A method and system for power driving an underwater device

Abstract

A power driving method and system of underwater equipment, real-time acquisition of energy system parameters, load system parameters and environmental system parameters of underwater equipment; based on the preset quantification rule, the collected energy system parameters are converted into the first quantification index, the load system parameters are converted into the second quantification index, and the scene mode information representing the current operation type of the equipment; based on the scene mode information, a preset multi-dimensional correlation rule library is called, and the first quantification index and the second quantification index are combined to generate a power distribution strategy; according to the power distribution strategy, control instructions are respectively sent to the energy module and the load module; real-time acquisition of feedback parameters after instruction execution, calculation of energy output deviation and load operation effect deviation; when the deviation meets the preset deviation condition, the energy output proportion or the load power quota is adjusted until the deviation returns to the preset deviation range. The present application improves the operation stability, energy efficiency and operation accuracy of the equipment in complex underwater environment.

Description

Technical Field

[0001] This invention relates to the field of underwater equipment control technology, and more specifically, to a power drive method and system for underwater equipment. Background Technology

[0002] With the increasing demand for underwater exploration and operations, underwater equipment is being used more and more widely in fields such as marine exploration, underwater rescue, and resource development. The power drive system of underwater equipment, as its core component, directly affects the equipment's operational efficiency, endurance, and environmental adaptability.

[0003] Traditional underwater equipment often employs a fixed power control mode, meaning it uses a single pre-set power distribution scheme for specific operational scenarios, failing to dynamically adjust based on changes in environmental parameters, energy status, and load demands. For example, in deep-sea exploration scenarios, using only a fixed energy output strategy may lead to equipment failure due to unreasonable energy distribution when encountering high-pressure extreme environments; in low-power exploration scenarios, it cannot effectively recover energy and optimize energy utilization, resulting in reduced endurance.

[0004] Meanwhile, in traditional power drive systems, the main and auxiliary functional modules are mostly integrated designs with fixed resource allocation. When system resources are insufficient, module loading failures or operational lags can easily occur, affecting the stability of equipment operation. In addition, the lack of an effective deviation feedback and correction mechanism means that when deviations occur in energy output or load operation, parameters cannot be adjusted in a timely manner, leading to reduced operational accuracy.

[0005] Therefore, the existing technology has problems and needs further improvement and development. Summary of the Invention

[0006] (I) Purpose of the invention: In order to solve the problems existing in the prior art, the purpose of the present invention is to provide a power drive method and system for underwater equipment, so as to solve the problems of poor adaptability of power control of traditional underwater equipment, low resource utilization and lack of effective deviation correction, and improve the operational stability, energy efficiency and operational accuracy of the equipment in complex underwater environments.

[0007] (II) Technical Solution: To solve the above-mentioned technical problems, this technical solution provides a power drive method for underwater equipment, including: The system acquires the energy system parameters, load system parameters, and environmental system parameters of the underwater equipment in real time. The energy system parameters represent the operating status of the energy module, the load system parameters represent the demand status of the load module, and the environmental system parameters represent the characteristic status of the environment in which the underwater equipment is located. Based on preset quantification rules, the collected energy system parameters are converted into a first quantification indicator reflecting the overall energy status, the load system parameters are converted into a second quantification indicator reflecting the urgency of load demand, and scenario mode information characterizing the current operating type of the equipment. Based on the scenario mode information, a preset multi-dimensional association rule library is invoked, and a power allocation strategy is generated by combining the first quantitative indicator and the second quantitative indicator. The multi-dimensional association rule library includes preset energy supply rules, load allocation rules and operation guarantee rules. According to the power distribution strategy, an energy output command is sent to the energy module, and a power distribution command is sent to the load module; feedback parameters after command execution are collected in real time, and energy output deviation and load operation effect deviation are calculated; when the deviation meets the preset deviation condition, the energy output ratio or load power quota is adjusted based on the preset correction rule until the deviation returns to the preset deviation range.

[0008] Preferably, the energy system parameters include the remaining capacity and real-time output power of the energy module; the load system parameters include the target operating intensity and real-time power consumption requirements of the load module; and the environmental system parameters include water depth, water flow velocity, water temperature, salinity, and water pressure.

[0009] Preferably, the scene mode information is a preset standardized operation scenario, including a main scene mode and auxiliary scene modes; the main scene mode includes cruise mode, heavy-load operation mode, deep-sea exploration mode, and low-power exploration mode; the auxiliary scene modes include attitude stabilization auxiliary kit, energy optimization auxiliary kit, robotic arm coordination auxiliary kit, power enhancement auxiliary kit, high-voltage protection auxiliary kit, environmental parameter monitoring auxiliary kit, energy recovery auxiliary kit, and sleep / wake-up auxiliary kit; the auxiliary scene modes are triggered by the loading command of the main scene mode and cannot run independently.

[0010] Preferably, the scene mode information is encapsulated through dynamic link library suites, each of which contains a power allocation strategy, suite metadata, and core functional sub-suites. The power allocation strategy includes an energy allocation algorithm, a load priority table, environmental adaptation parameters, and path planning parameters. The suite metadata includes resource requirement tags and energy compatibility tags; The core functional sub-package retains the core logic of energy output regulation and load power quota calculation.

[0011] Preferably, the scenario mode is dynamically deployed through the power service bus. When a scenario mode switching instruction is received, the power service bus calls the energy supply rules and load distribution rules in the multi-dimensional association rule base, reads the first quantitative indicator, and determines whether the kit metadata requirements are met. When the system resources meet the resource requirement tag and the first quantitative indicator meets the energy compatibility tag, load the complete dynamic link library suite and display the full-function mode and the current value of the first quantitative indicator. When system resources do not meet the resource requirement tag or the first quantitative indicator is lower than the energy compatibility tag threshold, switch to the core function sub-package, shut down non-core function processes, and display the function degradation.

[0012] Preferably, the multi-dimensional association rule base manages the main scene mode suite and the auxiliary scene mode suite through a collaborative dependency configuration table, and the collaborative dependency configuration table defines the dependency hierarchy through tags.

[0013] Preferably, the main scene mode suite and the auxiliary scene mode suite are isolated by memory sandboxes. Each suite is allocated an independent memory page table, and cross-sandbox write operations are prohibited. Environment parameters are exchanged through a read-only parameter buffer shared by the power service bus. The access permissions of the read-only parameter buffer are dynamically allocated by the power service bus. The main scene mode suite has read and write permissions, while the auxiliary scene mode suite has only read permissions.

[0014] Preferably, the memory quota of the main scene mode suite is fixed, while the memory of the auxiliary scene mode suite fluctuates according to demand. When the memory usage of the auxiliary scene mode suite exceeds the first usage threshold, it is expanded from the public memory pool. After expansion, the memory adjustment log is output to the interactive interface for display through the power service bus.

[0015] Preferably, the first quantitative indicator, the second quantitative indicator, scene mode information, and power allocation strategy are displayed in real time through an interactive interface; the interactive interface integrates a strategy configuration card, which encapsulates the power allocation strategy for each scene mode, including energy allocation algorithm, load priority table, and environment adaptation parameters; the strategy configuration card includes parameter snapshot, applicable scene tags, and resource requirement threshold.

[0016] This technical solution provides a power drive system for underwater equipment, used to execute a power drive method for underwater equipment, including: The data acquisition unit acquires the energy system parameters, load system parameters, and environmental system parameters of the underwater equipment in real time. The energy system parameters represent the operating status of the energy module, the load system parameters represent the demand status of the load module, and the environmental system parameters represent the characteristic status of the environment in which the equipment is located. Based on preset quantification rules, the data processing unit converts the collected energy system parameters into a first quantitative indicator reflecting the overall energy status, converts the load system parameters into a second quantitative indicator reflecting the urgency of load demand, and introduces scenario mode information that characterizes the current operating type of the equipment. The strategy generation unit calls a preset multi-dimensional association rule library based on the scenario mode information, and generates a power allocation strategy by combining the first quantitative indicator and the second quantitative indicator. The multi-dimensional association rule library includes preset energy supply rules, load allocation rules and operation guarantee rules. The dynamic execution unit sends energy output instructions to the energy module and power allocation instructions to the load module according to the power allocation strategy; it collects feedback parameters after the instructions are executed in real time, calculates energy output deviation and load operation effect deviation; when the deviation meets the preset deviation conditions, it adjusts the energy output ratio or load power quota based on the preset correction rules until the deviation returns to the preset deviation range.

[0017] (III) Beneficial Effects: The underwater equipment power drive method and system provided by this invention firstly combines scene mode information with a multi-dimensional association rule base to dynamically generate and adjust power allocation strategies based on energy status, load requirements, and environmental parameters. Simultaneously, it achieves collaborative loading and dynamic deployment of main and auxiliary scene suites, adapting to different underwater operation scenarios and environmental changes, thus solving the problem of poor adaptability of traditional fixed modes. Secondly, it employs memory sandbox isolation technology and dynamic memory quota management. The memory quota for the main scene mode suite is fixed, while the memory for the auxiliary scene mode suite floats as needed. Furthermore, it uses core functional sub-suites to achieve functional degradation when resources are insufficient, avoiding resource waste and improving system resource utilization. Thirdly, it has a real-time deviation feedback and correction mechanism, which can adjust the energy output ratio and load power quota in a timely manner to ensure deviations are within a preset range. In extreme environments, it automatically loads enhanced sub-suites to improve the stability and safety of equipment operation in complex environments, ensuring operational accuracy. Finally, it displays the system's operating status in real time through an interactive interface, encapsulating the power control logic into visual strategy configuration cards for easy viewing and operation, reducing the difficulty of use. Attached Figure Description

[0018] Figure 1 This is a flowchart of the steps of a power drive method for an underwater device according to the present invention; Figure 2 This is a schematic diagram of the structure of a power drive device for an underwater device according to the present invention; Figure 3 This is a detailed flowchart of step 2 of the present invention; Figure 4 This is a detailed flowchart of step 4 of the present invention. Detailed Implementation

[0019] The present invention will be further described in detail below with reference to preferred embodiments. More details are set forth in the following description in order to provide a full understanding of the present invention. However, the present invention can obviously be implemented in many other ways different from those described herein. Those skilled in the art can make similar extensions and derivations based on actual application situations without departing from the spirit of the present invention. Therefore, the scope of protection of the present invention should not be limited by the content of this specific embodiment.

[0020] The accompanying drawings are schematic diagrams of embodiments of the present invention. It should be noted that these drawings are for illustrative purposes only and are not drawn to scale, and should not be construed as limiting the actual scope of protection of the present invention.

[0021] A method for powering underwater equipment, such as Figure 1 As shown, the specific steps include: Step 1: Obtain the energy system parameters, load system parameters, and environmental system parameters of the underwater equipment in real time.

[0022] Step 2: Based on the preset quantification rules, convert the collected energy system parameters into the first quantification index, convert the load system parameters into the second quantification index, and determine the scenario mode information that characterizes the current operation type of the underwater equipment.

[0023] Step 3: Based on the scenario pattern information, call the preset multi-dimensional association rule library, and combine the first quantitative indicator and the second quantitative indicator to generate a power allocation strategy.

[0024] Step 4: According to the generated power distribution strategy, send control commands to the energy module and the load module respectively; collect feedback parameters after command execution in real time, calculate energy output deviation and load operation effect deviation, and adjust the energy output ratio or load power quota when the deviation exceeds the preset deviation condition until the deviation returns to the preset deviation range.

[0025] A power drive system for underwater equipment, applicable to the aforementioned power drive method for underwater equipment, such as... Figure 2As shown, the system includes a data acquisition unit, a data processing unit, a strategy generation unit, and a dynamic execution unit. The data acquisition unit acquires the energy system parameters, load system parameters, and environmental system parameters of the underwater equipment in real time. The data processing unit, based on preset quantification rules, converts the acquired energy system parameters into a first quantification index and the load system parameters into a second quantification index, and determines the scenario mode information representing the current operating type of the underwater equipment. The strategy generation unit, based on the scenario mode information, calls a preset multi-dimensional association rule library and combines the first and second quantification indicators to generate a power allocation strategy. The dynamic execution unit sends control commands to the energy module and load module respectively according to the generated power allocation strategy; it collects feedback parameters after command execution in real time, calculates energy output deviation and load operation effect deviation, and adjusts the energy output ratio or load power quota when the deviation exceeds a preset deviation condition until the deviation returns to the preset deviation range.

[0026] More specifically, the data acquisition unit in step 1 includes: The data acquisition unit acquires the energy system parameters, load system parameters, environmental system parameters, and path planning parameters of the underwater equipment in real time.

[0027] The data acquisition unit includes multiple sensors, with shielded twisted-pair cables for the sensors. The acquired data is digitized after A / D conversion, reducing underwater electromagnetic interference. The energy system parameters include the remaining capacity and real-time output power of the energy module, characterizing its operating status. The load system parameters include the target operating intensity and real-time power consumption requirements of the load module, characterizing its demand status. The environmental system parameters include water depth, current velocity, water temperature, salinity, and water pressure, characterizing the environmental conditions of the underwater equipment. The path planning parameters include the equipment's current location coordinates, target waypoint sequence, planned path length, path gradient distribution, estimated segment energy consumption coefficient, and obstacle avoidance path parameters, used to predict the energy demand trend of the underwater equipment in future segments.

[0028] Specifically, voltage and current sensors monitor the remaining battery capacity of the underwater equipment in real time; a power meter is connected in series in the energy output line to collect real-time output power with an accuracy of ±0.5%. The task controller receives instructions from the host computer to obtain the target workload; a current sensor detects the load circuit current, and the real-time power consumption requirement is calculated using the formula P=UI in conjunction with the voltage. The energy system parameters and the load system parameters are collected every 50ms to ensure the real-time nature of dynamic power allocation.

[0029] A piezoresistive water depth / pressure sensor is used, based on the conversion relationship between water pressure and water depth (1 MPa ≈ 100 m water depth), to collect water pressure and convert it into water depth. More preferably, core parameters such as water depth and water pressure employ a dual-sensor redundancy design, automatically switching to the backup sensor when the primary sensor fails, with a switching time of <10 ms. A Doppler current meter is used as the water flow velocity sensor, emitting acoustic signals and calculating water flow velocity through Doppler frequency shift, achieving an accuracy of ±0.01 m / s. A PT1000 PT1000 water temperature sensor is used, utilizing the temperature-dependent resistance characteristics of platinum resistance thermometers to collect water temperature data, achieving an accuracy of ±0.1℃. A conductivity sensor is used to measure water conductivity, and salinity is calculated using a water temperature compensation algorithm, achieving an accuracy of ±0.1 PSU. All environmental system parameters are collected every 100 ms and transmitted to the data processing unit via a CAN bus. The underwater equipment's three-dimensional position coordinates and attitude information are acquired in real time via an inertial navigation system (INS) and a depth gauge. A preset target waypoint sequence and planned path topology data are acquired via a mission controller. The position and attitude data are collected every 50ms and transmitted to the data processing unit via a CAN bus. More specifically, such as Figure 3 As shown, the data processing unit in step 2 specifically includes: Step 201: The data processing unit performs data preprocessing on the acquired energy system parameters and load system parameters to obtain standardized parameters.

[0030] The data preprocessing includes filtering the raw data acquired in real time, such as using moving average filtering to remove water flow noise, unit conversion, outlier detection, and performing redundant sensor verification when sensor data exceeds a reasonable range.

[0031] Specifically, the energy system parameters include the battery management system (BMS) which collects the current remaining capacity percentage and maps it to a base score; this is combined with the rated power P of the energy module. n Calculate the power load rate and map it to a dynamic adjustment score. The load system parameters have preset intensity levels based on the task type, mapped to a base score; this is then compared to the minimum guaranteed power P of the load module. min In comparison, the power consumption deficit rate is calculated as follows: power consumption deficit rate = (real-time power consumption demand - minimum guaranteed power) / minimum guaranteed power, which is mapped to a dynamic adjustment score.

[0032] Step 202: Perform weighted calculations on the standardized parameters of the energy system parameters and the load system parameters respectively to obtain the first quantitative index and the second quantitative index.

[0033] The comprehensive supply capacity of the energy module is evaluated by energy system parameters, and the energy system parameters are quantified into a first quantitative index, as shown in formula (1). The first quantitative index is a score of 0 to 100. The higher the score, the better the energy status.

[0034] (1), Wherein, SOC represents the remaining capacity of the energy module; α represents the weighting coefficient of SOC, indicating the priority of remaining capacity in the comprehensive energy status assessment, and is taken as 0.6 in this invention; P n P represents the rated power of the energy module; P represents the real-time output power of the energy module. β represents the power load rate; β represents the basic score for dynamic adjustment of the power load rate. When the power load rate is 0, the score for the dynamic adjustment item is 40 points; as the load rate increases, the score for this item decreases linearly.

[0035] For example, when SOC=75%, When the percentage is 60%, then the first quantitative indicator = 75 × 0.6 + (1 - 60%) × 40 = 45 + 16 = 61 points.

[0036] The urgency of the task of the load module is evaluated by load system parameters, and the load system parameters are quantified into a second quantitative index, as shown in formula (2), which is quantified into a score of 0 to 100. The higher the score of the second quantitative index, the more urgent the demand.

[0037] (2), Wherein, γ represents the weighting coefficient of the target task intensity, which indicates the priority of the urgency of the task itself in the load demand assessment. In this invention, the value is 0.7; δ represents the basic score of dynamic adjustment of power consumption deficit rate. When the power consumption deficit rate is 100%, the dynamic adjustment item score is 30 points. The lower the deficit rate, the lower the score.

[0038] For example, when the target workload is high, assigned a score of 80, and the power consumption deficit rate is 20%, then the second quantitative indicator = 80 × 0.7 + 20% × 30 = 62 points.

[0039] Step 203: Based on the first quantitative indicator, the second quantitative indicator, and environmental system parameters, match the preset standardized scenario mode.

[0040] A pre-defined scenario matching threshold table is provided in the multi-dimensional association rule base, as shown in Table 1. The first and second quantitative indicators, along with environmental system parameters calculated in real-time, are compared with the scenario matching threshold table. The main scenario mode that meets the conditions is automatically activated. If multiple scenario modes are met simultaneously, the main scenario mode with the highest second quantitative indicator is selected based on the principle of prioritizing load urgency.

[0041] The scenario modes are preset standardized operating scenarios, including a main scenario mode and auxiliary scenario modes. The main scenario mode is the core operation type, including cruise mode, heavy-load operation mode, deep-sea exploration mode, and low-power exploration mode. The auxiliary scenario modes are support function types, including attitude stabilization auxiliary kit, energy optimization auxiliary kit, robotic arm coordination auxiliary kit, power enhancement auxiliary kit, high-voltage protection auxiliary kit, environmental parameter monitoring auxiliary kit, energy recovery auxiliary kit, and sleep / wake-up auxiliary kit. The auxiliary scenario modes are triggered by the loading command of the main scenario mode and cannot run independently.

[0042] Table 1 Scene Matching Threshold Table

[0043] The matching of the main scene mode and the auxiliary scene mode is based on the functional dependency logic and scene operation requirements of the collaborative dependency configuration table, as shown in Table 2. The collaborative dependency configuration table defines the dependency hierarchy through tags, and the auxiliary scene mode is automatically parsed and loaded by the power service bus when the main scene mode is loaded.

[0044] The path planning module is initialized synchronously when the scene mode is loaded. In cruise mode, the path planning module adopts a large-radius semi-circular arc turning strategy, with straight segments as the main component. In heavy-load operation mode, the path planning module prioritizes ensuring coverage of the robotic arm's workspace. In deep-sea exploration mode, the path planning module generates a three-dimensional spiral coverage path, with a semi-circular arc transition on the vertical plane. In low-power exploration mode, the path planning module generates a minimum energy consumption path, maximizing the semi-circular arc radius.

[0045] The main scene mode and auxiliary scene mode are isolated by memory sandboxes. Each mode suite is allocated an independent memory page table and cross-sandbox write operations are prohibited. Environment parameters are exchanged through a read-only parameter buffer shared by the power service bus. The access permissions of the read-only parameter buffer are dynamically allocated by the power service bus. The main scene mode suite has read and write permissions, including updated environment parameters, current scene mode labels and switching status, energy output commands sent to the energy module, and power allocation commands sent to the load module.

[0046] The auxiliary scenario mode suite can only read the basic environmental state and system macro indicators in the power service bus shared buffer for its own logical operations, but it has no right to modify these data, including environmental system parameters, first quantitative indicators, second quantitative indicators, and global common system state.

[0047] Table 2 Matching Table between Main Scene Mode and Auxiliary Scene Mode

[0048] The memory quota of the main scene mode suite is fixed, while the memory of the auxiliary scene mode suite fluctuates according to demand. When the memory usage of the auxiliary scene mode suite exceeds the first usage threshold, it is expanded from the public memory pool. After expansion, the memory adjustment log is output to the interactive interface through the power service bus for display.

[0049] The memory of the auxiliary scene mode suite can be expanded by 10% each time, with a maximum of 30% of the total memory.

[0050] The dynamic execution unit in step 3 specifically includes: Based on the scenario mode information, a preset multi-dimensional association rule library is invoked, and a power allocation strategy adapted to the current work scenario is generated by combining the first quantitative indicator and the second quantitative indicator.

[0051] The interactive interface displays the first quantitative indicator, the second quantitative indicator, scenario mode information, and power allocation strategy in real time. The interface integrates strategy configuration cards, encapsulating power allocation strategies for each scenario mode, including energy allocation algorithms, load priority tables, and environmental adaptation parameters. Each strategy configuration card contains parameter snapshots, applicable scenario tags, and resource requirement thresholds. After each power allocation strategy adjustment, the corrected parameters are automatically updated to the multi-dimensional association rule base to optimize the generation of the next power allocation strategy.

[0052] The multi-dimensional association rule base includes preset energy supply rules, load allocation rules, operation guarantee rules, and a path planning module. The energy supply rules define the output priority of energy modules, check whether energy compatibility label conditions are met, and trigger energy output restrictions if not met; for example, in deep-diving mode, the main battery is used first, and low-power backup power is disabled. The load allocation rules specify the weight coefficients of load modules, sort the quota results according to the load priority table, ensuring that high-priority loads receive power first, and low-priority loads can have their quotas dynamically reduced. The operation guarantee rules set an upper limit for energy output and a minimum guaranteed power for the load, ensuring that the energy output command is less than or equal to the maximum output power of the energy module (i.e., the upper limit for energy output); and that the total load quota is greater than or equal to the minimum guaranteed power for the load. The path planning module generates candidate paths based on the current scenario mode information, calculates the instantaneous power of the thrusters for each segment of the candidate path, accumulates the total energy consumption of the path, and calculates the path tracking correction energy consumption.

[0053] Each dynamic link library suite contains a power allocation strategy, suite metadata, and core functional sub-suites, stored in a read-only directory on the device in .so format. The power allocation strategy includes an energy allocation algorithm, a load priority table, and environmental adaptation parameters. The suite metadata includes resource requirement tags and energy adaptation tags; the resource requirement tags confirm the CPU and memory requirements of the main suite and the resource requirement thresholds of the auxiliary suites; the energy adaptation tags extract the first quantitative indicator thresholds corresponding to the main and auxiliary suites. The core functional sub-suites retain the core logic for energy output regulation and load power quota calculation.

[0054] Scenario modes are dynamically deployed via the power service bus. When a scenario mode switching command is received, the power service bus calls the energy supply rules and load distribution rules in the multi-dimensional association rule base, reads the first quantitative indicator, and determines whether the suite metadata requirements are met. When system resources meet the resource requirement tag and the first quantitative indicator matches the energy adaptation tag, the complete dynamic link library suite is loaded, and the full-featured mode and the current value of the first quantitative indicator are displayed. When system resources do not meet the resource requirement tag or the first quantitative indicator is lower than the energy adaptation tag threshold, the system switches to the core function sub-suite, closes non-core function processes, and displays a function degradation message.

[0055] Specifically, based on the power allocation strategy corresponding to the scenario mode, energy allocation is performed by combining the first and second quantitative indicators. The energy allocation for the main scenario mode determines the basic energy output ratio based on the first quantitative indicator; for example, when the first quantitative indicator is 60 points, the basic output ratio is 65% of the rated power. The energy coordination for the auxiliary scenario mode adjusts the energy quota based on the second quantitative indicator; for example, when the second quantitative indicator is 70 points, an additional 10% of energy is allocated to ensure functional stability.

[0056] The load priority table in the dynamic link library suite is read, and the quota results are sorted according to the load priority table. Combined with the second quantitative indicator, load power is allocated to ensure that high-priority loads receive power first, while low-priority loads can have their quotas dynamically reduced, down to a minimum of 10% of their rated power. Load quotas in the main scenario mode are allocated according to the load priority table; load quotas in the auxiliary scenario mode are allocated to collaborative sub-suites according to load allocation rules, ensuring synchronized actions across multiple suitcases.

[0057] As shown in Table 3, the preset multi-dimensional association rule library is called according to the scene rule mapping relationship table to clarify the rules corresponding to the main scene mode and the auxiliary scene mode respectively, as well as the coordination priority among the energy supply rules, load distribution rules and operation guarantee rules in the multi-dimensional association rule library.

[0058] Specifically, energy supply rules and operational assurance rules are determined based on a multi-dimensional association rule base to ensure that the core requirements of the main scenario mode are met first. Auxiliary scenario modes are dynamically judged by combining environmental parameters, energy parameters, and load status to avoid invalid loading. When auxiliary scenario modes conflict in their triggering conditions, the operational assurance rules take precedence. When energy supply rules conflict with load allocation rules, such as when energy is insufficient but load demand is urgent, the operational assurance rules take precedence to ensure minimum power consumption.

[0059] Table 3 Scene Rule Mapping Relationship Table

[0060] The system invokes the environment adaptation module within the suite, dynamically adjusting strategies based on environmental system parameters. Each power suite has a built-in environment configuration mapping table, which contains environmental parameter thresholds and corresponding adjustment ranges for configuration parameters. The power service bus collects environmental system parameters every 300ms. When parameters exceed the current configuration threshold, the suite configuration is updated in real-time via API without restarting the suite, with an update latency of ≤80ms, and the configuration adjustment is displayed on the interactive interface. When environmental parameters reach extreme thresholds, the extreme environment enhancement sub-suite is automatically loaded, overriding the environment adaptation module of the main suite. The enhancement sub-suite shares a memory sandbox with the main suite; after loading, it replaces the critical functions of the main suite, and after unloading, it automatically restores them.

[0061] The path planning module in the suite is invoked to dynamically optimize the motion path by combining the device pose and the task objective. During the path generation phase, a multi-objective evaluation function is used to comprehensively evaluate each candidate path to obtain the final planned path.

[0062] The evaluation dimensions of the multi-objective evaluation function include at least two of the following: path energy consumption, path length, and job coverage.

[0063] The path planning module includes a path generation submodule, a path smoothing submodule, and a path tracking and correction submodule. The path generation submodule generates a three-dimensional candidate path based on the current position of the underwater equipment, the target position, and environmental system parameters. The path smoothing submodule performs curvature constraint verification on the three-dimensional candidate path; when the path curvature exceeds a preset curvature threshold, the path point distribution is adjusted. The path tracking and correction submodule collects the actual path of the underwater equipment in real time and compares it with the predetermined path; when the deviation between the actual path and the predetermined path exceeds a preset deviation threshold, a thruster power correction amount is generated based on the deviation, and the thruster power distribution is adjusted.

[0064] Each power kit has a built-in path energy consumption mapping table, which contains the unit energy consumption coefficient and expected segment duration corresponding to different optimized path topologies. The power service bus reads the underwater equipment position and waypoint deviation every 300ms. When the deviation from the planned path exceeds a preset threshold, without restarting the kit, the path planning parameters are updated in real time via the API interface, and the optimal path from the current segment to the target waypoint is replanned, with an update latency of ≤80ms. The path adjustment is displayed on the interactive interface. When an obstacle is detected or a sudden environmental change occurs, the obstacle avoidance replanning sub-kit is automatically loaded, overriding the path planning module of the main kit. The obstacle avoidance replanning sub-kit shares a memory sandbox with the main kit; after loading, it replaces the path planning function of the main kit, and after unloading, it automatically restores its original state.

[0065] Specifically, the path planning module generates a three-dimensional semi-circular spiral path. In deep-sea exploration mode or low-power exploration mode, the underwater equipment does not use the three-dimensional semi-circular spiral path; instead, the horizontal plane of the three-dimensional semi-circular spiral path uses a semi-circular arc turn, and the radius of the semi-circular arc is equal to the effective detection radius of the underwater equipment. The curvature of the three-dimensional semi-circular spiral path ,in The environmental correction factor is dynamically calculated from environmental system parameters. The vertical plane of the three-dimensional semi-circular spiral path is superimposed with the rising or falling semi-circular arc to form a three-dimensional spiral coverage trajectory. When the underwater equipment turns, a dual-thruster differential velocity strategy is employed, with the outer thruster velocity... inner thruster speed ,in , For the spacing between the two thrusters, The base speed is used. When the path curvature exceeds the preset maximum curvature threshold, the path planning module automatically adjusts the distribution of path points to ensure that the curvature meets the boundary constraints.

[0066] The path planning module also includes a trajectory tracking and correction submodule. Every 100ms, the actual path of the underwater equipment is acquired using a DVL (Doppler velocimeter) and inertial navigation fusion positioning system, and the three-dimensional angular deviation between this acquisition and the predetermined path is calculated. .when > Deviation Angle Threshold When the time is right, a correction is triggered, and the thruster power is adjusted via an energy output command to regulate the thruster power distribution. Simultaneously, the corrected thruster power is written into a multi-dimensional association rule base for optimizing the next path planning. Any additional energy consumption during the correction process is fed back to the first quantitative indicator in real time.

[0067] The deviation angle threshold described in this invention The value is 2°.

[0068] The path planning module also includes a path energy consumption prediction model, which predicts the total path energy consumption of the underwater equipment. ,in The thruster power for the i-th path segment is determined by the speed, path curvature, and environmental parameters. Let be the length of the i-th path segment. Additional energy consumption for trajectory tracking correction. The predicted path energy consumption serves as an input constraint for the energy allocation algorithm and load allocation rules, participating in the generation of the power allocation strategy along with the first and second quantification indicators. When the predicted path energy consumption exceeds a safety threshold compared to the current remaining capacity, the system automatically downgrades to a low-power path mode, which can involve increasing the path curvature radius or reducing speed.

[0069] The path energy consumption prediction model calculates the instantaneous power of the thrusters segment by segment for each candidate path. ,in , The hydrodynamic drag coefficient, obtained by the data acquisition unit, is determined by conducting uniform straight-line tests at different speeds in a still water test tank before the underwater equipment leaves the factory. The propeller power and speed data are recorded, and the coefficient is fitted using the least squares method. value. , For path curvature, The steering drag coefficient was obtained through a constant curvature rotation test. Before the underwater equipment left the factory, a constant curvature rotation test was conducted in a still water test tank with different radii of curvature. The thruster's additional power, curvature, and speed data were recorded, and the coefficient was fitted using the least squares method. value. , The angle between the heading and the direction of the water flow. The water flow coupling coefficient was obtained through straight-line tests at different flow velocities. Before the underwater equipment left the factory, straight-line tests were conducted in a circulating water tank at different water flow velocities, and the thruster power, flow velocity, and heading angle data were recorded. The least squares method was used for fitting. value.

[0070] Path curvature Calculation using the three-point method: ,when > In this process, the distribution of path points is automatically adjusted to ensure that the path curvature meets boundary constraints, thus avoiding additional energy consumption caused by sharp turns. The various scene modes in this invention correspond to... The values ​​are different: 0.05 rad / m for cruise mode, 0.08 rad / m for heavy-load operation mode, 0.06 rad / m for deep-sea detection mode, and 0.04 rad / m for low-power detection mode.

[0071] The path consists of n path points, and the length of each path segment is the Euclidean distance between adjacent path points. The total energy consumption of the path is output by the path energy consumption prediction model. With total path length and job coverage Together they constitute a multi-objective optimization function The path planning module calculates the results for each of the generated candidate paths. Value, selection The path with the shortest length is chosen as the final planned path.

[0072] When the path planning detects a narrow body of water, i.e., the distance traveled after turning is less than a preset distance threshold, the energy optimization auxiliary kit in the auxiliary scenario mode is automatically triggered to adjust the load power quota and ensure the underwater equipment's passability and operational stability in narrow body of water.

[0073] By introducing three-dimensional semi-circular spiral path planning, semi-circular turns replace traditional right-angle turns, avoiding jamming of underwater equipment in narrow waters or complex terrain, reducing energy surges caused by frequent start-stop of thrusters, and improving path smoothness. Secondly, through a trajectory tracking deviation correction mechanism, the actual path is collected every 100ms and compared with the predetermined path to adjust the thruster power distribution in a timely manner, ensuring operational accuracy.

[0074] like Figure 4 As shown, the dynamic execution unit in step 4 specifically includes: Step 401: According to the generated power distribution strategy, send control commands to the energy module and the load module respectively.

[0075] The control commands include energy output commands sent to the energy module and power allocation commands sent to the load module. The energy output commands specify the target power, duration, and priority, prioritizing core scenarios. The power allocation commands allocate quotas according to load type, setting a minimum guaranteed power for core loads and dynamically reducing power for non-core loads.

[0076] Step 402: Collect feedback parameters after command execution in real time, calculate energy output deviation and load operation effect deviation, and adjust the energy output ratio or load power quota when the deviation exceeds the preset deviation condition until the deviation returns to the preset deviation range.

[0077] As shown in formulas (3) and (4), key feedback parameters such as energy output power and core load operation intensity are collected, while non-core data are ignored to reduce system resource consumption.

[0078] (3), Among them, E eIndicates energy output deviation; P actual P represents the actual output power. target This indicates the target output power.

[0079] For example, when the target output power is 650W and the actual output power is 630W, then E e =∣(630-650) / 650∣×100%≈3.08%.

[0080] (4), Among them, E l Indicates the deviation in the effect of load operation; T targe t represents the target workload; Tactual represents the actual workload.

[0081] For example, when the target gripping force is 500N and the actual gripping force is 480N, then E l =∣(480-500) / 500∣×100%=4%.

[0082] The specific deviation threshold is preset according to the actual situation. In this invention, when the energy output deviation is greater than 5% and the load output deviation is greater than 10%, the load power quota is adjusted first, and then the energy output ratio is adjusted to avoid energy overload.

[0083] Dynamic adjustments are triggered by preset deviation conditions. If energy output is insufficient, the main energy source's share is increased first, followed by optimization of load quotas. The core load adjustment range is ≤10%, and non-core loads can be reduced to 0, ensuring that basic functions are not affected. When system resources are insufficient, non-core modules are automatically shut down, retaining only energy regulation and load quota calculation functions to maintain core control logic.

[0084] After each adjustment, the deviation is continuously monitored. Adjustments cease once the deviation remains stable within the preset range for three consecutive cycles, avoiding excessive intervention. Adjusted parameters are automatically synchronized to a multi-dimensional rule base to optimize the generation of the next power allocation strategy. This invention also supports manual parameter adjustment via a visual strategy configuration card, with adjustment records automatically archived for easy backtracking analysis.

[0085] Specifically, users manually adjust the policy configuration card and modify the load priority through the interactive interface. The system verifies whether the adjusted power allocation policy meets the operation guarantee rules; if the verification is successful, the system dynamically updates the multi-dimensional association rule base and suspends the automatic correction mechanism until the user exits manual mode.

[0086] As a further optimization of the above-mentioned power-driven method, the present invention also extends the configuration strategy between dynamic link library suites and system threads and memory resources to improve resource utilization efficiency and system stability during concurrent operation in multiple scenarios.

[0087] The power service bus allocates a dedicated thread to each loaded dynamic link library suite, with each dedicated thread corresponding one-to-one with the dynamic link library suite. The stack space of each dedicated thread is pre-calculated and allocated based on the resource requirement tags in the metadata of the dynamic link library suite. Specifically, the thread stack space size... ,in, For the basic stack space, For data tablespace, Reserved for heap space, This is the smallest unit of memory allocation. By rounding up the allocation, memory fragmentation can be effectively avoided, and memory utilization can be improved.

[0088] The power service bus maintains a thread scheduling table stored in RAM, which records the kit ID, kit name, dedicated thread ID, stack space size, and CPU time slice quota for each loaded kit.

[0089] The metadata for each dynamic link library suite includes a resource requirement tag containing the number of CPU instructions required per control cycle, the required stack space, the required heap space, the data table size, the execution priority, the maximum tolerable latency, and the recommended number of threads. The Power Service Bus reads this resource requirement tag when loading the suite, calculates, and allocates thread stack space.

[0090] When the total thread stack space requirement of all loaded dynamic link library suites exceeds the capacity of the shared memory pool, the power service bus sorts them from high to low and prioritizes loading suite combinations whose total stack space requirement does not exceed the capacity of the shared memory pool. The dedicated threads of suites not selected are suspended, their stack space is reclaimed to the shared memory pool, and the firmware remains in Flash for reloading when needed next.

[0091] When only partial compression of the thread stack space of each suite is required, the discard ratio coefficient is determined according to the current job urgency of each suite. : ,in This is the difference between the total stack space requirement and the shared memory pool capacity. It represents the discard ratio for high-urgency packages. Smaller values ​​mean fewer items to discard; the discard ratio for packages with low urgency. Larger values ​​are discarded. The discarded thread stack space should at least retain the minimum stack space required for the core functional sub-packages to run.

[0092] At the start of each cycle, the power service bus writes to a read-only parameter buffer. Each dedicated thread of each dynamic link library suite accesses this buffer in read-only mode during the cycle execution phase. Intermediate calculation results and local variables of each dedicated thread are stored in its own stack space, which is independently partitioned within a sandbox and does not overlap. Because all threads access the shared read-only parameter buffer in read-only mode, and the power service bus is the only writer, there is no mutex lock contention between threads, eliminating the need for semaphores or critical sections for synchronization.

[0093] Each dedicated thread's stack space is independently partitioned within a sandbox, with access permissions controlled by an independent page table. Each suite's page table maps virtual addresses to physical addresses, prohibiting cross-sandbox write operations. Threads interact with environment parameters only through a read-only parameter buffer shared by the Power Service Bus. Access permissions for this read-only parameter buffer are dynamically allocated by the Power Service Bus; the main scene mode suite has read and write permissions, while the auxiliary scene mode suite has only read-only permissions. Each thread can only execute within its own stack space; exceeding this space triggers a page fault, which is caught and handled by the Power Service Bus to prevent illegal memory access from causing system crashes.

[0094] The Power Service Bus centrally manages the dedicated thread lifecycle, stack space allocation, CPU time slice scheduling, and sandbox memory quota for each dynamic link library suite, significantly improving resource utilization efficiency and system real-time performance during concurrent operation in multiple scenarios. When the total stack space requirement exceeds the capacity of the shared memory pool, priority is selected based on a comprehensive calculation using static priority, the first quantitative indicator, and the second quantitative indicator. Priority is given to ensuring resource supply for core scenario modes, while threads of suites not selected are suspended and their memory is reclaimed, ensuring stable system operation even under resource constraints. Simultaneously, a discard ratio is determined based on job urgency, with fewer high-urgency suites discarded and more low-urgency suites discarded, balancing fairness with critical task assurance.

[0095] This invention relates to a power drive method and system for underwater equipment. Firstly, by combining scene mode information with a multi-dimensional association rule base, it can dynamically generate and adjust power allocation strategies based on energy status, load requirements, and environmental parameters. Simultaneously, it enables the collaborative loading and dynamic deployment of primary and secondary scene modules, adapting to different underwater operating scenarios and environmental changes, thus solving the problem of poor adaptability of traditional fixed modes. Secondly, it employs memory sandbox isolation technology and dynamic memory quota management. The memory quota for the primary scene mode module is fixed, while the memory for the secondary scene mode module floats as needed. Furthermore, it utilizes core functional sub-modules to degrade functions when resources are insufficient, avoiding resource waste and improving system resource utilization. Thirdly, it features a real-time deviation feedback and correction mechanism, which can promptly adjust the energy output ratio and load power quota to ensure deviations remain within preset ranges. In extreme environments, it automatically loads enhanced sub-modules to improve the stability and safety of equipment operation in complex environments, ensuring operational accuracy. Finally, it displays the system's operating status in real time through an interactive interface, encapsulating the power control logic into visual strategy configuration cards for easy viewing and operation, reducing the difficulty of use.

[0096] The above description illustrates preferred embodiments of the present invention and helps those skilled in the art to more fully understand the technical solution of the present invention. However, these embodiments are merely illustrative and should not be construed as limiting the specific implementation of the present invention to these embodiments. For those skilled in the art, several simple deductions and modifications can be made without departing from the inventive concept, and all such modifications should be considered within the protection scope of the present invention.

Claims

1. A power drive method for underwater equipment, characterized in that, include: The system acquires the energy system parameters, load system parameters, and environmental system parameters of the underwater equipment in real time. The energy system parameters represent the operating status of the energy module, the load system parameters represent the demand status of the load module, and the environmental system parameters represent the characteristic status of the environment in which the underwater equipment is located. Based on preset quantification rules, the collected energy system parameters are converted into a first quantification indicator reflecting the overall energy status, the load system parameters are converted into a second quantification indicator reflecting the urgency of load demand, and scenario mode information characterizing the current operating type of the equipment. Based on the scenario mode information, a preset multi-dimensional association rule library is invoked, and a power allocation strategy is generated by combining the first quantitative indicator and the second quantitative indicator. The multi-dimensional association rule library includes preset energy supply rules, load allocation rules and operation guarantee rules. According to the power distribution strategy, an energy output command is sent to the energy module, and a power distribution command is sent to the load module; feedback parameters after command execution are collected in real time, and energy output deviation and load operation effect deviation are calculated; when the deviation meets the preset deviation condition, the energy output ratio or load power quota is adjusted based on the preset correction rule until the deviation returns to the preset deviation range.

2. The power drive method for an underwater device according to claim 1, characterized in that, The energy system parameters include the remaining capacity and real-time output power of the energy module; the load system parameters include the target operating intensity and real-time power consumption requirements of the load module; and the environmental system parameters include water depth, water flow velocity, water temperature, salinity, and water pressure.

3. The power drive method for an underwater device according to claim 1, characterized in that, The scenario mode information consists of preset standardized operating scenarios, including a main scenario mode and auxiliary scenario modes. The main scenario mode includes cruise mode, heavy-load operation mode, deep-sea exploration mode, and low-power exploration mode. The auxiliary scenario modes include attitude stabilization auxiliary kit, energy optimization auxiliary kit, robotic arm coordination auxiliary kit, power enhancement auxiliary kit, high-voltage protection auxiliary kit, environmental parameter monitoring auxiliary kit, energy recovery auxiliary kit, and sleep / wake-up auxiliary kit. The auxiliary scenario modes are triggered by the loading command of the main scenario mode and cannot run independently.

4. The power drive method for an underwater device according to claim 3, characterized in that, The scene mode information is encapsulated through dynamic link library suites. Each dynamic link library suite contains a power allocation strategy, suite metadata, and core functional sub-suites. The power allocation strategy includes an energy allocation algorithm, a load priority table, environmental adaptation parameters, and path planning parameters. The suite metadata includes resource requirement tags and energy compatibility tags; The core functional sub-package retains the core logic of energy output regulation and load power quota calculation.

5. The power drive method for an underwater device according to claim 4, characterized in that, The scenario mode is dynamically deployed through the power service bus. When a scenario mode switching instruction is received, the power service bus calls the energy supply rules and load distribution rules in the multi-dimensional association rule base, reads the first quantitative indicator, and determines whether the kit metadata requirements are met. When the system resources meet the resource requirement tag and the first quantitative indicator meets the energy compatibility tag, load the complete dynamic link library suite and display the full-function mode and the current value of the first quantitative indicator. When system resources do not meet the resource requirement tag or the first quantitative indicator is lower than the energy compatibility tag threshold, switch to the core function sub-package, shut down non-core function processes, and display the function degradation.

6. The power drive method for an underwater device according to claim 1, characterized in that, The multi-dimensional association rule base manages the main scene mode suite and the auxiliary scene mode suite through a collaborative dependency configuration table, which defines the dependency hierarchy through tags.

7. The power drive method for an underwater device according to claim 6, characterized in that, The main scene mode suite and the auxiliary scene mode suite are isolated by memory sandboxes. Each suite is allocated an independent memory page table and cross-sandbox write operations are prohibited. Environment parameters are exchanged through a read-only parameter buffer shared by the power service bus. The access permissions of the read-only parameter buffer are dynamically allocated by the power service bus. The main scene mode suite has read and write permissions, while the auxiliary scene mode suite has only read permissions.

8. The power drive method for an underwater device according to claim 7, characterized in that, The memory quota of the main scene mode suite is fixed, while the memory of the auxiliary scene mode suite fluctuates according to demand. When the memory usage of the auxiliary scene mode suite exceeds the first usage threshold, it is expanded from the public memory pool. After expansion, the memory adjustment log is output to the interactive interface through the power service bus for display.

9. The power drive method for an underwater device according to claim 1, characterized in that, The interactive interface displays the first quantitative indicator, the second quantitative indicator, scene mode information, and power allocation strategy in real time. The interactive interface integrates a strategy configuration card, which encapsulates the power allocation strategy for each scene mode, including energy allocation algorithm, load priority table, and environment adaptation parameters. The strategy configuration card includes parameter snapshots, applicable scene tags, and resource requirement thresholds.

10. A power drive system for an underwater device, used to execute a power drive method for an underwater device, characterized in that, include: The data acquisition unit acquires the energy system parameters, load system parameters, and environmental system parameters of the underwater equipment in real time. The energy system parameters represent the operating status of the energy module, the load system parameters represent the demand status of the load module, and the environmental system parameters represent the characteristic status of the environment in which the equipment is located. Based on preset quantification rules, the data processing unit converts the collected energy system parameters into a first quantitative indicator reflecting the overall energy status, converts the load system parameters into a second quantitative indicator reflecting the urgency of load demand, and introduces scenario mode information that characterizes the current operating type of the equipment. The strategy generation unit calls a preset multi-dimensional association rule library based on the scenario mode information, and generates a power allocation strategy by combining the first quantitative indicator and the second quantitative indicator. The multi-dimensional association rule library includes preset energy supply rules, load allocation rules and operation guarantee rules. The dynamic execution unit sends energy output instructions to the energy module and power allocation instructions to the load module according to the power allocation strategy; it collects feedback parameters after the instructions are executed in real time, calculates energy output deviation and load operation effect deviation; when the deviation meets the preset deviation conditions, it adjusts the energy output ratio or load power quota based on the preset correction rules until the deviation returns to the preset deviation range.

Images