Internet-of-things intelligent warehousing service robot control method and system

Abstract

The invention discloses an Internet of Things intelligent storage service robot control method and system, and relates to the technical field of intelligent robot control, and the method comprises the steps: achieving path planning through combining a first fusion algorithm and a second algorithm; an instruction is generated through a first voice conversion technology, and intelligent interaction is realized in combination with a second language processing technology and a second voice synthesis technology; and comprehensive service management is realized through the first management system. According to the method, through the multi-sensor data fusion technology and the A * algorithm, the robot can perceive the environment in real time, the position of the robot can be accurately positioned, the route is dynamically adjusted according to the environment change, collision and jamming are effectively avoided, and therefore the operation efficiency and accuracy are improved; man-machine interaction is realized through cooperation of ASR, NLP and TTS technologies, and the operation convenience is improved; by constructing the knowledge base, the robot can learn and store knowledge and intelligently answer and make decisions according to the content of the knowledge base, and the intelligent level of homework is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent robot control, and specifically to a control method and system for an Internet of Things intelligent warehousing service robot. Background Art

[0002] In recent years, with the rapid development of Internet of Things technology and the rise of the intelligentization wave, the technology of intelligent warehousing service robots has received extensive attention and application. The traditional warehousing management mode has problems such as low efficiency, high labor cost, and inaccurate inventory management, and can no longer meet the needs of the modern logistics industry. Intelligent warehousing service robots can autonomously complete operation processes such as warehousing, storage, picking, and outbound through integrating multiple sensors, navigation technologies, and artificial intelligence algorithms, significantly improving the warehousing efficiency and management level. However, there are still some deficiencies in the existing control methods of intelligent warehousing service robots, such as insufficient navigation accuracy, difficulty in coping with complex and changeable warehousing environments; lack of effective anti-collision and anti-falling functions, and difficult to guarantee safety; low integration degree with the background management system, and difficult to achieve data sharing and collaborative management.

[0003] In view of the above problems, the present invention proposes a control method and system for an Internet of Things intelligent warehousing service robot. This method realizes the precise perception and path planning of the robot for complex environments through multi-sensor data fusion navigation technology, improving the navigation accuracy and flexibility; at the same time, combining multi-sensor data such as lidar, ultrasonic sensors, depth cameras, and infrared sensors, it realizes the anti-collision and anti-falling functions, enhancing the safety of the robot; in addition, this system is deeply integrated with the background management system, realizing data sharing and collaborative management, and improving the efficiency and accuracy of warehousing management. Summary of the Invention

[0004] In view of the existing problems above, the present invention is proposed.

[0005] Therefore, the technical problems solved by the present invention are: the existing methods for intelligent warehousing service robots have insufficient navigation accuracy, poor safety, low integration degree with the background management system, and how to achieve precise navigation, anti-collision and anti-falling, and deep integration with the background management system.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: A control method for an Internet of Things intelligent warehousing service robot, including real-time collecting first environmental data, generating a first environmental map through a first fusion algorithm, and realizing path planning based on the first environmental map through a second algorithm; generating instructions through a first voice conversion technology, and realizing intelligent interaction by combining a second language processing technology and a second voice synthesis technology; connecting to a first server through a first communication means, and realizing comprehensive service management through a first management system.

[0007] As a preferred embodiment of the control method for the novel Internet of Things intelligent warehousing service robot of the present invention, wherein: generating the first environmental map through the first fusion algorithm includes performing weighted fusion on the first environmental data through the first fusion algorithm to generate the first environmental map.

[0008] As a preferred embodiment of the control method for the novel Internet of Things intelligent warehousing service robot of the present invention, wherein: implementing path planning through the second algorithm includes planning a path through the second algorithm and dynamically adjusting the path in combination with real-time data.

[0009] As a preferred embodiment of the control method for the novel Internet of Things intelligent warehousing service robot of the present invention, wherein: generating an instruction through the first voice conversion technology includes converting voice into text through the first voice conversion technology and extracting keywords to generate an instruction.

[0010] As a preferred embodiment of the control method for the novel Internet of Things intelligent warehousing service robot of the present invention, wherein: implementing intelligent interaction includes matching answers from the knowledge base through the second language processing technology.

[0011] As a preferred embodiment of the control method for the novel Internet of Things intelligent warehousing service robot of the present invention, wherein: implementing intelligent interaction further includes converting the matched answers into voice through the second voice synthesis technology to achieve intelligent interaction.

[0012] As a preferred embodiment of the control method for the novel Internet of Things intelligent warehousing service robot of the present invention, wherein: implementing comprehensive service management through the first management system includes implementing knowledge management, operation management, and monitoring management through the first management system.

[0013] Another object of the present invention is to provide a control system for a novel Internet of Things intelligent warehousing service robot, which can solve the problems of insufficient navigation accuracy and poor safety in the current intelligent warehousing service robot technology through multi-sensor data fusion navigation technology.

[0014] As a preferred embodiment of the control system for the novel Internet of Things intelligent warehousing service robot of the present invention, wherein: it includes a motion control module, a voice interaction module, and a service management module; the motion control module is used to collect first environmental data in real time, generate a first environmental map through the first fusion algorithm, and implement path planning through the second algorithm based on the first environmental map; the voice interaction module is used to generate an instruction through the first voice conversion technology and implement intelligent interaction in combination with the second language processing technology and the second voice synthesis technology; the service management module is used to connect to the first server through the first communication means and implement comprehensive service management through the first management system.

[0015] A computer device includes a memory and a processor. The memory stores a computer program, and the execution of the computer program by the processor implements the steps of a control method for a new type of Internet of Things intelligent warehousing service robot.

[0016] A computer-readable storage medium stores a computer program thereon. When the computer program is executed by a processor, it implements the steps of a control method for a new type of Internet of Things intelligent warehousing service robot.

[0017] Advantages of the present invention: The control method for the new type of Internet of Things intelligent warehousing service robot provided by the present invention uses multi-sensor data fusion technology and the A* algorithm. Not only can the robot construct a high-precision environmental map and perform real-time environmental perception, but it can also accurately locate its own position and dynamically adjust the route according to environmental changes, effectively avoiding collisions and freezes, thereby improving the operation efficiency and accuracy; through the coordination of ASR real-time speech transcription technology, NLP natural language processing technology, and TTS speech synthesis technology, the robot can understand and execute voice commands, realizing human-machine interaction and improving the operation convenience; by constructing a knowledge base, the robot can learn and store knowledge, and make intelligent answers and decisions according to the content of the knowledge base, enhancing the intelligence level of the operation; through the background management system, not only can the running state of the robot be monitored in real time and remote control and management be carried out, faults can be detected and solved in time to ensure the safe operation of the robot, but also data sharing between the robot and the background management system can be realized, and the warehousing data can be updated in real time. Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for description in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 It is the overall flowchart of a control method for an Internet of Things intelligent warehousing service robot provided by the first embodiment of the present invention.

[0020] Figure 2 It is the overall flowchart of a control system for a new type of Internet of Things intelligent warehousing service robot provided by the third embodiment of the present invention. Detailed Embodiments

[0021] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will provide a detailed description of the specific embodiments of the present invention with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0022] Example 1, referring to Figure 1 , which is an embodiment of the present invention, provides a control method for an Internet of Things intelligent warehousing service robot, including:

[0023] S1: Real-time collect the first environmental data, generate the first environmental map through the first fusion algorithm, and implement path planning based on the first environmental map through the second algorithm.

[0024] Furthermore, real-time collecting the first environmental data includes real-time collecting the first environmental data through multiple sensors carried by the robot.

[0025] The multiple sensors include, but are not limited to, lidar, ultrasonic sensors, depth cameras, and infrared sensors, etc.

[0026] The first environmental data includes, but is not limited to, distance data, contour data, and obstacle position data collected by lidar; close-range obstacle data and dynamic obstacle data collected by ultrasonic sensors; three-dimensional space data, dynamic object recognition data, and visual obstacle avoidance data collected by depth cameras, and heat source detection data and low-light environment data collected by infrared cameras, etc.

[0027] It should be noted that generating the first environmental map through the first fusion algorithm includes performing weighted fusion on the first environmental data through the first fusion algorithm to generate the first environmental map.

[0028] The first fusion algorithm can be a Kalman filter algorithm, a Bayesian estimation algorithm, or other data fusion algorithms suitable for generating a high-precision environmental map after weighted fusion.

[0029] The first environmental map is a high-precision environmental map generated by performing weighted fusion on the first environmental data through the first fusion algorithm.

[0030] In the embodiments of the present application, the first fusion algorithm uses the Kalman filter algorithm to perform weighted fusion on the first environmental data to generate the first environmental map. Specifically, the first environmental data collected is preprocessed according to the embodiments of the present application; the preprocessing according to the embodiments of the present application includes denoising, calibration, and time synchronization to ensure the consistency and accuracy of the data; the state variables and the error covariance matrix of the Kalman filter are initialized, where the state variables include the position, speed, and environmental map information of the robot; the data of each sensor is weighted, and the weights are dynamically adjusted according to the sensor accuracy and reliability. Among them, the lidar data has the highest weight to improve the high-precision distance information; the ultrasonic sensor and depth camera data have the second highest weight to supplement the lidar blind area; the infrared sensor data has the lowest weight to detect heat sources and living bodies; the prediction step of the Kalman filter is used to predict the state at the next moment based on the current state variables and the system dynamic model. In the update step, the predicted state is compared with the actual sensor measurement value, the residual is calculated, and according to the residual and the sensor weights, the Kalman gain is updated, and then the state variables and the error covariance matrix are updated, and the environmental map is constructed using the updated state variables.

[0031] In an alternative embodiment, the first fusion algorithm uses the Bayesian estimation algorithm to perform weighted fusion on the first environmental data to generate the first environmental map. Specifically, the collected data is preprocessed according to the alternative embodiment. The preprocessing according to the alternative embodiment includes denoising, filtering, and normalization to ensure the data quality; the Bayesian estimation algorithm is used to perform weighted fusion on the preprocessed data, that is, an initial weight is assigned to each sensor, and the weights are assigned based on the historical performance and reliability of the sensors. According to the uncertainty of the sensor data, the weights are updated. The weight update formula is expressed as:

[0032]

[0033] where W i represents the weight of the i-th sensor, and P(S i |O) represents the posterior probability of the sensor S i data under the observation O; the data of each sensor is mapped to the same coordinate system using the weighted fusion data, and the Bayesian estimation fusion algorithm is used to combine the weights and data of each sensor to generate a high-precision comprehensive environmental map.

[0034] It should also be noted that the path planning is implemented through the second algorithm, including planning the path through the second algorithm and dynamically adjusting the path in combination with real-time data.

[0035] The second algorithm can be the A* algorithm, or the D*Lite algorithm, or other algorithms suitable for path planning.

[0036] The real-time data includes, but is not limited to, real-time sensor data.

[0037] In the embodiment of the present application, the second algorithm uses the A* algorithm combined with real-time data for path planning and dynamically adjusts the path. Specifically, an initial environment map is constructed based on the multi-sensor data fusion algorithm, the A* algorithm is used to initialize the path planning, the starting point and the target point are set, and the heuristic function is calculated to estimate the cost from the current point to the target point; the starting point is added to the open list of the A* algorithm, and path search is performed until the target point is found or the open list is empty; the path search includes selecting the node with the lowest f(n) value from the open list, which is expressed as:

[0038] f(n) = g(n) + h(n)

[0039] where g(n) represents the actual cost from the starting point to the current node, and h(n) represents the heuristic cost; the node with the lowest f(n) value is moved to the closed list; the adjacent nodes of this node are expanded, and the g(n) and f(n) values of each adjacent node are calculated; if the adjacent node is already in the open list and the new g(n) value is lower, then the f(n) value and the parent node are updated; if the adjacent node is not in the open list, then the adjacent node is added to the open list; during the path search process of the robot, real-time sensor data is received, and the following steps are performed:

[0040] Step 1: When the ultrasonic sensor or the depth camera detects an obstacle, the A search is immediately paused.

[0041] Step 2: Use the real-time map data of the lidar to re-evaluate the environment and update the environment map.

[0042] Step 3: Re-plan the affected path segment and re-calculate the path from the current node using the A algorithm.

[0043] Step 4: After determining the best path to reach the target point, the robot moves along the calculated path again and continuously receives sensor data. If the sensor data indicates that a new obstacle appears on the predetermined route, it immediately stops moving forward and returns to Step 1 to re-plan the path; if the sensor data indicates that the predetermined path is safe, it continues to move along the current path until the robot reaches the target point.

[0044] In an alternative embodiment, the second algorithm uses the D*Lite algorithm for path planning and dynamically adjusts the path. Specifically, on the initialized environmental map, the D*Lite algorithm is used to plan the initial path from the current position of the robot to the target position, and the key nodes and path costs of the initial path are recorded. Among them, the path costs include distance cost, energy consumption cost, safety cost, time cost, and knowledge base cost. During the movement of the robot, the sensor data is continuously monitored, and the environmental map is updated in real time. When the ultrasonic sensor or depth camera detects an obstacle, the path replanning mechanism is triggered. Based on the latest environmental map, the D*Lite algorithm is used to recalculate the path; the newly added or removed obstacles are identified according to the sensor data; the path costs are re-evaluated, and the path with the lowest cost is selected; if the target position changes, the path is replanned. After the path planning, the robot moves along the newly planned path, and at the same time, the sensor data is continuously monitored. If the obstacles on the path change, the movement is immediately paused, and the path is replanned according to the D*Lite algorithm until the robot reaches the target position.

[0045] S2: Generate instructions through the first voice conversion technology, and realize intelligent interaction by combining the second language processing technology and the second speech synthesis technology.

[0046] Furthermore, generating instructions through the first voice conversion technology includes converting voice into text through the first voice conversion technology and extracting keywords to generate instructions.

[0047] The first voice conversion technology includes, but is not limited to, the ASR real-time speech transcription technology.

[0048] In the embodiment of the present application, the robot is configured with a ring-shaped six-array microphone for collecting voice.

[0049] It should be noted that realizing intelligent interaction includes matching answers from the knowledge base through the second language processing technology.

[0050] The second language processing technology includes, but is not limited to, the NLP natural language processing technology.

[0051] The knowledge base includes supporting custom keyword groups and answer templates, and manages the knowledge base through operations such as knowledge acquisition, update, deletion, and release; the system supports the regular matching function, matches user questions through sentence regular grammar, and automatically binds the answers corresponding to different instance parameters to achieve accurate replies.

[0052] It should also be noted that realizing intelligent interaction further includes converting the matched answers into voice through the second speech synthesis technology to achieve intelligent interaction.

[0053] The second speech synthesis technology includes, but is not limited to, the TTS speech synthesis technology.

[0054] S3: Connect to the first server through the first communication means and implement comprehensive service management through the first management system.

[0055] Furthermore, implementing comprehensive service management through the first management system includes implementing knowledge management, operation management, and monitoring management through the first management system.

[0056] The first communication means includes but is not limited to 4G network, 5G network, Wi-Fi, Bluetooth, etc.

[0057] The first server includes but is not limited to cloud servers, physical servers, dedicated servers, etc.

[0058] The first management system includes but is not limited to a background management system.

[0059] It should be noted that the first management system monitors the operating status of the robot through monitoring management, including but not limited to battery power, location, load conditions, fault diagnosis, etc., and remotely manages it; through operation management, it is responsible for the resource usage of the robot, such as memory, storage, network bandwidth, and is used to allocate and schedule tasks to the robot to ensure efficient system operation; through knowledge management, it is used for knowledge base maintenance and update, making the knowledge base highly customizable.

[0060] Embodiment 2, an embodiment of the present invention, provides a control method for an Internet of Things intelligent warehousing service robot. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0061] First, this simulation experiment is carried out in a simulated warehouse environment to compare the performance differences between the traditional manual management mode and the technical solution of the present invention. Among them, the traditional manual management mode is the control group, and the technical solution of the present invention is the experimental group. The simulated warehouse size is set to 50m × 30m × 5m, and the inbound area, storage area, picking area, and outbound area are delimited. Dynamic obstacles are set, such as mobile shelves, randomly stacked packages, etc., and static obstacles are set, such as columns, blind spots of shelves, etc.

[0062] The robot in the experimental group is equipped with a lidar, ultrasonic sensors, a depth camera, and a ring six-array microphone, and is connected to a cloud server. Among them, the lidar detection range is 0.1 - 30m, and the accuracy is ±2cm; the ultrasonic sensor detection range is 0.2 - 5m; the depth camera resolution is 1280 × 720, and the frame rate is 30fps; the sound pickup radius of the ring six-array microphone is 5m, and the 4G network latency of the cloud server is ≤50ms. The control group uses a traditional manual operation and a warehousing robot with single lidar navigation.

[0063] Secondly, navigation tests, voice tests, and inventory tests were conducted. In the navigation test, 10 sets of random path tasks were set, with the distance from the starting point to the target point being 20 - 40 m. The completion time, obstacle avoidance success rate, and path deviation error of the experimental group and the control group were statistically analyzed. In the voice test, under a background noise of 70 dB, through 10 sets of standardized voice commands, the voice recognition accuracy rate and response time were tested. In the inventory test, the inbound, picking, and outbound processes of 1000 items were simulated, and the inventory data update delay and error rate were statistically analyzed.

[0064] During the experiment, in the navigation test, after the experimental group robot received the target coordinates, the multi - sensor fusion algorithm was started, and the A* algorithm was used for path planning. The control group robot relied only on lidar, and the path planning was the fixed A algorithm. In the voice test, when the user issued an instruction such as "Please transport spare part A3 - 05 to the picking area", the experimental group generated instructions through ASR and NLP, and the control group input manually. In the inventory test, the experimental group automatically updated the inventory data through RFID tags, and the background system was synchronized in real - time. The control group scanned the code manually, and the average manual scanning speed was about 2 seconds per piece. The experimental data shown in Table 1 below were obtained.

[0065] Table 1 Comparison table of test index data between the present invention and traditional technologies

[0066] Test Index Unit Experimental Group (This Invention) Control Group (Traditional Technology) Path Planning Time Seconds / Task 12.3 18.7 Obstacle Avoidance Success Rate ％ 98.5 82.3 Speech Recognition Accuracy Rate ％ 93.7 75.4 Speech Command Response Time Seconds 1.2 3.5 Inventory Data Error Rate ％ 0.05 1.2 Task Scheduling Efficiency Tasks / Hour 45 28

[0067] It can be seen from the experimental data that in the navigation test of the present invention, the path planning time was shortened by 34.2%, and the obstacle avoidance success rate was increased by 16.2%. By providing a high - precision map through lidar, ultrasonic waves and depth cameras were used to detect dynamic obstacles in real - time. Combining with the improved A* algorithm, it avoided the risk of path detours or collisions caused by environmental changes in traditional single sensors. For example, in the dynamic obstacle scenario, the present invention detected the moving shelf in advance through an infrared sensor, and the path replanning took only 0.8 seconds. While the traditional technology relied on a static map and required manual intervention to adjust the path.

[0068] In the voice test, the voice recognition accuracy rate of the present invention was increased by 18.3%, and the response time was reduced by 65.7%. The 360° sound source localization and echo cancellation of the annular six - array microphone effectively suppressed environmental noise interference. The NLP technology accurately extracted instruction parameters through regular matching, while the traditional technology was prone to input errors or delays due to manual input.

[0069] In the inventory test, the inventory error rate of the present invention was reduced by 95.8%, and the task scheduling efficiency was increased by 60.7%. The background management system dynamically associated inventory data with business processes through a knowledge base, while the traditional technology of manual scanning was prone to missed inspections and could not synchronize data in real - time.

[0070] In summary, through the multi-sensor fusion technology combined with the A* algorithm, intelligent voice interaction, and the background management system, the present invention significantly improves the automation level and reliability of warehousing operations. At the same time, due to modularization and knowledge management, the method of the present invention has high scalability and can be widely applied to complex warehousing scenarios in fields such as power and logistics.

[0071] Example 3. Refer to Figure 2 , which is an embodiment of the present invention, provides a control system for a new type of Internet of Things intelligent warehousing service robot, including a motion control module, a voice interaction module, and a service management module.

[0072] Among them, the motion control module is used to collect first environmental data in real time, generate a first environmental map through a first fusion algorithm, and implement path planning based on the first environmental map through a second algorithm; the voice interaction module is used to generate instructions through a first voice conversion technology and realize intelligent interaction by combining a second language processing technology and a second voice synthesis technology; the service management module is used to connect to a first server through a first communication means and realize comprehensive service management through a first management system.

[0073] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present invention. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, etc., which can store program codes.

[0074] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, which can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0075] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which a program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or otherwise processing it as appropriate, and then storing it in a computer memory.

[0076] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

[0077] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A control method for an Internet of Things intelligent warehousing service robot, characterized in that, Including: Collecting first environmental data in real time, generating a first environmental map through a first fusion algorithm, and implementing path planning based on the first environmental map through a second algorithm; Generating instructions through a first voice conversion technology, and implementing intelligent interaction by combining a second language processing technology and a second speech synthesis technology; Connecting to a first server through a first communication means, and implementing comprehensive service management through a first management system.

2. The control method of the novel Internet of Things intelligent warehousing service robot according to claim 1, characterized in that: The generating of the first environmental map through the first fusion algorithm includes performing weighted fusion on the first environmental data through the first fusion algorithm to generate the first environmental map.

3. The control method of the new IoT intelligent warehousing service robot according to claim 2, wherein: The implementing of path planning through the second algorithm includes planning a path through the second algorithm and dynamically adjusting the path in combination with real-time data.

4. The control method of the new IoT intelligent warehousing service robot according to claim 3, characterized in that: The generating of instructions through the first voice conversion technology includes converting voice into text through the first voice conversion technology and extracting keywords to generate instructions.

5. The control method of the new Internet of Things intelligent warehousing service robot according to claim 4, characterized in that: The implementing of intelligent interaction includes matching answers from a knowledge base through the second language processing technology.

6. The control method of the new IoT intelligent warehousing service robot according to claim 5, characterized in that: The implementing of intelligent interaction further includes converting the matched answers into voice through the second speech synthesis technology to achieve intelligent interaction.

7. The control method of the new IoT intelligent warehousing service robot according to claim 6, characterized in that: The implementing of comprehensive service management through the first management system includes implementing knowledge management, operation management, and monitoring management through the first management system.

8. A system adopting the control method of the new Internet of Things intelligent warehousing service robot as described in any one of claims 1 to 7, characterized in that: Including a motion control module, a voice interaction module, and a service management module; The motion control module is used for collecting first environmental data in real time, generating a first environmental map through a first fusion algorithm, and implementing path planning based on the first environmental map through a second algorithm; The voice interaction module is used for generating instructions through a first voice conversion technology and implementing intelligent interaction by combining a second language processing technology and a second speech synthesis technology; The service management module is used for connecting to a first server through a first communication means and implementing comprehensive service management through a first management system.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the control method of the new type of Internet of Things intelligent warehousing service robot according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the control method of the new type of Internet of Things intelligent warehousing service robot according to any one of claims 1 to 7 are implemented.

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