Elevator collaborative scheduling method and system based on man-machine transport capacity balance

Abstract

The invention relates to an elevator collaborative scheduling method and system based on man-machine transport capacity balance, and the method comprises the steps: receiving and analyzing a robot task state packet, and determining the task urgency degree, the task timeliness and the key resource state; building passenger transport state data are collected in real time, and current passenger flow pressure data and elevator system state data are analyzed; carrying out transport capacity balance and scheduling cost decision making, and calculating comprehensive weighted cost of different scheduling strategies based on a robot task state packet and building passenger transport state data; and according to the strategy of the lowest comprehensive weighting cost, differentiated two-way instructions are output, and control instructions are sent to the robot terminal and the elevator control system. According to the scheme provided by the invention, the elevator transport capacity can be dynamically and elastically distributed between the requirements of people and robots, transport capacity waste or local congestion is avoided, the overall vertical transport efficiency of a building is improved, and the problem that the passenger transport service quality is reduced due to improper scheduling of the robots is relieved.

Description

Technical Field

[0001] This application relates to the field of building robot scheduling technology, and in particular to an elevator collaborative scheduling method and system based on human-machine capacity balance. Background Technology

[0002] With the rapid development of smart buildings and unmanned service scenarios, the application of building robots (such as delivery robots, inspection robots, and medical emergency robots) is becoming increasingly widespread. When performing cross-floor tasks, these robots must rely on elevators for vertical movement, thus creating a scenario of "humans and robots sharing elevators".

[0003] Traditional elevator scheduling strategies primarily cater to human passengers, failing to adequately consider the unique characteristics of robotic tasks. This leads to inefficient obstacle avoidance mechanisms in actual operation. Some systems employ a "perception-avoidance" model, where robots use sensors to perceive elevator passenger flow and operational status in real time and passively adjust their paths. During peak hours, elevators operate under high load, often causing robots to wait indefinitely without a chance to ride, resulting in task delays or even interruptions, particularly impacting the execution of emergency tasks.

[0004] Another common robot scheduling scheme is to set static priorities. Static priority scheduling interferes with the passenger experience. This static priority mechanism fails to distinguish between the urgency of the task and the real-time passenger flow status. During peak periods, a non-urgent robot calling for an elevator may occupy elevator resources, resulting in a significant increase in the average waiting time for all passengers, causing conflicts between humans and robots for elevators, and seriously affecting the quality of building passenger service and user satisfaction.

[0005] Furthermore, existing technologies lack a mechanism for quantitatively evaluating and dynamically balancing the benefits of robot task execution with the costs of elevator passenger disruption. This makes it impossible to achieve intelligent coordination between robot tasks and passenger travel needs while ensuring the overall building capacity efficiency. Especially when dealing with sudden emergency tasks, the system lacks the ability for flexible scheduling and dynamic priority adjustment.

[0006] Therefore, there is an urgent need to propose an elevator collaborative scheduling method and system that can proactively assess task value, perceive passenger flow status in real time, and achieve dynamic balance between human and machine transportation capacity, so as to improve the overall transportation efficiency of buildings, ensure the execution of critical tasks, and optimize the passenger elevator experience. Summary of the Invention

[0007] To address or partially address the problems existing in related technologies, this application provides an elevator collaborative scheduling method and system based on human-machine capacity balance, aiming to solve the human-machine conflict problem in the building robot scheduling process.

[0008] This application provides an elevator collaborative scheduling method based on human-machine capacity balance, including: Receive and parse robot task status packets to determine task urgency, timeliness, and status of key resources; Real-time collection of building passenger transport status data; analysis of current passenger flow pressure data and elevator system status data; To make capacity balancing and scheduling cost decisions, the comprehensive weighted cost of different scheduling strategies is calculated based on robot task status packages and building passenger transport status data. Based on the strategy of minimizing overall weighted cost, differentiated bidirectional instructions are output to send control commands to the robot terminal and elevator control system.

[0009] Optionally, perform capacity balancing and scheduling cost calculations, including: Total weighted cost = robot task waiting cost + increased cost of waiting time for all passengers; The waiting cost for robot tasks is dynamically calculated based on the urgency and timeliness of the task. The cost of increased waiting time for all passengers is calculated based on passenger flow pressure data, the number of affected floors, predicted delay increments, time-sensitive coefficients, and capacity loss coefficients.

[0010] Optionally, calculate the comprehensive weighted cost of different scheduling strategies, including: Immediate elevator dispatch - human-machine co-riding strategy: Total cost = robot task waiting cost + increased waiting time cost for all passengers due to this strategy; Immediate elevator dispatch - robot-specific strategy: Total cost = robot task waiting cost + cost of increased waiting time for all passengers due to occupying one elevator; Task suspension - off-peak execution strategy: Total cost = acceptable waiting cost of robot task + 0.

[0011] Optional, output differentiated bidirectional instructions, including: When an emergency task is determined, the dedicated elevator strategy is immediately executed, sending a control command to the elevator control system to clear the elevator and notify the passengers in the car, and sending a command to the robot terminal to go to the elevator for exclusive use. When a time-sensitive task is determined and the current passenger flow pressure is low, the human-machine co-riding strategy is implemented. A control instruction to assign an optimal elevator is sent to the elevator control system, and a command to go to the corresponding elevator is sent to the robot terminal. When a task is determined to be a routine task and there is high passenger flow pressure, a task suspension strategy is implemented, and instructions to be executed during off-peak hours are sent to the robot terminal.

[0012] Optionally, receive and parse robot task status packets, including: Structured data packets include elevator call information and a complete task description; The complete task description covers task urgency, task timeliness, and the status of key resources. Task urgency is divided into emergency medical care, low battery return, time-sensitive delivery, and routine inspection. Time-sensitive delivery tasks must be completed within 20 minutes, while routine inspection tasks can be completed within 2 hours; Key resource statuses include the urgency of medical care and the robot's current remaining battery power.

[0013] Optionally, analyze current passenger flow pressure data and elevator system status data, including: Building passenger flow data includes average waiting time on each floor, number of people waiting for elevators, and real-time load rate of elevator cars. The number of people waiting for elevators is estimated through AI cameras or historical big data. Elevator system status data includes elevator health status, operating mode, and work status. Operating modes include morning peak mode and fire protection mode.

[0014] The second aspect of this application provides an elevator collaborative scheduling system based on human-machine capacity balance, comprising: The human-machine balance decision-making module includes the following sub-modules: The task parsing unit is used to receive and parse robot task status packets. The data acquisition unit is used to collect real-time building passenger transport status data; The cost decision-making unit is used to make decisions on capacity balancing and scheduling costs. The instruction output unit is used to output differentiated collaborative instructions to the robot terminal and elevator control system.

[0015] Optional, the data acquisition unit includes: Building passenger flow data acquisition unit and elevator system status data acquisition unit; The building passenger flow data collection unit collects the average waiting time, number of people waiting for the elevator, and real-time load rate of the elevator car on each floor, and estimates the number of people waiting for the elevator through AI cameras or historical big data. The elevator system status data acquisition unit is connected to the elevator control system to collect elevator health status, operating mode, and work status. The operating modes include morning peak mode and fire protection mode.

[0016] Optional cost decision-making units include: Robot waiting cost algorithm unit, passenger elevator waiting cost algorithm unit, and cost-benefit balance decision algorithm unit; The robot waiting cost algorithm unit dynamically calculates the robot task waiting cost based on the tasks in the structured data packet; The passenger waiting cost algorithm unit calculates the total additional waiting time for all passengers in the building caused by the robot performing the current task by using the number of affected floors, the number of people waiting on the kth floor, the predicted delay increment on the kth floor, the time period sensitivity coefficient, and the capacity loss coefficient. This is the total waiting cost for all passengers. The cost-benefit balance decision-making algorithm unit calculates the comprehensive weighted cost of different scheduling strategies, including the immediate elevator dispatch-human-machine co-riding strategy, the immediate elevator dispatch-robot dedicated strategy, and the task suspension-off-peak execution strategy.

[0017] The technical solution provided in this application may include the following beneficial effects: By introducing a capacity balancing decision-making mechanism, the benefits of robot task execution and the interference costs to building passenger transport are dynamically evaluated. Based on real-time passenger flow data and elevator status, intelligent balancing is carried out, enabling elevator capacity to be dynamically and flexibly allocated between the needs of humans and robots. This avoids capacity waste or local congestion, improves the overall vertical transportation efficiency of the building, and alleviates the problem of declining passenger service quality caused by improper robot scheduling.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0019] The above and other objects, features and advantages of this application will become more apparent from the more detailed description of exemplary embodiments thereof in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments thereof.

[0020] Figure 1 This is a flowchart illustrating the elevator collaborative scheduling method based on human-machine capacity balance in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of an elevator collaborative scheduling system based on human-machine capacity balance, as shown in an embodiment of this application. Detailed Implementation

[0021] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While embodiments of this application are shown in the drawings, it should be understood that this application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to make this application more thorough and complete, and to fully convey the scope of this application to those skilled in the art.

[0022] The technical solutions of the embodiments of this application are described in detail below with reference to the accompanying drawings.

[0023] Figure 1 This is a schematic flowchart illustrating the elevator collaborative scheduling method based on human-machine capacity balance in an embodiment of this application.

[0024] In some implementations, see Figure 1 An elevator collaborative scheduling method based on human-machine capacity balance includes: S101. Receive and parse the robot task status packet to determine the task urgency, task timeliness and key resource status. Specifically, the robot task status package includes elevator call information and a complete task description. The complete task description covers at least the task urgency, task timeliness, and key resource status. The task urgency is divided into P0 emergency medical care, P1 low battery return, P2 time-sensitive delivery, and P3 routine inspection. P2 tasks must be completed within 20 minutes, and P3 tasks can be completed within 2 hours. Key resource status includes the urgency of medical care and the robot's current remaining battery power.

[0025] S102. Real-time collection of building passenger transport status data, analysis of current passenger flow pressure data and elevator system status data; Specifically, building passenger flow data includes average waiting time on each floor, number of people waiting, and real-time load rate of the elevator car. The number of people waiting is estimated through AI cameras or historical big data. Elevator system status data includes elevator health status, operating mode, and work status. Operating modes include morning peak mode and fire protection mode.

[0026] S103. Make capacity balancing and scheduling cost decisions. Based on robot task status packages and building passenger transport status data, calculate the comprehensive weighted cost of different scheduling strategies. Specifically, the waiting cost for robot tasks and the waiting cost for all passengers are dynamically calculated, and the comprehensive weighted cost of different scheduling strategies is calculated. Specific strategies include, but are not limited to, the following: Immediate elevator dispatch - human-robot co-riding strategy: Overall cost = (robot task waiting cost) + (cost of increased waiting time for all passengers due to this strategy); Immediate elevator dispatch - robot-specific strategy: Total cost = (robot task waiting cost) + (cost of increased waiting time for all passengers due to occupying one elevator); Task suspension - off-peak execution strategy: Total cost = (acceptable waiting cost for robot tasks) + (increased cost of passenger waiting time), and increased cost of passenger waiting time ≈ 0; The robot task waiting cost is dynamically calculated based on the task in the received structured data packet. For example, the waiting cost of task P3 is very low, while the waiting cost of task P1 with low battery increases dramatically over time.

[0027] The total passenger waiting cost refers to the total additional waiting time that would be incurred by all passengers in the building if the robot were to perform the current task. This cost is obtained by inputting parameters into the model algorithm. Specific parameters include: the number of affected floors, the number of people waiting on the kth floor, the predicted delay increment on the kth floor, the time-sensitivity coefficient, and the capacity loss coefficient.

[0028] S104. Based on the strategy of minimizing the overall weighted cost, output differentiated bidirectional instructions and send control instructions to the robot terminal and elevator control system.

[0029] Specifically, the output differentiated bidirectional instructions include: Decision Output A: When a P0 emergency task is determined, execute the immediate elevator dispatch-robot dedicated strategy, send a control command to the elevator control system to clear the elevator and notify the passengers in the car; send a command to the robot terminal to go to the elevator for exclusive use.

[0030] Decision Output B: When the task is determined to be a P2 time-sensitive task and the current passenger flow pressure is low, execute the immediate elevator dispatch-human-machine co-riding strategy, send a control instruction to the elevator control system to assign an optimal elevator, and send a human-machine co-riding instruction to the robot terminal.

[0031] Decision Output C: When the task is determined to be a regular P3 task and the current passenger flow pressure is high, the task suspension-off-peak execution strategy is implemented, and the robot terminal is sent an instruction that the task has been received and will be executed during off-peak hours.

[0032] In some implementations, corresponding to the aforementioned application function implementation device embodiments, this application also provides an elevator collaborative scheduling system based on human-machine capacity balance and corresponding embodiments.

[0033] See Figure 2 An elevator collaborative scheduling system based on human-machine capacity balance includes: The human-machine balance decision-making module includes the following sub-modules: Task analysis unit, data acquisition unit, cost decision-making unit, and instruction output unit; The task parsing unit is used to receive and parse robot task status packets. The data acquisition unit is used to collect real-time building passenger transport status data. The data acquisition unit includes a building passenger flow data acquisition unit and an elevator system status data acquisition unit. The building passenger flow data acquisition unit collects the average waiting time, number of people waiting, and real-time load rate of the elevator car on each floor, and estimates the number of people waiting by using AI cameras or historical big data. The elevator system status data acquisition unit is connected to the elevator control system to collect the elevator health status, operating mode, and work status. The operating modes include morning peak mode and fire protection mode.

[0034] The cost decision unit is used to perform capacity balancing and scheduling cost decisions. The cost decision unit includes a robot waiting cost algorithm unit, a passenger elevator waiting cost algorithm unit, and a cost-benefit balance decision algorithm unit. The robot waiting cost algorithm unit dynamically calculates the robot task waiting cost based on the tasks in the structured data packet. The passenger elevator waiting cost algorithm unit calculates the total additional waiting time for all passengers in the building caused by the robot performing its current task, i.e., the total passenger elevator waiting cost, using the number of affected floors, the number of passengers waiting on the k-th floor, the predicted delay increment on the k-th floor, the time-sensitivity coefficient, and the capacity loss coefficient. The cost-benefit balance decision algorithm unit calculates the comprehensive weighted cost of different scheduling strategies, including immediate elevator dispatch-human-robot co-riding strategy, immediate elevator dispatch-robot dedicated strategy, and task suspension-off-peak execution strategy.

[0035] The instruction output unit is used to output differentiated collaborative instructions to the robot terminal and the elevator control system. The instruction output unit includes an instruction sending unit to the robot terminal and a control instruction sending unit to the elevator control system. Based on the calculation results of the cost decision unit, the strategy with the lowest comprehensive weighted cost is selected and differentiated bidirectional instructions are output. The differentiated bidirectional instructions are sent through the instruction sending unit to the robot terminal and the control instruction sending unit to the elevator control system, respectively.

[0036] The various embodiments of this application have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. An elevator collaborative scheduling method based on human-machine capacity balance, characterized in that, include: Receive and parse robot task status packets to determine task urgency, timeliness, and status of key resources; Real-time collection of building passenger transport status data; analysis of current passenger flow pressure data and elevator system status data; To make capacity balancing and scheduling cost decisions, the comprehensive weighted cost of different scheduling strategies is calculated based on the robot task status package and building passenger transport status data. Based on the strategy of minimizing overall weighted cost, differentiated bidirectional instructions are output to send control commands to the robot terminal and elevator control system.

2. The elevator collaborative scheduling method based on human-machine capacity balance according to claim 1, characterized in that, The calculation of capacity balancing and scheduling costs includes: Total weighted cost = robot task waiting cost + increased cost of waiting time for all passengers; The robot task waiting cost is dynamically calculated based on the task urgency and timeliness. The increased cost of waiting time for all passengers is calculated based on passenger flow pressure data, the number of affected floors, predicted delay increments, time-sensitivity coefficients, and capacity loss coefficients.

3. The elevator collaborative scheduling method based on human-machine capacity balance according to claim 1, characterized in that, The calculation of the comprehensive weighted cost of different scheduling strategies includes: Immediate elevator dispatch - human-machine co-riding strategy: Total cost = robot task waiting cost + increased waiting time cost for all passengers due to this strategy; Immediate elevator dispatch - robot-specific strategy: Total cost = robot task waiting cost + cost of increased waiting time for all passengers due to occupying one elevator; Task suspension - off-peak execution strategy: Total cost = acceptable waiting cost of robot task + 0.

4. The elevator collaborative scheduling method based on human-machine capacity balance according to claim 1, characterized in that, The output differentiated bidirectional instructions include: When an emergency task is determined, the dedicated elevator strategy is immediately executed, sending a control command to the elevator control system to clear the elevator and notify the passengers in the car, and sending a command to the robot terminal to go to the elevator for exclusive use. When a time-sensitive task is determined and the current passenger flow pressure is low, the human-machine co-riding strategy is implemented. A control instruction to assign an optimal elevator is sent to the elevator control system, and a command to go to the corresponding elevator is sent to the robot terminal. When a task is determined to be a routine task and there is high passenger flow pressure, a task suspension strategy is implemented, and instructions to be executed during off-peak hours are sent to the robot terminal.

5. The elevator collaborative scheduling method based on human-machine capacity balance according to claim 1, characterized in that, The process of receiving and parsing the robot task status packet includes: The robot task status package includes elevator call information and a complete task description; The complete task description covers task urgency, task timeliness, and the status of key resources. Task urgency is divided into emergency medical care, low battery return, time-sensitive delivery, and routine inspection. Time-sensitive delivery tasks must be completed within 20 minutes, while routine inspection tasks can be completed within 2 hours; Key resource statuses include the urgency of medical care and the robot's current remaining battery power.

6. The elevator collaborative scheduling method based on human-machine capacity balance according to claim 1, characterized in that, The analysis of current passenger flow pressure data and elevator system status data includes: The building passenger transport status data includes the average waiting time for elevators on each floor, the number of people waiting for elevators, and the real-time load rate of the elevator car. The number of people waiting for elevators is estimated through AI cameras or historical big data. The elevator system status data includes elevator health status, operating mode, and work status. The operating modes include morning peak mode and fire protection mode.

7. An elevator collaborative scheduling system based on human-machine capacity balance, used to execute the elevator collaborative scheduling method based on human-machine capacity balance as described in any one of claims 1-6, characterized in that, include: A human-machine balance decision-making module, wherein the sub-modules of the human-machine balance decision-making module include: The task parsing unit is used to receive and parse robot task status packets. The data acquisition unit is used to collect real-time building passenger transport status data; The cost decision-making unit is used to make decisions on capacity balancing and scheduling costs. The instruction output unit is used to output differentiated collaborative instructions to the robot terminal and elevator control system.

8. The elevator collaborative scheduling system based on human-machine capacity balance according to claim 7, characterized in that, The data acquisition unit includes: Building passenger flow data acquisition unit and elevator system status data acquisition unit; The building passenger flow data collection unit collects the average waiting time, number of people waiting for the elevator, and real-time load rate of the elevator car on each floor, and estimates the number of people waiting for the elevator through AI cameras or historical big data. The elevator system status data acquisition unit is connected to the elevator control system to collect elevator health status, operating mode, and work status. The operating modes include morning peak mode and fire protection mode.

9. The elevator collaborative scheduling system based on human-machine capacity balance according to claim 7, characterized in that, The cost decision-making unit includes: Robot waiting cost algorithm unit, passenger elevator waiting cost algorithm unit, and cost-benefit balance decision algorithm unit; The robot waiting cost algorithm unit dynamically calculates the robot task waiting cost based on the tasks in the structured data packet; The passenger waiting cost algorithm unit calculates the total additional waiting time for all passengers in the building caused by the robot performing the current task by the number of affected floors, the number of people waiting on the kth floor, the predicted delay increment on the kth floor, the time period sensitivity coefficient, and the capacity loss coefficient, which is the total passenger waiting cost. The cost-benefit balance decision algorithm unit calculates the comprehensive weighted cost of different scheduling strategies, including the immediate elevator dispatch-human-machine co-ride strategy, the immediate elevator dispatch-robot dedicated strategy, and the task suspension-off-peak execution strategy.

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