A Smart Monitoring Method and System for a Logistics Park

Abstract

The present application relates to the field of intelligent monitoring technologies, and provides an intelligent monitoring method and system for a logistics park. The method includes: obtaining a floor plan of the logistics park, identifying key areas and arranging an intelligent sensing network; obtaining first monitoring data based on real-time monitoring by fixed edge nodes; obtaining area tasks and evaluating activities, and calculating a task complexity factor; adjusting dynamic nodes based on the complexity factor to obtain area activity data; inputting the activity data into a deep analysis model to obtain a logistics intelligent sensing set; inputting the sensing set and the complexity factor into a risk assessment function to perform multi-level early warning matching and feedback. The present application solves the technical problems of unreasonable resource allocation and untimely risk early warning caused by the difficulty in accurately monitoring the changing frequency of dynamic activities in the logistics park, and realizes the effect of efficiently monitoring and accurately warning by adjusting dynamic edge nodes in real time through an intelligent sensing network and combining multi-dimensional task complexity factors.

Description

Technical Field

[0001] This application relates to the field of intelligent monitoring technology, and specifically relates to a smart monitoring method and system for a logistics park. Background Art

[0002] In the management of modern logistics parks, with the increasing frequency of logistics activities and the expansion of park scale, traditional monitoring means can no longer meet the increasingly complex operation requirements of the park. The dynamic data of various aspects such as cargo loading and unloading, vehicle entry and exit, and environmental monitoring in the logistics park change frequently. The monitoring system needs to process a large amount of real-time data and respond quickly to sudden abnormal situations. However, existing monitoring systems usually rely on fixed nodes for monitoring, unable to flexibly cope with the fluctuations of logistics activities, easily causing resource waste or monitoring blind spots. In addition, the lack of accurate perception and intelligent analysis of key task areas leads to low monitoring efficiency, untimely risk warning, and brings potential safety hazards and management problems to the park operation. Summary of the Invention

[0003] This application provides a smart monitoring method and system for a logistics park, aiming to solve the technical problems of unreasonable resource allocation and untimely risk warning caused by the difficulty in accurately monitoring the changing frequency of dynamic activities in the logistics park.

[0004] In view of the above problems, this application provides a smart monitoring method and system for a logistics park.

[0005] In the first aspect disclosed in this application, a smart monitoring method for a logistics park is provided. The method includes: obtaining a floor plan of the logistics park, identifying key areas based on the floor plan, and arranging an intelligent perception network according to the key area identification results; performing real-time monitoring of the key areas based on the fixed edge nodes of the intelligent perception network to obtain multiple first real-time monitoring data; obtaining the area tasks of the key areas, evaluating the area activities based on the area tasks and the first real-time monitoring data to obtain multi-dimensional indicators of the area tasks, and calculating the area task complexity factor based on the multi-dimensional indicators of the area tasks; adjusting the dynamic edge nodes of the intelligent perception network based on the area task complexity factor, and obtaining multiple area activity data according to the adjustment results; inputting the multiple area activity data into a dynamic perception depth analysis model for multi-layer perception analysis to obtain a global logistics intelligent perception set; inputting the vehicle flow index, cargo loading and unloading index, and environmental risk index of the global logistics intelligent perception set and the area task complexity factor into an area risk assessment function, matching a multi-level early warning mechanism according to the function calculation results, and performing warning feedback based on the matching results.

[0006] Another aspect disclosed in this application provides a smart monitoring system for a logistics park. The system includes: a key area identification module, which is used to obtain the floor plan of the logistics park, identify key areas based on the floor plan, and arrange an intelligent perception network according to the key area identification results; a real-time monitoring module, which is used to perform real-time monitoring of the key areas based on the fixed edge nodes of the intelligent perception network and obtain a plurality of first real-time monitoring data; a regional activity evaluation module, which is used to obtain the regional tasks of the key areas, evaluate regional activities based on the regional tasks and the first real-time monitoring data, obtain multi-dimensional indicators of regional tasks, and calculate a regional task complexity factor based on the multi-dimensional indicators of regional tasks; a dynamic edge node adjustment module, which is used to adjust the dynamic edge nodes of the intelligent perception network based on the regional task complexity factor, and obtain a plurality of regional activity data according to the adjustment results; a multi-layer perception analysis module, which is used to input the plurality of regional activity data into a dynamic perception depth analysis model for multi-layer perception analysis to obtain a global logistics intelligent perception set; a multi-level early warning mechanism matching module, which is used to input the vehicle flow index, cargo handling index, environmental risk index of the global logistics intelligent perception set and the regional task complexity factor into a regional risk assessment function, perform multi-level early warning mechanism matching according to the function calculation results, and perform early warning feedback based on the matching results.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The above-mentioned smart monitoring method for a logistics park obtains the floor plan of the logistics park, identifies key areas in the park by analyzing the floor plan, and arranges an intelligent perception network based on the identification results. Subsequently, fixed edge nodes are used to perform real-time monitoring of these key areas, collect the first batch of real-time monitoring data, then obtain the task information of these key areas, and combine the real-time monitoring data to evaluate regional activities, generating multi-dimensional task indicators. Based on these indicators, the complexity factor of each regional task is calculated. According to this complexity factor, the layout of the edge nodes in the intelligent perception network is dynamically adjusted to ensure reasonable resource allocation, and regional activity data is obtained through the adjusted layout. Then, these regional activity data are input into a dynamic perception depth analysis model for multi-level analysis, generating a global logistics intelligent perception set, which includes a vehicle flow index, a cargo handling index, and an environmental risk index. Finally, these indexes and the regional task complexity factor are input into a regional risk assessment model, a suitable multi-level early warning mechanism is matched based on the calculation results, and an early warning feedback is generated according to the matching results to ensure the safe and efficient operation of the logistics park.

[0009] The above description is only an overview of the technical solution of this application. In order to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specific embodiments of this application are specifically given. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0011] Figure 1 It is a schematic flowchart of a smart monitoring method for a logistics park in an embodiment.

[0012] Figure 2 It is an architecture diagram of a smart monitoring system for a logistics park in an embodiment.

[0013] Description of the reference numerals: Key area identification module 1, Real-time monitoring module 2, Area activity evaluation module 3, Dynamic edge node adjustment module 4, Multi-layer perception analysis module 5, Multi-level early warning mechanism matching module 6. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0014] The embodiments of this application provide a smart monitoring method and system for a logistics park, and solve the technical problems of unreasonable resource allocation and untimely risk warning caused by the difficulty in accurately monitoring the changing frequency of dynamic activities in the logistics park.

[0015] The following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the drawings in the embodiments of this application. Obviously, the described embodiments are only some embodiments of this application, rather than all embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this application.

[0016] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server including a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0017] Embodiment 1, as Figure 1 shown, this application provides a smart monitoring method for a logistics park, and the method includes:

[0018] Obtain the floor plan of the logistics park, identify key areas based on the floor plan, and arrange the intelligent perception network according to the key area identification results.

[0019] In the embodiment of the present application, the system terminal first obtains the complete floor plan of the logistics park, which includes the detailed layout of each functional area in the park, such as the cargo loading and unloading area, the storage area, the vehicle access channels, the office area, and other auxiliary facilities. Subsequently, by analyzing this floor plan, the functions of different areas in the park are classified to identify key areas. These areas usually include the cargo loading and unloading area, the vehicle import and export area, the storage area, etc. Then, fixed edge nodes are arranged in each key area of the logistics park. These nodes will cover the basic monitoring requirements of the entire park, including cargo loading and unloading, vehicle scheduling, and environmental monitoring. The system terminal combines the spatial layout in the park and the area monitoring requirements to determine the location of each fixed edge node, ensuring seamless connection of the monitoring coverage and minimizing monitoring blind spots as much as possible. Then, according to the functions of different areas, appropriate sensor types are configured for each fixed edge node, such as cameras, temperature and humidity sensors, infrared sensors, etc., to meet the monitoring requirements of different areas. In the arrangement of the intelligent perception network, not only the arrangement of fixed edge nodes is required, but also the arrangement of dynamic edge nodes. The system terminal analyzes the historical logistics activity data and historical environmental change data of the logistics park, and combines redundant processing to configure the upper limit of dynamic edge nodes for the entire logistics park. Then, the dynamic edge nodes are arranged according to the configured upper limit of dynamic edge nodes. Each dynamic edge node includes all the sensor types required by the logistics park to facilitate the free control of the dynamic edge nodes. After the arrangement of fixed edge nodes and dynamic edge nodes is completed, the system terminal reasonably arranges the intelligent perception network to ensure real-time monitoring and efficient management of the park.

[0020] Furthermore, the present application provides the arrangement of dynamic edge nodes for the intelligent perception network, including:

[0021] Obtain the historical logistics activity data and historical environmental change data of the logistics park; input the historical logistics activity data and the historical environmental change data into a demand prediction model for minimum coverage demand prediction to obtain the minimum coverage value of the logistics park; perform redundant processing on the minimum coverage value of the logistics park to obtain the upper limit of dynamic edge nodes; arrange dynamic edge nodes based on the upper limit of dynamic edge nodes.

[0022] Preferably, the system terminal first obtains the logistics activity data and environmental change data of the logistics park in the past period from the Internet of Things devices in the park. Among them, the logistics activity data includes cargo loading and unloading data, vehicle entry and exit data, cargo circulation data, etc. The environmental change data includes temperature, humidity, air quality, etc. in the park. Subsequently, the obtained historical logistics activity data and historical environmental change data are input into the demand prediction model. The demand prediction model combines the historical logistics activity data and environmental change data, and uses the learned mapping relationship to predict the monitoring requirements of the entire logistics park in the future, and outputs the prediction result as the minimum coverage value of the logistics park. To avoid monitoring blind spots or monitoring failures caused by equipment failures, communication delays, etc. during the monitoring process, the system terminal will perform redundancy processing on the basis of the minimum coverage value. Specifically, the system terminal sets a certain redundancy coefficient according to the actual needs and importance of the park, and this redundancy coefficient is usually 0.2 or 0.3. Then multiply this redundancy coefficient by the minimum coverage value of the logistics park, and add the product to the minimum coverage value of the logistics park to calculate the upper limit of the dynamic edge nodes required for the entire logistics park. This represents that in the densest state, the dynamic edge nodes cannot exceed this calculated upper limit to ensure that resources are not wasted excessively. After that, according to the calculated upper limit of the dynamic edge nodes, the system terminal starts to deploy dynamic edge nodes in the logistics park, that is, configures all the dynamic edge nodes in the logistics park and adjusts these dynamic edge nodes to the sleep state for dynamic scheduling in case of increased tasks or anomalies, improving the monitoring flexibility.

[0023] For the demand prediction model, the system terminal obtains the sample logistics activity data, sample environmental change data, and sample edge node quantity from the sample library, and divides these samples to obtain the training set and the test set. Subsequently, a neural network model is designed as the demand prediction model according to the characteristics of the input data and the complexity of the problem. Then, the weights and biases of the demand prediction model are initialized using random numbers. After that, the training set is input into the demand prediction model, and the predicted edge node quantity is calculated layer by layer through forward propagation, and the mean square error (MSE) is used to calculate the loss value between the predicted edge node quantity and the corresponding sample edge node quantity. Then, through backpropagation, the partial derivative of the loss function with respect to each weight is calculated layer by layer from the output layer to the input layer as the gradient, and then the weights and bias terms of each layer are updated through the gradient descent method, making the network gradually optimized and reducing the value of the loss function until the maximum number of iterations. After the training is completed, the system terminal uses evaluation metrics such as mean square error and coefficient of determination to test the prediction performance of the demand prediction model on the test set. When the test result meets the expected expectation, the system terminal outputs the current demand prediction model; otherwise, it adjusts the learning rate, the number of hidden layers, the number of neurons, etc. to find the optimal neural network structure.

[0024] Based on the fixed edge nodes of the intelligent perception network, real-time monitoring of the key area is carried out to obtain multiple first real-time monitoring data.

[0025] In one embodiment, in the intelligent perception network, the system terminal uses the deployed fixed edge nodes to monitor the key areas in the logistics park, and real-time collects the logistics activities and environmental change data in the key areas. Through these fixed edge nodes, the activities in these key areas can be continuously monitored to generate the first real-time monitoring data. These data include the entry and exit of vehicles, the progress of cargo loading and unloading, the fluctuation of environmental temperature and humidity, etc., providing basic support for subsequent dynamic adjustment and risk assessment.

[0026] Obtain the area tasks of the key area, conduct area activity evaluation based on the area tasks and the first real-time monitoring data, obtain multi-dimensional area task indicators, calculate the area task complexity factor based on the multi-dimensional area task indicators; perform dynamic edge node adjustment of the intelligent perception network based on the area task complexity factor, and obtain multiple area activity data according to the adjustment result.

[0027] In one embodiment, the system terminal obtains the tasks currently being executed in each key area from the task scheduling platform. The area tasks include but are not limited to cargo loading and unloading, vehicle scheduling, environmental monitoring, etc. In order to quantitatively evaluate the area activities of the area tasks, the system terminal calculates the area priority, task urgency and task scale of each key area to form a multi-dimensional area task indicator. Subsequently, the multi-dimensional area task indicators of the key areas with anomalies are sequentially input into the pre-constructed area complexity function to generate the area task complexity factors of these key areas with anomalies. Then, the number of edge nodes required for the abnormal area is calculated according to each area task complexity factor and compared with the fixed edge nodes to obtain the number of dynamic edge nodes required for the area. Then, according to the obtained abnormal key areas and the corresponding number of dynamic edge nodes, the nearest dynamic edge nodes are retrieved to monitor the abnormal key areas, obtaining multiple second real-time monitoring data, and combining these second real-time monitoring data with multiple first real-time monitoring data to generate multiple area activity data, laying a foundation for subsequent early warning feedback.

[0028] Furthermore, the present application provides obtaining the area tasks of the key area, conducting area activity evaluation based on the area tasks and the first real-time monitoring data, obtaining multi-dimensional area task indicators, calculating the area task complexity factor based on the multi-dimensional area task indicators, including:

[0029] Perform current time truncation on the multiple cargo handling data, multiple vehicle flow data, and multiple environmental data of the multiple first real-time monitoring data according to a preset time step, and perform normalization processing on the truncation result to obtain multiple cargo handling data segments, multiple vehicle flow data segments, and multiple environmental data segments; based on the multiple cargo handling data segments, the multiple vehicle flow data segments, and the multiple environmental data segments, perform multi-dimensional frequency calculation in combination with the preset time step to obtain multiple handling frequencies, multiple vehicle in-and-out frequencies, and multiple environmental frequencies; obtain multiple area priorities based on the multiple handling frequencies, the multiple vehicle in-and-out frequencies, and the multiple environmental frequencies and add them to the area task multi-dimensional index.

[0030] Preferably, the system terminal sets the time step according to the type and monitoring requirements of the task. For example, every 5 minutes or 10 minutes is a time period, and data segments are regularly extracted from the real-time data stream for analysis. Subsequently, according to the set time step, data information within the corresponding time period is intercepted from the multiple cargo handling data, vehicle flow data, and environmental data of the multiple first real-time monitoring data to generate multiple groups of data segments. Each group of data segments corresponds to a key area and includes a cargo handling data segment, a vehicle flow data segment, and an environmental data segment. Then, these intercepted data segments are normalized to eliminate biases or outliers in the data, enabling different types of data to be analyzed on a unified scale. For example, the data is mapped to the range of 0-1 through the maximum-minimum method. After that, based on the cargo handling data segment within each time step, that is, by calculating the ratio of the total amount of loaded and unloaded goods in the cargo handling data segment to the time step, the cargo handling frequency of each key area is obtained. The handling frequency can reflect the amount of handling tasks completed per unit time. According to the vehicle flow data segment within each time step, that is, by calculating the ratio of the number of in-and-out vehicles in the vehicle flow data segment to the time step, the vehicle in-and-out frequency is obtained, and the vehicle in-and-out frequency represents the number of vehicle in-and-outs per unit time. According to the environmental data segment within each time step, that is, by adding the absolute temperature change amount and the absolute humidity change amount in the environmental data segment, and then calculating the ratio of the sum result to the time step, the environmental frequency is obtained. Then, the system terminal sums up the calculated handling frequency, vehicle in-and-out frequency, and environmental frequency of each key area, and uses the sum result as the area priority of the key area. Finally, the calculated multiple area priorities are added to the area task multi-dimensional index as a part of the task multi-dimensional index to further optimize resource allocation and provide a basis for subsequent monitoring and node scheduling.

[0031] Further, the present application provides a regional task for obtaining the key area. Based on the regional task and the first real-time monitoring data, a regional activity assessment is carried out to obtain multi-dimensional indicators of the regional task. Based on the multi-dimensional indicators of the regional task, a regional task complexity factor is calculated, including:

[0032] Perform threshold verification on the multiple handling frequencies, the multiple vehicle entry and exit frequencies, and the multiple environmental frequencies. According to the verification results, obtain the abnormal activity areas from the key areas; extract the regional tasks of the abnormal activity areas, obtain the basic data of the regional tasks, calculate the task urgency and task scale based on the basic data of the regional tasks, and add them to the multi-dimensional indicators of the regional task; input the multi-dimensional indicators of the regional task into the regional complexity function to generate the regional task complexity factor.

[0033] Optionally, the system terminal sets a threshold for the handling frequency, vehicle entry and exit frequency, and environmental frequency respectively according to historical data and expert suggestions to identify whether the activity is abnormal. Subsequently, compare the multiple handling frequencies, multiple vehicle entry and exit frequencies, and multiple environmental frequencies with the corresponding thresholds respectively. If the handling frequency is higher than the handling frequency threshold, it means that the handling activity in this key area is abnormally active and may require more monitoring resources. At this time, mark this key area as abnormal. If the vehicle entry and exit frequency is higher than the vehicle entry and exit frequency threshold, it means that the vehicle flow in this area is too frequent, which may lead to risks such as traffic congestion or poor scheduling. At this time, mark this key area as abnormal. If the environmental frequency is higher than the environmental frequency threshold, it means that the environmental conditions fluctuate violently, which may affect the warehousing or logistics process. At this time, mark this key area as abnormal. After that, the system terminal extracts these key areas with abnormal marks from the key areas to generate abnormal activity areas. Once the abnormal activity areas are determined, the system terminal extracts the tasks currently being executed in these abnormal activity areas to obtain the basic data of the regional tasks of these abnormal activity areas. These basic data of the regional tasks include task type, task objective, task time requirements, etc., such as the scheduled completion time, task deadline, quantity of goods, etc. Then, input these basic data of the regional tasks into the task urgency calculation formula and task scale calculation formula respectively to obtain the task urgency and task scale of these abnormal activity areas, and add them to the multi-dimensional indicators of the regional task to comprehensively reflect the complexity and urgency of the tasks in the abnormal activity areas. Then, the system terminal inputs the multi-dimensional indicators of the regional task into the pre-constructed regional complexity function to calculate the task complexity factor of these abnormal activity areas. This complexity factor is used for subsequent dynamic edge node adjustment to ensure reasonable allocation of monitoring resources according to the task complexity.

[0034] Combined with the node load capacity, the number of dynamic edge nodes is obtained according to the regional task complexity factor and the fixed edge nodes; a node scheduling instruction is generated based on the abnormal activity area and the number of dynamic edge nodes, and dynamic edge node movement is executed based on the node scheduling instruction; real-time monitoring of the abnormal activity area is performed based on the dynamic edge nodes, second real-time monitoring data is obtained, and combined with the multiple first real-time monitoring data, multiple regional activity data are generated.

[0035] Optionally, the system terminal multiplies the regional task complexity factor of the abnormal activity area by the regional coverage requirement (coverage area) corresponding to the area, and then calculates the ratio with the load capacity of a single node to obtain the total number of edge nodes required for the abnormal activity area. Subsequently, the difference between this total number of edge nodes and the fixed edge nodes of the abnormal activity area is calculated to obtain the required number of dynamic edge nodes. After obtaining the number of dynamic edge nodes, the system terminal combines the abnormal activity area with the corresponding number of dynamic edge nodes to generate a node scheduling instruction, which indicates the destination where the dynamic edge nodes need to move and the number of nodes that need to move. Then, the node scheduling instructions are sorted in descending order according to the regional task complexity factor of the abnormal activity area, that is, the areas with larger regional task complexity factors are processed first. For the abnormal activity area with the largest regional task complexity factor, the system terminal evaluates the movement cost of each dynamic edge node based on the Dijkstra algorithm, considering the cost of each step. Then, multiple dynamic edge nodes with the smallest distances are extracted from all dynamic edge nodes according to the number of dynamic edge nodes in the node scheduling instruction and moved to the destination indicated in the node scheduling instruction. When the dynamic edge nodes reach the corresponding abnormal activity area, they will immediately start to execute monitoring tasks, including data collection and sensor working mode switching. For example, monitoring the goods loading and unloading situation, vehicle entry and exit frequency, or environmental parameters, etc. These dynamic edge nodes will collect the second real-time monitoring data of the abnormal activity area and add these data to the first real-time monitoring data of the corresponding area to jointly form multiple regional activity data. These data will be used as the basis for the next step of monitoring, scheduling, and decision-making.

[0036] Furthermore, the present application provides a regional complexity function, including:

[0037] The regional complexity function is specifically as follows:

[0038]

[0039] Optionally, the regional complexity function is used to calculate the complexity factor C of the regional task task , which combines the urgency, scale, and priority of the task and reflects the complexity of task execution. This complexity factor provides a basis for subsequent dynamic node adjustment. The regional complexity function is specifically as follows: Among them, C task is the regional task complexity factor. is the task urgency. T standard is the standard completion time, which is the time required for the task to be completed as expected. T actual is the actual execution time of the task, that is, the time already consumed by the task. P priorityl is the regional priority, reflecting the importance of the task. is the task scale. A storage is the storage area. The larger the storage area, the larger the task scale, and the more monitoring resources and nodes are required. Q cargo is the quantity of goods. The more the quantity of goods, the higher the complexity of the loading and unloading operations, and more dynamic edge nodes are needed to cover the monitoring requirements. N vehicle is the number of vehicles. The number of vehicles is directly proportional to the transportation complexity of the logistics task. More vehicles require more complex scheduling and monitoring.

[0040] Input the multiple regional activity data into the dynamic perception depth analysis model for multi-layer perception analysis to obtain the global logistics intelligent perception set.

[0041] In one embodiment, after obtaining the multiple regional activity data, the system terminal inputs these regional activity data into a pre-constructed dynamic perception depth analysis model. The dynamic perception depth analysis model performs mapping calculations on the vehicle flow data, goods loading and unloading data, and environmental data in the regional activity data through the internal vehicle flow data calculation layer, goods loading and unloading data calculation layer, and environmental data calculation layer to generate the global logistics intelligent perception set. This global logistics intelligent perception set includes the vehicle flow index, goods loading and unloading index, and environmental risk index for subsequent regional risk assessment.

[0042] For the dynamic perception depth analysis model, the system terminal first extracts the sample vehicle flow data, sample goods loading and unloading data, sample environmental data, and sample global logistics intelligent perception set from the sample library, and divides these sample data into a training set and a test set. Subsequently, based on the multi-layer neural network, construct the dynamic perception depth analysis model structure, including the input layer, hidden layer, vehicle flow data calculation layer, goods loading and unloading data calculation layer, environmental data calculation layer, output layer, etc. Then, use random numbers to initialize the constructed dynamic perception depth analysis model, set the weights and biases of each layer. Then, use the same method as the aforementioned training demand prediction model to train the dynamic perception depth analysis model based on the training set until the maximum number of iterations, and use the test set to evaluate the dynamic perception depth analysis model. When the evaluation passes, output the current dynamic perception depth analysis model.

[0043] Input the vehicle flow index, cargo handling index, and environmental risk index of the global logistics intelligent perception set and the regional task complexity factor into the regional risk assessment function, match the multi-level early warning mechanism according to the function calculation result, and give early warning feedback based on the matching result.

[0044] In one embodiment, after obtaining the global logistics intelligent perception set, the system terminal combines and inputs the vehicle flow index, cargo handling index, environmental risk index, and regional task complexity factor in the global logistics intelligent perception set into a pre-constructed regional risk assessment function to calculate the regional risk coefficient of each key area. Subsequently, the calculated regional risk coefficient of each key area is matched with the multi-level early warning mechanism, that is, it is judged whether the regional risk coefficient falls within the corresponding threshold. Among them, the multi-level early warning mechanism includes primary response early warning, enhanced response early warning, and in-depth intervention early warning. The primary response early warning is a reminder notice early warning, indicating that the task complexity in this area is low, the vehicle flow is stable, the cargo handling volume is moderate, and the environmental risk is controllable. This reminder aims to maintain continuous monitoring of the regional situation. The enhanced response early warning is a preventive intervention early warning, indicating that there are certain potential risks in this area, such as an increase in task complexity, an increase in vehicle flow, a significant increase in cargo handling volume, or a deterioration of the environmental condition. At this time, preventive intervention suggestions are sent to the management personnel, prompting them to take preventive intervention measures in advance, such as increasing the monitoring frequency or dispatching more resources. This early warning aims to prevent potential problems from escalating. The in-depth intervention early warning is an active intervention early warning, indicating that the regional situation has deteriorated significantly, such as extremely high task complexity, excessive vehicle concentration, high cargo handling pressure, or a sharp rise in environmental risk. At this time, the management personnel will be immediately notified to take active intervention measures. For example, adding dynamic edge nodes, reallocating logistics resources, or reducing logistics activities in the area. This early warning aims to quickly respond to high-risk situations and prevent more serious consequences.

[0045] Furthermore, the present application provides a regional risk assessment function, including:

[0046] The regional risk assessment function is specifically as follows:

[0047] Preferably, the regional risk assessment function is used to evaluate the risk level of each area in the logistics park. By combining indexes in multiple dimensions such as vehicle flow, cargo handling, and environmental changes, the regional risk coefficient R of the area is obtained through function calculation, providing a basis for the subsequent early warning mechanism. The regional risk assessment function is specifically as follows: Among them, R is the regional risk coefficient, indicating the overall risk level of the area after evaluation. The higher the risk coefficient, the greater the potential risk in the area, and more monitoring resources and response measures are required. I vehicle is the vehicle flow index, reflecting the flow frequency and intensity of vehicles in this area. Icargo is the cargo handling index, reflecting the frequency, speed, and throughput of cargo handling within the region. I env is the environmental risk index, used to quantify the potential impact of the environment on logistics activities. t impact is the time dynamic factor, reflecting the time sensitivity of risks. C task is the regional task complexity factor, reflecting the complexity of regional tasks.

[0048] In summary, the embodiments of the present application have at least the following technical effects:

[0049] In the embodiments of the present application, by obtaining the floor plan of the park and identifying key areas, an intelligent perception network is arranged based on the identification results, including fixed edge nodes and dynamic edge nodes. Then, real-time monitoring is carried out through the fixed nodes, and the regional activities are evaluated by combining regional tasks and the first real-time monitoring data to generate multi-dimensional indicators and calculate the regional task complexity factor. Subsequently, the layout and scheduling of the dynamic nodes are adjusted according to the complexity factor, and then the regional activity data is obtained and multi-level analysis is carried out, and finally a global logistics intelligent perception set is formed. By inputting the vehicle flow index, cargo handling index, environmental risk index, and complexity factor in the global logistics intelligent perception set into the regional risk assessment function, the risk coefficient is calculated and matched with a multi-level early warning mechanism, and feedback is carried out based on the matching result. The layout of the dynamic nodes also predicts the minimum coverage requirements based on historical data and sets the node upper limit. Complex tasks are determined by calculating the frequencies of multiple real-time data, and the nodes are dynamically adjusted through threshold verification and the multi-dimensional indicators of regional tasks to cope with abnormal activity areas. These technical effects together solve the technical problems of unreasonable resource allocation and untimely risk warning caused by the difficulty in accurately monitoring the changing frequency of dynamic activities in the logistics park, and achieve the effect of efficiently monitoring and accurately warning by combining the intelligent perception network to adjust the dynamic edge nodes in real time and the multi-dimensional task complexity factor.

[0050] Embodiment 2, based on the same inventive concept as a smart monitoring method for a logistics park in the foregoing embodiment, such as Figure 2As shown in the figure, the present application provides a smart monitoring system for a logistics park. The system includes: Key area identification module 1: The key area identification module 1 is used to obtain the floor plan of the logistics park, identify key areas based on the floor plan, and arrange an intelligent perception network according to the key area identification results; Real-time monitoring module 2: The real-time monitoring module 2 is used to perform real-time monitoring of the key areas based on the fixed edge nodes of the intelligent perception network, and obtain a plurality of first real-time monitoring data; Area activity evaluation module 3: The area activity evaluation module 3 is used to obtain the area tasks of the key areas, evaluate area activities based on the area tasks and the first real-time monitoring data, obtain multi-dimensional indicators of area tasks, and calculate area task complexity factors based on the multi-dimensional indicators of area tasks; Dynamic edge node adjustment module 4: The dynamic edge node adjustment module 4 is used to adjust the dynamic edge nodes of the intelligent perception network based on the area task complexity factors, and obtain a plurality of area activity data according to the adjustment results; Multi-layer perception analysis module 5: The multi-layer perception analysis module 5 is used to input the plurality of area activity data into a dynamic perception depth analysis model for multi-layer perception analysis, and obtain a global logistics intelligent perception set; Multi-level early warning mechanism matching module 6: The multi-level early warning mechanism matching module 6 is used to input the vehicle flow index, cargo handling index, environmental risk index of the global logistics intelligent perception set and the area task complexity factor into an area risk assessment function, perform multi-level early warning mechanism matching according to the function calculation results, and perform early warning feedback based on the matching results.

[0051] Further, the key area identification module 1 is further used to execute the following method:

[0052] Obtain the historical logistics activity data and historical environmental change data of the logistics park; Input the historical logistics activity data and the historical environmental change data into a demand prediction model for minimum coverage demand prediction to obtain the minimum coverage value of the logistics park; Perform redundancy processing on the minimum coverage value of the logistics park to obtain the upper limit of dynamic edge nodes; Arrange dynamic edge nodes based on the upper limit of dynamic edge nodes.

[0053] Further, the area activity evaluation module 3 is further used to execute the following method:

[0054] Intercept the current time for the multiple cargo handling data, multiple vehicle flow data, and multiple environmental data among the multiple first real-time monitoring data according to a preset time step, and perform standardization processing on the intercepted results to obtain multiple cargo handling data segments, multiple vehicle flow data segments, and multiple environmental data segments; based on the multiple cargo handling data segments, the multiple vehicle flow data segments, and the multiple environmental data segments, perform multi-dimensional frequency calculation in combination with the preset time step to obtain multiple handling frequencies, multiple vehicle in-and-out frequencies, and multiple environmental frequencies; based on the multiple handling frequencies, the multiple vehicle in-and-out frequencies, and the multiple environmental frequencies, obtain multiple area priorities and add them to the area task multi-dimensional index.

[0055] Further, the area activity evaluation module 3 is also used to execute the following method:

[0056] Perform threshold verification on the multiple handling frequencies, the multiple vehicle in-and-out frequencies, and the multiple environmental frequencies, and obtain abnormal activity areas from the key areas according to the verification results; extract the area tasks of the abnormal activity areas, obtain the basic data of the area tasks, calculate the task urgency and task scale based on the basic data of the area tasks and add them to the area task multi-dimensional index; input the area task multi-dimensional index into the area complexity function to generate an area task complexity factor; in combination with the node load capacity, obtain the number of dynamic edge nodes according to the area task complexity factor and the fixed edge node; generate a node scheduling instruction based on the abnormal activity area and the number of dynamic edge nodes, and execute the movement of the dynamic edge node based on the node scheduling instruction; perform real-time monitoring of the abnormal activity area based on the dynamic edge node, obtain second real-time monitoring data, and combine it with the multiple first real-time monitoring data to generate multiple area activity data.

[0057] Further, the area activity evaluation module 3 is also used to execute the following method:

[0058] The area complexity function is specifically as follows:

[0059] Among them, C task is the area task complexity factor, is the task urgency, T standard is the standard completion time, T actual is the actual execution time of the task, P priorityl is the area priority, is the task scale, A storage is the storage area, Q cargo is the quantity of goods, N vehicle is the number of vehicles.

[0060] Further, the area activity evaluation module 3 is further configured to execute the following method:

[0061] For the area activity frequency that passes the threshold verification, obtain the normal activity area; judge the composition of the edge nodes in the normal activity area. If there are dynamic edge nodes, perform the dynamic edge node sleep operation.

[0062] Further, the multi-level early warning mechanism matching module 6 is further configured to execute the following method:

[0063] The area risk assessment function is specifically as follows: Wherein, R is the area risk coefficient, I vehicle is the vehicle flow index, I cargo is the cargo handling index, I env is the environmental risk index, t impact is the time dynamic factor, C task is the area task complexity factor.

[0064] It should be noted that the above-mentioned order of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above describes specific embodiments of this specification. The processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0065] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included within the protection scope of the present application.

[0066] This specification and the drawings are only exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A smart monitoring method for a logistics park, characterized in that, The method includes: Obtain the floor plan of the logistics park, identify key areas based on the floor plan, and arrange the intelligent perception network according to the key area identification results; Perform real-time monitoring of the key areas based on the fixed edge nodes of the intelligent perception network, and obtain multiple first real-time monitoring data; Obtain the area tasks of the key areas, evaluate the area activities based on the area tasks and the first real-time monitoring data, obtain multi-dimensional indicators of the area tasks, and calculate the area task complexity factor based on the multi-dimensional indicators of the area tasks; Adjust the dynamic edge nodes of the intelligent perception network based on the area task complexity factor, and obtain multiple area activity data according to the adjustment results; Input the multiple area activity data into the dynamic perception depth analysis model for multi-layer perception analysis to obtain the global logistics intelligent perception set; Input the vehicle flow index, cargo handling index, and environmental risk index of the global logistics intelligent perception set and the area task complexity factor into the area risk assessment function, match the multi-level early warning mechanism according to the function calculation results, and perform early warning feedback based on the matching results; Obtain the area tasks of the key areas, evaluate the area activities based on the area tasks and the first real-time monitoring data, obtain multi-dimensional indicators of the area tasks, and calculate the area task complexity factor based on the multi-dimensional indicators of the area tasks. The method includes: Perform current time truncation on the multiple cargo handling data, multiple vehicle flow data, and multiple environmental data of the multiple first real-time monitoring data according to the preset time step, and perform standardization processing on the truncation results to obtain multiple cargo handling data segments, multiple vehicle flow data segments, and multiple environmental data segments; Based on the multiple cargo handling data segments, the multiple vehicle flow data segments, and the multiple environmental data segments, perform multi-dimensional frequency calculation in combination with the preset time step to obtain multiple handling frequencies, multiple vehicle in-and-out frequencies, and multiple environmental frequencies; Obtain multiple area priorities based on the multiple handling frequencies, the multiple vehicle in-and-out frequencies, and the multiple environmental frequencies and add them to the multi-dimensional indicators of the area tasks; Obtain the area tasks of the key areas, evaluate the area activities based on the area tasks and the first real-time monitoring data, obtain multi-dimensional indicators of the area tasks, and calculate the area task complexity factor based on the multi-dimensional indicators of the area tasks. The method includes: Verify the thresholds of the multiple handling frequencies, the multiple vehicle in-and-out frequencies, and the multiple environmental frequencies, and obtain the abnormal activity areas from the key areas according to the verification results; Extract the area tasks of the abnormal activity areas, obtain the basic data of the area tasks, and calculate the task urgency and task scale based on the basic data of the area tasks and add them to the multi-dimensional indicators of the area tasks; Input the multi-dimensional indicators of the area tasks into the area complexity function to generate the area task complexity factor; Combine the node load capacity, and obtain the number of dynamic edge nodes according to the area task complexity factor and the fixed edge nodes; Generate a node scheduling instruction based on the abnormal activity area and the number of dynamic edge nodes, and execute the dynamic edge node movement based on the node scheduling instruction; Based on the dynamic edge nodes, perform real-time monitoring of the abnormal activity area, obtain second real-time monitoring data, and combine it with the multiple first real-time monitoring data to generate multiple regional activity data.

2. The method according to claim 1, wherein Perform the layout of dynamic edge nodes for the intelligent perception network. The method includes: Obtain the historical logistics activity data and historical environmental change data of the logistics park; Input the historical logistics activity data and the historical environmental change data into a demand prediction model for minimum coverage demand prediction to obtain the minimum coverage value of the logistics park; Perform redundancy processing on the minimum coverage value of the logistics park to obtain the upper limit of dynamic edge nodes; Perform the layout of dynamic edge nodes based on the upper limit of dynamic edge nodes.

3. The method according to claim 1, wherein The regional complexity function is specifically as follows: Among them, C task is the regional task complexity factor, is the task urgency, T standard is the standard completion time, T actual is the actual execution time of the task, P priorityl is the regional priority, is the task scale, A storage is the storage area, Q cargo is the quantity of goods, N vehicle is the number of vehicles.

4. The method according to claim 1, wherein The method includes: For the regional activity frequency that passes the threshold verification, obtain the normal activity area; Judge the composition of edge nodes for the normal activity area. If there are dynamic edge nodes, perform the dynamic edge node sleep operation.

5. The method according to claim 1, wherein The regional risk assessment function is specifically as follows: Among them, R is the regional risk coefficient, I vehicle is the vehicle flow index, I cargo is the cargo handling index, I env is the environmental risk index, t impact is the time dynamic factor, C task is the regional task complexity factor.

6. A smart monitoring system for a logistics park, characterized in that, Steps for implementing the intelligent monitoring method of a logistics park according to any one of claims 1 to 5, including: Key area identification module: Obtain the floor plan of the logistics park, identify key areas based on the floor plan, and arrange the intelligent perception network according to the key area identification results; Real-time monitoring module: Based on the fixed edge nodes of the intelligent perception network, perform real-time monitoring of the key area to obtain multiple first real-time monitoring data; Regional activity evaluation module: Obtain the regional tasks of the key area, perform regional activity evaluation based on the regional tasks and the first real-time monitoring data to obtain multi-dimensional indicators of the regional tasks, and calculate the regional task complexity factor based on the multi-dimensional indicators of the regional tasks; Dynamic edge node adjustment module: Adjust the dynamic edge nodes of the intelligent perception network based on the regional task complexity factor, and obtain multiple regional activity data according to the adjustment results; Multi-layer perception analysis module: Input the multiple regional activity data into a dynamic perception depth analysis model for multi-layer perception analysis to obtain a global logistics intelligent perception set; Multi-level early warning mechanism matching module: Input the vehicle flow index, cargo handling index, environmental risk index of the global logistics intelligent perception set and the regional task complexity factor into the regional risk assessment function, perform multi-level early warning mechanism matching according to the function calculation results, and perform early warning feedback based on the matching results.

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