Portable terminal for robot data acquisition and integration

Abstract

The invention relates to the technical field of data acquisition and integration, and discloses a portable terminal for robot data acquisition and integration, a pluggable expansion module, a data processing module, a scheduling and management module and a connection module are arranged in the portable terminal, and an integrated system of multi-source data acquisition, integration and communication management is constructed. The efficiency and stability of a robot system in the multi-source data acquisition, information integration and multi-robot cooperative communication process can be improved, more accurate data processing, more intelligent communication scheduling and more reasonable resource allocation are realized, and thus the cooperative operation capability of a robot group and the reliability of system operation are remarkably enhanced.

Description

Technical Field

[0001] This invention relates to the field of data acquisition and integration technology, and in particular to a portable terminal for robot data acquisition and integration. Background Technology

[0002] With the rapid development of intelligent manufacturing, warehousing and logistics, public safety, and special operations, the types and application scenarios of robot systems are constantly expanding, forming a multi-type, multi-task system architecture represented by mobile robots, collaborative robots, inspection robots, and special operations robots. During task execution, various robots often need to collect multi-source data, including vision, posture, temperature, pressure, and position information, to perceive and make decisions regarding environmental conditions and their own operational status. To meet the needs of on-site deployment, rapid access, and synchronous analysis of multi-source data, data acquisition and management terminals for robot systems have emerged, playing an increasingly important role in networked robot management and multi-machine collaborative control. Portable terminal devices, in particular, with their small size, flexible deployment, and mobile operation, are gradually becoming important auxiliary control and data relay nodes in robot operation sites.

[0003] However, existing portable robot terminals mostly focus on single-machine control or data monitoring functions, and still have significant shortcomings in multi-source data acquisition, multi-protocol communication, and parallel management of multiple robots. This results in poor device scalability, complex on-site deployment, and an inability to adaptively schedule based on task characteristics and link status, easily causing communication conflicts or increased latency for critical tasks. These problems severely limit the application effectiveness of portable terminals in complex work scenarios. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a portable terminal for robot data acquisition and integration, so as to solve the technical problems of low scalability, poor multi-source data integration capability and poor multi-robot adaptive communication scheduling capability of existing portable terminals.

[0005] This invention discloses a portable terminal for robot data acquisition and integration, the portable terminal comprising: A pluggable expansion module is used to provide interfaces and functional circuits, and to complete the physical connection, power management and drive loading of different types of expansion units through the interfaces and functional circuits; The data processing module is used to acquire heterogeneous data from the robot's exterior and the robot's body from the pluggable expansion module, and to integrate and process the heterogeneous data to obtain an integrated processing result; the integrated processing result includes communication task feature information. The scheduling and management module is used to determine the communication task requirement parameters based on the communication task characteristic information output by the data processing module and the preset scheduling strategy. A connection module is used to establish parallel communication links with multiple robots and allocate the bandwidth of the parallel communication links based on the communication task requirement parameters. The scheduling and management module is also used to coordinate the interaction between the pluggable expansion module, the data processing module, and the connection module.

[0006] Furthermore, the expansion unit includes a sensor unit, a camera unit, and a communication unit; wherein, The communication unit is used to establish a connection with the robot body through a wired or wireless interface, acquire robot body data, and interact with external communication objects.

[0007] Furthermore, the functional circuit includes an interface identification circuit, a power management circuit, and a drive control circuit; wherein, The interface identification circuit is used to identify the type of the expansion unit based on the pin level characteristics and the startup protocol message when the expansion unit is inserted into the interface, and obtain the identification result. The power management circuit is used to allocate power supply voltage and current matching the expansion unit according to the identification result, and to perform power allocation and overcurrent protection based on a priority queue algorithm when multiple expansion units are connected in parallel. The drive control circuit is used to perform a matching test based on the type of the extension unit and the built-in driver library, and when the test result is a match, it calls the driver file to load the driver for the extension unit.

[0008] Furthermore, when the drive control circuit detects that the type of the expansion unit does not fully match the built-in drive library, it performs a fault-tolerant compensation adaptation operation; the fault-tolerant compensation adaptation operation includes correcting the power supply parameters, communication protocol fields, and data sampling rate. When the drive control circuit detects that the type of the extended unit is completely incompatible with the built-in driver library, it calls the compatible driver library to complete the driver loading.

[0009] Furthermore, the process of integrating the heterogeneous data specifically includes: Based on the timestamps of each heterogeneous data, a time alignment operation is performed on the heterogeneous data, and the time alignment window is dynamically adjusted when sampling frequency fluctuations are detected. Mapping robot external data from different sources to robot body data into a unified coordinate system for spatial alignment; The difference between the data after time and space alignment is completed is detected. If the difference exceeds a preset threshold or statistical deviation range, it is determined to be conflicting data. When conflicting data is identified, a confidence score is calculated for each data source. Based on the confidence scores, a weighted fusion operation is performed on the conflicting data to obtain integrated data.

[0010] Furthermore, the process of determining the communication task feature information specifically includes: Based on the integrated data, feature extraction operations are performed to obtain data type features, real-time features, bandwidth features, and priority features; Anomaly sensitivity is determined based on the data type characteristics and a preset sensitivity mapping table; the anomaly sensitivity represents the tolerance of the communication task to abnormal data fluctuations. Based on the terminal power consumption status monitoring results, energy consumption parameters are collected, and energy consumption constraints are calculated based on the energy consumption parameters and the bandwidth characteristics to obtain energy consumption constraint characteristics; The data type characteristics, real-time characteristics, bandwidth characteristics, priority characteristics, anomaly sensitivity, and energy consumption constraint characteristics are used as communication task feature information.

[0011] Furthermore, when determining the communication task requirement parameters, the scheduling and management module includes: Select communication parameter mapping rules based on the characteristics of the data type; Under the selected communication parameter mapping rules, the allowable delay threshold is determined based on the real-time characteristics; bandwidth mapping is performed according to the bandwidth characteristics and energy consumption constraint characteristics to obtain the target bandwidth allocation value; priority sorting is performed according to the priority characteristics and anomaly sensitivity to obtain the communication task priority sequence. The allowed latency threshold, target bandwidth allocation value, and communication task priority sequence are combined to generate the first communication task requirement parameters.

[0012] Furthermore, after generating the first communication task requirement parameters, the scheduling and management module also performs a dynamic correction operation on the first communication task requirement parameters based on a preset scheduling strategy, and uses the corrected first communication task requirement parameters as the final communication task requirement parameters; the dynamic correction operation includes: When the current link occupancy rate exceeds the threshold, reduce the bandwidth allocation value for low-priority communication tasks; When the terminal power consumption is lower than the preset power threshold, increase the weight of energy consumption constraint features in bandwidth mapping. When a communication task with high abnormal sensitivity is detected, the priority sorting information is adjusted and the bandwidth allocation value is increased.

[0013] Furthermore, when establishing parallel communication links with multiple robots, the connection module includes: Obtain the type characteristic information of the target robot, and select the link establishment type based on the type characteristic information; Based on the selected link establishment type, a corresponding communication channel is established on the physical link, and virtualization and isolation operations are performed on the communication channel; The specific steps of selecting the link establishment type based on type feature information include: For mobile robots, select latency-priority links; for stationary robots, select bandwidth-priority links; and for flying robots, select redundant links.

[0014] Furthermore, after establishing communication channels for different robots and performing virtualization and isolation operations, the connection module detects whether there is a bandwidth conflict between different communication channels. If there is a conflict, it adjusts the bandwidth allocation of the communication channels based on priority parameters. When multiple robots are detected to belong to the same collaborative communication task, the corresponding communication channels are merged into a communication task group channel, and bandwidth sharing and data deduplication operations are performed within the communication task group channel. When a conflict is detected between the communication task group channel and other communication channels, cross-channel coordination is performed based on the communication task priority.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention constructs an integrated system for multi-source data acquisition, integration, and communication management by incorporating a pluggable expansion module, a data processing module, a scheduling and management module, and a connection module in a portable terminal. This system can improve the efficiency and stability of robot systems in multi-source data acquisition, information integration, and multi-robot collaborative communication processes, achieving more accurate data processing, more intelligent communication scheduling, and more rational resource allocation, thereby significantly enhancing the collaborative operation capability of robot groups and the reliability of system operation. Attached Figure Description

[0016] The accompanying drawings, which are provided to further illustrate embodiments of the invention and form part of this economic application, do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the structure of a portable terminal for robot data acquisition and integration disclosed in Embodiment 1 of the present invention. Detailed Implementation

[0017] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0018] Example 1: This invention discloses a portable terminal for robot data acquisition and integration. Please refer to [link / reference]. Figure 1 , Figure 1This is a schematic diagram of the structure of a portable terminal for robot data acquisition and integration disclosed in an embodiment of the present invention. The portable terminal includes: A pluggable expansion module is used to provide interfaces and functional circuits, and to complete the physical connection, power management and drive loading of different types of expansion units through the interfaces and functional circuits; The data processing module is used to acquire heterogeneous data from the robot's exterior and the robot's body from the pluggable expansion module, and to integrate and process the heterogeneous data to obtain an integrated processing result; the integrated processing result includes communication task feature information. The scheduling and management module is used to determine the communication task requirement parameters based on the communication task characteristic information output by the data processing module and the preset scheduling strategy. A connection module is used to establish parallel communication links with multiple robots and allocate the bandwidth of the parallel communication links based on the communication task requirement parameters. The scheduling and management module is also used to coordinate the interaction between the pluggable expansion module, the data processing module, and the connection module.

[0019] Furthermore, the expansion unit includes a sensor unit, a camera unit, and a communication unit; wherein, The communication unit is used to establish a connection with the robot body through a wired or wireless interface, acquire robot body data, and interact with external communication objects.

[0020] Furthermore, the functional circuit includes an interface identification circuit, a power management circuit, and a drive control circuit; wherein, The interface identification circuit is used to identify the type of the expansion unit based on the pin level characteristics and the startup protocol message when the expansion unit is inserted into the interface, and obtain the identification result. The power management circuit is used to allocate power supply voltage and current matching the expansion unit according to the identification result, and to perform power allocation and overcurrent protection based on a priority queue algorithm when multiple expansion units are connected in parallel. The drive control circuit is used to perform a matching test based on the type of the extension unit and the built-in driver library, and when the test result is a match, it calls the driver file to load the driver for the extension unit.

[0021] Furthermore, when the drive control circuit detects that the type of the expansion unit does not fully match the built-in drive library, it performs a fault-tolerant compensation adaptation operation; the fault-tolerant compensation adaptation operation includes correcting the power supply parameters, communication protocol fields, and data sampling rate. When the drive control circuit detects that the type of the extended unit is completely incompatible with the built-in driver library, it calls the compatible driver library to complete the driver loading.

[0022] Specifically, in this embodiment of the invention, the pluggable expansion module in the portable terminal is mainly used to achieve rapid access and unified management of multiple types of expansion units. Structurally, this module includes two parts: an interface component and functional circuitry, which form a coordinated working system. The interface component provides physical connection channels for various expansion units, while the functional circuitry is responsible for operations such as plug-in / plug-out detection, power distribution, protocol identification, and driver loading. The key feature of this module is ensuring stable connection and automatic identification of expansion units under different operating states, enabling the terminal to quickly expand its sensing and communication capabilities in complex field environments.

[0023] The pluggable expansion module's interface employs a standardized and error-proof design. Standardization ensures consistency in interface pin definitions, power supply levels, and signal line configurations, enabling compatibility with expansion units from different manufacturers or of different specifications. The error-proof structure uses asymmetrical insertion slots and mechanical positioning pins to prevent incorrect insertion. The interface supports both digital and analog dual-channel signal transmission modes, automatically switching operating modes based on detected voltage levels and handshake signals during expansion unit insertion. When insertion occurs, the functional circuit first monitors minute changes in the interface contact voltage and triggers an insertion interruption signal, subsequently initiating the identification and power supply process.

[0024] Pluggable expansion modules support various types of expansion units, including but not limited to sensor units, camera units, and communication units. Sensor units typically integrate multiple sensing elements such as temperature, humidity, pressure, light intensity, magnetic fields, or ultrasound to acquire external physical information about the robot's working environment. This data, after being input through a data processing module, can be fused with the robot's own status data to provide environmental feedback for task execution. Camera units are primarily used to collect external visual data and can be equipped with infrared, visible light, or multispectral imaging modules as needed. Their output data is typically a high-bandwidth image stream signal, requiring a dedicated channel and stable power supply upon insertion to prevent video signal frame loss. Communication units are mainly responsible for data exchange, enabling connections to the robot via wired interfaces (such as CAN, Ethernet, or RS-485) or wireless interfaces (such as Wi-Fi, ZigBee, 5G modules, or Bluetooth modules), thereby facilitating the acquisition of robot operating data and data interaction with external systems. The communication unit can select bidirectional communication mode, multicast mode or broadcast mode according to different task requirements, and can dynamically adjust the transmission power according to the link status to reduce energy consumption while ensuring transmission reliability.

[0025] In this architecture, external robot data and robot body data are uniformly defined as heterogeneous data. Heterogeneous data refers to data that differs in source, format, and sampling reference. For example, temperature or illumination data from external sensors are typically continuous analog quantities, converted into periodically updated values ​​after sampling and quantization. Data output from camera units, on the other hand, is a two-dimensional pixel matrix or video frame sequence, typical of high-dimensional unstructured data. Robot body data is mostly discrete numerical information, including motor speed, posture angle, acceleration, joint angle, or battery voltage. These data differ in time-domain sampling frequency, spatial reference frame, and semantic representation. Direct fusion would lead to time drift, coordinate mismatch, and data distortion. Therefore, pluggable expansion modules ensure the correct acquisition, distribution, and encapsulation of this data through underlying circuitry and interface protocols, allowing the data processing module to perform subsequent alignment and integration.

[0026] The functional circuitry is the core of the pluggable expansion module, including but not limited to interface identification circuitry, power management circuitry, and drive control circuitry. The interface identification circuitry identifies the unit type and communication mode the moment the expansion unit is inserted. The identification principle is based on pin level characteristics and protocol handshake signal analysis. When the expansion unit is inserted, the interface identification circuitry first detects changes in the pin resistance network to identify the hardware category of the unit. It then reads its startup protocol message, parsing fields such as module ID, communication rate, data type, and device signature to obtain the precise type of the expansion unit. This process is automatically executed by the MCU, without manual configuration, thus achieving plug-and-play recognition of the expansion unit.

[0027] After identification, the power management circuit automatically configures the power supply parameters based on the identification result. Internally, it employs programmable voltage regulators and hierarchical power allocation logic to adjust the output voltage and current in real time. For example, when the identification result is a high-power camera unit, a stable 5V DC power is output, and instantaneous current compensation is provided during startup to prevent voltage drop; when the identification result is a low-power sensor unit, a 3.3V voltage is output and energy-saving mode is entered, thereby reducing overall power consumption. When multiple expansion units are connected simultaneously, the power management circuit uses a priority queue algorithm to perform power allocation according to the importance and real-time requirements of each expansion unit. High-priority tasks (such as communication units or critical sensors) receive power resources first, while low-priority tasks enter a waiting queue when power is insufficient. When the total current approaches the set upper limit, the power management circuit activates the overcurrent detection component, determines the load status by detecting the voltage drop across the current sampling resistor, and performs phased power switching when necessary to avoid power overload.

[0028] The driver control circuit is responsible for the driver loading and adaptation control of the expansion unit. This circuit first reads the corresponding driver file from the terminal's local driver library based on the interface identification result, and performs a matching check using the device identifier and version number. When the detection result is a perfect match, the driver control circuit loads the driver program through the bus interface, initializes the device registers, establishes a communication channel, and enables the expansion unit to start working.

[0029] When the detection result shows only a partial match, a fault-tolerant compensation adaptation mechanism is executed to ensure the stable operation of the expansion unit. The specific process of fault-tolerant compensation includes adjusting the power supply output to accommodate minor differences in electrical parameters, mapping communication protocol fields and rewriting commands to ensure compatibility with different vendor protocols, and balancing transmission rate and data integrity by modifying the sampling rate and data buffer length, thereby achieving dynamic adaptation at the hardware and software levels. When the detection result shows a complete mismatch between the expansion unit and the device, the drive control circuit will call the general driver program in the compatible driver library to perform device initialization and data transmission through a standardized interface protocol, enabling the terminal to continue operating even when an unknown device is connected.

[0030] As a further preferred embodiment, the functional circuitry also integrates a communication management circuit and a signal modulation circuit. The communication management circuitry enables communication scheduling and signal priority management between expansion units, while the signal modulation circuitry performs filtering, amplification, and analog-to-digital conversion on analog signals to improve data acquisition accuracy and anti-interference performance. This multi-circuit collaborative design allows the pluggable expansion modules to simultaneously process multiple types of signals and maintain system stability in complex field environments.

[0031] Through the above operations, the pluggable expansion module can not only automatically identify the device type and complete power supply and drive loading when the expansion unit is connected, but also maintain the continuous operation of the system through a fault-tolerant compensation mechanism even when devices are not fully compatible. The introduction of this module enables the portable terminal to achieve plug-and-play and automatic adaptation of multiple types of expansion units, significantly reducing the complexity of on-site deployment and manual configuration. Furthermore, the multi-level identification and fault-tolerant mechanism ensures compatible access for devices from different manufacturers and with different protocols, and the adaptive adjustment of power supply and drive improves the stability and security of the system, providing a solid foundation for subsequent heterogeneous data processing and multi-robot communication scheduling.

[0032] Furthermore, the process of integrating the heterogeneous data specifically includes: Based on the timestamps of each heterogeneous data, a time alignment operation is performed on the heterogeneous data, and the time alignment window is dynamically adjusted when sampling frequency fluctuations are detected. Mapping robot external data from different sources to robot body data into a unified coordinate system for spatial alignment; The difference between the data after time and space alignment is completed is detected. If the difference exceeds a preset threshold or statistical deviation range, it is determined to be conflicting data. When conflicting data is identified, a confidence score is calculated for each data source. Based on the confidence scores, a weighted fusion operation is performed on the conflicting data to obtain integrated data.

[0033] Furthermore, the process of determining the communication task feature information specifically includes: Based on the integrated data, feature extraction operations are performed to obtain data type features, real-time features, bandwidth features, and priority features; Anomaly sensitivity is determined based on the data type characteristics and a preset sensitivity mapping table; the anomaly sensitivity represents the tolerance of the communication task to abnormal data fluctuations. Based on the terminal power consumption status monitoring results, energy consumption parameters are collected, and energy consumption constraints are calculated based on the energy consumption parameters and the bandwidth characteristics to obtain energy consumption constraint characteristics; The data type characteristics, real-time characteristics, bandwidth characteristics, priority characteristics, anomaly sensitivity, and energy consumption constraint characteristics are used as communication task feature information.

[0034] In this embodiment of the invention, the data processing module is configured to continuously receive heterogeneous data streams from the outside of the robot and the robot body from the pluggable expansion module.

[0035] Specifically, a unified data acquisition buffer and time reference are first established. To eliminate the impact of sampling periods and clock drift from different data sources, the module introduces a timestamp-based alignment mechanism. At the receiving end, using the terminal's high-precision timer as a reference, the deviation between the timestamp of each data packet and the reference time is calculated. The instantaneous sampling frequency is obtained through linear drift estimation and jitter statistics, and the width of the alignment window is adaptively adjusted accordingly. The dynamic update of the window width follows the principle that the larger the jitter, the wider the window, but is capped by the maximum delay threshold to ensure that alignment does not introduce timeout. After alignment, the module maps the external sensor measurements to the robot's body coordinate system based on the extrinsic parameter matrix and sensor pose obtained during the calibration phase. Image / point cloud data undergoes rigid body transformation through the extrinsic parameter matrix, and inertial and odometry data are fused through attitude to obtain velocity and position quantities in a unified reference system, thereby achieving a consistent representation in spatial dimensions.

[0036] After completing temporal and spatial alignment, the module proceeds to the conflict detection phase. The essence of conflict is that multiple source observations under the same spatiotemporal reference give significantly inconsistent estimates of the same physical quantity. The module calculates residuals for candidate observations within the same time slice, at the same coordinate location or region. The residuals include scalar differences, vector norms, projection errors, and are standardized by combining in-source statistical characteristics.

[0037] Furthermore, to accommodate different noise distributions, this invention supports both fixed threshold and statistical threshold strategies. Fixed thresholds are used for sensors with known ranges and accuracies in engineering applications, while statistical thresholds employ a mean-variance model or Mahalanobis distance model within a sliding window for anomaly determination. When the residual exceeds a preset threshold or statistical deviation range, it is marked as conflicting data. To avoid misjudgments caused by short-term spikes, the module introduces a minimum duration threshold for the residual sequence; a conflict is only confirmed when several consecutive frames exceed the threshold.

[0038] After conflict confirmation, data fusion is performed based on confidence levels. Confidence levels are derived from three quantifiable metrics: first, source stability, estimated based on the short-term noise variance, jitter, and packet loss rate of the data source; second, signal quality, such as frame sharpness and exposure stability for image streams, SNR or RSSI for RF links, and Allan variance for inertial / magnetic sensing; and third, consistency metrics, i.e., the relative deviation between this source and other sources at the same time. These metrics are normalized and then combined using a convex combination to obtain a comprehensive confidence level, which is then fused according to weights. Fusion uses weighted least squares updates on scalar quantities and element-wise fusion on vector fields (such as optical flow, normals, or 3D points) using weighted fields. If a source's confidence level is below the rejection threshold, it is first filtered using Huber loss before participating in the weighted fusion. If temporary frame loss or link jitter exists, interpolation reconstruction is performed using neighborhood time slices. The interpolation strategy is tied to the real-time level of the channel to avoid introducing excessive waiting time for low-latency tasks. After the above operations, integrated data with a unified structure, consistent temporal and spatial dimensions, and conflict resolution is obtained, which can be used for subsequent feature extraction and task mapping.

[0039] In determining the characteristic information of communication tasks, this invention first performs feature extraction through both semantic and statistical channels. The semantic path classifies the integrated data into types such as video streams, sensor data streams, and control / status messages based on the source identifier, payload type, and protocol tag in the packet header. The statistical path calculates real-time and bandwidth-related quantities from the arrival interval, jitter, packet loss, and payload byte count measured from the integrated data.

[0040] More specifically, the real-time characteristic is obtained by combining the mean, 99th percentile, and jitter index of the arrival interval sequence to obtain the maximum acceptable waiting time and jitter tolerance range of the data stream without degrading task quality. The bandwidth characteristic estimates the instantaneous and sliding window average bandwidth based on the unit-time payload and encoding / decoding configuration, while recording the peak factor for subsequent peak suppression or protection. The priority characteristic comes from two sources: first, a baseline mapping of the task importance of the data type (e.g., control commands are higher than logs, security alarms are higher than regular telemetry); second, task-level weights from upper-layer policies or on-site configurations. These two are normalized and combined to obtain the current priority scalar. The data type characteristic is used both to characterize communication semantics and as a priori condition for subsequent mapping rule selection.

[0041] Furthermore, in this embodiment of the invention, the determination of anomaly sensitivity first involves retrieving a baseline sensitivity from a preset sensitivity mapping table based on the data type. For example, control / security-related messages are set to extremely high sensitivity, navigation and positioning to high sensitivity, video surveillance to medium sensitivity, and routine status and logs to low sensitivity. Subsequently, this baseline value undergoes behavioral-level correction. The correction amount is determined by the frequency and impact of recent anomalies. The module maintains timeout, packet loss, and conflict records within a sliding window and assigns weights according to their impact on the task. For channels exhibiting continuous anomalies that affect the stability of the task's closed loop, their sensitivity scores are increased; for channels that can be buffered and masked by retransmission, the correction magnitude is reduced. This two-stage method ensures that the sensitivity reflects the essence of the task while also adapting to the on-site conditions.

[0042] During the determination of energy consumption constraints, the data processing module periodically reads the battery's state of charge, instantaneous current, and temperature rise, and estimates the available energy budget using the remaining available energy and the expected energy consumption rate. Simultaneously, it uses bandwidth characteristics and coding complexity to estimate the power consumption contribution of the data stream. When the battery level is below a threshold or the temperature approaches its upper limit, the energy consumption constraint is gradually tightened from lenient to stringent, providing limit prompts for high-bandwidth but non-critical data streams. Conversely, when external power is supplied or the system is under low load, the energy consumption constraint tends to be more lenient.

[0043] As a further preferred embodiment, in order to avoid the energy consumption strategy from having a negative impact on critical security tasks, the module adds a priority and abnormal sensitivity suppression term to the energy consumption constraint calculation. That is, the high priority / high sensitivity channel has a lower degree of effectiveness of energy consumption constraints, thereby ensuring the transmission quality of security-related links.

[0044] After the above extraction is completed, the characteristics such as data type, real-time performance, bandwidth, priority, anomaly sensitivity and energy consumption constraints are combined to form communication task feature information, and output together with necessary statistical side information (such as peak factor, recent anomaly count and energy budget time).

[0045] Through the above operations, the data processing module not only achieves a consistent representation of multi-source data at the temporal and spatial levels, but also obtains more robust integrated data through interpretable conflict determination and confidence-based fusion. Based on this, it generates communication task feature information that takes into account task semantics, statistical behavior, and energy status, providing quantifiable and traceable basis for subsequent parameter mapping and link scheduling. This significantly reduces latency jitter and conflict misjudgment caused by multi-source heterogeneous data, improves fusion accuracy and data continuity, and enhances the targeting and stability of communication resource allocation through precise characterization of task feature information, ultimately improving real-time performance in multi-robot collaborative operations.

[0046] Furthermore, when determining the communication task requirement parameters, the scheduling and management module includes: Select communication parameter mapping rules based on the characteristics of the data type; Under the selected communication parameter mapping rules, the allowable delay threshold is determined based on the real-time characteristics; bandwidth mapping is performed according to the bandwidth characteristics and energy consumption constraint characteristics to obtain the target bandwidth allocation value; priority sorting is performed according to the priority characteristics and anomaly sensitivity to obtain the communication task priority sequence. The allowed latency threshold, target bandwidth allocation value, and communication task priority sequence are combined to generate the first communication task requirement parameters.

[0047] Furthermore, after generating the first communication task requirement parameters, the scheduling and management module also performs a dynamic correction operation on the first communication task requirement parameters based on a preset scheduling strategy, and uses the corrected first communication task requirement parameters as the final communication task requirement parameters; the dynamic correction operation includes: When the current link occupancy rate exceeds the threshold, reduce the bandwidth allocation value for low-priority communication tasks; When the terminal power consumption is lower than the preset power threshold, increase the weight of energy consumption constraint features in bandwidth mapping. When a communication task with high abnormal sensitivity is detected, the priority sorting information is adjusted and the bandwidth allocation value is increased.

[0048] In this embodiment of the invention, the scheduling and management module is used to determine the communication task requirement parameters based on the communication task characteristic information output by the data processing module, so as to realize the dynamic allocation and priority scheduling of communication resources. Specifically, it receives the communication task characteristic information output by the data processing module and then enters the communication parameter mapping stage. The core of this stage is to select communication parameter mapping rules based on data type characteristics. Different types of data have different key requirements at the communication level. For example, video stream data emphasizes sufficient bandwidth and continuity, while control command data emphasizes minimizing latency and packet loss rate. To this end, the module has a pre-set set of communication parameter mapping rules, and each rule defines the correspondence between different features and communication parameters. The module retrieves the rule set based on the data type characteristic value. For example, when the data type is identified as video stream, it automatically selects a rule dominated by bandwidth allocation; when the data type is identified as control command data, it selects a rule dominated by latency threshold; and if it is status or log data, it selects a comprehensive balanced rule to ensure the rationality of resource allocation. The selection of mapping rules not only affects the calculation method of subsequent parameters, but also determines the weight distribution of different features in the target parameter generation process.

[0049] After selecting the mapping rule, the allowable latency threshold is determined based on real-time characteristics. This process calculates the indicators representing communication latency fluctuations within the real-time characteristics and combines them with the task-level Quality of Service (QoS) level, using a non-linear function mapping to determine the specific threshold. The mapping function preferably adopts an exponential decay form to ensure that the latency threshold decreases rapidly when real-time characteristics are high, thereby strengthening the weight of low-latency guarantees. When the task priority or anomaly sensitivity is high, an additional correction factor is introduced into the calculation to further tighten the latency threshold, prioritizing data transmission paths for critical tasks.

[0050] During bandwidth allocation, a bandwidth mapping operation is performed based on bandwidth characteristics and energy consumption constraints. This operation first calculates the theoretical bandwidth requirements of each task and forms a bandwidth allocation matrix by combining this with the current total bandwidth resources. Then, the allocation result is adjusted under the constraints of energy consumption characteristics. Specifically, when power consumption is low or temperature is close to the upper limit, the bandwidth of high-power tasks is compressed by increasing the energy consumption constraint weight; when under external power supply or low load conditions, the constraint weight is reduced to release bandwidth and improve throughput. This mapping operation is implemented based on a dynamic linear programming model. By monitoring bandwidth usage and energy consumption changes in real time, the optimization equation is solved periodically to maximize communication efficiency while satisfying energy consumption constraints. For sudden high-bandwidth tasks, this invention introduces a sliding window average bandwidth and a peak suppression factor in the mapping operation to prevent short-term peaks from saturating link resources.

[0051] In the priority ranking stage, a comprehensive priority for communication tasks is calculated based on priority characteristics and anomaly sensitivity. Priority characteristics reflect the importance of the task, while anomaly sensitivity reflects the task's tolerance for data fluctuations. The module combines the two using a weighting function, inverting the anomaly sensitivity and weighting it onto the priority characteristics to form a comprehensive ranking index. The calculation results are normalized before being used for priority ranking to ensure consistency in the sequence of similar tasks when priorities change. The ranking results form a priority sequence of communication tasks, with higher-priority tasks receiving priority access to bandwidth and low-latency configurations during link allocation and conflict scheduling.

[0052] More preferably, to prevent excessive skewness from causing low-priority tasks to wait for a long time, the module introduces a time decay factor during the sorting process, so that the weight of low-priority tasks gradually increases over time, thereby achieving long-term fairness in resource allocation.

[0053] Once the allowed latency threshold, target bandwidth allocation value, and communication task priority sequence are calculated, the module combines these three to generate the first communication task requirement parameters. To maintain adaptability in complex link environments, the scheduling and management module performs a dynamic correction operation after generating the first communication task requirement parameters. The main principle of dynamic correction is to adjust the parameters based on real-time link occupancy, energy consumption status, and task anomalies, so that the scheduling results are automatically optimized as the terminal status changes.

[0054] Specifically, when the link occupancy rate exceeds a preset threshold, the module monitors the bandwidth utilization curve in real time, calculates the link saturation index using differential calculation, and automatically reduces the bandwidth allocation value for low-priority tasks, reallocating the released bandwidth to high-priority or high-sensitivity tasks to avoid link congestion. When the terminal power consumption is below a preset power threshold, the module increases the weight of energy consumption constraints in the bandwidth mapping, tending to compress the bandwidth requirements of high-energy-consuming tasks and prioritizing the communication needs of low-power tasks to extend the overall runtime. When a communication task with high anomaly sensitivity is detected, the module reorders and adjusts the priority sequence while simultaneously increasing the bandwidth allocation value to ensure stable and reliable data transmission for such tasks, thereby improving the response capability in the event of sudden anomalies.

[0055] It should be further explained that the communication tasks of this invention do not directly correspond to the specific business operations performed by the robot, but rather to an abstract expression of the terminal's internal requirements for multi-source data transmission. For example, when the robot generates control command streams, status monitoring streams, video image streams, or sensor data streams, the terminal treats each as an independent communication task and performs differentiated management based on their respective characteristics such as real-time performance, bandwidth, energy consumption, and anomaly sensitivity.

[0056] Through the aforementioned scheduling and management module, this invention achieves dynamic generation and intelligent adjustment of communication task requirement parameters, enabling communication resource allocation to adapt to changes in task characteristics and terminal status in real time. This module can not only automatically select mapping rules and calculate reasonable latency and bandwidth parameters based on different data types, but also adaptively correct parameter configurations under conditions of link contention or energy constraints. This significantly improves resource utilization efficiency and transmission stability in multi-task communication environments, allowing terminals to ensure the real-time performance of critical tasks while maintaining overall energy balance and long-term operational stability.

[0057] Furthermore, when establishing parallel communication links with multiple robots, the connection module includes: Obtain the type characteristic information of the target robot, and select the link establishment type based on the type characteristic information; Based on the selected link establishment type, a corresponding communication channel is established on the physical link, and virtualization and isolation operations are performed on the communication channel; The specific steps of selecting the link establishment type based on type feature information include: For mobile robots, select latency-priority links; for stationary robots, select bandwidth-priority links; and for flying robots, select redundant links.

[0058] Furthermore, after establishing communication channels for different robots and performing virtualization and isolation operations, the connection module detects whether there is a bandwidth conflict between different communication channels. If there is a conflict, it adjusts the bandwidth allocation of the communication channels based on priority parameters. When multiple robots are detected to belong to the same collaborative communication task, the corresponding communication channels are merged into a communication task group channel, and bandwidth sharing and data deduplication operations are performed within the communication task group channel. When a conflict is detected between the communication task group channel and other communication channels, cross-channel coordination is performed based on the communication task priority.

[0059] In this embodiment of the invention, the connection module of the portable terminal is mainly used to establish parallel communication links with multiple robots under the guidance of communication task requirement parameters, and to realize dynamic allocation and conflict coordination of communication resources. It should be noted that all scheduling decisions of the connection module are based on the communication task requirement parameters generated by the scheduling and management module. When establishing parallel communication links, the connection module dynamically adjusts the link establishment method, the number of channels, and the bandwidth allocation ratio according to these parameters. The communication task requirement parameters are not only used for configuration during the link initialization phase, but also throughout the entire process of link maintenance, conflict scheduling, and task group channel optimization.

[0060] In its implementation, the connection module first receives communication task requirement parameters from the scheduling and management module, and parses out the target robot identifier and its corresponding type characteristic information associated with each communication task. The type characteristic information can be proactively reported by the robot itself during the initial handshake phase, including robot category, communication protocol version, real-time performance level, and redundancy / fault-tolerant configuration. The connection module selects the link establishment type based on the parsing results, with different communication strategies corresponding to different robot types. For mobile robots, considering their position changes and link volatility during operation, the module prioritizes a latency-priority link. This link is designed with low buffering, short-cycle acknowledgment, and high-frequency heartbeat mechanisms to reduce retransmission delays caused by movement. For stationary robots, due to their constant position and data throughput as the primary task, the module uses a bandwidth-priority link, improving throughput through long frame encapsulation and flow control aggregation mechanisms. For flying robots, redundant links are prioritized, and a backup channel is maintained simultaneously in addition to the primary channel. If the primary link signal quality deteriorates or is interrupted, the module immediately switches to the backup channel to ensure continuous communication for critical tasks.

[0061] After selecting the link type, the connection module establishes the corresponding communication channel at the physical link layer. The channel establishment process includes three steps: channel negotiation, link authentication, and logical mapping. The module's hardware structure includes, but is not limited to, a multi-protocol communication interface, electrical signal scheduling circuitry, and a virtualized control unit. The module automatically identifies the target robot's communication protocol stack through the interface and negotiates the transmission rate and frame structure based on the bandwidth and latency constraints in its communication task requirements. Next, it performs a link authentication process, verifying communication security through device identification, key exchange, and message verification mechanisms. Finally, the established physical channel is virtualized into multiple logical communication channels, each corresponding to a single communication task. The virtualized control unit allocates independent identifiers and buffer spaces to different channels, achieving logical isolation of physical link resources and preventing data conflicts and bandwidth contention between tasks. At this point, the priority sequence in the communication task requirements is transformed into the scheduling weight of the virtual channel, directly determining the transmission order of data frames in the physical channel queue.

[0062] In parallel communication environments, bandwidth conflicts are inevitable due to different robots simultaneously sending or receiving data. After the logical channel is established, the connection module monitors the bandwidth occupancy of each channel in real time, calculating the conflict coefficient by comparing the target bandwidth allocation value in the task requirement parameters with the actual bandwidth usage. When the conflict coefficient exceeds a threshold, it is determined to be a bandwidth conflict and triggers a dynamic adjustment mechanism. During the adjustment process, the module reallocates available bandwidth according to the communication task priority sequence. The channel bandwidth for high-priority or highly sensitive tasks remains unchanged or is increased, while the bandwidth for low-priority tasks is linearly compressed or temporarily suspended. This process is implemented in hardware through the DMA scheduling logic of the virtualized control unit to achieve real-time frame rate adjustment, and in software through a rate control algorithm to perform bandwidth balancing, ensuring low latency and continuity for critical tasks.

[0063] To further improve communication efficiency in multi-robot collaborative tasks, the connection module has the capability to dynamically create and manage task group channels. When multiple robots are detected to belong to the same collaborative communication task, such as synchronous multi-robot handling or formation navigation, the module determines that they belong to the same task set based on task identifiers and timestamp correlation, and merges the corresponding communication channels into communication task group channels. The establishment of group channels adopts a logical aggregation approach, with multiple individual channels sharing the same scheduling queue and bandwidth pool at the link layer, and performing bandwidth sharing and data deduplication operations within the group channel. Bandwidth sharing is achieved through a bandwidth pool, allocating resources on demand according to the task weight of each channel to avoid duplicate occupation of physical links. Data deduplication is achieved by comparing the sequence number and hash check value of data packets to identify data from the same source uploaded by multiple robots, retaining only the latest or highest quality copy to reduce the load on the link from redundant transmissions. This operation is particularly important when multiple robots perform collaborative sensing tasks, significantly reducing the total communication volume while maintaining data synchronization.

[0064] After the task group channel is established, resource conflicts between the group channel and other independent channels are continuously monitored. When a conflict is detected, the connection module performs cross-channel coordination based on the communication task priority. The principle of cross-channel coordination is to achieve dynamic balance among tasks by adjusting the scheduling weights and packet sending rates of different channels. Specifically, the scheduling latency and bandwidth difference of each channel are calculated in real time, and the scheduling matrix is ​​reconstructed according to the priority sequence and energy consumption constraint correction factor. High-priority tasks gain scheduling priority during conflicts, while the scheduling cycle of low-priority tasks is appropriately extended to ensure the stability and fairness of overall communication. The entire coordination process is completed within a millisecond timescale, thus achieving real-time response and imperceptible adjustment.

[0065] To ensure the stability and reliability of connections between multiple robot types, the connection module is designed to be compatible with various communication protocol stacks at the protocol layer and implements a multi-mode switching mechanism at the link layer. When different robot types are detected, the module can automatically switch between TCP / IP, UDP, DDS, CAN, or a custom protocol to ensure interoperability between heterogeneous robots. Simultaneously, the module continuously monitors signal quality, packet loss rate, and latency jitter through a link status detection unit. When abnormal fluctuations are detected, it can automatically adjust the sending window or switch redundant channels according to the real-time threshold set in the communication task requirements parameters, thereby preventing link interruption. This feature enables the terminal to maintain a stable connection with multiple types of robots simultaneously in complex environments, ensuring the continuity and reliability of communication.

[0066] Through the above operations, the connection module in this embodiment combines communication task requirement parameters with multi-type robot connection mechanisms to achieve a complete mapping from task-level communication requirements to physical link resources. The module can not only dynamically select link establishment strategies based on robot type, but also achieve multi-channel parallel communication and adaptive scheduling of bandwidth conflicts through virtualization and isolation mechanisms. Furthermore, in multi-robot collaborative scenarios, it improves link utilization and data transmission consistency through task group channels and cross-channel coordination mechanisms. This setup significantly enhances the communication efficiency and system stability of portable terminals in multi-robot collaborative environments, providing reliable assurance for real-time data interaction in complex task scenarios.

[0067] It is understood that the scheduling and management module in this embodiment not only generates communication task requirement parameters, but also undertakes the system coordination and control functions of the entire portable terminal, ensuring the orderly interaction of data flow and control commands between the pluggable expansion module, data processing module, and connection module. During operation, the modules do not work independently, but rather coordinate under the interaction sequence and task dependencies set by the scheduling and management module, achieving real-time linkage between data acquisition, task parsing, parameter generation, and link scheduling.

[0068] It should be noted that the "task" in this embodiment includes operational tasks issued by the upper-level control platform or external server, as well as communication tasks generated by this terminal after parsing the operational tasks. Operational tasks indicate the business objectives of the terminal system, such as which robots need to be collaborated with; communication tasks characterize the data interaction and communication parameters required to complete the business. Upon receiving an operational task, the scheduling and management module first parses the task content, identifies the target robot objects involved, and, in conjunction with the robot registration information registered by the pluggable expansion module, determines the currently connectable robot types and communication capability parameters. Based on the requirements of the operational task and the robot characteristic information, the module divides each operational task into several communication task units and assigns a unique task identifier to each communication task.

[0069] After completing task parsing, the scheduling and management module sends a data channel initialization command to the data processing module, requesting it to obtain data sources related to the target robot from the pluggable expansion module, including external environment data and robot body operation data. After data integration, the data processing module outputs communication task characteristic information, which the scheduling and management module uses to generate communication task requirement parameters. This parameter set includes the quality of service requirements for each communication task (such as allowable latency, target bandwidth, priority order, etc.) and the corresponding target robot identifier and type characteristic information. Subsequently, the scheduling and management module transmits the complete communication task requirement parameters to the connection module.

[0070] After receiving the data, the connection module not only reads the communication parameters but also parses the corresponding target robot information based on the task identifier. It then automatically selects the link establishment strategy according to the robot type. The entire link establishment and resource allocation process is coordinated by the scheduling and management module through a time synchronization mechanism and a status feedback mechanism to ensure that parameter updates and link scheduling are executed under a unified time base.

[0071] The core of this coordination mechanism lies in binding data flow and control flow through task identifiers. Each communication task carries a unique identifier from generation to execution. The scheduling and management module uses this identifier to establish a task mapping table within the terminal system, enabling corresponding management of data channels and link resources. When upper-layer operation tasks change or new robots are added, the module can quickly update the communication task requirement parameters based on the task identifier and reissue them, thereby maintaining the real-time performance and consistency of the terminal system in dynamic environments.

[0072] Through the above operations, the scheduling and management module achieves unified control of task parsing, resource allocation, and inter-module information synchronization at the terminal system level. This module ensures consistent transmission of operational tasks, communication tasks, and target robot information among various modules, enabling the connection module to accurately identify communication objects and their characteristics, thereby establishing optimal communication links. Ultimately, it achieves efficient collaboration in data integration and communication scheduling in multi-robot collaborative scenarios, giving the terminal adaptive management and global coordination capabilities in multi-robot collaborative scenarios, significantly improving the communication reliability and task scheduling efficiency of the terminal system.

[0073] Finally, it should be noted that the above-described embodiments include multiple parallel implementations of the present invention. Deleting or otherwise adjusting one or more of these implementations will not affect the implementation of the solution. Furthermore, the portable terminal for robot data acquisition and integration disclosed in the embodiments of the present invention is merely a preferred embodiment of the present invention and is only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A portable terminal for robotic data collection and integration, comprising: The portable terminal comprises: A pluggable expansion module for providing an interface and a functional circuit, and completing physical connection, power management and drive loading of different types of expansion units through the interface and the functional circuit; A data processing module for obtaining heterogeneous data of a robot external body and a robot body from the pluggable expansion module, and performing integration processing on the heterogeneous data to obtain an integration processing result; the integration processing result comprises communication task characteristic information; A scheduling and management module for determining communication task demand parameters based on the communication task characteristic information output by the data processing module and a preset scheduling strategy; A connection module for establishing parallel communication links with multiple robots, and allocating bandwidth of the parallel communication links based on the communication task demand parameters; The scheduling and management module is further configured to coordinate interaction between the pluggable expansion module, the data processing module and the connection module.

2. The portable terminal for robotic data collection and integration of claim 1, wherein, The expansion unit comprises a sensor unit, a camera unit and a communication unit; wherein, The communication unit is configured to establish a connection with the robot body through a wired interface or a wireless interface, obtain robot body data, and perform data interaction with an external communication object.

3. The portable terminal for robotic data collection and integration of claim 2, wherein, The functional circuit comprises an interface identification circuit, a power management circuit and a drive control circuit; wherein, The interface identification circuit is configured to identify the type of the expansion unit based on pin level characteristics and start protocol messages when the expansion unit is inserted into the interface, and obtain an identification result; The power management circuit is configured to allocate power voltage and current matched with the expansion unit according to the identification result, and perform power allocation and overcurrent protection based on a priority queue algorithm when multiple expansion units are accessed in parallel; The drive control circuit is configured to perform matching detection based on the type of the expansion unit and a built-in drive library, and call a drive file to drive the expansion unit when the detection result is matching.

4. The portable terminal for robotic data collection and integration of claim 3, wherein, The drive control circuit performs fault-tolerant compensation adaptation operation when it is detected that the type of the expansion unit and the built-in drive library are not completely matched; the fault-tolerant compensation adaptation operation comprises modifying power supply parameters, communication protocol fields and data sampling rates; The drive control circuit calls a compatible drive library to complete drive loading when it is detected that the type of the expansion unit and the built-in drive library are completely unmatched.

5. The portable terminal for robotic data collection and integration of claim 1, wherein, The process of integrating the heterogeneous data comprises: Performing time alignment operation on the heterogeneous data based on the time stamps of the heterogeneous data, and dynamically adjusting the time alignment window when it is detected that the sampling frequency fluctuates; Mapping robot external body data and robot body data of different sources to a unified coordinate system for spatial alignment; Detecting differences between the data after time and space alignment, and determining that there is conflict data if the differences exceed a preset threshold or a statistical deviation range; When it is determined that there is conflict data, calculating a confidence score for each data source, and performing weighted fusion operation on the conflict data according to the confidence score to obtain integrated data.

6. The portable terminal for robotic data collection and integration of claim 5, wherein, The process of determining the communication task characteristic information comprises: Performing feature extraction operation based on the integrated data to obtain data type feature, real-time feature, bandwidth feature and priority feature; Determine abnormal sensitivity according to the data type feature and preset sensitivity mapping table; the abnormal sensitivity represents the tolerance of communication task to data abnormal fluctuation; Collect energy consumption parameter based on the terminal power consumption state monitoring result, and calculate energy consumption constraint based on energy consumption parameter and the bandwidth feature to obtain energy consumption constraint feature; Take the data type feature, real-time feature, bandwidth feature, priority feature, abnormal sensitivity and energy consumption constraint feature as communication task feature information.

7. The portable terminal for robotic data collection and integration of claim 6, wherein, The scheduling and management module includes the following when determining the communication task demand parameter: Select communication parameter mapping rule according to the data type feature; Determine allowed time delay threshold value based on the real-time feature under the selected communication parameter mapping rule; perform bandwidth mapping operation according to the bandwidth feature and energy consumption constraint feature to obtain target bandwidth allocation value; perform priority sorting operation according to the priority feature and abnormal sensitivity to obtain communication task priority sequence; Combine the allowed time delay threshold value, target bandwidth allocation value and communication task priority sequence to generate first communication task demand parameter.

8. The portable terminal for robotic data collection and integration of claim 7, wherein, After generating the first communication task demand parameter, the scheduling and management module further performs dynamic correction operation on the first communication task demand parameter based on preset scheduling strategy, and takes the first communication task demand parameter after correction as the final communication task demand parameter; The dynamic correction operation includes: When the current link occupancy rate exceeds the threshold value, reduce the bandwidth allocation value of low-priority communication task; When the terminal power consumption state is lower than the preset power threshold, increase the weight of energy consumption constraint feature in bandwidth mapping; When detecting a communication task with high abnormal sensitivity, adjust the priority sorting information and increase the bandwidth allocation value.

9. The portable terminal for robotic data collection and integration of claim 1, wherein, The connection module includes the following when establishing parallel communication link between multiple robots: Obtain type feature information of target robot, and select link establishment type according to type feature information; Establish corresponding communication channel on physical link according to selected link establishment type, and perform virtualization and isolation operation on the communication channel; The type feature information includes the following: Select latency priority type link for mobile robot, select bandwidth priority type link for fixed robot, and select redundant link for flying robot.

10. The portable terminal for robotic data collection and integration of claim 9, wherein, After establishing communication channel for different robots and performing virtualization and isolation operation, the connection module detects whether the bandwidth of different communication channels conflicts, and performs bandwidth allocation adjustment on the communication channel based on priority parameter when there is conflict; When detecting that multiple robots belong to the same cooperative communication task, merge the corresponding communication channels into communication task group channel, and perform bandwidth sharing and data deduplication operation in the communication task group channel; when detecting that there is conflict between communication task group channel and other communication channels, perform cross-channel coordination based on communication task priority.

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