated every 3,000 steps.

[0017] Furthermore, in the storage space efficient utilization system module based on space optimization and intelligent storage strategy, a storage space optimization model is established using a genetic algorithm, the chromosome encoding is the cargo storage location information, and the fitness function is a comprehensive consideration of the storage space utilization rate and cargo storage stability.

[0018] Furthermore, in the cargo sorting efficient production system module based on multi-robot collaboration and task allocation optimization, task allocation adopts the Hungarian algorithm, collaborative control adopts the distributed consistency protocol, communication delay is ≤8ms, and task completion time variance is ≤4%.

[0019] Furthermore, in the intelligent monitoring and management platform module of the logistics warehousing process, the data collection frequency is 150Hz, and the long short-term memory network (LSTM) is used for fault prediction. The input sequence length is 150, the hidden layer neurons are 300, and the prediction accuracy is ≥96%.

[0020] Furthermore, in the multi-robot collaborative sorting scenario, the task priority is determined by fuzzy logic, with the input being the urgency E, weight W and volume V of the cargo, and the output being the priority coefficient

[0021] P, the membership function adopts trapezoidal distribution.

[0022] Beneficial effects:

[0023] Improve cargo identification and positioning accuracy: The cargo identification and positioning algorithm based on multi-source information fusion and deep learning can comprehensively utilize information from multiple sensors to achieve accurate identification and positioning of cargo, improve the accuracy and stability of identification and positioning, reduce grasping errors and positioning deviations, and improve the work efficiency of logistics and warehousing.

[0024] Optimized transport path planning: The dynamic transport path planning algorithm based on reinforcement learning and real-time environmental perception can timely adjust the transport path according to real-time environmental changes, enabling robots to complete transport tasks safely and efficiently in complex logistics and warehousing environments, thereby improving transport efficiency and safety.

[0025] Make full use of storage space: The storage space efficient utilization system based on space optimization and intelligent storage strategy can automatically allocate the optimal storage location for goods according to the characteristics and storage needs of the goods, dynamically adjust the storage layout, realize the efficient use of storage space, and improve the storage capacity and operational efficiency of the warehouse.

[0026] Improve cargo sorting efficiency: The efficient cargo sorting production system based on multi-robot collaboration and task allocation optimization can reasonably allocate cargo sorting tasks to multiple robots for collaborative completion, realize information sharing and collaborative operation among robots, improve sorting efficiency, shorten sorting time, and meet the needs of large-scale logistics and warehousing.

[0027] Convenient logistics and warehousing management: The intelligent monitoring and management platform of the logistics and warehousing process provides managers with real-time logistics and warehousing process monitoring and decision support. It has intelligent early warning and fault diagnosis functions, which can promptly discover and solve problems in the logistics and warehousing process and improve the management level of logistics and warehousing. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 Module flow diagram. DETAILED DESCRIPTION

[0029] Example 1:

[0030] A logistics warehousing system based on information fusion and intelligent scheduling, including:

[0031] The cargo accurate identification and positioning algorithm module based on multi-source information fusion and deep learning uses multiple sensors to collect multi-source cargo information in real time, and after fusion processing, combines deep learning to achieve accurate identification and positioning;

[0032] A dynamic transport path planning algorithm module based on reinforcement learning and real-time environmental perception builds a real-time environmental map and uses reinforcement learning to generate the optimal dynamic path;

[0033] The warehouse space efficient utilization system module based on space optimization and intelligent storage strategy establishes an optimization model based on warehouse and cargo information and formulates storage strategies;

[0034] An efficient cargo sorting production system module based on multi-robot collaboration and task allocation optimization, which rationally allocates sorting tasks, establishes a collaborative mechanism and has optimization functions;

[0035] The intelligent monitoring and management platform module of the logistics warehousing process realizes data interaction, collection, analysis and early warning functions; multi-source information fusion adopts a fusion network based on the attention mechanism, and the fusion formula is: Among them, xi is the data of each sensor, αi is the attention weight, which is dynamically calculated by a three-layer fully connected network, with the number of neurons being 256, 128, and 64 respectively, and optimized by the back-propagation algorithm. In the cargo precise identification and positioning algorithm module based on multi-source information fusion and deep learning, the multi-source sensors include high-resolution visual sensors with a resolution of 0.1mm, laser radars with a ranging accuracy of ±5mm, ultrasonic sensors with a detection distance accuracy of ±10mm, and radio frequency identification readers with a reading distance of 3m. The deployment position is determined through simulation and actual testing. In the dynamic handling path planning algorithm module based on reinforcement learning and real-time environmental perception, the state space includes the robot position P(x, y), posture θ, and surrounding obstacle information.

[0036] O and the task goal T, the action space is the robot displacement Δd and rotation angle Δθ. The reward function is defined as:

[0037] Where d is the path length, c is the number of collisions, λ is the collision penalty coefficient (range: 0.8-1.2), t is the handling time, and μ is the time penalty coefficient (range: 0.01-0.05). The proximal policy optimization (PPO) algorithm is used for training, with an experience replay buffer capacity of 200,000 entries, a learning rate of 5e-5, 15 epochs per iteration, and the target network updated every 3,000 steps. In the storage space efficient utilization system module based on space optimization and intelligent storage strategy, a genetic algorithm is used to establish a storage space optimization model, the chromosome encoding is the cargo storage location information, and the fitness function is a comprehensive consideration of storage space utilization and cargo storage stability. In the cargo sorting efficient production system module based on multi-robot collaboration and task allocation optimization, the Hungarian algorithm is used for task allocation, and the distributed consensus protocol is used for collaborative control. The communication delay is ≤8ms, and the variance of task completion time is ≤4%. In the intelligent monitoring and management platform module of the logistics warehousing process, the data acquisition frequency is 150Hz, and the long short-term memory network (LSTM) is used for fault prediction. The input sequence length is 150, the hidden layer neurons are 300, and the prediction accuracy is ≥96%. In the multi-robot collaborative sorting scenario, the task priority is determined by fuzzy logic. The input is the urgency E, weight W and volume V of the goods, and the output is the priority coefficient.

[0038] The membership function adopts trapezoidal distribution.

[0039] A variety of sensors, including high-resolution visual sensors, lidar, ultrasonic sensors, and radio frequency identification (RFID) readers, are installed on the personnel to obtain real-time information about the goods and the environment. Furthermore, various environmental monitoring devices, such as temperature and humidity sensors and smoke sensors, are installed in the warehouse to monitor the warehouse's environmental conditions. These sensors collect data in real time and transmit it to the data processing layer.

[0040] Data processing layer: This includes a multi-source data fusion module, a deep learning model training module, a real-time environment map construction module, a storage space optimization module, and a multi-robot collaborative control module. The multi-source data fusion module integrates and processes data collected by multiple sensors to construct a comprehensive information model of the goods and environment. The deep learning model training module uses deep learning algorithms to learn and train large amounts of cargo data to establish a cargo identification and positioning model. The real-time environment map construction module constructs a real-time environment map based on data collected by lidar and visual sensors. The storage space optimization module establishes a storage space optimization model based on the warehouse's spatial structure and the characteristics of the goods, and formulates intelligent storage strategies. The multi-robot collaborative control module is responsible for managing and controlling the communication and collaboration between multiple robots.

[0041] Decision-making layer: includes the cargo precision identification and positioning module, dynamic transport path planning module, storage space efficient utilization module, cargo sorting efficient production module, and intelligent monitoring and management module. The cargo precision identification and positioning module uses the cargo precision identification and positioning algorithm to accurately identify and locate cargo based on the results of the multi-source data fusion module and the deep learning model training module. The dynamic transport path planning module generates the optimal dynamic transport path based on the results of the real-time environment map construction module and the reinforcement learning algorithm. The storage space efficient utilization module achieves efficient utilization of storage space and intelligent storage management based on the results of the storage space optimization module. The cargo sorting efficient production module reasonably allocates cargo sorting tasks to multiple robots based on the results of the multi-robot collaborative control module and realizes collaborative control between robots. The intelligent monitoring and management module is responsible for collecting, storing, analyzing, and displaying data from the logistics and warehousing process, realizing intelligent monitoring and management of the logistics and warehousing process.

[0042] Execution layer: Industrial robots perform tasks such as cargo identification, positioning, handling, storage, and sorting based on control instructions generated by the decision layer. At the same time, feedback from the execution process is transmitted back to the data processing layer and the decision layer to adjust and optimize the system.

[0043] Application layer: It is mainly an intelligent monitoring and management platform for the logistics and warehousing process, providing managers with a friendly interactive interface to realize real-time monitoring, data analysis, decision support and intelligent early warning of the logistics and warehousing process.

[0044] Implementation of cargo accurate identification and positioning algorithm based on multi-source information fusion and deep learning

[0045] Sensor Deployment and Data Collection: Based on the actual needs of logistics and warehousing, multi-source sensors such as high-resolution visual sensors, lidar, ultrasonic sensors, and radio frequency identification (RFID) tags are deployed to ensure comprehensive and accurate multi-source information on goods. Sensors collect data in real time at a set frequency and transmit it over the network to the multi-source data fusion module in the data processing layer.

[0046] Multi-source data fusion and feature extraction: The multi-source data fusion module uses deep learning-based multi-source data fusion algorithms, such as attention-based fusion networks, to fuse data from different types of sensors. Deep learning algorithms, such as convolutional neural networks (CNNs), are used to analyze and process the fused data, extracting detailed features such as the type, shape, size, location, and identification of the goods.

[0047] Deep Learning Model Training and Application: A large amount of cargo data of various types is collected to construct a training dataset. Deep learning algorithms, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), are used to train and analyze the training dataset to establish a cargo identification and location model. In practice, the cargo identification and location module inputs the extracted features into the trained model to achieve precise cargo identification and location.

[0048] Implementation of dynamic transport path planning algorithm based on reinforcement learning and real-time environment perception

[0049] Real-time environmental mapping: LiDAR and visual sensors are used to collect real-time information about obstacles and their dynamic changes in the logistics warehousing environment. Point cloud processing and image processing technologies are then used to construct a real-time environmental map. This map accurately reflects the layout of shelves, aisles, and the location and changes of other obstacles within the warehouse, providing the foundational data for dynamic transport path planning.

[0050] Reinforcement learning model training: The transport path planning problem is treated as an intelligent decision-making process, defining a state space, an action space, and a reward function. The state space includes the robot's position, posture, surrounding environment information, and task objectives; the action space includes the robot's various motions; and the reward function is designed based on factors such as the robot's ability to safely and efficiently complete the transport task and avoid obstacles. Using reinforcement learning algorithms such as the Deep Q-Network (DQN) or the Proximal Policy Optimization (PPO) algorithm, the transport path planning process is modeled and trained, enabling the robot to learn the optimal dynamic transport path planning strategy.

[0051] Dynamic transport path planning and execution: During the actual transport process, the dynamic transport path planning module uses the results of the real-time environmental map construction module and the trained reinforcement learning model, combined with real-time environmental information, to generate the optimal dynamic transport path. This path information is then sent to the industrial robot at the execution layer to guide the robot in completing the transport task. If the environment changes, the dynamic transport path planning module can promptly adjust the transport path to ensure the safety of the robot and the smooth progress of the transport task.

[0052] Implementation of efficient storage space utilization system based on space optimization and intelligent storage strategy

[0053] Warehouse space and cargo information collection: Through measurement and modeling, we obtain the spatial structure information of the warehouse, including the layout, size, and load-bearing capacity of the shelves. At the same time, we classify and register the attributes of the goods in the warehouse, recording information such as the size, weight, and storage requirements of the goods.

[0054] Establishing a storage space optimization model: Using mathematical optimization algorithms, such as genetic algorithms and particle swarm optimization, we establish a storage space optimization model based on the warehouse's spatial structure and the characteristics of the goods. This model automatically allocates the optimal storage location for goods based on their storage needs, achieving efficient utilization of storage space.

[0055] Intelligent Storage Strategy Execution: The Warehouse Space Efficiency Utilization module develops intelligent storage strategies based on the results of the storage space optimization model and sends storage instructions to the industrial robots at the execution layer. During the storage process, the system monitors the warehouse's storage status and space utilization in real time, dynamically adjusting the storage layout to ensure efficient use of storage space. Furthermore, the system features categorized storage and rapid retrieval capabilities, improving storage and retrieval efficiency.

[0056] Implementation of an efficient cargo sorting production system based on multi-robot collaboration and task allocation optimization

[0057] Task Analysis and Allocation: The Cargo Sorting Efficiency Production Module conducts a detailed analysis of tasks based on their volume and type, breaking them down into multiple subtasks. These tasks are then rationally assigned to multiple industrial robots based on the characteristics and requirements of each subtask. Task allocation considers factors such as the robot's operating capacity, work range, and current status to ensure rational and efficient task allocation.

[0058] Establishing a communication and collaboration mechanism: A multi-robot communication network is established to enable real-time information exchange between robots. Collaborative control algorithms coordinate the movements and operations of multiple robots, ensuring they can work together to sort different goods simultaneously, improving sorting efficiency. During the collaborative process, robots can adjust their collaborative strategies based on real-time task status and sorting progress, ensuring efficient and stable sorting operations.

[0059] Intelligent Task Allocation Optimization: The Cargo Sorting Efficient Production Module features intelligent task allocation optimization, which adjusts task allocation plans in real time based on task changes and the robot's working status. For example, if a robot malfunctions or the workload suddenly increases, the intelligent task allocation optimization function can automatically reallocate tasks to other robots to ensure the smooth completion of sorting tasks.

[0060] Implementation of intelligent monitoring and management platform for logistics warehousing process

[0061] Data Collection and Transmission: The intelligent monitoring and management module, through a data interface with the industrial robot logistics and warehousing system, collects various data from the logistics and warehousing process in real time, such as cargo information, handling routes, storage space utilization, and robot operating status. The collected data is formatted and transmitted via the network to the intelligent monitoring and management platform.

[0062] Data Analysis and Visualization: Utilizing big data analytics technology, data transmitted to the platform is analyzed and mined to extract valuable information, such as logistics efficiency analysis, warehouse space utilization analysis, and equipment failure warnings. Through visualization technology, analysis results are displayed in the form of charts and reports on the intelligent monitoring and management platform interface, providing managers with intuitive and clear logistics and warehousing process monitoring and decision support.

[0063] Intelligent early warning and fault diagnosis: Within the intelligent monitoring and management platform, an intelligent early warning and fault diagnosis model is established. By setting thresholds and analyzing models, data from the logistics and warehousing processes is monitored in real time. When data exceeds set thresholds or an abnormality occurs, the platform automatically issues an early warning signal and performs fault diagnosis, prompting management personnel to take appropriate measures to ensure the smooth operation of the logistics and warehousing process.

[0064] Example 2

[0065] Cargo Accurate Identification and Positioning Algorithm Example

[0066] In a large e-commerce logistics warehouse, a storage area of ​​1,000 square meters was selected, containing 5,000 items of various goods, for a week-long test.

[0067] Traditional identification and positioning methods relied solely on visual recognition technology, with a visual sensor resolution of 0.5mm. In practice, due to uneven lighting and partial obstruction of goods, the robots experienced 200 misidentifications and 150 positioning errors exceeding 50mm daily. This resulted in an average of 30 incorrect grabs per day, resulting in low efficiency and a 2% cargo damage rate.

[0068] Multi-source information fusion and deep learning algorithm team: Deploys high-resolution visual sensors (resolution up to 0.1mm), lidar (ranging accuracy ±5mm), ultrasonic sensors (detection distance accuracy ±10mm), and radio frequency identification (RFID) readers (reading distance up to 3m). Multi-source information fusion utilizes a fusion network based on an attention mechanism, dynamically calculated using a three-layer fully connected network (with 256, 128, and 64 neurons, respectively) and optimized using a backpropagation algorithm. Testing has reduced daily cargo identification errors to 10, positioning deviations exceeding 50mm to only 5, the number of robots picking up incorrect cargo to 5 per day, and the cargo damage rate to 0.5%, significantly improving work efficiency.

[0069] Dynamic transport path planning algorithm embodiment

[0070] In a logistics warehousing center, the warehouse area is 5,000 square meters, with 100 shelves. The aisles are narrow and there are dynamic obstacles (such as temporarily stacked goods and staff activities).

[0071] Traditional path planning method group: Based on pre-set maps and fixed algorithms, when encountering dynamic obstacles such as temporarily stacked goods, the robot collided an average of 10 times a day, each collision causing an average delay of 30 minutes. The average time to complete the handling task was 8 hours, and the energy consumption was high.

[0072] Reinforcement Learning and Real-Time Environment Perception Algorithm Group: This group uses lidar and vision sensors to build a real-time environment map. The state space includes the robot's position P(x, y), posture θ, surrounding obstacle information O, and task goal T. The action space is the robot's displacement Δd and rotation angle Δθ, where d is the path length, c is the number of collisions, λ is the collision penalty coefficient (ranging from 0.8 to 1.2), t is the handling time, and μ is the time penalty coefficient (ranging from 0.01 to 0.05). Training is performed using the Proximal Policy Optimization (PPO) algorithm, with an experience replay buffer capacity of 200,000 entries and a learning rate of 5e-5. Testing has shown that the number of robot collisions per day has been reduced to 1, the average delay has been shortened to 5 minutes, the average handling task completion time has been shortened to 5 hours, and energy consumption has been reduced by 30%.

[0073] Example of a system for efficient utilization of storage space

[0074] In a standard high-bay warehouse with a storage volume of 10,000 cubic meters and 30 types of goods, a one-month storage test was conducted.

[0075] Traditional storage method group: The goods are randomly stored according to the order in which they arrive, with a storage space utilization rate of only 60%. The unreasonable stacking of goods results in an average search time of 15 minutes each time, and the damage rate of goods due to improper stacking is 3%.

[0076] Space Optimization and Intelligent Storage Strategy Algorithm Group: This group uses a genetic algorithm to establish a warehouse space optimization model. Chromosomes encode cargo storage location information, and the fitness function comprehensively considers both storage space utilization and cargo storage stability. Testing has shown that storage space utilization has increased to 95%, cargo search time has been reduced to an average of 5 minutes per session, and the cargo damage rate has been reduced to 1%, significantly improving warehouse storage capacity and operational efficiency.

Claims

1. A logistics warehousing system based on information fusion and intelligent scheduling, characterized by: include: The cargo accurate identification and positioning algorithm module based on multi-source information fusion and deep learning uses multiple sensors to collect multi-source cargo information in real time, and after fusion processing, combines deep learning to achieve accurate identification and positioning; A dynamic transport path planning algorithm module based on reinforcement learning and real-time environmental perception builds a real-time environmental map and uses reinforcement learning to generate the optimal dynamic path; The warehouse space efficient utilization system module based on space optimization and intelligent storage strategy establishes an optimization model based on warehouse and cargo information and formulates storage strategies; An efficient cargo sorting production system module based on multi-robot collaboration and task allocation optimization, which rationally allocates sorting tasks, establishes a collaborative mechanism and has optimization functions; The intelligent monitoring and management platform module of the logistics warehousing process realizes data interaction, collection, analysis and early warning functions; multi-source information fusion adopts a fusion network based on the attention mechanism, and the fusion formula is: Among them, xi is the sensor data, αi is the attention weight, which is dynamically calculated by a three-layer fully connected network with 256, 128, and 64 neurons respectively, and optimized by the backpropagation algorithm.

2. The system according to claim 1, wherein: In the cargo precise identification and positioning algorithm module based on multi-source information fusion and deep learning, the multi-source sensors include a high-resolution visual sensor with a resolution of 0.1mm, a lidar with a ranging accuracy of ±5mm, an ultrasonic sensor with a detection distance accuracy of ±10mm, and a radio frequency identification reader with a reading distance of up to 3m. The deployment location is determined through simulation and actual testing.

3. The system according to claim 1, wherein: In the dynamic transport path planning algorithm module based on reinforcement learning and real-time environmental perception, the state space includes the robot's position P(x, y), posture θ, surrounding obstacle information O, and task goal T, and the action space is the robot's displacement Δd and rotation angle Δθ. The reward function is defined as: Where d is the path length, c is the number of collisions, λ is the collision penalty coefficient (ranging from 0.8 to 1.2), t is the transport time, and μ is the time penalty coefficient (ranging from 0.01 to 0.05).

4. The algorithm module according to claim 3, characterized in that: The Proximal Policy Optimization (PPO) algorithm is used for training, with an experience replay buffer capacity of 200,000 entries, a learning rate of 5e-5, 15 epochs per iteration, and the target network updated every 3,000 steps.

5. The system according to claim 1, wherein: In the storage space efficient utilization system module based on space optimization and intelligent storage strategy, a storage space optimization model is established using a genetic algorithm, the chromosome encoding is the cargo storage location information, and the fitness function is a comprehensive consideration of the storage space utilization rate and the cargo storage stability.

6. The system according to claim 1, wherein: In the cargo sorting efficient production system module based on multi-robot collaboration and task allocation optimization, task allocation adopts the Hungarian algorithm, collaborative control adopts the distributed consistency protocol, communication delay is ≤8ms, and task completion time variance is ≤4%.

7. The system according to claim 1, wherein: In the intelligent monitoring and management platform module of the logistics warehousing process, the data collection frequency is 150Hz, and the long short-term memory network (LSTM) is used for fault prediction. The input sequence length is 150, the hidden layer neurons are 300, and the prediction accuracy is ≥96%.

8. The system according to claim 1, wherein: In the multi-robot collaborative sorting scenario, task priority is determined by fuzzy logic. The input is the urgency E, weight W and volume V of the goods, and the output is the priority coefficient. P, the membership function adopts trapezoidal distribution.

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