Mobile energy storage robot communication scheduling method for energy scheduling

Abstract

The invention discloses a mobile energy storage robot communication scheduling method for energy scheduling, and relates to the field of energy scheduling and communication scheduling, and the method comprises the steps: obtaining the demand data of an area needing energy scheduling and the real-time state data of a mobile energy storage robot; a layered state acquisition mechanism is adopted, and an exclusive communication time slot is distributed to each robot; calculating the accumulated length of a road and the predicted driving time, and obtaining an initial candidate robot set; based on a greedy algorithm, screening a final mobile energy storage robot combination as an optimal scheduling scheme; constructing a communication signal quality evaluation model, and dynamically adjusting robot replacement scheduling based on the optimal scheduling scheme; and dynamically adjusting a communication link between the robot and the base station by combining the position of the communication base station and the boundary coordinates of the base station. The method has the advantages that in a large-scale mobile energy storage robot cluster scene, efficient, reliable and adaptive cooperation of energy scheduling and communication scheduling is realized.

Description

Technical Field

[0001] This invention relates to the fields of energy dispatching and communication dispatching, specifically to a communication dispatching method for a mobile energy storage robot used in energy dispatching. Background Technology

[0002] With the increasing penetration of renewable energy and the development of distributed energy systems, the problem of spatiotemporal energy imbalance is becoming increasingly prominent. Mobile energy storage robots, as a flexible energy carrier, have emerged to address this issue. They can transport electricity from surplus nodes (such as overloaded photovoltaic power generation areas) to shortage nodes (such as production workshops or electric vehicle charging stations), realizing "energy logistics." This effectively facilitates peak shaving and valley filling, emergency power supply, and alleviates local network congestion. Existing technical solutions typically revolve around a central control platform, with energy scheduling algorithms at their core. These algorithms are mostly derived from traditional vehicle routing problems or fixed energy storage scheduling models, primarily based on the robot's location, state of charge, and task requirements, aiming for maximum economic efficiency or minimum energy loss. At the execution level, robots are typically considered independent execution units, and their communication with the control center is mostly based on standard, relatively simple IoT communication protocols (such as 4G / 5G, LoRa, or Wi-Fi). Their main function is to issue commands and report status. The communication itself is usually regarded as an ideal, latency-free, and uninterrupted channel. Meanwhile, in the field of multi-robot systems, there is also a lot of research on robot task allocation and path planning. These studies focus on optimizing the robot's movement path to avoid collisions, reduce travel time, and solve the task coordination problem between multiple agents. However, most of these studies assume that the robot maintains a perfect and continuous communication connection with the control center during task execution.

[0003] In short, existing technologies treat energy scheduling, robot path planning, and communication assurance as three relatively independent subsystems, and optimize them in a fragmented manner. Energy scheduling algorithms do not consider communication constraints and may select robots with unreliable communication. Path planning algorithms do not consider base station coverage boundaries, which may cause robots to enter communication blind spots during critical task phases. The communication system passively provides services and cannot provide forward-looking quality warnings for upper-level scheduling decisions. This fragmented optimization mode results in a large number of inherent uncertainties in the entire system, making it impossible to form a stable, reliable, and adaptive collaborative whole. This severely restricts the practical application of mobile energy storage robot technology in large-scale industrial scenarios. Summary of the Invention

[0004] To address the aforementioned technical problems, this paper provides a communication scheduling method for mobile energy storage robots used for energy dispatch. This technical solution solves the problem mentioned in the background technology that treats energy dispatch, robot path planning, and communication assurance as three relatively independent subsystems, which are then fragmented and optimized separately. This results in a large number of inherent uncertainties in the entire system, making it impossible to form a stable, reliable, and adaptive collaborative whole, which seriously restricts the practical application of mobile energy storage robot technology in large-scale industrial scenarios.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A communication scheduling method for mobile energy storage robots for energy dispatching, comprising: A central control platform for communication scheduling of mobile energy storage robots based on energy dispatch acquires demand data for areas requiring energy dispatch and real-time status data of mobile energy storage robots. To acquire real-time status data for all mobile energy storage robots, a hierarchical status acquisition mechanism is adopted, allocating a dedicated communication time slot for each robot. Based on the robot's current location and the geographical location of the area requiring energy dispatch, combined with online map services, the cumulative road length and estimated travel time are calculated to obtain an initial set of candidate robots. Based on the initial set of candidate robots, a greedy algorithm is used to select the final combination of mobile energy storage robots as the optimal scheduling scheme. Based on the communication data between the robot and the central control platform, a communication signal quality assessment model is constructed, and the robot replacement scheduling is dynamically adjusted based on the optimal scheduling scheme. Based on online map services, and combined with the location and boundary coordinates of communication base stations, the communication link between the robot and the base station is dynamically adjusted.

[0006] Preferably, the acquisition of real-time status data for all mobile energy storage robots adopts a hierarchical status acquisition mechanism, specifically allocating a dedicated communication time slot for each robot, including: A fixed period is preset for all robots, and they send heartbeat data packets to the central control platform periodically. The data packets include at least the robot's ID and current position coordinates. The heartbeat data packets are sent using a random backoff mechanism to avoid channel conflicts. When the central control platform executes energy dispatching decisions, it first selects a preliminary set of geographical candidates based on the location coordinates in the newly received heartbeat data packets and the geographical location of the area requiring energy dispatching. Based on the geographic candidate set, the central control platform sends a time-division multiplexing precise status query command and communication scheduling table to the robots in the set; The communication scheduling table refers to a communication time window starting from the current moment, which is divided into several consecutive micro-slots of equal length, and each robot in the geographical candidate set is uniquely mapped to one of the specific micro-slots. After receiving the instruction and schedule, each robot is configured to parse its assigned time slot number and send its complete real-time status data to the central control platform during the corresponding time slot opening time. During other time slots, communication reception or silence is maintained to avoid interfering with the information transmission of other robots.

[0007] Preferably, the step of calculating the cumulative road length and estimated travel time based on the robot's current location and the geographical location of the area requiring energy dispatch, combined with online map services, to obtain the initial candidate robot set specifically includes: Based on online map services, obtain the path information and road traffic coefficient of each robot in the geographic candidate set to reach the area requiring energy dispatch; Based on the robot's current location and the geographical location of the area requiring energy dispatch, combined with path information and road traffic coefficient, calculate the cumulative road length and estimated travel time for the robot to reach the area requiring energy dispatch. Based on the cumulative road length and estimated travel time, a comprehensive evaluation value of the robot's scheduling accessibility is calculated using a weighted summation formula. Based on the demand data of the energy dispatch area, set multi-parameter thresholds for robot dispatch accessibility, and obtain an initial set of candidate robots through a threshold comparison mechanism; The threshold comparison mechanism specifically includes: Based on the scheduling time limit of the required energy dispatch area, a reserved value is subtracted from the scheduling time limit to form a time limit threshold, i.e., time limit threshold = scheduling time limit - reserved value; Based on the cumulative length of roads leading to the area requiring energy dispatch, an upper limit for the cumulative road length is set. Robots exceeding the upper limit for the cumulative road length will not participate in this dispatch. Based on the comprehensive evaluation value of robot scheduling reachability, an reachability boundary threshold is set, and a set of robots with a comprehensive evaluation value less than the reachability boundary threshold is obtained; When screening robots, the threshold comparison mechanism follows this order: first, screening based on scheduling time limits; then, screening based on cumulative road length; and finally, screening based on a comprehensive evaluation value of scheduling reachability.

[0008] Preferably, the step of selecting the final combination of mobile energy storage robots as the optimal scheduling scheme based on the initial candidate robot set and a greedy algorithm specifically includes: From the initial set of candidate robots, select a single robot whose current remaining directly schedulable energy is greater than or equal to the total energy demand multiplied by the redundancy factor; Wherein, the redundancy coefficient is a preset value greater than 1; If such a robot exists, the robot with the shortest cumulative road length will be selected as the optimal scheduling solution. If there is no single robot that meets the conditions, then: Sort all robots in the initial candidate robot set from highest to lowest according to their current remaining directly schedulable energy. Starting with the robot with the highest remaining directly dispatchable energy, its energy is added sequentially until the total added amount is greater than or equal to the total energy demand multiplied by the redundancy coefficient. At this point, the combination of robots participating in the addition is the optimal scheduling scheme.

[0009] Preferably, the step of constructing a communication signal quality assessment model based on the communication data between the robot and the central control platform, and dynamically adjusting the robot replacement scheduling based on the optimal scheduling scheme, specifically includes: Based on the communication data between the robot and the central control platform, a communication signal quality assessment model is constructed, and the communication signal quality assessment score of each robot in the optimal scheduling scheme is calculated in real time. When generating the optimal scheduling scheme, the communication signal quality qualification threshold is set. If the evaluation score of each robot in the scheme is greater than the qualification threshold, the optimal scheduling scheme is used as the final scheduling scheme. If not, robots with evaluation scores less than the qualification threshold are removed. The optimal scheduling scheme is then re-selected from the initial candidate robot set based on a greedy algorithm. The process is repeated until all robots that meet the qualification threshold are selected as the final scheduling scheme. During the execution of the final scheduling plan, the central control platform records and updates the communication signal quality assessment score of each energy scheduling robot at a first fixed time interval T1. When the evaluation score of any robot at K consecutive sampling points shows a strictly monotonically decreasing trend, the robot is determined to enter a Level 1 warning state, and the time interval for its status reporting is shortened from T1 to a second fixed time interval T2, where T2... <T1; An emergency threshold is set for the communication signal quality assessment score, which is lower than the acceptable threshold. When a robot in a Level 1 warning state has its evaluation score drop below the emergency threshold, or when the evaluation score of any energy dispatching robot drops directly below the emergency threshold, the robot is determined to enter a Level 2 emergency state. The central control platform immediately selects a backup robot from the initial candidate robot set based on a greedy algorithm, issues a replacement command to the backup robot, and simultaneously issues a command to the robot in the level 2 emergency state to terminate energy dispatch and return.

[0010] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention proposes a communication scheduling method for mobile energy storage robots for energy dispatch. It introduces a hierarchical state acquisition mechanism and communication quality-driven dynamic fault-tolerant scheduling as the core guarantee for the collaborative scheduling of mobile energy storage robot clusters. Based on a central control platform, it integrates robot location information, energy status, and real-time communication quality data to construct a closed-loop control system encompassing "perception-decision-execution-fault tolerance," enabling efficient and reliable scheduling of large-scale mobile energy storage robots in complex environments. Furthermore, it avoids channel conflicts through a time-division multiplexing communication scheduling mechanism, and uses a greedy algorithm-based robot combination selection to ensure that energy dispatch requirements are met with the minimum number of calls. A communication signal quality assessment model is used to achieve seamless integration of pre-screening and in-process dynamic control. Combined with a base station link dynamic adjustment mechanism, it ensures that robots maintain reliable communication connections and controllable states while performing energy dispatch tasks. This effectively solves the "blind control" problem caused by communication interruptions in traditional methods, greatly improving the collaborative efficiency and task reliability of mobile energy storage robot clusters, and providing a solid technical guarantee for the spatiotemporal stability and reliability of energy dispatch. Attached Figure Description

[0011] Figure 1 This is a flowchart of a communication scheduling method for a mobile energy storage robot for energy dispatch according to the present invention. Figure 2 The present invention employs a hierarchical state acquisition mechanism, assigning a dedicated communication time slot flowchart to each robot; Figure 3 A flowchart for obtaining the initial candidate robot set based on the cumulative road length and estimated travel time of the computational robot of the present invention to reach the area requiring energy dispatch. Figure 4 This is a structural diagram of the electronic device proposed in this invention; Figure 5 This is a schematic diagram of the structure of the computer-readable storage medium proposed in this invention. Detailed Implementation

[0012] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0013] Reference Figure 1As shown, a communication scheduling method for mobile energy storage robots used for energy dispatch includes: A central control platform for communication scheduling of mobile energy storage robots based on energy dispatch acquires demand data for areas requiring energy dispatch and real-time status data of mobile energy storage robots. To acquire real-time status data for all mobile energy storage robots, a hierarchical status acquisition mechanism is adopted, allocating a dedicated communication time slot for each robot. Based on the robot's current location and the geographical location of the area requiring energy dispatch, combined with online map services, the cumulative road length and estimated travel time are calculated to obtain an initial set of candidate robots. Based on the initial set of candidate robots, a greedy algorithm is used to select the final combination of mobile energy storage robots as the optimal scheduling scheme. Based on the communication data between the robot and the central control platform, a communication signal quality assessment model is constructed, and the robot replacement scheduling is dynamically adjusted based on the optimal scheduling scheme. Based on online map services, and combined with the location and boundary coordinates of communication base stations, the communication link between the robot and the base station is dynamically adjusted.

[0014] This solution addresses communication conflicts in large-scale robot swarms through a hierarchical state acquisition mechanism, ensuring reliable data collection. It achieves minimal robot scheduling while maintaining sufficient energy supply through collaborative selection using a geographic information system and a greedy algorithm. A communication quality assessment system and dynamic fault-tolerance mechanism transform communication reliability from a static parameter into a dynamic control indicator, enabling full-process monitoring from task allocation to execution assurance. Finally, proactive base station handover technology based on location prediction ensures communication continuity during movement. This system architecture, deeply coupling energy scheduling, path planning, and communication assurance, allows each component to support and verify each other, collectively forming a highly efficient and reliable complete technical solution.

[0015] The central control platform for communication scheduling of mobile energy storage robots based on energy dispatch acquires demand data for areas requiring energy dispatch and real-time status data of mobile energy storage robots, specifically including: Demand data includes: total energy demand, geographical location, and dispatch timeframe; Real-time status data includes: robot ID, current position coordinates, current energy consumption, current energy scheduling status, and communication data. Energy scheduling status includes: idle state and energy scheduling in progress. Communication data includes: signal strength value, channel collision rate, and network latency. The system obtains the amount of energy that can be directly scheduled when the robot is idle, and the estimated timestamp and estimated remaining amount of energy that can be directly scheduled when the energy scheduling ends.

[0016] This can be explained by the following: Total energy demand is the core basis for scheduling, directly determining the threshold of total supply capacity of robots to be called; Geographical location is the core parameter for calculating robot scheduling distance and planning the optimal path, while also providing the basis for communication hierarchy division; Scheduling time limit constrains scheduling efficiency from a time dimension, driving the platform to quickly match and adapt resources. These three together constitute the basic demand boundary for energy scheduling; Robot ID enables precise positioning and control of individual robots, avoiding problems such as instruction confusion and energy scheduling allocation errors when multiple robots are scheduled in parallel; Current location coordinates are the key basis for measuring the time it takes for a robot to reach the scheduling area and establishing hierarchical communication links, directly affecting scheduling response speed and communication stability; Communication data is an important basic data to ensure pre-screening and process control of energy scheduling; Current self-consumption energy specifically refers to the energy used by the robot for movement. The available energy quantity is such that, since the robot is usually stationary when in energy scheduling, it generates almost no additional energy consumption for movement. This data can stably reflect the remaining energy available for movement of the robot at present and after the energy scheduling is completed. There is no need to calculate the energy loss of the robot itself during energy scheduling. The directly scheduling energy quantity refers to the amount of energy that the robot carries that can be supplied to the outside world. The energy quantity types include, but are not limited to, electricity and fuel. In particular, the current energy scheduling status, the robot's expected end time stamp in energy scheduling, and the expected remaining directly scheduling energy quantity can help the platform predict the potential energy resources that can be called in the future, plan the scheduling connection sequence in advance, realize forward scheduling, and avoid missing available resources. In summary, these data items cover the core scheduling needs in a concise and efficient way, and together constitute the core information foundation required for an accurate and predictable dynamic resource scheduling model.

[0017] Reference Figure 2 As shown, a hierarchical state acquisition mechanism is adopted, allocating a dedicated communication time slot to each robot, specifically including: A fixed cycle is preset for all robots, which send heartbeat data packets to the central control platform periodically. The data packets include at least the robot's ID and current position coordinates. The heartbeat data packets are sent using a random backoff mechanism to avoid channel conflicts. When the central control platform executes energy dispatching decisions, it first selects a preliminary set of geographical candidates based on the location coordinates in the newly received heartbeat data packets and the geographical location of the area requiring energy dispatching. Based on the geographic candidate set, the central control platform sends a time-division multiplexing precise status query command and communication scheduling table to the robots in the set; The communication scheduling table refers to a communication time window that starts from the current moment and is divided into several consecutive micro-slots of equal length, and each robot in the geographical candidate set is uniquely mapped to one of the specific micro-slots. After receiving the instruction and schedule, each robot is configured to parse its assigned time slot number and send its complete real-time status data to the central control platform during the corresponding time slot opening time. During other time slots, communication reception or silence is maintained to avoid interfering with the information transmission of other robots.

[0018] It can be explained that when the scale of mobile energy storage robot clusters expands to dozens or even hundreds of units, the system will face severe challenges without an effective communication and scheduling mechanism. In the status reporting stage, if all robots concurrently transmit complete status data at the same cycle, the limited wireless channels will quickly become congested due to the excessive size and number of data packets. Signals will collide and overlap in the air, leading to distortion and loss. The central control platform will be unable to parse valid information, resulting in "collective deafness." In the command issuance stage, the centralized transmission of massive control commands may also cause confusion due to timing misalignment or address resolution errors, causing robot A to receive a command pointing to robot B, or the logical sequence of key commands to be reversed, ultimately leading to execution disorder and coordination failure of the entire energy scheduling system. Therefore, this solution... A hierarchical state acquisition mechanism is proposed, dividing the communication between the robot and the central control platform into two stages: Before energy scheduling decisions, the robot sends heartbeat data packets (containing only the robot ID and current position coordinates) at fixed intervals, and a random backoff mechanism (e.g., based on the classic binary exponential backoff algorithm or its variants) is used during transmission. When channel occupancy is detected, random retry is performed to reduce the probability of channel collisions from the source. During energy scheduling decisions, the platform initially filters out a set of geographical candidates based on the location information in the heartbeat packets (the set of geographical candidates where the robot can reach the target area in time within the scheduling time limit, i.e., the set where the time obtained by dividing the cumulative road length by the robot's average speed is less than the scheduling time limit). Here, geographical location is the key factor in the decision. The core constraint of whether robots can arrive at the scene in a timely manner is that this screening can quickly eliminate robots that are not timely due to distance. The robot's identification ID is the foundation for accurate addressing and management. Together, they constitute the core basis for the initial screening. Subsequently, the platform only initiates time-division multiplexing precise status queries to the geographical candidate set. This mechanism decouples control signaling from data load, controlling both the communication scale and data volume, and reducing uplink data collisions through time slot allocation, thus solving the problem of uplink communication paralysis such as "mass deafness". At the same time, the time slot rules of time-division multiplexing can be reused for downlink command issuance. The platform sends commands in the dedicated time slot of the corresponding robot, ensuring accurate delivery and reliable timing of downlink commands. In addition, the micro-slot duration is adapted to the data transmission volume and communication time. The dynamic matching set size of the inter-window further ensures the efficiency and stability of this communication mechanism under high load and large-scale scenarios, thus building a solid underlying communication foundation for the reliable collaborative scheduling of large-scale mobile energy storage robots. At this point, the geographical candidate set output by this hierarchical state acquisition mechanism is no longer a simple list of IDs, but a rich dataset containing complete real-time state data of each robot, which can be used for advanced decision-making. The determination of the fixed period can refer to the M / D / 1 queuing theory model, and establish a quantitative relationship between network load and latency through parameters such as maximum allowable latency, number of robots, data packet size, and channel rate, while ensuring the channel utilization threshold (usually taken as 0.3-0) to guarantee system stability.7) Solve for the optimal period within the timeframe.

[0019] Reference Figure 3 As shown, the calculation of the cumulative road length and estimated travel time for the robot to reach the area requiring energy dispatch, and the acquisition of the initial candidate robot set specifically include: Based on online map services, obtain the path information and road traffic coefficient of each robot in the geographic candidate set to reach the area requiring energy dispatch; Based on the robot's current location and the geographical location of the area requiring energy dispatch, combined with path information and road traffic coefficient, calculate the cumulative road length and estimated travel time for the robot to reach the area requiring energy dispatch. Based on the cumulative road length and estimated travel time, a comprehensive evaluation value of the robot's scheduling accessibility is calculated using a weighted summation formula. Based on the demand data of the energy dispatch area, set multi-parameter thresholds for robot dispatch accessibility, and obtain an initial set of candidate robots through a threshold comparison mechanism; The threshold comparison mechanism specifically includes: Based on the scheduling time limit of the required energy dispatch area, a reserved value is subtracted from the scheduling time limit to form a time limit threshold, i.e., time limit threshold = scheduling time limit - reserved value; Based on the cumulative length of roads leading to the area requiring energy dispatch, an upper limit for the cumulative road length is set. Robots exceeding the upper limit for the cumulative road length will not participate in this dispatch. Based on the comprehensive evaluation value of robot scheduling reachability, an reachability boundary threshold is set, and a set of robots with a comprehensive evaluation value less than the reachability boundary threshold is obtained; When screening robots, the threshold comparison mechanism follows this order: first, screening based on scheduling time limits; then, screening based on cumulative road length; and finally, screening based on a comprehensive evaluation value of scheduling reachability.

[0020] Explained by this, path planning and threshold screening are the core of ensuring scheduling accessibility and robustness. The estimated travel time directly determines whether the robot can meet the time-bound constraints of scheduling energy, while the cumulative road length is directly related to the robot's mobile energy consumption, serving as a rigid energy constraint for determining its safe arrival. This solution, based on a geographical candidate set, uses a threshold comparison mechanism for efficient screening: Time Limit Screening: Subtracting a reserved value from the scheduling time limit forms a "time limit threshold." This reserved value is determined based on the 95th percentile of historical delay data, used to absorb uncertainties such as road congestion and temporary failures, ensuring that the selected robots have time redundancy; Distance Screening: Setting an upper limit for the cumulative road length, based on the maximum safe travel distance of robots in historical successful scheduling data, and combining this with reverse verification of the robot's rated energy consumption per unit distance and common self-energy consumption limits, directly excluding robots with excessively high energy consumption due to excessive distance or those unable to arrive in time, thus meeting the rigid energy constraint; Comprehensive Evaluation Screening: For robots that pass the first two rounds of screening, their scheduling accessibility is calculated... The comprehensive assessment value of accessibility, i.e., comprehensive assessment value = k1 × standardized estimated travel time + k2 × standardized cumulative road length, is standardized using the min-max normalization method, and a accessibility boundary threshold is set for final optimization. This threshold is also based on the statistical analysis of historical successful scheduling data, which can screen out candidates that are optimal in terms of both time and energy consumption. Through this threshold comparison mechanism, the efficiency and reliability of scheduling decisions are ensured, which not only meets the urgent needs of energy scheduling, but also ensures the safety and stability of the robot's execution process. This lays the foundation for the stable operation of the energy scheduling system from the perspective of decision-making logic. Among them, k1 and k2 are used to balance the relative importance of estimated travel time and cumulative road length in the comprehensive assessment. Their specific values ​​are adjusted according to the actual scheduling strategy: if the scheduling strategy emphasizes timeliness, a larger value is assigned to k1; if it emphasizes energy saving and economy, a larger value is assigned to k2. A specific implementation method includes, but is not limited to: based on historical successful scheduling data, using regression analysis or machine learning methods (such as linear regression) to backfit the optimal combination of k1 and k2. The estimated travel time expression is: ; In the formula, For the estimated travel time, The cumulative length of roads obtained from online map services, This is the robot's rated average moving speed under ideal, unobstructed road conditions, a constant preset based on the robot's performance. The road traffic coefficient is calculated based on online map service data; The cumulative road length refers to the cumulative road length that the robot travels to the target area in the online map service. The road traffic coefficient is a value in the range [0,1] obtained from a preset mapping relationship by looking up a table based on the real-time traffic condition level (e.g., smooth, slow, congested) returned by the online map service. For example, smooth traffic is mapped to 1.0, slow traffic to 0.6, and congested traffic to 0.3. The closer the road traffic coefficient value is to 1, the smoother the road is, and the closer it is to 0, the more congested the road is.

[0021] Based on the initial candidate robot set, and using a greedy algorithm, the final combination of mobile energy storage robots is selected as the optimal scheduling scheme, specifically including: From the initial set of candidate robots, select a single robot whose current remaining directly schedulable energy is greater than or equal to the total energy demand multiplied by the redundancy factor; The redundancy coefficient is a preset value greater than 1. If such a robot exists, the robot with the shortest cumulative road length will be selected as the optimal scheduling solution. If there is no single robot that meets the conditions, then: Sort all robots in the initial candidate robot set from highest to lowest according to their current remaining directly schedulable energy. Starting with the robot with the highest remaining directly dispatchable energy, its energy is added sequentially until the total added amount is greater than or equal to the total energy demand multiplied by the redundancy coefficient. At this point, the combination of robots participating in the addition is the optimal scheduling scheme.

[0022] This can be explained by the fact that the optimal scheduling decision has "minimizing the number of robots to be called" as its core optimization objective. This objective directly reduces the system's scheduling complexity, communication load, and collaborative control difficulty. To achieve this objective, this solution employs a highly efficient optimization algorithm based on a greedy strategy: First, it attempts to find a single robot that can satisfy all requirements, which is the most economical solution; if this does not exist, it uses a greedy strategy of "accumulating from large to small" to quickly find the combination that minimizes the number of robots required under the constraint of total energy demand multiplied by a redundancy coefficient, which has safety redundancy. This algorithm ensures the quality of the solution. Meanwhile, the computational efficiency is extremely high, which can meet the real-time requirements of energy dispatching scenarios. The optimal dispatching scheme introduces a redundancy coefficient, the core purpose of which is to establish an energy safety margin for the dispatching system to absorb the risks brought about by uncertainties such as robot movement energy consumption, power monitoring errors, and energy dispatching delays. This fundamentally ensures the success rate of energy dispatching and the robustness of the system. The redundancy coefficient is set based on historical data and engineering experience. A preferred implementation method is to calculate the ratio of the actual total energy consumption to the total demand based on historical successful dispatching data, and take its 95th percentile as the benchmark value of the redundancy coefficient.

[0023] Based on the communication data between the robot and the central control platform, a communication signal quality assessment model is constructed, and the robot replacement scheduling is dynamically adjusted based on the optimal scheduling scheme. Specifically, this includes: Based on the communication data between the robot and the central control platform, a communication signal quality assessment model is constructed, and the communication signal quality assessment score of each robot in the optimal scheduling scheme is calculated in real time. When generating the optimal scheduling scheme, the communication signal quality qualification threshold is set. If the evaluation score of each robot in the scheme is greater than the qualification threshold, the optimal scheduling scheme is used as the final scheduling scheme. If not, robots with evaluation scores less than the qualification threshold are removed. The optimal scheduling scheme is then re-selected from the initial candidate robot set based on a greedy algorithm. The process is repeated until all robots that meet the qualification threshold are selected as the final scheduling scheme. During the execution of the final scheduling plan, the central control platform records and updates the communication signal quality assessment score of each energy scheduling robot at a first fixed time interval T1. When the evaluation score of any robot at K consecutive sampling points shows a strictly monotonically decreasing trend, the robot is determined to enter a Level 1 warning state, and the time interval for its status reporting is shortened from T1 to a second fixed time interval T2, where T2... <T1; An emergency threshold is set for the communication signal quality assessment score, which is lower than the acceptable threshold. When a robot in a Level 1 warning state has its evaluation score drop below the emergency threshold, or when the evaluation score of any energy dispatching robot drops directly below the emergency threshold, the robot is determined to enter a Level 2 emergency state. The central control platform immediately selects a backup robot from the initial candidate robot set based on a greedy algorithm, issues a replacement command to the backup robot, and simultaneously issues a command to the robot in the level 2 emergency state to terminate energy dispatch and return.

[0024] This can be explained by the fact that the energy dispatching of mobile energy storage robots is divided into pre-screening (i.e., the final dispatching scheme) and energy dispatching process control (i.e., the setting of qualified thresholds and emergency thresholds). These two aspects constitute a dual line of defense to ensure the success of energy dispatching tasks, and neither can be omitted. Pre-screening is a static optimization based on the current known state, and its core lies in "optimization." It aims to assemble a high-quality team of robots with reliable communication and sufficient energy at the beginning of the task, minimizing systemic risks caused by individual robot failures from the outset, and establishing a stable initial foundation for the entire dispatching process. Energy dispatching process control, as the second line of defense, focuses on "tolerance." The system employs real-time monitoring and dynamic adjustment of robot replacement scheduling to provide early warnings, control, and even real-time replacement for robots exhibiting communication deterioration or performance degradation. This endows the system with resilience and adaptability to cope with uncertainty. Secondly, by setting an emergency threshold far below the acceptable threshold, this solution aims to define a critical point where the risk of communication failure is "unacceptable." When a robot's communication quality assessment score falls below this emergency threshold, it does not necessarily mean that the robot cannot reach the target location. Rather, it signifies that the command and control link between the central control platform and the robot is extremely fragile, and the platform can no longer reliably obtain its real-time status or respond to sudden changes. Even when effective instructions are issued in response to unforeseen circumstances (such as road changes or task updates), there is no guarantee that the robot will safely return after the task is completed. In this state, the robot transforms from a "controllable unit" into an "uncertain factor," and its continued execution of tasks poses a significant risk to the determinism, security, and robustness of the entire scheduling system. Therefore, the strategy adopted in this solution is: instead of sacrificing the overall controllability of the system to gamble on the probability of a single node's success, it proactively avoids risk by replacing it with a reliably communicating and controllable unit. This fundamentally ensures the ultimate success of the scheduling task and the safety of system assets. The specific value of the emergency threshold is based on... The sampling interval is set based on the statistical analysis of the inflection point where the probability of link loss and command loss increases sharply in historical communication data. T1 is the sampling interval of the system in the non-warning state, which can be directly set as the maximum state update delay that the service can accept. T2 is the sampling interval of the system in the warning state. Based on historical warning data, the worst-case rate of signal quality (μ+3σ) is calculated using the 3σ criterion. The warning sampling interval is directly quantified and determined by the formula T2=communication interruption threshold / (μ+3σ). The communication interruption threshold is determined by using structured expert decision-making based on the Delphi method to ensure that the system can still maintain effective intervention capability in near-worst conditions. The expression for the communication signal quality assessment model is as follows: ; ; In the formula, This is a score for evaluating the quality of communication signals. This is the signal strength value. For channel collision rate, For network latency duration, These are the weighting coefficients; Weighting coefficient The values ​​are obtained by fitting historical communication data using a linear regression algorithm. Specifically, a large amount of data is collected, including signal strength S, channel collision rate C, network latency D, and their corresponding communication quality expert scores. The sample data, with S, C, and D as independent variables, Performing linear regression on the dependent variable yields regression coefficients, which are the optimized weighting coefficients. .

[0025] Based on online map services, and combined with the location and boundary coordinates of communication base stations, the communication link between the robot and the base station is dynamically adjusted, specifically including: The robot's location is obtained in real time based on online map services, and the robot's movement trajectory is predicted based on road information on the map; The location and boundary coordinates of the base stations are obtained based on online map services, and the base station handover boundaries are set by manual marking. The robot's current position is continuously compared with the predicted trajectory, and its distance from the current serving base station and the handover boundary of the neighboring base station is dynamically calculated to determine whether it is about to enter or has already entered the base station handover boundary. If so, the robot is instructed to pre-register with the target base station, synchronously negotiate communication parameters and conduct link connectivity tests. Then, dual-link redundancy is established with the target base station. Once the dual-link data synchronization error is less than a preset threshold and the connection is stable, the robot is instructed to release the connection with the original base station. If the pre-registration or dual-link synchronization is abnormal, the robot immediately returns to the original base station link and triggers a handover warning. Otherwise, the original communication link is maintained.

[0026] It can be explained that the energy scheduling of mobile energy storage robots is based on a reliable and continuous communication link, which is divided into pre-screening and energy scheduling process control. If the robot encounters a communication interruption due to base station switching during movement, the entire system will instantly degenerate into a "blind control" state, and the scheduling algorithm will become infeasible. Therefore, this solution predicts the movement trajectory through an online map and presets the base station switching boundary, realizing a proactive and seamless base station switching of "location trigger, build first, then disconnect". This builds a reliable communication channel for the moving robot, ensuring a continuous and reliable connection between the central control platform and the robot. It provides the underlying communication guarantee for real-time scheduling command transmission, full-process status monitoring, and dynamic fault-tolerant decision-making. It is the core foundation for maintaining the determinism, reliability, and security of the entire energy scheduling system. The preset threshold is determined through testing based on the tolerance of historical business data. The connection stability refers to the fact that the data synchronization error of the two links is less than the preset threshold for several consecutive communication cycles. The pre-registration refers to the robot using the original base station that is still connected as a registration point before physically moving to the coverage area of ​​the new base station. Following the process of establishing a logical connection with the target base station in advance, completing identity authentication and resource reservation, the dual-link synchronization anomaly refers to the monitoring during the handover process, where the data synchronization error of the dual links exceeds the preset threshold for several consecutive communication cycles, indicating that the quality of the new link does not meet the handover conditions. Since the robot can only travel along a fixed path, that is, it can only travel on the road and cannot directly cross obstacles or travel off-road, the route for the robot to reach the target area is actually already determined, just like map navigation. Based on the robot's route, the next path of the robot can be known, that is, the robot's movement trajectory can be predicted. Even if there are multiple forks in the road, it will not affect the robot. After determining a path, the system has sufficient time to judge the robot's movement trajectory. The specific content of determining whether it is about to or has already entered the base station handover boundary is to predict the robot's movement trajectory based on the robot's real-time position coordinates, continuously calculate the spatial intersection of its movement path and the dynamic boundaries of each base station, so as to predict the handover timing before the robot actually crosses the boundary or trigger the handover operation at the moment of crossing the boundary.

[0027] Furthermore, the method according to the embodiments of this application can also be achieved by means of... Figure 4 The architecture of the electronic device shown is used to implement this. For example... Figure 4As shown, the electronic device 500 may include a bus 501, one or more CPUs 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, a communication port 505 connected to a network, an input / output component 506, a hard disk 507, etc. The storage device in the electronic device 500, such as the ROM 503 or the hard disk 507, may store a communication scheduling method for a mobile energy storage robot for energy dispatching provided in this application. The electronic device 500 may also include a user interface 508. Of course, Figure 4 The architecture shown is merely exemplary and can be omitted as needed when implementing different devices. Figure 4 One or more components in the illustrated electronic device.

[0028] Figure 5 This is a schematic diagram of a computer-readable storage medium structure provided in one embodiment of this application. Figure 5 The diagram illustrates a computer-readable storage medium 600 according to one embodiment of this application. The computer-readable storage medium 600 stores computer-readable instructions. When executed by a processor, the computer-readable instructions can perform a mobile energy storage robot communication scheduling method for energy dispatch according to an embodiment of this application, as described above with reference to the accompanying drawings. The storage medium 600 includes, but is not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0029] In summary, the advantages of this invention are: it achieves efficient, reliable, and adaptive coordination of energy scheduling and communication scheduling in large-scale mobile energy storage robot cluster scenarios.

[0030] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A communication scheduling method for mobile energy storage robots used in energy dispatching, characterized in that, include: A central control platform for communication scheduling of mobile energy storage robots based on energy dispatch acquires demand data for areas requiring energy dispatch and real-time status data of mobile energy storage robots. To acquire real-time status data for all mobile energy storage robots, a hierarchical status acquisition mechanism is adopted, allocating a dedicated communication time slot for each robot. Based on the robot's current location and the geographical location of the area requiring energy dispatch, combined with online map services, the cumulative road length and estimated travel time are calculated to obtain an initial set of candidate robots. Based on the initial set of candidate robots, a greedy algorithm is used to select the final combination of mobile energy storage robots as the optimal scheduling scheme. Based on the communication data between the robot and the central control platform, a communication signal quality assessment model is constructed, and the robot replacement scheduling is dynamically adjusted based on the optimal scheduling scheme. Based on online map services, and combined with the location and boundary coordinates of communication base stations, the communication link between the robot and the base station is dynamically adjusted.

2. The communication scheduling method for a mobile energy storage robot for energy dispatch according to claim 1, characterized in that, The central control platform for communication scheduling of mobile energy storage robots based on energy dispatch specifically acquires demand data for the areas requiring energy dispatch and real-time status data of the mobile energy storage robots, including: The demand data includes: total energy demand, geographical location, and scheduling time limit; The real-time status data includes: robot ID, current position coordinates, current energy consumption, current energy scheduling status, and communication data. The energy scheduling status includes: idle state and energy scheduling in progress. The communication data includes: signal strength value, channel collision rate, and network latency. The system obtains the amount of energy that can be directly scheduled when the robot is idle, and the estimated timestamp and estimated remaining amount of energy that can be directly scheduled when the energy scheduling ends.

3. The communication scheduling method for a mobile energy storage robot for energy dispatch according to claim 2, characterized in that, The acquisition of real-time status data for all mobile energy storage robots adopts a hierarchical status acquisition mechanism, allocating a dedicated communication time slot for each robot, specifically including: A fixed period is preset for all robots, and they send heartbeat data packets to the central control platform periodically. The data packets include at least the robot's ID and current position coordinates. The heartbeat data packets are sent using a random backoff mechanism to avoid channel conflicts. When the central control platform executes energy dispatching decisions, it first selects a preliminary set of geographical candidates based on the location coordinates in the newly received heartbeat data packets and the geographical location of the area requiring energy dispatching. Based on the geographic candidate set, the central control platform sends a time-division multiplexing precise status query command and communication scheduling table to the robots in the set; The communication scheduling table refers to a communication time window starting from the current moment, which is divided into several consecutive micro-slots of equal length, and each robot in the geographical candidate set is uniquely mapped to one of the specific micro-slots. After receiving the instruction and schedule, each robot is configured to parse its assigned time slot number and send its complete real-time status data to the central control platform during the corresponding time slot opening time. During other time slots, communication reception or silence is maintained to avoid interfering with the information transmission of other robots.

4. A communication scheduling method for a mobile energy storage robot for energy dispatch according to claim 3, characterized in that, The process of calculating the cumulative road length and estimated travel time based on the robot's current location and the geographical location of the area requiring energy dispatch, combined with online map services, to obtain the initial candidate robot set specifically includes: Based on online map services, obtain the path information and road traffic coefficient of each robot in the geographic candidate set to reach the area requiring energy dispatch; Based on the robot's current location and the geographical location of the area requiring energy dispatch, combined with path information and road traffic coefficient, calculate the cumulative road length and estimated travel time for the robot to reach the area requiring energy dispatch. Based on the cumulative road length and estimated travel time, a comprehensive evaluation value of the robot's scheduling accessibility is calculated using a weighted summation formula. Based on the demand data of the energy dispatch area, set multi-parameter thresholds for robot dispatch accessibility, and obtain an initial set of candidate robots through a threshold comparison mechanism; The threshold comparison mechanism specifically includes: Based on the scheduling time limit of the required energy dispatch area, a reserved value is subtracted from the scheduling time limit to form a time limit threshold, i.e., time limit threshold = scheduling time limit - reserved value; Based on the cumulative length of roads leading to the area requiring energy dispatch, an upper limit for the cumulative road length is set. Robots exceeding the upper limit for the cumulative road length will not participate in this dispatch. Based on the comprehensive evaluation value of robot scheduling reachability, an reachability boundary threshold is set, and a set of robots with a comprehensive evaluation value less than the reachability boundary threshold is obtained; When screening robots, the threshold comparison mechanism follows this order: first, screening based on scheduling time limits; then, screening based on cumulative road length; and finally, screening based on a comprehensive evaluation value of scheduling reachability.

5. A communication scheduling method for a mobile energy storage robot for energy dispatch according to claim 4, characterized in that, The step of selecting the final combination of mobile energy storage robots as the optimal scheduling scheme based on the initial candidate robot set and a greedy algorithm specifically includes: From the initial set of candidate robots, select a single robot whose current remaining directly schedulable energy is greater than or equal to the total energy demand multiplied by the redundancy factor; Wherein, the redundancy coefficient is a preset value greater than 1; If such a robot exists, the robot with the shortest cumulative road length will be selected as the optimal scheduling solution. If there is no single robot that meets the conditions, then: Sort all robots in the initial candidate robot set from highest to lowest according to their current remaining directly schedulable energy. Starting with the robot with the highest remaining directly dispatchable energy, its energy is added sequentially until the total added amount is greater than or equal to the total energy demand multiplied by the redundancy coefficient. At this point, the combination of robots participating in the addition is the optimal scheduling scheme.

6. A communication scheduling method for a mobile energy storage robot for energy dispatch according to claim 5, characterized in that, The process of constructing a communication signal quality assessment model based on communication data between the robot and the central control platform, and dynamically adjusting robot replacement scheduling based on the optimal scheduling scheme, specifically includes: Based on the communication data between the robot and the central control platform, a communication signal quality assessment model is constructed, and the communication signal quality assessment score of each robot in the optimal scheduling scheme is calculated in real time. When generating the optimal scheduling scheme, the communication signal quality qualification threshold is set. If the evaluation score of each robot in the scheme is greater than the qualification threshold, the optimal scheduling scheme is used as the final scheduling scheme. If not, robots with evaluation scores less than the qualification threshold are removed. The optimal scheduling scheme is then re-selected from the initial candidate robot set based on a greedy algorithm. The process is repeated until all robots that meet the qualification threshold are selected as the final scheduling scheme. During the execution of the final scheduling plan, the central control platform records and updates the communication signal quality assessment score of each energy scheduling robot at a first fixed time interval T1. When the evaluation score of any robot at K consecutive sampling points shows a strictly monotonically decreasing trend, the robot is determined to enter a Level 1 warning state, and the time interval for its status reporting is shortened from T1 to a second fixed time interval T2, where T2... <T1; An emergency threshold is set for the communication signal quality assessment score, which is lower than the acceptable threshold. When a robot in a Level 1 warning state has its evaluation score drop below the emergency threshold, or when the evaluation score of any energy dispatching robot drops directly below the emergency threshold, the robot is determined to enter a Level 2 emergency state. The central control platform immediately selects a backup robot from the initial candidate robot set based on a greedy algorithm, issues a replacement command to the backup robot, and simultaneously issues a command to the robot in the level 2 emergency state to terminate energy dispatch and return.

7. A communication scheduling method for a mobile energy storage robot for energy dispatch according to claim 6, characterized in that, The method of dynamically adjusting the communication link between the robot and the base station based on online map services, combined with the location and boundary coordinates of the communication base station, specifically includes: The robot's location is obtained in real time based on online map services, and the robot's movement trajectory is predicted based on road information on the map; The location and boundary coordinates of the base stations are obtained based on online map services, and the base station handover boundaries are set by manual marking. The robot's current position is continuously compared with the predicted trajectory, and its distance from the current serving base station and the handover boundary of the neighboring base station is dynamically calculated to determine whether it is about to enter or has already entered the base station handover boundary. If so, the robot is instructed to pre-register with the target base station, synchronously negotiate communication parameters and conduct link connectivity tests. Then, dual-link redundancy is established with the target base station. Once the dual-link data synchronization error is less than a preset threshold and the connection is stable, the robot is instructed to release the connection with the original base station. If the pre-registration or dual-link synchronization is abnormal, the robot immediately returns to the original base station link and triggers a handover warning. Otherwise, the original communication link is maintained.

8. An electronic device, characterized in that, include: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform a mobile energy storage robot communication scheduling method for energy dispatch as described in any one of claims 1-7.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements a mobile energy storage robot communication scheduling method for energy dispatch as described in any one of claims 1-7.

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