Operating method and system for programmable modularized distribution network uninterruptible power operation robot

Abstract

The invention provides an operation method and system for a programmable modular distribution network uninterrupted operation robot, and the method comprises the steps: receiving and analyzing a distribution network operation task instruction, so as to deconstruct the distribution network operation task instruction into a standardized task sequence composed of a plurality of atomic operations; based on the standardized task sequence, dynamically matching and generating an executable modularized operation program from a virtualized module function library, and pre-storing program modules corresponding to logic functions of a plurality of physical function modules in the module function library; and executing the modular operation program, sending an instruction to a robot system, and controlling a robot to automatically complete each atomic operation in the standardized task sequence according to program logic. According to the invention, the operation threshold and dependence on a specific hardware platform can be reduced while the safety and efficiency of non-power-cut operation can be obviously improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of robot operation, and in particular to a programmable modular distribution network live-line operation robot operation method and system. BACKGROUND

[0002] Live-line operation of distribution network is a key measure to improve power supply reliability. However, traditional manual live-line operation mode faces inherent challenges such as high labor intensity, high safety risk, and high skill requirement for operation personnel. In recent years, robot technology has been introduced into this field to replace manual operation. However, existing robot operation still relies on remote manual control by operators or execution of a single fixed process. This method lacks intelligence and cannot flexibly adapt to complex field operation environment. Moreover, each new task needs to be reprogrammed and deployed, resulting in low operation efficiency and difficulty in large-scale application.

[0003] In addition, existing robot operation systems generally have the problem of tight coupling of software and hardware. The operation method is deeply bound to specific hardware such as mechanical arm and end effector, and lacks universality and flexibility. When the operation task changes or the hardware module is upgraded, the entire system needs to be redeveloped and debugged, resulting in huge repeated development cost. Therefore, the industry urgently needs a robot operation method that can break the hardware bondage, has autonomous decision-making and adaptive ability, and can quickly deploy new tasks through software programming, so as to realize the evolution of live-line operation of distribution network to high intelligence and flexibility. SUMMARY

[0004] The present application aims to provide a programmable modular distribution network live-line operation robot operation method and system, which aims to solve at least one problem in the background art.

[0005] In a first aspect, the present application provides a programmable modular distribution network live-line operation robot operation method, which comprises:

[0006] receiving and analyzing a distribution network operation task instruction to decompose it into a standardized task sequence composed of multiple atomic operations;

[0007] based on the standardized task sequence, dynamically matching and generating an executable modular operation program from a virtualized module function library, the module function library pre-storing program modules corresponding to the logical functions of multiple physical function modules;

[0008] executing the modular operation program and sending instructions to the robot system to control the robot to automatically complete each atomic operation in the standardized task sequence according to the program logic.

[0009] In some embodiments, the step of dynamically matching and generating an executable modular operation program comprises:

[0010] mapping the standardized task sequence with the virtualized module function library to identify required program modules;

[0011] based on predefined job logic, the identified program modules are arranged and linked to form a complete workflow program.

[0012] In some embodiments, during the execution of the modular job program, the method further comprises:

[0013] receiving a multi-modal data stream from a robot perception system;

[0014] based on the multi-modal data stream, performing state verification at a specific node of program execution to determine whether the actual state of the current job scene is consistent with the expected state of the program;

[0015] if consistent, the program continues to execute, and if inconsistent, a dynamic decision-making process is triggered.

[0016] In some embodiments, the step of triggering a dynamic decision-making process comprises:

[0017] calling an adaptive correction subprogram pre-installed in the modular job program and corresponding to the current atomic operation to fine-tune the execution path or parameters;

[0018] or, suspending the execution of the current program and generating a decision request containing on-site data and a proposed solution to be sent to a remote monitoring end, and after receiving a remote confirmation or correction instruction, resuming program execution.

[0019] In some embodiments, the method further comprises, before the step of receiving and parsing a distribution network job task instruction:

[0020] loading the standardized task sequence and the corresponding modular job program in a digital twin environment for simulation running;

[0021] based on the results of the simulation running, optimizing key parameters in the modular job program, and locking the optimized program as the final executable version.

[0022] In some embodiments, the method further comprises:

[0023] after the completion of the physical job task, collecting all timing data in the actual execution process, including perception data, program instructions and state verification results;

[0024] comparing and analyzing the timing data with the pre-play data in the digital twin environment to iteratively optimize the logic and parameters of the modular job program or the adaptive correction subprogram.

[0025] In a second aspect, the present application provides a programmable modular power distribution network operation robot operating system, comprising:

[0026] an instruction parsing module configured to receive and parse a power distribution network operation task instruction to decompose it into a standardized task sequence composed of multiple atomic operations;

[0027] a matching module configured to dynamically match and generate an executable modular operation program from a virtualized module function library based on the standardized task sequence, the module function library pre-storing program modules corresponding to the logical functions of multiple physical function modules;

[0028] an instruction execution module configured to execute the modular operation program and send instructions to the robot system to control the robot to automatically complete each atomic operation in the standardized task sequence according to the program logic.

[0029] In a third aspect, the present application provides a storage medium storing one or more programs, which, when executed by a processor, implement the programmable modular power distribution network operation robot operating method described above.

[0030] In a fourth aspect, the present application provides an electronic device comprising a memory and a processor, wherein:

[0031] the memory is configured to store a computer program;

[0032] the processor is configured to execute the computer program stored on the memory to implement the programmable modular power distribution network operation robot operating method described above.

[0033] Compared with the prior art, the present application has the following advantages:

[0034] The present application decomposes specific power distribution network operation tasks into standardized task sequences, and dynamically generates, verifies and optimizes executable programs based on a virtualized module function library, thereby realizing software definition and flexible deployment of robot operation processes, effectively improving the intelligent level and adaptability of operation; the method constructs a complete closed loop from program generation, real-time state verification to dynamic decision-making, and integrates digital twin pre-play and iterative learning mechanisms, so that the robot can autonomously cope with the uncertainty of complex field environment, ultimately significantly improving the safety and efficiency of non-stop operation while reducing the operation threshold and dependence on specific hardware platforms. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 A flowchart of a programmable modular power distribution network operation robot operating method according to an embodiment of the present application;

[0036] Figure 2 A structural schematic diagram of a programmable modular network distribution non-power operation robot operating system is provided for an embodiment of the present application.

[0037] The following specific embodiments will further illustrate the present application in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0038] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of the present application. Unless otherwise defined, the technical terms or scientific terms used herein should be understood as their common meanings to those of ordinary skill in the art to which the present application belongs. The words such as “comprise” and the like used herein mean that the elements or objects before the words encompass the elements or objects listed after the words and their equivalents, and do not exclude other elements or objects.

[0039] As shown in Figure 1 An embodiment of the present application provides a programmable modular network distribution non-power operation robot operating method, which comprises steps S101 to S103, wherein:

[0040] Step S101: receiving and analyzing a network distribution operation task instruction to decompose it into a standardized task sequence composed of multiple atomic operations;

[0041] This step realizes the digitization and standardization of complex operation tasks by decomposing high-order, fuzzy natural language or simple instructions into a standardized task sequence composed of indivisible “atomic operations”.

[0042] In addition, in some embodiments, before analyzing the instruction, the standardized task sequence and the corresponding modular operation program need to be loaded in the digital twin environment for simulation running; and based on the results of the simulation running, the key parameters in the modular operation program are optimized, and the optimized program is locked as the final executable version.

[0043] Through the pre-rehearsal optimization driven by the digital twin, potential problems are solved before the entity operation. Before the real execution, the operation program is simulated and run in the virtual environment, which can discover logical conflicts, unreasonable parameters or possible collision interference problems in advance, and optimize the key parameters.

[0044] Step S102: Based on the standardized task sequence, dynamically match and generate an executable modular job program from a virtualized module function library, wherein the module function library pre-stores program modules corresponding to the logical functions of various physical function modules.

[0045] It should be noted that by dynamically matching and generating a job program from the "virtualized module function library", the software definition and flexible combination of the robot job capability are realized. In addition, the capability of the physical function module is abstracted as a reusable software program module, so that when facing different job tasks, there is no need to replace or redesign the hardware, and only through the modular arrangement at the software level, a new job program can be quickly generated, greatly improving the flexibility and adaptability of the system, and reducing the research and development cost of function expansion and iteration.

[0046] In addition, in some embodiments, during the dynamic matching process, the standardized task sequence needs to be mapped with the virtualized module function library to identify the required program modules; based on the pre-defined job logic, the identified program modules are arranged and linked to form a complete workflow program.

[0047] By accurately mapping the standardized task sequence with the virtual module library, the required program modules are automatically identified, and then arranged into a complete workflow according to the pre-set job logic. This process converts complex robot programming work into efficient and reusable modular splicing, thereby greatly reducing the difficulty and time cost of program development, ensuring the structural specification and logical correctness of the generated program under different job tasks, and realizing the automatic, rapid and accurate conversion from "task demand" to "executable code".

[0048] Step S103: Execute the modular job program and send instructions to the robot system to control the robot to automatically complete each atomic operation in the standardized task sequence according to the program logic.

[0049] In this step, by executing the generated modular job program, the robot system is controlled to automatically complete all atomic operations according to the predetermined program logic, realizing the fundamental change from "human driving" to "program driving". It ensures the high standardization and consistency of the job process, eliminates the uncertainty and quality fluctuations caused by manual operation, and at the same time, changes the operation personnel from high-risk and high-intensity field operation to task supervisors and managers, thereby improving the operation quality and reliability while greatly ensuring the safety of personnel and power grid.

[0050] Further, in some embodiments, during execution of the modular job procedure, the method further comprises: receiving a multi-modal data stream from a robot perception system; based on the multi-modal data stream, performing state verification at a specific node of the procedure execution to determine whether an actual state of a current job scene is consistent with an expected state of the procedure; if consistent, the procedure continues to execute, and if inconsistent, triggering a dynamic decision-making process.

[0051] By introducing real-time multi-modal data streams and performing state verification at key nodes of procedure execution, a closed-loop feedback mechanism is constructed. This enables the robot to no longer blindly execute preset instructions, but to perceive the environment, understand the state, and determine whether the procedure execution meets expectations based on this, thereby upgrading static automation to a dynamic execution process with preliminary intelligence that can cope with on-site uncertainties, significantly improving the reliability and intelligence level of the job.

[0052] Specifically, the step of triggering a dynamic decision-making process includes:

[0053] calling an adaptive correction sub-procedure pre-stored in the modular job procedure and corresponding to the current atomic operation to fine-tune the execution path or parameters;

[0054] Alternatively, suspending execution of the current procedure and generating a decision request containing on-site data and a proposed solution, which is sent to a remote monitoring end, and after receiving a remote confirmation or correction instruction, resuming procedure execution.

[0055] For foreseeable deviations, autonomous fine-tuning is performed by calling the built-in adaptive sub-procedure, ensuring the smoothness and efficiency of the job; for complex or unpredictable abnormalities, on-site data and proposed solutions are reported to a remote person for decision-making through human-machine collaboration. This design ensures the autonomy of the robot in most cases, while retaining the highest decision-making authority of humans in critical issues, achieving an optimal balance between safety, efficiency, and autonomy.

[0056] Further, in some embodiments, after completion of the physical job task, all time-series data during actual execution are collected, including perception data, procedure instructions, and state verification results; the time-series data are compared and analyzed with pre-play data in the digital twin environment to iteratively optimize the logic and parameters of the modular job procedure or the adaptive correction sub-procedure.

[0057] By collecting the full amount of data of entity operation and comparing and analyzing the pre-play data in digital twin, the differences between the simulation environment and the real world and the deficiencies of the program itself can be accurately found. Using these data to iteratively optimize the operation program and the correction subprogram makes the execution strategy of the system more and more accurate and more and more adaptive to the real complex environment, and finally makes the whole operation method have the evolution ability of learning and improving from practical experience.

[0058] In summary, according to the programmable modular power distribution operation robot operation method described above, by decomposing specific power distribution operation tasks into standardized task sequences and dynamically generating, verifying and optimizing executable programs based on a virtualized module function library, the software definition and flexible deployment of the robot operation process are realized, and the intelligent level and adaptability of the operation are effectively improved. The method builds a complete closed loop from program generation, real-time state verification to dynamic decision making, and integrates digital twin pre-play and iterative learning mechanism, so that the robot can autonomously cope with the uncertainty of complex site environment, and finally significantly improve the safety and efficiency of non-stop power operation while reducing the operation threshold and dependence on specific hardware platforms.

[0059] As shown in Figure 2 An embodiment of the present application provides a programmable modular power distribution operation robot operation system, which comprises:

[0060] An instruction analysis module 10 is configured to receive and analyze a power distribution operation task instruction to decompose it into a standardized task sequence composed of multiple atomic operations.

[0061] A matching module 20 is configured to dynamically match and generate an executable modular operation program from a virtualized module function library based on the standardized task sequence, wherein the module function library pre-stores program modules corresponding to the logical functions of multiple physical function modules.

[0062] An instruction execution module 30 is configured to execute the modular operation program and send instructions to the robot system to control the robot to automatically complete each atomic operation in the standardized task sequence according to the program logic.

[0063] In another aspect, the present application also provides a storage medium having one or more programs stored thereon, which, when executed by a processor, implement the programmable modular power distribution operation robot operation method described above.

[0064] In another aspect, the present application also provides an electronic device comprising a memory and a processor, wherein the memory is configured to store a computer program, and the processor is configured to execute the computer program stored on the memory to implement the programmable modular power distribution operation robot operation method described above.

[0065] Those skilled in the art will appreciate that the logic and / or steps represented in the flow diagrams, or otherwise described herein, for example, can be thought of as a list of executable instructions for implementing logic functions, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor- containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions, or a combination thereof. For the purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

[0066] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

[0067] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following technologies, known in the art, or combinations thereof, can be used: a discrete logic circuit having logic gates for implementing logic functions upon data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), and / or the like.

[0068] While the embodiments of the application have been illustrated and described in detail, it will be clear to those skilled in the art that various modifications and changes can be made to the embodiments without departing from the scope and spirit of the application as described in the claims. Moreover, the application described herein can have other embodiments and be practiced or carried out in various ways.

Claims

1. A programmable modular power distribution network work without power robot operation method, characterized in that, The method comprises: receiving and parsing a power distribution task instruction to deconstruct it into a standardized task sequence composed of multiple atomic operations; based on the standardized task sequence, dynamically matching and generating an executable modular job program from a virtualized module function library, the module function library pre-storing program modules corresponding to the logical functions of multiple physical function modules; executing the modular job program and sending instructions to the robot system to control the robot to automatically complete each atomic operation in the standardized task sequence according to the program logic.

2. The programmable modular network commissioning robot operating method of claim 1, wherein, The step of dynamically matching and generating an executable modular job program comprises: mapping the standardized task sequence with the virtualized module function library to identify the required program modules; based on the pre-defined job logic, arranging and linking the identified multiple program modules to form a complete workflow program.

3. The programmable modular network commissioning robot operating method of claim 2, wherein, During the execution of the modular job program, the method further comprises: receiving a multi-modal data stream from the robot perception system; based on the multi-modal data stream, performing state verification at a specific node of program execution to determine whether the actual state of the current job scene is consistent with the expected state of the program; if consistent, the program continues to execute, and if inconsistent, a dynamic decision-making process is triggered.

4. The programmable modular network commissioning robot operating method of claim 3, wherein, The step of triggering a dynamic decision-making process comprises: calling an adaptive correction sub-program pre-stored in the modular job program and corresponding to the current atomic operation to fine-tune the execution path or parameters; or, pausing the execution of the current program and generating a decision request containing on-site data and a proposed solution, which is sent to the remote monitoring end, and after receiving the remote confirmation or correction instruction, the program execution is resumed.

5. The programmable modular network commissioning robot operating method of claim 4, wherein, The step of receiving and parsing a power distribution task instruction further comprises: loading the standardized task sequence and the corresponding modular job program in the digital twin environment for simulation running; based on the results of the simulation running, optimizing the key parameters in the modular job program, and locking the optimized program as the final executable version.

6. The programmable modular network commissioning robot operating method of claim 5, wherein, The method further comprises: after the completion of the entity job task, collecting all the timing data in the actual execution process, including perception data, program instructions and state verification results; comparing and analyzing the timing data with the pre-play data in the digital twin environment to iteratively optimize the logic and parameters of the modular job program or the adaptive correction sub-program.

7. A programmable modular power distribution network work without electricity robot operating system, characterized in that, The system comprises: an instruction parsing module for receiving and parsing a power distribution task instruction to deconstruct it into a standardized task sequence composed of multiple atomic operations; a matching module for dynamically matching and generating an executable modular job program from a virtualized module function library based on the standardized task sequence, the module function library pre-storing program modules corresponding to the logical functions of multiple physical function modules; The instruction execution module is configured to execute the modular operation program and send instructions to the robot system to control the robot to automatically complete each atomic operation in the standardized task sequence according to the program logic.

8. A storage medium, characterized by The storage medium stores one or more programs, which are executed by the processor to implement the programmable modular network distribution uninterrupted operation robot operation method according to any one of claims 1-6. 9.An electronic device, comprising a memory and a processor, wherein: The memory is configured to store a computer program; The processor is configured to execute the computer program stored on the memory to implement the programmable modular network distribution uninterrupted operation robot operation method according to any one of claims 1-6.

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