Material light guiding method

Abstract

The invention provides a material light guiding method. The method comprises the following steps that row indicator lights are installed on each row of goods shelves, and layer indicator lights are installed on each layer of goods shelves. All the indicating lamps are connected to the central control system through the wireless network controller; the central control system maintains a database and records position information of all goods and corresponding goods allocation numbers. After an operator inputs a query instruction through the tablet personal computer or the PC, the system searches the goods allocation number corresponding to the goods in the database according to the instruction, and sends a signal through the network controller to activate the row indicator light and the layer indicator light of the corresponding goods shelf; the system introduces an adaptive learning algorithm, and can automatically adjust the display mode of the indicator lamp according to historical operation data and real-time environment change. Through combined use of the row indicating lamps and the layer indicating lamps, the goods picking personnel can quickly locate the target goods allocation without assistance of handheld equipment, so that the goods picking speed is remarkably improved, the guiding accuracy is ensured, and errors caused by manual misoperation are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics and warehousing management, and in particular to a material lighting guidance method. Background Art

[0002] Modern logistics and warehouse management rely on various automated information systems to improve operational efficiency and accuracy. Traditional picking systems usually locate goods and guide the picking process based on barcode scanning, RFID (radio frequency identification) tag reading, and LED indicator lights. However, these technologies have certain limitations. For example, handheld scanning devices increase the complexity of operations and reduce the speed of picking. Although the existing LED indicator-based system provides intuitive guidance, it lacks intelligence and flexibility, making it difficult to achieve efficient inventory management and reduce error rates. In addition, traditional systems are not able to adapt to environmental changes and cannot fully consider the impact of factors such as light and temperature in different areas of the warehouse on work efficiency. Therefore, it is necessary to develop a more intelligent and humanized material lighting guidance method to overcome the above problems and improve overall operational efficiency. Summary of the invention

[0003] In view of the shortcomings of the prior art, the present invention provides a material lighting guidance method. In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions: A material lighting guidance method comprises the following steps: Install row indicator lights on each row of shelves; Install layer indicator lights on each shelf level; Accurately bind the indicator light to the physical location of the shelf and the shelf number in the system; Remotely control the status of the indicator light through the network controller; The system background maintains a database to record the location information of all goods and the corresponding cargo location numbers; After the operator inputs the query command through the system interface, the system searches the corresponding shelf number of the item in the database according to the command, and sends a signal through the network controller to activate the row indicator and layer indicator of the corresponding shelf, so that the picker can intuitively see the row and layer where the item is located and quickly go to the designated location to pick up the item; The system introduces an adaptive learning algorithm, which automatically adjusts the indicator light display mode according to historical operation data and real-time environmental changes to optimize the picking path and efficiency; The formula for adjusting the brightness of the indicator light is: ; in, It's time The color of is the initial color, is the amount of color change, Is based on time and historical operation data The adjustment function.

[0004] Furthermore, the system also includes a welcome indicator light arranged at the warehouse entrance, which can respond to the operator's identity information and automatically light up when the operator enters the warehouse to guide the operator to the nearest workstation or picking starting point.

[0005] Furthermore, the indicator light uses a color-changing LED, and different colors indicate different working states or priorities, red for urgent orders, green for regular orders, and blue for inventory checks; The color change mode of the indicator light can be customized and programmed to support a variety of color combinations and flashing frequencies to adapt to different business scenarios and user preferences. At the same time, the indicator light has a predictive maintenance function that can predict future demand changes based on historical data and adjust the display mode in advance; The color change formula is: ; in, It's time The color of is the initial color, is the amount of color change, Is based on time and demand forecast data The adjustment function.

[0006] Furthermore, the system also includes an environmental sensing module for detecting the light intensity, temperature and humidity in the warehouse, and automatically adjusting the brightness and flashing frequency of the indicator light according to the detection results.

[0007] Furthermore, the system also includes an intelligent path planning algorithm. When multiple picking tasks are concurrent, the system calculates the optimal path, and the indicator lights light up in sequence according to the optimized path sequence to guide the pickers to complete the task efficiently; The intelligent path planning algorithm is based on a reinforcement learning model, which can continuously learn from historical data and optimize the path selection strategy. It also supports real-time updates and can adjust the path instantly according to the dynamic changes of new goods and temporary obstacles in the warehouse. The path adjustment formula is: ; in, It is a new path. It is The weight of the node, From the current node to The distance between nodes, is the time cost coefficient, is the total time, is the obstacle influence coefficient, The influence of obstacles. is the workload factor, is the workload of the picker, is the task priority coefficient, is the priority of the task, is the historical data coefficient, It is based on learning and prediction of historical operation data. is the real-time traffic situation coefficient, It is the real-time traffic flow in the warehouse.

[0008] Furthermore, the system includes a voice prompt function, which cooperates with the light guidance to provide additional sound guidance.

[0009] Furthermore, the indicator light has a self-checking and fault reporting function. When a fault is detected, an alarm is sent to the system and a backup indicator light is activated at the same time.

[0010] Furthermore, the system also includes a virtual reality training module, which provides an immersive training experience for new employees by simulating a real warehouse environment. The module can generate realistic three-dimensional scenes based on the actual warehouse layout and operating procedures to help employees become familiar with the warehouse structure and operating procedures. At the same time, the virtual reality training module can also record employees' operating data and provide personalized improvement suggestions.

[0011] Furthermore, the system also includes an intelligent dispatch center that monitors all activities within the warehouse in real time and automatically adjusts task priorities and personnel arrangements based on the current workload and resource allocation. The intelligent dispatch center uses big data analysis and machine learning algorithms to predict future logistics needs and prepare in advance.

[0012] Furthermore, the intelligent dispatching center is seamlessly integrated with ERP and WMS supply chain management systems.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. By combining the row indicator light and the layer indicator light, the picker can quickly locate the target cargo location without the assistance of handheld devices, which significantly improves the picking speed; 2. Each indicator light is associated with a unique cargo location number, ensuring the accuracy of the guidance and reducing errors caused by manual operation errors; 3. The introduction of the environmental perception module can dynamically adjust the display parameters of the indicator light according to the actual situation in the warehouse, improving the practicality and energy efficiency of the system; 4. Intelligent path planning algorithms can provide pickers with the shortest path suggestions, saving time and physical energy. Combined with voice prompts and visual guidance, it provides pickers with a more friendly working experience and reduces their workload. 5. Through functions such as welcome indicator lights and intelligent path planning, the interaction between people and machines is enhanced, promoting a more harmonious and efficient workflow. By collecting user feedback, the system can continuously optimize itself to improve the accuracy of guidance and user satisfaction. DETAILED DESCRIPTION

[0014] The technical solution of the present invention is further described below in conjunction with embodiments.

[0015] Embodiment 1 The present invention provides a material light guidance method, comprising the following steps: Install row indicators on each row of shelves and layer indicators on each layer of shelves. All indicators are connected to the central control system through a wireless network controller; The central control system maintains a database that records the location information of all goods and the corresponding cargo location numbers. After the operator enters the query command through the tablet or PC, the system searches the database for the corresponding cargo location number of the item according to the command, and sends a signal through the network controller to activate the row indicator and layer indicator of the corresponding shelf; The system introduces an adaptive learning algorithm that can automatically adjust the indicator light display mode based on historical operation data and real-time environmental changes. For example, the system can dynamically adjust the indicator light color and flashing frequency according to the daily peak picking hours to improve visibility and response speed; The formula for adjusting the brightness of the indicator light is: ; in, It's time The color of is the initial color, is the amount of color change, Is based on time and historical operation data The adjustment function.

[0016] Through adaptive learning algorithms, the system can predict peak hours based on historical data and adjust the indicator light display mode in advance, so that pickers can find the target location in the shortest time, significantly improving the picking speed; each indicator light is associated with a unique location number, ensuring the accuracy of the guidance and reducing errors caused by manual operation; by collecting user feedback, the system can continuously optimize itself to improve the accuracy of guidance and user satisfaction. The sentiment analysis algorithm can identify the emotional state of pickers, helping the system better understand user needs and provide more personalized services.

[0017] Embodiment 2 On the basis of the first embodiment, a welcome indicator light is set at the warehouse entrance, equipped with a facial recognition camera and a voice prompt device. The welcome indicator light is linked with other intelligent devices (such as access control system, security monitoring) through the wireless communication module; when the operator enters the warehouse, the facial recognition system automatically identifies his identity and dynamically plans the optimal path according to his personal work habits and task arrangements. The welcome indicator light will light up automatically, and guide the operator to the nearest workstation or picking starting point through light guidance and voice prompts; Navigation path optimization formula: ; in, is the optimal path, It is The weight of the node, From the current node to The distance between nodes, is the time cost coefficient, is the total time.

[0018] The welcome light combined with facial recognition technology can quickly identify operators and provide personalized navigation guidance, reducing entry waiting time and path-finding time; the welcome light is linked with other smart devices to form a comprehensive security management system, ensuring that only authorized personnel can enter the warehouse, thereby improving warehouse safety.

[0019] Embodiment 3 Based on the first or second embodiment, the indicator light adopts a color-changing LED, supporting a variety of color combinations and flashing frequencies. Each indicator light is equipped with a micro sensor for monitoring environmental parameters (such as temperature, humidity, and light intensity); The system sets the color of the indicator light according to different working status or priority, such as red for urgent orders, green for regular orders, and blue for inventory checks. The color change mode of the indicator light can be customized and supported for personalized settings. At the same time, the system has a predictive maintenance function that can predict future demand changes based on historical data and adjust the display mode in advance; The color change formula is: ; in, It's time The color of is the initial color, is the amount of color change, Is based on time and demand forecast data The adjustment function.

[0020] By using LED indicators of different colors, the system can intuitively convey the priority and status of the task, helping pickers quickly identify the target storage location and reducing the possibility of misoperation; the color and flashing frequency of the indicator lights can be custom programmed according to business scenarios and user preferences to adapt to different operational needs, enhancing the flexibility of the system.

[0021] Embodiment 4 Based on the third embodiment, multiple environmental sensing modules are installed in the warehouse to detect light intensity, temperature and humidity. Each module is equipped with a wireless communication interface and can be interconnected with other smart devices (such as temperature control system, lighting system); The environmental perception module automatically adjusts the brightness and flashing frequency of the indicator light according to the detection results to adapt to different working conditions, improve visibility and energy efficiency. The module also has a self-calibration function that can automatically adjust sensor parameters after long-term operation to ensure data accuracy and system stability.

[0022] The environmental perception module can monitor the changes in light, temperature and humidity in the warehouse in real time, and automatically adjust the display parameters of the indicator light according to the actual situation to ensure the best visual guidance in different environments; the environmental perception module has a self-calibration function to ensure high-precision data collection and processing after long-term operation, thereby improving the stability and reliability of the system.

[0023] Embodiment 5 Based on the fourth embodiment, each shelf and channel in the warehouse is equipped with sensors for real-time monitoring of obstacles, traffic flow and other information. The indicator light is connected to the central control system through a wireless network controller; The system also includes an intelligent path planning algorithm. When multiple picking tasks are concurrent, the system calculates the optimal path, and the indicator lights light up in sequence according to the optimized path sequence to guide the pickers to complete the task efficiently; The intelligent path planning algorithm is based on a reinforcement learning model, which can continuously learn from historical data and optimize the path selection strategy. The system will adjust the path in real time based on multiple factors (such as distance, obstacles, workload, task priority, historical data, and real-time traffic conditions) to ensure the smoothness and accuracy of the picking process; The path adjustment formula is: ; in, It is a new path. It is The weight of the node, From the current node to The distance between nodes, is the time cost coefficient, is the total time, is the obstacle influence coefficient, The influence of obstacles. is the workload factor, is the workload of the picker, is the task priority coefficient, is the priority of the task, is the historical data coefficient, It is based on learning and prediction of historical operation data. is the real-time traffic situation coefficient, It is the real-time traffic flow in the warehouse.

[0024] The intelligent path planning algorithm can comprehensively consider multiple factors and dynamically adjust the picking path to ensure that pickers complete the task in the shortest time, reducing unnecessary walking and waiting time. By real-time monitoring of traffic flow and obstacles in the warehouse, the system can adjust the path in time to avoid congestion and detours, thereby improving overall work efficiency.

[0025] Embodiment 6 Based on Example 5, a voice prompt device is installed near each indicator light, supporting multi-language and personalized sound effect settings. The voice prompt device is connected to the central control system via a wireless network. The voice prompt function integrates natural language processing technology, which can customize the sound content according to the operator's language preferences and hearing needs. The system can also be integrated with virtual assistants (such as AI customer service) to provide real-time help and support to solve complex problems. Through voice prompts, pickers can confirm the cargo location number more accurately, reducing the possibility of misoperation, especially in low light or obstructed vision.

[0026] Preferably, each indicator light is equipped with redundant circuits and backup power supplies, which can automatically switch to the backup circuit when the main circuit fails to keep the indicator light running continuously. The indicator light is also equipped with a wireless communication module, which can upload fault information to the cloud; the system has a self-check function, regularly checks the health status of the indicator light, and issues a warning before a fault occurs. The professional team can remotely diagnose and maintain the system through the cloud platform to ensure the continuity and reliability of the system. Through the self-check and predictive maintenance functions, the system can issue a warning before a fault occurs, avoid downtime, ensure the continuous operation of the indicator light, and improve the reliability and availability of the system.

[0027] Embodiment 7 On the basis of Example 6, it also includes a user feedback mechanism, which allows pickers to evaluate the accuracy of the guidance system. The system continuously learns and improves based on user feedback, and gradually improves the guidance accuracy and user experience; the user feedback mechanism integrates a sentiment analysis algorithm, which can identify the emotional state of the pickers (such as satisfaction, dissatisfaction, confusion), and adjust the system's response method based on emotional feedback, such as providing more detailed operating instructions or directly contacting customer service support. The system also generates reports regularly to analyze user feedback trends and hot issues to provide data support for subsequent product iterations. In addition, the system can automatically generate personalized operating guides based on long-term user feedback to help new employees adapt to the work environment more quickly and improve overall work efficiency Embodiment 8 Based on the seventh embodiment, the system also includes a virtual reality training module, which provides an immersive training experience for new employees by simulating a real warehouse environment. The module can generate realistic three-dimensional scenes based on the actual warehouse layout and operating procedures, helping employees to become familiar with the warehouse structure and operating procedures, reducing training time and costs. At the same time, the virtual reality training module can also record employees' operating data and provide personalized improvement suggestions to ensure that each employee can achieve the best operating level.

[0028] Embodiment 9 Based on Example 8, the system also includes an intelligent scheduling center that monitors all activities in the warehouse in real time and automatically adjusts task priorities and personnel arrangements based on the current workload and resource allocation. The intelligent scheduling center uses big data analysis and machine learning algorithms to predict future logistics needs, prepare in advance, and ensure the efficiency and flexibility of warehouse operations.

[0029] Preferably, the intelligent dispatching center is seamlessly integrated with the ERP and WMS supply chain management systems to achieve digital management of the entire process.

[0030] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A material lighting guidance method, characterized in that: The following steps are involved: Install row indicator lights on each row of shelves; Install layer indicator lights on each shelf level; Accurately bind the indicator light to the physical location of the shelf and the shelf number in the system; Remotely control the status of the indicator light through the network controller; The system background maintains a database to record the location information of all goods and the corresponding cargo location numbers; After the operator inputs the query command through the system interface, the system searches the corresponding shelf number of the item in the database according to the command, and sends a signal through the network controller to activate the row indicator and layer indicator of the corresponding shelf, so that the picker can intuitively see the row and layer where the item is located and quickly go to the designated location to pick up the item; The system introduces an adaptive learning algorithm, which automatically adjusts the indicator light display mode according to historical operation data and real-time environmental changes to optimize the picking path and efficiency; The formula for adjusting the brightness of the indicator light is: ； in, It's time The color of is the initial color, is the amount of color change, Is based on time and historical operation data The adjustment function.

2. A material light guidance method according to claim 1, characterized in that: The system also includes a welcome indicator light arranged at the warehouse entrance, which can respond to the operator's identity information and automatically light up when the operator enters the warehouse to guide the operator to the nearest workstation or picking starting point.

3. A material light guidance method according to claim 1, characterized in that: The indicator light adopts a color-changing LED, and different colors indicate different working states or priorities. Red indicates urgent orders, green indicates regular orders, and blue indicates inventory checks. The color change mode of the indicator light can be customized and programmed to support a variety of color combinations and flashing frequencies to adapt to different business scenarios and user preferences. At the same time, the indicator light has a predictive maintenance function that can predict future demand changes based on historical data and adjust the display mode in advance; The color change formula is: ； in, It's time The color of is the initial color, is the amount of color change, Is based on time and demand forecast data The adjustment function.

4. A material light guidance method according to claim 1, characterized in that: The system also includes an environmental sensing module for detecting the light intensity, temperature and humidity in the warehouse, and automatically adjusting the brightness and flashing frequency of the indicator light according to the detection results.

5. A material light guidance method according to claim 1, characterized in that: The system also includes an intelligent path planning algorithm. When multiple picking tasks are concurrent, the system calculates the optimal path, and the indicator lights light up in sequence according to the optimized path sequence to guide the pickers to complete the task efficiently; The intelligent path planning algorithm is based on a reinforcement learning model, which can continuously learn from historical data and optimize the path selection strategy. It also supports real-time updates and can adjust the path instantly according to the dynamic changes of new goods and temporary obstacles in the warehouse. The path adjustment formula is: ； in, It is a new path. It is The weight of the node, From the current node to The distance between nodes, is the time cost coefficient, is the total time, is the obstacle influence coefficient, The influence of obstacles. is the workload factor, is the workload of the picker, is the task priority coefficient, is the priority of the task, is the historical data coefficient, It is based on learning and prediction of historical operation data. is the real-time traffic situation coefficient, It is the real-time traffic flow in the warehouse.

6. A material light guidance method according to claim 1, characterized in that: The system includes a voice prompt function, which cooperates with the light guidance to provide additional sound guidance.

7. A material light guidance method according to claim 1, characterized in that: The indicator light has self-checking and fault reporting functions. When a fault is detected, an alarm is sent to the system and a backup indicator light is activated at the same time.

8. A material lighting guidance method according to claim 1, characterized in that: The system also includes a virtual reality training module, which provides an immersive training experience for new employees by simulating a real warehouse environment. The module can generate realistic three-dimensional scenes based on the actual warehouse layout and operating procedures to help employees become familiar with the warehouse structure and operating procedures. At the same time, the virtual reality training module can also record employees' operating data and provide personalized improvement suggestions.

9. A material lighting guidance method according to any one of claims 1 to 8, characterized in that: The system also includes an intelligent dispatch center that monitors all activities within the warehouse in real time and automatically adjusts task priorities and personnel arrangements based on current workload and resource allocation. The intelligent dispatch center uses big data analysis and machine learning algorithms to predict future logistics needs and prepare in advance.

10. A material light guidance method according to claim 9, characterized in that: The intelligent dispatching center is seamlessly integrated with the ERP and WMS supply chain management systems.

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