Wheeled robot communication network dynamic load balancing method, device and equipment

Abstract

The invention provides a wheeled robot communication network dynamic load balancing method, device and equipment, and relates to the technical field of communication networks, and the method comprises the steps: obtaining a first offset value associated with a macro base station and a second offset value associated with a small base station in a target area; selecting an associated base station according to the real-time communication channel condition of the wheeled robot in the target area and the initial offset value, and collecting related data to identify an overload base station and a light-load base station; a first wheeled robot meeting a migration preset condition is selected from the overload base stations, the first wheeled robot is switched to the light-load base station in an associated mode, the switching success rate is obtained, and the switching success rate is used for dynamically adjusting the first offset value and the second offset value. And by dynamically adjusting the offset value, balanced distribution of the wheeled robots among the base stations is guided, and the utilization rate of network resources is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of communication networks, and in particular, to a method, device, and equipment for dynamic load balancing of a communication network of a wheeled robot. Background Art

[0002] With the development of intelligent transportation systems, the communication of wheeled robots has higher and higher requirements for network reliability and latency. Traditional network resource sharing methods are difficult to meet the needs of different services. Especially in a high-density wheeled robot environment, the problems of network congestion and uneven bandwidth allocation are particularly prominent. A large number of wheeled robot terminals access the network simultaneously, which may lead to unbalanced load of base stations, some base stations being overloaded while other base stations' resources are idle, thus affecting communication quality.

[0003] At the same time, due to the high mobility of wheeled robots, the transfer of service traffic in space also has great diversity. This leads to continuous changes in the user load of the entire wheeled robot communication network, thus bringing great challenges to the radio resource management between slices of the wheeled robot communication network. Summary of the Invention

[0004] The embodiments of the present application provide a method for dynamic load balancing of a communication network of a wheeled robot. By dynamically adjusting the bias values of macro base stations and small base stations, it intelligently guides the balanced distribution of wheeled robot terminals among base stations, significantly improving the utilization rate of network resources.

[0005] In a first aspect, the embodiments of the present application provide a method for dynamic load balancing of a communication network of a wheeled robot, including: Obtain the initial bias values of base stations in a target area, where the base stations include macro base stations and small base stations, the initial bias values include a first bias value and a second bias value, the first bias value is associated with the macro base station, and the second bias value is associated with the small base station; Select an associated base station according to the real-time communication channel conditions of wheeled robots in the target area and the initial bias values, and collect the load data of the base station, the channel state of the wheeled robot, and the mobility performance of the wheeled robot, and identify overloaded base stations and lightly loaded base stations; Select a first wheeled robot that meets the migration preset conditions from the overloaded base stations, associate and switch the first wheeled robot to the lightly loaded base station, and obtain the switching success rate, where the switching success rate is used to dynamically adjust the first bias value and the second bias value.

[0006] It can be understood that the bias value is a configurable parameter of a base station (such as a macro base station MBS and a small base station SBS), which is used to adjust the priority of a wheeled robot to select an associated base station. By differentially setting the bias value to guide users to migrate from high-load base stations to low-load base stations, local congestion can be avoided. By dynamically adjusting the bias value, it is ensured that high-priority services (such as autonomous driving) are always associated with base stations with sufficient resources. In this way, by dynamically adjusting the bias values of macro base stations and small base stations, the intelligent guidance of the balanced distribution of wheeled robot terminals among base stations significantly improves the network resource utilization rate.

[0007] In a possible implementation manner, the initial bias value includes a first bias value and a second bias value, and the first bias value is less than the second bias value.

[0008] In a possible implementation manner, the handover success rate is used to dynamically adjust the first bias value and the second bias value, including: When the handover success rate is higher than a preset value, the first bias value is decreased and the second bias value is increased.

[0009] It can be seen that by automatically suppressing the access of high-load base stations through a negative feedback mechanism, when the handover success rate is high (for example, >90%), it indicates that the network state is stable. At this time, reducing the bias value of the macro base station can reduce the access of new users, and at the same time, increasing the bias value of the small base station can effectively divert the wheeled robot. The actual measurement shows that this strategy can significantly reduce the load difference between base stations. At the same time, the dynamic adjustment keeps the handover success rate within the optimal range (for example, 85%-95%), avoids the ping-pong effect caused by sudden changes in the bias value, controls the handover interruption time of high-speed moving wheeled robots within 20 ms, and at the same time ensures that the throughput fluctuation of services such as video streams does not exceed 15%. This closed-loop control can enable the network to always work at the best load balance point.

[0010] In a possible implementation manner, select the first wheeled robot that meets the migration preset conditions from the overloaded base stations, and associate and switch the first wheeled robot to the lightly loaded base station. The preset conditions include at least one of the following: After migration, the target base station can meet the QoS requirements of the wheeled robot, the wheeled robot with a moving speed lower than the set speed threshold, and the wheeled robot with a channel condition better than the set threshold.

[0011] In a possible implementation manner, the method further includes: Predict the moving path of the wheeled robot and the set of potential handover target base stations, pre-enact the handover process in the digital twin environment, calculate the predicted QoS metric set, and virtually migrate the target wheeled robot to the candidate base station; When all the predicted QoS metrics meet the corresponding service requirement thresholds, trigger the actual base station handover operation.

[0012] It is understandable that by pre-screening eligible migrating wheeled robots and combining digital twin verification, the overall network resource utilization rate is improved on the premise of ensuring service quality, effectively avoiding the coexistence of resource idle and congestion situations.

[0013] In a possible implementation manner, the method further includes: When the QoS index after actual handover deviates from the predicted value by more than a set threshold, roll back to the connection with the original base station.

[0014] It is understandable that when QoS indexes such as actual latency / packet loss rate deviate from the predicted value by more than a preset value (for example, 10%) (such as the latency of the autonomous driving service deteriorates from the predicted 15 ms to 30 ms), a rollback operation is immediately triggered to ensure safety. At the same time, by quickly rolling back to reduce the number of ineffective handovers, the signaling overhead can be reduced.

[0015] In a possible implementation manner, the digital twin model is automatically retrained by a rollback event, and the prediction accuracy can be continuously improved. Combining historical rollback data can identify network weak points (such as blind areas in the coverage of base stations in specific areas), enabling the system to have the ability to learn from anomalies and reducing the prediction deviation rate.

[0016] In a possible implementation manner, the method further includes: When there are high-priority wheeled robots in the target area, set a bias value adjustment exemption flag for the high-priority wheeled robots, and the base stations associated with the high-priority wheeled robots do not participate in the dynamic reduction of the bias value. The high-priority wheeled robots include wheeled robots performing emergency tasks or authorized emergency communication wheeled robots.

[0017] It can be seen that in the embodiments of the present application, by establishing a pyramid structure of service levels, it is ensured that emergency communication wheeled robots always obtain the optimal network resources. By setting a bias value adjustment exemption signaling flag mechanism, the exemption decision latency can be effectively reduced.

[0018] In a second aspect, the embodiments of the present application further provide a dynamic load balancing device for a wheeled robot communication network, including: An acquisition module, configured to acquire the initial bias values of base stations in the target area, where the base stations include macro base stations and small base stations, the initial bias values include a first bias value and a second bias value, the first bias value is associated with the macro base station, and the second bias value is associated with the small base station; An identification module, configured to select associated base stations according to the real-time communication channel conditions of the wheeled robots in the target area and the initial bias values, and collect the load data of the base stations, the channel states of the wheeled robots, and the mobility performance of the wheeled robots, and identify overloaded base stations and lightly loaded base stations; A switching module, configured to select a first wheeled robot that meets the migration preset conditions from the overloaded base stations, associate and switch the first wheeled robot to the lightly loaded base station, and obtain a switching success rate, where the switching success rate is used to dynamically adjust the first bias value and the second bias value.

[0019] In a possible implementation manner, the initial bias value includes a first bias value and a second bias value, and the first bias value is less than the second bias value.

[0020] In a possible implementation manner, the switching module is further configured to: When the switching success rate is higher than a preset value, reduce the first bias value and increase the second bias value.

[0021] In a possible implementation manner, the switching module is further configured to: select a first wheeled robot that meets the migration preset conditions from the overloaded base stations, associate and switch the first wheeled robot to the lightly loaded base station, where the preset conditions include at least one of the following: After migration, the target base station can meet the QoS requirements of the wheeled robot, the wheeled robot with a moving speed lower than the set speed threshold, and the wheeled robot with a channel condition better than the set threshold.

[0022] In a possible implementation manner, the device further includes a prediction module, configured to: Predict the moving path of the wheeled robot and the set of potential switching target base stations, pre - simulate the switching process in the digital twin environment, calculate the predicted QoS metric set, and virtually migrate the target wheeled robot to the candidate base station; When all the predicted QoS metrics meet the corresponding service requirement thresholds, trigger the actual base station switching operation.

[0023] In a possible implementation manner, the device further includes a rollback module, configured to: When the QoS metric after actual switching deviates from the predicted value by more than the set threshold, roll back to the original base station connection.

[0024] In a possible implementation manner, the device further includes an adjustment module, configured to: When there are high - priority wheeled robots in the target area, set a bias value adjustment exemption flag for the high - priority wheeled robots, and the base stations associated with the high - priority wheeled robots do not participate in the dynamic reduction of the bias value. The high - priority wheeled robots include wheeled robots performing emergency tasks or authorized emergency communication wheeled robots.

[0025] Combined with the first aspect, in the second aspect, the relevant beneficial effects can be referred to the first aspect, and the embodiments of the present application will not be elaborated herein.

[0026] In a third aspect, an embodiment of the present application further provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the electronic device to execute the method corresponding to any one of the embodiments in the first aspect of the embodiments of the present application.

[0027] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement any one of the methods in the first aspect of the embodiments of the present application.

[0028] In a fifth aspect, the present disclosure further provides a computer program product, which includes computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the method corresponding to any one of the embodiments in the first aspect of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present disclosure and, together with the specification, are used to explain the principles of the present disclosure.

[0030] To more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0031] Figure 1 It is a schematic flow chart of a method for dynamic load balancing of a communication network of a wheeled robot provided by an embodiment of the present application.

[0032] Figure 2 It is a schematic diagram of a device for dynamic load balancing of a communication network of a wheeled robot provided by an embodiment of the present application.

[0033] Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] When the following description relates to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments are only examples of devices and methods that are consistent with some aspects of the embodiments of the present application.

[0035] The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in some embodiments. The embodiments of the present application will be described below with reference to the accompanying drawings.

[0036] It should be noted that, in this document, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0037] The embodiments of the present disclosure can be applied to the control of various devices such as multiple wheeled robots, mobile robots, aircraft, ships, intelligent rail rapid transit systems (ART), etc. The wheeled robot can be, but is not limited to, a passenger car, a commercial vehicle (e.g., a truck, a bus, a freight vehicle, etc.), a special-purpose wheeled robot (e.g., an ambulance, a fire truck, an engineering vehicle, a rescue vehicle, etc.), an agricultural and industrial wheeled robot (e.g., a harvester, a forklift, etc.), a transportation and logistics wheeled robot (e.g., a container truck, a refrigerated truck, etc.), a new energy wheeled robot (e.g., an electric vehicle, a hybrid vehicle), a special vehicle (e.g., a garbage truck, a sprinkler truck, etc.). In other words, the "wheeled robot" in the embodiments of the present disclosure is equivalent to the foregoing various devices. It can be used in scenarios such as driverless vehicles, manned vehicles, and mixed traffic flows.

[0038] The embodiments of the present disclosure can be applied to scenarios such as urban traffic, highways, ports, mines, farms, closed parks, etc., and are applicable to many aspects such as ride-hailing, public transportation, logistics distribution, unmanned transportation, last-mile delivery, automated agricultural operations, automated environmental sanitation, etc. Of course, the embodiments of the present disclosure can also be applied to any other intelligent control scenarios involving devices such as wheeled robots, etc. The present disclosure does not limit the application scenarios and applicable fields of the embodiments of the present disclosure.

[0039] Exemplarily, the embodiments of the present disclosure can be applied to the following four aspects: 1) Urban roads: In a complex urban traffic environment, the embodiments of the present disclosure can handle changing traffic flows and diverse traffic participants and provide accurate trajectory prediction.

[0040] 2) Highways: On highways, the disclosed embodiments can efficiently, quickly and accurately predict the trajectory of a wheeled robot traveling at high speeds.

[0041] 3) Closed areas such as ports and parks: In closed or semi-closed environments, applying the embodiments of the present disclosure to an autonomous driving wheeled robot can achieve efficient, fast and accurate navigation of the autonomous driving wheeled robot, thereby improving efficiency and reducing labor costs.

[0042] 4) Shared travel services: Integrating the embodiments of the present disclosure into self-driving taxis or shared wheeled robots can help provide safe and reliable travel services.

[0043] The network used for the wheeled robot data transmission resource in the embodiment of the present application may be, but is not limited to, a long-range radio (LoRa, Long Range Radio) module, a narrowband Internet of Things (NB-IoT, Narrow Band Internet of Things) module, an enhanced machine type communication (eMTC, Enhanced Machine-Type Communication) module, or other similar communication modules. It may also be, but is not limited to, a module that supports one or more of the following wireless communication methods: mobile communication, long-term evolution technology wheeled robot communication (LTE-V, LTE-Vehicle-to-Everything), dedicated short-range communication (DSRC, Dedicated Short-Range Communication), cellular vehicle network (C-V2X, Cellular Vehicle-to-Everything), and vehicle wireless communication technology (V2X, Vehicle-to-Everything).

[0044] In the examples of the present application, the following terms are explained exemplarily.

[0045] Macro Base Station (MBS): usually refers to a high-power base station with a coverage radius of ≥500m. It operates in the Sub-3GHz frequency band and has a wide coverage range, making it suitable for serving high-speed mobile wheeled robots.

[0046] Small Base Station (SBS): usually refers to a low-power node with a coverage radius of ≤200m, and an operating frequency band of 3.5-28GHz. It has a small coverage range and high capacity, and is suitable for serving low-speed mobile wheeled robots or wheeled robots in hot spots.

[0047] Macro base station bias value (MBS): that is, the first bias value, which is lower and encourages wheeled robots to associate with small base stations first.

[0048] Small base station bias value (SBS): that is, the second bias value, which is higher and attracts the wheeled robot to associate with the small base station.

[0049] Overload Base Station: Reduced bias value to reduce new wheeled robot association.

[0050] Light load base station: Increase the bias value to attract more wheeled robots to associate.

[0051] Cell Individual Offset (CIO): A parameter used to adjust the wheeled robot's switching behavior. By artificially increasing or decreasing the signal measurement value of the base station (such as RSRP / RSRQ), it affects the terminal's tendency to choose to access the macro base station or small base station. Its core functions include: (1) Load balancing: By dynamically adjusting the CIO, terminals are guided to preferentially access base stations with lighter loads (e.g., increasing the CIO of small base stations to divert the pressure from macro base stations).

[0052] (2) Switching optimization: In high-speed mobile scenarios, adjusting the CIO can reduce ping-pong switching (for example, increasing the macro station CIO allows wheeled robots to switch earlier).

[0053] (3) Business classification: Set fixed CIO exemptions for high-priority businesses (such as emergency wheeled robots) to ensure quality of service (QoS).

[0054] Figure 1 A dynamic load balancing method for a wheeled robot communication network provided in an embodiment of the present application dynamically adjusts the bias values ​​of macro base stations and small base stations, intelligently guides the balanced distribution of wheeled robot terminals among base stations, and significantly improves network resource utilization.

[0055] exist Figure 1 In the method, the method comprises step S101, step S102 and step S103. Specifically: Step S101, obtain the initial bias value of the base station in the target area, the base station includes a macro base station and a small base station, the initial bias value includes a first bias value and a second bias value, the first bias value is associated with the macro base station, and the second bias value is associated with the small base station.

[0056] In the embodiment of the present application, the initial bias value includes two independent parameters: the first bias value is used for the macro base station, and the second bias value is used for the small base station. Exemplarily, the first bias value Bias_MBS∈[-5dB, 0dB] is used for the macro base station, and the second bias value Bias_SBS∈[+3dB, +10dB] is used for the small base station.

[0057] Step S102: selecting an associated base station according to the real-time communication channel condition of the wheeled robot in the target area and the initial bias value, collecting the load data of the base station, the channel status of the wheeled robot and the mobility performance of the wheeled robot, and identifying overloaded base stations and lightly loaded base stations.

[0058] In a possible implementation, the first wheeled robot that meets a preset migration condition is selected from the overloaded base station, and the first wheeled robot is associated and switched to the lightly loaded base station, wherein the preset condition includes at least one of the following: After the migration, the target base station can meet the QoS requirements of the wheeled robots, the wheeled robots whose moving speed is lower than the set speed threshold, and the wheeled robots whose channel conditions are better than the set threshold.

[0059] For example, in the specific implementation, the following multi-dimensional conditions must be met simultaneously when selecting wheeled robots to be migrated from overloaded base stations: first, the target base station must have the ability to guarantee the service quality of the wheeled robot, that is, it can continue to meet the QoS indicators such as latency and bandwidth required by the wheeled robot service after migration; second, the wheeled robot terminal with low speed movement is preferred, and its speed value must be lower than the speed threshold dynamically calculated according to the coverage range of the base station (v_th = R_cell / T_handover, where R_cell is the coverage radius of the base station and T_handover is the handover delay); finally, the wireless channel condition of the candidate wheeled robot at the current base station should be better than the preset threshold value, including key indicators such as reference signal received power (RSRP>-85dBm) and signal to interference and noise ratio (SINR>15dB). The combined application of these three conditions not only ensures the continuity of the service after migration, but also minimizes the risk of handover failure caused by the high-speed movement of the wheeled robot, while ensuring the communication quality of the new link after migration.

[0060] In the embodiment of the present application, a virtual signal quality evaluation model is constructed based on the wireless channel measurement data (including key indicators such as reference signal receiving power and signal-to-noise ratio) reported in real time by the wheeled robot terminal, combined with the pre-configured differentiated bias value parameters; secondly, the load status information such as the resource utilization rate of each base station and the number of access users is obtained in real time through the control plane interface between the core network and the base station; at the same time, a multi-dimensional base station selection decision matrix is ​​established using the mobility parameters (such as position coordinates, movement speed, acceleration, etc.) provided by the vehicle-mounted communication unit. On this basis, a dynamic threshold judgment algorithm is used to mark base stations whose resource utilization rate continuously exceeds the preset upper limit as overloaded nodes, and base stations whose resource utilization rate is long-term below the preset lower limit are identified as lightly loaded nodes, thereby providing an accurate decision basis for subsequent load balancing adjustments.

[0061] Exemplarily, the wheeled robot terminal measures the reference signal receiving power RSRP (range -140dBm to -44dBm) and the signal-to-interference-plus-noise ratio SINR (accuracy ±0.5dB) of the serving base station and neighboring cells every 200ms, and calculates the virtual signal strength by combining the pre-configured initial bias values (macro base station [-5dB, 0dB], small base station [+3dB, +10dB]): RSRP virtual =RSRP measured +Bias + ΔHys (hysteresis parameter 3dB) After screening out candidate base stations with RSRP virtual >-110dBm, they are sorted in descending order of SINR and the optimal base station that meets the service QoS requirements is selected; at the same time, base station load data is collected through the X2 interface (including PRB utilization rate sampling period of 1 second, active user count statistical granularity of 5 seconds, backhaul link load rate accuracy of ±2%), combined with the real-time speed vector provided by the in-vehicle OBU (GPS / INS fusion positioning error <0.5m), acceleration data (range ±2g), and motion direction angle (accuracy ±3°). When it is detected that the PRB utilization rate of the base station continues to be >80% for 30 seconds and the number of users >90% of the capacity, it is determined as an overloaded base station; when the PRB utilization rate continues to be <40% for 60 seconds and the number of users <50% of the capacity, it is determined as a lightly loaded base station. During the process, Kalman filtering is used to compensate for missing data and eliminate outliers, and finally the base station selection accuracy and the load recognition delay within 300ms are achieved.

[0062] Step S103: Select the first wheeled robot that meets the migration preset conditions from the overloaded base stations, associate and switch the first wheeled robot to the lightly loaded base station, and obtain the handover success rate, which is used to dynamically adjust the first bias value and the second bias value.

[0063] In the embodiment of the present application, an intelligent user migration strategy is adopted to achieve load balancing. A candidate wheeled robot terminal that meets the conditions (i.e., the first wheeled robot) is selected from the overloaded base stations, and its service connection is intelligently switched to the lightly loaded base station, and at the same time, the success rate of the handover operation is statistically monitored in real time. This handover success rate is used as a key feedback parameter to dynamically adjust the bias weights of the macro base station (corresponding to the first bias value) and the small base station (corresponding to the second bias value), thereby forming a closed-loop optimization control mechanism.

[0064] Exemplarily, target wheeled robots (the first wheeled robots) meeting the requirements are screened out from overloaded base stations. The migration preset conditions include, but are not limited to: 1) Wheeled robot channel quality indicators (RSRP≥-85dBm and SINR≥15dB); 2) Mobility characteristics (speed≤preset threshold and motion trajectory stability≥0.8); 3) Service demand matching degree (the QoS level provided by the target base station≥the current service requirement level). Through signaling interaction, the service connection of the screened first wheeled robots is seamlessly switched to the pre-identified lightly loaded base station, and a dual confirmation mechanism is adopted during the switching process to ensure service continuity. The system monitors and records the success rate of the switching operation in real time (defined as the number of successful switches / total number of attempted switches×100%). When the switching success rate within the statistical window period (default 300 seconds) exceeds the set threshold (such as 90%), a bias value dynamic adjustment algorithm is triggered: According to the load balancing state and network performance indicators, with a step of 0.5dB, the first bias value of the macro base station and the second bias value of the small base station are collaboratively optimized and adjusted, where the adjustment direction of the first bias value is negatively correlated with the switching success rate, and the adjustment direction of the second bias value is positively correlated with the switching success rate, thereby achieving adaptive balance of network load. This mechanism can ensure that while improving resource utilization, the probability of service interruption can be effectively reduced.

[0065] It can be understood that in the embodiments of the present application, the bias value is a configurable parameter of the base station (such as macro base station MBS and small base station SBS), which is used to adjust the priority of the wheeled robot to select the associated base station. By differentially setting the bias value to guide users to migrate from high-load base stations to low-load base stations, local congestion can be avoided. By dynamically adjusting the bias value, it is ensured that high-priority services (such as autonomous driving) are always associated with base stations with sufficient resources. In this way, by dynamically adjusting the bias values of the macro base station and the small base station, the intelligent guidance of the balanced distribution of wheeled robot terminals among base stations significantly improves the network resource utilization rate.

[0066] In a possible implementation manner, the initial bias value includes a first bias value and a second bias value, and the first bias value is less than the second bias value.

[0067] In a possible implementation manner, the switching success rate is used to dynamically adjust the first bias value and the second bias value, including: When the switching success rate is higher than the preset value, the first bias value is reduced and the second bias value is increased.

[0068] Exemplarily, when the handover success rate is monitored to be higher than a preset threshold (such as 95%), the first bias value is reduced by 1 - 3 dB, making the wheeled robot more inclined to switch to the small base station. At the same time, the second bias value is increased by 1 - 3 dB to enhance the access attractiveness of the small base station. It can be understood that when the handover success rate is lower than the threshold (such as 95%), the parameters are adjusted in the opposite direction, increasing the first bias value by 1 - 3 dB and reducing the second bias value by 1 - 3 dB. In addition, a gradient adjustment algorithm can also be introduced to adjust the handover success rate. For example:

[0069] Among them, ΔOffset is the adjustment amount of the bias value. By quantifying the deviation between the current success rate and the target success rate, the bias value (the first / second bias value) is dynamically adjusted to make the handover success rate approach the ideal threshold; α is the learning rate (for example, set to 0.5). During the morning and evening peak hours, due to large traffic changes, the success rate threshold will be automatically relaxed by 5%.

[0070] It can be seen that the access of high-load base stations is automatically suppressed through the negative feedback mechanism. When the handover success rate is high (for example, >95%), it indicates that the network state is stable. At this time, reducing the macro base station bias value can reduce the access of new users, and at the same time, increasing the small base station bias value can effectively divert the wheeled robot. The actual measurement shows that this strategy can significantly reduce the load difference between base stations. At the same time, the dynamic adjustment keeps the handover success rate in the optimal range (for example, 85% - 95%), avoiding the ping-pong effect caused by sudden changes in the bias value, controlling the handover interruption time of the high-speed moving wheeled robot within 20 ms, and ensuring that the throughput fluctuation of services such as video streams does not exceed 15%. This closed-loop control can enable the network to always operate at the best load balance point.

[0071] In the embodiment of the present application, the bias value is dynamically adjusted according to the base station load rate. The bias value of the overloaded base station is updated according to the following formula:

[0072] where α is the adjustment coefficient, Bias new is the new adjusted bias value, Bias old is the current bias value (before adjustment), Load current is the current load rate of the base station, Load threshold is the preset load threshold.

[0073] In the above formula, if the difference is positive (current load > threshold), the base station is in an overloaded state, and the bias value needs to be reduced to reduce the access of new wheeled robots. If the difference is negative (current load < threshold), the base station is in a lightly loaded state, and the bias value can be appropriately increased to attract new wheeled robots. The larger α is, the more drastic the bias value responds to load changes (rapid adjustment, but may cause oscillation), and the smaller α is, the smoother the adjustment (high stability, but delayed response).

[0074] In the above formula, when the load is seriously overloaded, an exponential function is used to accelerate the adjustment:

[0075] In a possible implementation, a multi-parameter fusion method can also be used to dynamically calculate the offset value. The specific formula is as follows:

[0076] where Δ load : Load deviation (in the above overload base station bias value calculation formula, ( ), P i,norm : is the normalized QoS parameter (delay / packet loss / jitter, etc.) of the i-th type of service, β: the weight coefficient of the load item (usually set to 0.5~0.7), w i : Parameter weight corresponding to the service type. Service types include but are not limited to autonomous driving, 4K video streaming, and IoT telemetry.

[0077] In the above formula, when there is an emergency scenario trigger: when any QoS parameter exceeds the threshold, its weight is automatically increased by the following formula:

[0078] Among them, w i ': is the adjusted service type weight, which is used for subsequent bias value calculation or resource allocation; P i : is the actual QoS parameter value of the current service (such as delay, packet loss rate, jitter, etc.); Pthresh: is the threshold of the QoS parameter of the service (the maximum / minimum critical value allowed).

[0079] In a possible implementation, the method further includes: Predict the wheeled robot's moving path and potential switching target base station set, rehearse the switching process in the digital twin environment, calculate the predicted QoS indicator set, and virtually migrate the target wheeled robot to the candidate base station; When all predicted QoS indicators meet the corresponding service demand thresholds, the actual base station switching operation is triggered.

[0080] Exemplarily, in an embodiment of the present application, an intelligent handover pre - rehearsal method based on digital twin is provided. First, by fusing the motion parameters of a wheeled robot (GPS coordinates, velocity vector, acceleration) and high - precision map data, an improved LSTM neural network is used to predict the moving path in the next 10 seconds and the set of potential handover base stations. Subsequently, a virtual simulation platform containing real - time network status (base station load, channel condition, backhaul delay) is constructed in the digital twin environment to pre - rehearse the handover process and accurately calculate key QoS metrics such as delay and interruption duration. When all predicted metrics simultaneously meet the service requirement threshold (e.g., the autonomous driving delay ≤ 20 ms) and a 10% safety margin is reserved, the actual handover operation is triggered. At the same time, a real - time monitoring and feedback mechanism is established. When the actual performance deviates from the predicted value by more than 5%, it automatically rolls back and optimizes the model parameters, ultimately achieving a handover success rate of 98.5%, compressing the service interruption time to within 10 ms, and reducing ineffective handovers.

[0081] It can be understood that by pre - screening eligible migrating wheeled robots and combining digital twin verification, while ensuring service quality, the overall network resource utilization rate is improved, effectively avoiding the situation of co - existence of resource idleness and congestion.

[0082] In a possible implementation manner, the method further includes: When the QoS metrics after the actual handover deviate from the predicted value by more than the set threshold, roll back to the original base station connection.

[0083] It can be understood that when QoS metrics such as actual delay / packet loss rate deviate from the predicted value by more than the preset value (e.g., 10%) (such as the autonomous driving service delay deteriorates from the predicted 15 ms to 30 ms), the roll - back operation is immediately triggered to ensure safety. At the same time, by quickly rolling back, the number of ineffective handovers is reduced, and the signaling overhead can be reduced.

[0084] In a possible implementation manner, the digital twin model is automatically triggered for retraining by the roll - back event, and the prediction accuracy can be continuously improved. Combining historical roll - back data can identify network weak points (such as base station coverage blind spots in specific areas), enabling the system to have the ability to learn from anomalies and reducing the prediction deviation rate.

[0085] In a possible implementation manner, the method further includes: When there are high - priority wheeled robots in the target area, a bias value adjustment exemption flag is set for the high - priority wheeled robots, and the base stations associated with the high - priority wheeled robots do not participate in the dynamic bias value reduction. The high - priority wheeled robots include wheeled robots performing emergency tasks or authorized emergency communication wheeled robots.

[0086] Exemplarily, when the system detects that there are special wheeled robots with high priority in the target service area (such as emergency wheeled robots performing emergency tasks like ambulances, fire trucks, police cars, etc., or emergency communication support wheeled robots authorized by the competent department), the network control unit will automatically set a bias value adjustment exemption flag for these high-priority wheeled robots. This flag will trigger the following protection mechanism: The base stations that establish communication connections with the high-priority wheeled robots will be excluded from the scope of execution of the dynamic bias value reduction strategy, and their service bias values will be maintained at the optimal level to ensure communication quality. This design ensures that during the base station load balancing process, the communication links of critical wheeled robots always enjoy the highest level of resource guarantee, including: 1) The base stations associated with the exemption flag wheeled robots do not participate in the dynamic bias value reduction process in the load balancing algorithm; 2) The system resource scheduler will reserve the necessary wireless resource margin for such base stations; 3) The network management system will monitor the performance indicators of the exempt base stations in real time, and will give priority to triggering the resource expansion mechanism when detecting the risk of communication quality decline. The implementation of this priority strategy effectively solves the problem of possible communication quality fluctuations faced by emergency wheeled robots in traditional load balancing algorithms, providing a reliable communication guarantee foundation for the execution of emergency tasks.

[0087] In a possible implementation manner, specifically, first, a digital certificate verification request is sent to the emergency management platform through the in-vehicle eSIM card. After double authentication by the traffic management system and the emergency management database, the network slicing manager generates a dynamic exemption token with a timestamp and a geographical fence. After receiving the registration request carrying the exemption token, the base station immediately starts the dedicated resource pool reservation program, and at the same time locks the MIMO channel parameters of this wheeled robot in the cell-level beamforming configuration to ensure the RSRP reception strength that can still be maintained in complex electromagnetic environments such as dense urban areas. When multiple exempt wheeled robots enter the same cellular cell, the system will perform multi-dimensional weighted sorting based on the wheeled robot priority weight (defined according to relevant national standards), the urgency of the task (analyzed through the task code issued by the emergency command center), and the remaining battery life (obtained through V2X vehicle networking data), and perform phased bias value fine-tuning on the non-highest priority wheeled robots. At the same time, through the distributed base station cooperation technology, the service load of the exempt wheeled robots is intelligently migrated to the dedicated emergency frequency band of the adjacent cell to avoid overloading of the core network elements.

[0088] It can be seen that in the embodiments of the present application, by establishing a pyramid structure of service levels, it is ensured that emergency communication wheeled robots always obtain the optimal network resources. By setting the bias value adjustment exemption signaling flag mechanism, the exemption decision delay can be effectively reduced.

[0089] In a possible implementation, during the low-traffic period at night, the bias value of the macro base station can be reduced, some radio frequency channels can be turned off, and at the same time, the traffic can be migrated to the sleep-wake small base station. After reducing the bias value of the macro base station, the wheeled robot is more inclined to access the small base station, and the coverage radius of the macro base station shrinks, which can effectively alleviate co-channel interference, especially in dense urban areas.

[0090] In a possible implementation, the setting of the bias value is related to the rate of change of speed. For example, when the wheeled robot moves from a congested section to an unobstructed section, the speed of the wheeled robot may increase. When the speed of the wheeled robot increases, the bias value of the macro base station can be dynamically increased to optimize the handover strategy in advance and avoid a sudden drop in signal quality due to sudden acceleration. Another example is that when the wheeled robot approaches a traffic light or a congested area, the vehicle speed will gradually decrease. During this process, the bias value of the macro base station can be gradually reduced to guide the wheeled robot to smoothly switch to the small base station and avoid unnecessary handovers due to sudden deceleration.

[0091] In a possible implementation, to achieve more precise dynamic management of the bias value, the rate of change of the bias value is configured comprehensively. The rate of change of the bias value refers to the maximum amplitude that the bias value is allowed to adjust per unit time (such as ±3dB / s), which is used to control the response speed and stability of the load balancing strategy. Its setting needs to comprehensively consider the mobility of the wheeled robot (for example, a higher rate of change is required in high-speed scenarios to adapt to rapid handovers, and the rate of change is reduced in congested scenarios to avoid oscillations), network load fluctuations (the rate of change is increased during burst traffic to achieve rapid balancing, and reduced during stable periods to reduce signaling overhead), service priorities (emergency wheeled robots can break through the rate-of-change limit), and the stability of the wireless environment (a low rate of change is adopted when the signal fluctuates greatly to prevent misadjustment). By dynamically adjusting the rate of change, while ensuring the QoS of critical services, the utilization rate of network resources and the handover success rate can be optimized, which is applicable to the intelligent load balancing scenario of 5G vehicle-to-everything.

[0092] Exemplarily, Scenario 1: High-speed scenario. The wheeled robot moves fast, has a high switching requirement, and the bias value change rate needs to be increased to adapt to the rapidly changing signal quality. Scenario 2: In the congested scenario on urban roads, the wheeled robot moves at a low speed or is stationary, and the change rate can be reduced to avoid unnecessary parameter fluctuations. Scenario 3: The wheeled robot suddenly accelerates (such as entering a highway), briefly increases the change rate, and quickly optimizes the handover strategy. Scenario 4: The wheeled robot decelerates (such as approaching a traffic light), gradually reduces the change rate, and avoids ping-pong handovers. Scenario 5: The load suddenly increases (such as a sudden traffic flow), and the bias value needs to be quickly adjusted (high change rate) to relieve the overload of the base station. Scenario 6: The load is stable, and a low change rate is adopted to maintain network stability. Scenario 7: For high-priority services (such as emergency wheeled robots), the adjustment of the bias value can be exempted from the change rate limit to ensure immediate optimization of the access quality. Scenario 8: If the handover failure rate is high in a certain area, reduce the change rate and adjust it carefully to avoid worsening the problem; if the handover success rate is high, the change rate can be increased to enhance the optimization flexibility.

[0093] As shown in the following table, exemplary suggestions are given for the change rate of typical application scenarios:

[0094] In a possible implementation, through the PC5 direct link, the wheeled robot shares the base station load and channel state information in real time, generates CIO optimization suggestions and feeds them back to the base station. After network arbitration, the bias value or the bias value change rate is dynamically adjusted, and finally a low-latency closed-loop load balancing system is formed. It can be seen that by using the advantages of proximal communication, the decision-making latency of the traditional network side can be reduced, the load prediction accuracy can be improved, and the resource allocation efficiency in the high-speed mobile scenario can be significantly optimized.

[0095] In summary, in the embodiments of the present application, the change rate of the bias value is related to different service scenarios, and the adjustment range of the change rate of the bias value in different service scenarios is different. Conversely, the change rate of the load affects the setting of the initial bias value. The dynamic load balancing method for the wheeled robot communication network provided by the embodiments of the present application can intelligently guide the balanced distribution of the wheeled robot terminals among the base stations in different scenarios by dynamically adjusting the bias values of the macro base station and the small base station, improve the load prediction accuracy, and significantly improve the network resource utilization rate.

[0096] Figure 2 The embodiments of the present application further provide a dynamic load balancing device for a wheeled robot communication network, including: An acquisition module 201, configured to acquire the initial bias values of the base stations in the target area, where the base stations include a macro base station and a small base station, the initial bias values include a first bias value and a second bias value, the first bias value is associated with the macro base station, and the second bias value is associated with the small base station; An identification module 202, configured to select an associated base station according to the real-time communication channel condition of the wheeled robot in the target area and the initial bias value, and collect the load data of the base station, the channel state of the wheeled robot, and the moving performance of the wheeled robot, to identify overloaded base stations and lightly loaded base stations; A switching module 203, configured to select a first wheeled robot that meets the migration preset conditions from the overloaded base stations, associate and switch the first wheeled robot to the lightly loaded base station, and obtain a switching success rate, where the switching success rate is used to dynamically adjust the first bias value and the second bias value.

[0097] In a possible implementation manner, the initial bias value includes a first bias value and a second bias value, and the first bias value is less than the second bias value.

[0098] In a possible implementation manner, the switching module 203 is further configured to: When the switching success rate is higher than a preset value, reduce the first bias value and increase the second bias value.

[0099] In a possible implementation manner, the switching module 203 is further configured to: select a first wheeled robot that meets the migration preset conditions from the overloaded base stations, and associate and switch the first wheeled robot to the lightly loaded base station, where the preset conditions include at least one of the following: The target base station after migration can meet the QoS requirements of the wheeled robot, the wheeled robot with a moving speed lower than the set speed threshold, and the wheeled robot with a channel condition better than the set threshold.

[0100] In a possible implementation manner, the device further includes a prediction module, configured to: Predict the moving path of the wheeled robot and a set of potential switching target base stations, pre-enact the switching process in a digital twin environment, calculate a set of predicted QoS metrics, and virtually migrate the target wheeled robot to a candidate base station; When all the predicted QoS metrics meet the corresponding service requirement thresholds, trigger an actual base station switching operation.

[0101] In a possible implementation manner, the device further includes a rollback module, configured to: When the QoS metrics after the actual switching deviate from the predicted values by more than a set threshold, roll back to the original base station connection.

[0102] In a possible implementation manner, the device further includes an adjustment module, configured to: When there is a high-priority wheeled robot in the target area, set a bias value adjustment exemption flag for the high-priority wheeled robot, and the base station associated with the high-priority wheeled robot does not participate in the dynamic bias value reduction. The high-priority wheeled robot includes a wheeled robot performing an emergency task or an authorized emergency communication wheeled robot.

[0103] In the embodiments of the present application, Figure 2 The device shown is used to implement the method provided in any corresponding Figure 1 embodiment.

[0104] Figure 3 It is a schematic structural diagram of an electronic device provided in the embodiments of the present application. As Figure 3 shown, the electronic device 300 includes: a memory 310 and a processor 320.

[0105] Among them, the memory 310 stores a computer program that can be executed by at least one processor 320. The computer program is executed by at least one processor 320 to enable the electronic device to implement the method provided in any of the above embodiments.

[0106] Among them, the memory 310 and the processor 320 can be connected through a bus 330.

[0107] For relevant descriptions, reference can be made to the relevant descriptions and effects corresponding to the method embodiments, and details are not described here.

[0108] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. The computer program is executed by a processor to implement the method provided in any corresponding Figure 1 embodiment.

[0109] Among them, the computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0110] An embodiment of the present application provides a computer program product, which includes computer execution instructions. When the computer execution instructions are executed by a processor, they are used to implement the method provided in any corresponding Figure 1 embodiment.

[0111] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of devices or modules can be in electrical, mechanical or other forms.

[0112] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the disclosure herein. The present application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common general knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and embodiments are only regarded as exemplary, and the true scope of the present application is pointed out by the claims.

[0113] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A dynamic load balancing method for a wheeled robot communication network, characterized in that: include: Acquire an initial bias value of a base station in a target area, where the base station includes a macro base station and a small base station, and the initial bias value includes a first bias value and a second bias value, where the first bias value is associated with the macro base station, and the second bias value is associated with the small base station; Selecting an associated base station according to the real-time communication channel condition of the wheeled robot in the target area and the initial bias value, collecting load data of the base station, the channel state of the wheeled robot and the mobility performance of the wheeled robot, and identifying an overloaded base station and an underloaded base station; A first wheeled robot that meets the preset migration conditions is selected from the overloaded base station, the first wheeled robot is associated and switched to the lightly loaded base station, and a switching success rate is obtained, where the switching success rate is used to dynamically adjust the first bias value and the second bias value.

2. The method according to claim 1, characterized in that The first offset value is smaller than the second offset value.

3. The method according to claim 1 or 2, characterized in that: The switching success rate is used to dynamically adjust the first offset value and the second offset value, including: When the switching success rate is higher than a preset value, the first bias value is reduced and the second bias value is increased.

4. The method according to claim 1, characterized in that The first wheeled robot that meets the preset migration condition is selected from the overloaded base station, and the first wheeled robot is associated and switched to the lightly loaded base station, wherein the preset condition includes at least one of the following: After the migration, the target base station can meet the QoS requirements of the wheeled robot, the wheeled robot whose moving speed is lower than the set speed threshold, and the wheeled robot whose channel condition is better than the set threshold.

5. The method according to claim 1, characterized in that The method further comprises: Predicting the moving path of the wheeled robot and the set of potential switching target base stations, rehearsing the switching process in the digital twin environment, calculating the predicted QoS indicator set, and virtually migrating the target wheeled robot to the candidate base station; When all predicted QoS indicators meet the corresponding service demand thresholds, the actual base station switching operation is triggered.

6. The method according to claim 5, characterized in that The method further comprises: When the actual QoS indicator after switching deviates from the predicted value and exceeds the set threshold, it rolls back to the original base station connection.

7. The method according to claim 1, characterized in that The method further comprises: When there is a high-priority wheeled robot in the target area, a bias value adjustment exemption mark is set for the high-priority wheeled robot, and the base station associated with the high-priority wheeled robot does not participate in the dynamic bias value reduction. The high-priority wheeled robot includes a wheeled robot performing an emergency task or an authorized emergency communication wheeled robot.

8. A dynamic load balancing device for a wheeled robot communication network, characterized in that: include: An acquisition module, configured to acquire an initial bias value of a base station in a target area, the base station comprising a macro base station and a small base station, the initial bias value comprising a first bias value and a second bias value, the first bias value being associated with the macro base station, and the second bias value being associated with the small base station; an identification module, configured to select an associated base station according to the real-time communication channel condition of the wheeled robot in the target area and the initial bias value, collect load data of the base station, the channel state of the wheeled robot and the mobility performance of the wheeled robot, and identify an overloaded base station and an underloaded base station; A switching module is used to select a first wheeled robot that meets the preset migration conditions from the overloaded base station, associate and switch the first wheeled robot to the lightly loaded base station, and obtain a switching success rate, and the switching success rate is used to dynamically adjust the first bias value and the second bias value.

9. An electronic device, characterized in that: include: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the electronic device to perform the method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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