Intelligent water meter auxiliary debugging device and method, electronic equipment and storage medium

Abstract

The one or more embodiments of the specification relate to the field of intelligent water meter manufacturing, in particular to an intelligent water meter auxiliary debugging device, method, electronic equipment and storage medium. The auxiliary debugging device comprises a connection device configured to establish a communication connection with the intelligent water meter to be debugged; an auxiliary debugging host configured to connect the intelligent water meter through the connection device and run a debugging program, wherein the debugging program comprises a program auxiliary subprogram, a strategy auxiliary subprogram and an operation auxiliary subprogram. The beneficial technical effects of the one or more embodiments of the specification include: the intelligent water meter auxiliary debugging device is connected with the intelligent water meter, and the debugging program run by the intelligent water meter auxiliary debugging device improves the automation level of the debugging process and improves the efficiency of the intelligent water meter debugging.

Description

Technical Field

[0001] This specification relates to one or more embodiments in the field of smart water meter manufacturing, specifically to smart water meter auxiliary debugging devices, methods, electronic devices, and storage media. Background Technology

[0002] A smart water meter is a new type of water meter that uses modern microelectronics, sensing, and smart IC card technology to measure water consumption and transmit and settle water usage data. Compared to traditional water meters, which generally only have the functions of flow rate collection and mechanical pointer display, smart meters have more practical functions. For example, they can record and electronically display water consumption; they can control water consumption according to agreements and automatically calculate water fees based on tiered pricing; they can also store water usage data; and they can transmit data and settle transactions via IC cards, offering convenient transactions, accurate calculations, and the ability to use banks for settlement. Summary of the Invention

[0003] This specification describes one or more embodiments of an auxiliary debugging device, method, electronic device, and storage medium for smart water meters, which are used to assist in the debugging process of smart water meters and improve the efficiency of debugging.

[0004] Firstly, embodiments of this specification provide an auxiliary debugging device for smart water meters, comprising: A connection device for establishing a communication connection with the smart water meter to be debugged; An auxiliary debugging host is used to connect to the smart water meter via the connection device and run the auxiliary debugging program. The auxiliary debugging program includes program auxiliary subroutines, strategy auxiliary subroutines, and operation auxiliary subroutines. The program-aided subroutine monitors the writing of debugging programs or receives natural language tasks for writing and debugging programs, and provides reference program snippets. The strategy-assistance subroutine generates a debugging strategy based on the debugging program. The operation assistance subroutine interacts with the smart water meter according to the debugging strategy and obtains the debugging results based on the interacting data.

[0005] Secondly, this specification provides an auxiliary debugging method for smart water meters, including the following steps: Establish a communication connection with the smart water meter to be debugged; The smart water meter is connected via the connection device, and an auxiliary debugging program is run. The auxiliary debugging program includes program auxiliary subroutines, strategy auxiliary subroutines, and operation auxiliary subroutines. The program-aided subroutine monitors the writing of debugging programs or receives natural language tasks for writing and debugging programs, and provides reference program snippets. The strategy-assistance subroutine generates a debugging strategy based on the debugging program. The operation assistance subroutine interacts with the smart water meter according to the debugging strategy and obtains the debugging results based on the interacting data.

[0006] Thirdly, embodiments of this specification provide an electronic device, including a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code stored in the memory to perform the method as described above.

[0007] Fourthly, embodiments of this specification provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described above.

[0008] The beneficial technical effects of one or more embodiments of this specification include: connecting the smart water meter auxiliary debugging device to the smart water meter, and improving the automation level of the debugging process and efficiency of smart water meter debugging by means of the auxiliary debugging program running on the smart water meter auxiliary debugging device. Assisting in the generation of the debugging program through program auxiliary subroutines not only improves the generation efficiency of the debugging program but also reduces the probability of errors in the debugging program to a certain extent. Assisting in the generation of debugging strategies automatically based on the debugging program through strategy auxiliary subroutines helps improve testing efficiency and is more conducive to ensuring the reliability of test results, while reducing the workload of testers.

[0009] Other features and advantages of one or more embodiments of this specification will be disclosed in detail in the following detailed description and accompanying drawings. Attached Figure Description

[0010] The following description, in conjunction with the accompanying drawings, further illustrates this specification: Figure 1 This is a schematic diagram illustrating the application scenario of the intelligent water meter auxiliary debugging device in the embodiments of this specification.

[0011] Figure 2 This is a schematic diagram of the auxiliary debugging host running the debugging program in an embodiment of this specification.

[0012] Figure 3 This is a schematic diagram of the connection device structure in an embodiment of this specification.

[0013] Figure 4This is a schematic diagram of another connecting device structure according to an embodiment of this specification.

[0014] Figure 5 A flowchart of a reference program segment is provided for the program auxiliary subroutines in the embodiments of this specification.

[0015] Figure 6 This is a schematic diagram of the process for generating the program model in an embodiment of this specification.

[0016] Figure 7 This is a schematic diagram of the generation and debugging strategy process in the embodiments of this specification.

[0017] Figure 8 This is a schematic diagram of the auxiliary debugging method for smart water meters in an embodiment of this specification.

[0018] Figure 9 This is a schematic diagram of the electronic device structure according to an embodiment of this specification. Detailed Implementation

[0019] The technical solutions of the embodiments of this specification will be explained and described below with reference to the accompanying drawings. However, the following embodiments are only preferred embodiments of this specification and not all of them. Other embodiments obtained by those skilled in the art based on the embodiments in the implementation methods without creative effort are all within the protection scope of this specification.

[0020] In the following description, terms such as “inner,” “outer,” “upper,” “lower,” “left,” and “right” are used only to facilitate the description of the embodiments and to simplify the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this specification.

[0021] Definitions: Smart water meter The smart water meter 300 referred to in this specification includes water meters with at least the functions of near-field communication, remote transmission, metering, data storage, and automatic valve opening / closing. Near-field communication includes short-range wireless communication methods such as Bluetooth, RFID, and ZigBee, as well as wired communication methods such as USB, RS485, and serial ports. Remote transmission refers to the smart water meter 300's ability to establish communication with a remote server via 3G / 4G / 5G or the Internet of Things. Metering refers to the measurement of the amount of water flowing through the smart water meter 300. Data storage refers to the smart water meter 300's ability to store and retrieve data locally. Automatic valve opening / closing refers to the smart water meter 300 having a solenoid valve and corresponding control module, capable of automatically or according to instructions to perform valve opening or closing operations.

[0022] Auxiliary debugging host The auxiliary debugging host 100 referred to in this specification is a device capable of running programs, interacting with users, and establishing data interaction connections with the connection device 200. The auxiliary debugging host 100 should have a processor, memory, bus, and input / output devices. For example, personal computers, workstations, remote servers, tablet computers, laptops, super mobile personal computers, handheld computers, and personal digital assistants can all serve as auxiliary debugging hosts 100.

[0023] Natural Language Tasks The natural language tasks referred to in this specification encompass a variety of tasks involving the processing and understanding of natural language text using computers. Examples include text classification, information extraction, machine translation, text generation, question answering systems, language models, sentiment analysis, text clustering and text similarity, and language understanding.

[0024] convergence In this specification, convergence means that the natural language model, when responding to the same natural language question, exhibits sufficient similarity to the supervision signal and its response content is relatively stable. The determination of relatively stable response content does not consider any deliberate generation of different responses by the natural language model to avoid repeating answers. When the natural language model's response converges, it indicates that the natural language model has mastered the necessary knowledge 21 and is able to produce the responses required for other steps.

[0025] PLM PLM stands for Pre-trained Language Model. A pre-trained language model is a deep learning model that has been trained on large-scale text data. By learning extensive contextual information and linguistic rules, a pre-trained language model can understand and generate natural language text. Furthermore, based on the knowledge already acquired by the pre-trained language model, it can be used as a classification model, with or without further training. It can quickly serve as a qualified machine learning model for classification or generative tasks. Examples include BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pre-trained Transformer), RoBERTa (Robustly Optimized BERT Pretraining Approach), and XLNet (eXtreme Learning Network).

[0026] Reference program snippet Reference program snippets are relevant program segments used to improve the efficiency of generating debugger 101. A reference program snippet can be one or more complete program statements, or it can be an incomplete statement. Examples include recommended functions, recommended parameters, and recommended conditional statements.

[0027] Debugging results The debugging results involve interacting with the smart water meter 300 according to the debugging program 101 to obtain interactive data, and determining whether the interactive data meets the test pass conditions based on the debugging pass condition judgment statement in the debugging program 101. That is, the debugging program 101 includes not only operation statements for the smart water meter 300, but also interactive data acquisition statements, and judgment statements for whether the debugging has passed. For example, in debugging communication delay, the program first sends and receives data to a specified remote server, then reads the data sending and receiving delay, and finally executes a judgment statement that compares the delay with a set threshold. The output of this judgment statement indicates whether the communication delay debugging has passed.

[0028] Application Scenarios This specification describes one or more embodiments relating to auxiliary debugging devices, methods, electronic devices, and storage media for smart water meters 300, used for debugging and quality inspection of smart water meters 300 before they leave the factory, and for troubleshooting or inspection of smart water meters 300 that have already been installed in homes. Please refer to the appendix. Figure 1 One or more smart water meters 300 are connected to an auxiliary debugging host 100 via a connection device 200, and the debugging program 101 runs on the auxiliary debugging host 100. Alternatively, the debugging program 101 runs on a dedicated host, but data interaction is established between the auxiliary debugging host 100 and the dedicated host. The debugging program 101 can be fixed or edited and compiled on-site by debugging personnel, then imported into the smart water meter 300 and run on the smart water meter 300. When used to develop the control program for the smart water meter 300, the debugging program 101 is generated to determine the function calls and parameters of the control program. In this case, the preferred implementation of the debugging program 101 is that it is edited and compiled on-site by debugging personnel and imported into the smart water meter 300 for execution. The debugging personnel adjust or determine the debugging program 101 by observing the operation of the smart water meter 300, and then adjust or determine the control program of the smart water meter 300 based on the debugging program 101. When used for factory testing, a preferred implementation of the debugging program 101 is to use a pre-compiled, fixed program, connect the smart water meter 300 to be tested via the connection device 200, and then execute the debugging program 101 once. The debugging program 101 automatically acquires the interaction data with the smart water meter 300 and automatically determines whether the debugging is successful. If the debugging is successful, the test is considered successful; otherwise, if the debugging is unsuccessful, the test is considered unsuccessful.

[0029] This manual provides an auxiliary debugging device for a smart water meter 300. Please refer to the appendix again. Figure 1 ,include: The connecting device 200 is used to establish a communication connection with the smart water meter 300 to be debugged; The auxiliary debugging host 100 is used to connect to the smart water meter 300 via the connection device 200 and run the debugging program 101. Please see the appendix Figure 2 The debugging program 101 includes a program assistance subroutine 1011, a strategy assistance subroutine 1012, and an operation assistance subroutine 1013. The program-assistant subroutine 101 monitors the writing or receiving of natural language tasks by the debugging program 101, and provides reference program snippets. The strategy assistance subroutine 1012 generates a debugging strategy based on the debugging program 101. The operation assistance subroutine 1013 interacts with the smart water meter 300 according to the debugging strategy and obtains the debugging results based on the interactive data.

[0030] On the other hand, in another embodiment, the connection device 200 includes a power supply module 201, a tap module 202, and several wireless communication modules 203. Please refer to the appendix. Figure 3 The tapping module 202 has a first communication interface for connecting to the auxiliary debugging host 100 and several second communication interfaces for connecting to the wireless communication module 203. The wireless communication module 203 is used to establish a communication connection with the smart water meter 300. Please refer to the appendix. Figure 4 , Figure 4The diagram illustrates a specific implementation of the tapping module 202. In this implementation, the tapping module 202 includes a conversion chip 2021 and several tapping chips 2022, all of which are connected to the conversion chip 2021. Each tapping chip 2022 is connected to several wireless communication modules 203. The conversion chip 2021 is used to convert communication protocols and expand the number of communication interfaces; for example, it can convert one USB communication interface into four serial communication interfaces. The tapping chip 2022 is used to expand a single communication interface into multiple communication interfaces, such as expanding a single serial communication interface into multiple serial communication interfaces. For example, it can expand a single serial communication interface into four serial communication interfaces. Each serial communication interface is connected to one wireless communication module 203. The exemplary wireless communication module 203 is a Bluetooth communication module. For example, the conversion chip 2021 is implemented using an SL2.1A chip, the tapping chip 2022 is implemented using a CH344Q chip, and the Bluetooth communication module is implemented using a CH582F chip. The solutions described in this specification can also be implemented using other types of chips disclosed in this field.

[0031] On the other hand, in another implementation, please refer to the appendix. Figure 5 When the program auxiliary subroutine 1011 provides a reference program fragment, it performs the following steps: Step 102) Input the read program segment of the debug program 101 or the received natural language task into the pre-generated program generation model and obtain the response of the program generation model; Step 104) Generate a reference program fragment based on the response and display the reference program fragment.

[0032] Using natural language tasks can improve the efficiency of the program-aided subroutine 1011 in providing reference program fragments, and it is closer to the language habits of debuggers, which can effectively improve the writing efficiency of the debugging program 101.

[0033] On the other hand, in another embodiment, the program generation model is generated by the program auxiliary subroutine 1011, see Appendix. Figure 6 When generating the program generation model, the program auxiliary subroutine 1011 performs the following steps: Step 202) Receive the sample debugging program, and construct a program to generate a learning task based on the sample debugging program; Step 204) Submit the learning task generated by the program to the pre-connected PLM, monitor the output of the PLM until the output of the PLM converges; Step 206) Obtain the program generation model based on the PLM.

[0034] The sample debugger is an unlabeled debugger 101. You can directly use the existing debugger 101. After creating a learning task, input it into the PLM, and the PLM will learn automatically. The learning task is a natural language task, for example: "Learn the following sample debugger: <Debugger 101 segment>". The learning task can be instruction learning or command learning, with a preferred implementation combining both. For example: "Learn the following sample debugger and try to fill in the blanks: <Debugger 101 segment>. Try to fill in the blanks in the following program: <Debugger 101 segment>__<blank>__<Debugger 101 segment>. Reference program segments: <Reference program segment 1>, <Reference program segment 2>, <Reference program segment 3>". The question sets the content to be filled in. The debugger 101 segments before and after the blanks serve as prompts and guidance for the PLM to answer, while the reference program segments further assist the PLM in answering, guiding the PLM to learn more quickly; this is instruction learning. The instruction to “learn the following sample debugging program and try to fill in the blanks: <Debug program 101 segment>” is an example of instructional learning, which guides the PLM to learn by requiring it to complete a task.

[0035] On the other hand, in another implementation, please refer to the appendix. Figure 7 When the strategy assistance subroutine 1012 generates a debugging strategy based on the debugging program 101, it performs the following steps: Step 302) Receive sample debugging strategy, the sample debugging strategy includes a debugger segment 101 and an associated debugging strategy; Step 304) Establish a neural network model, use the debugging strategy as a label, train and test the neural network model using the sample debugging strategy, and obtain a strategy generation model based on the neural network model; Step 306) Establish a program segmentation model, wherein the program segmentation model divides the debug program 101 into several debug program 101 segments; Step 308) Read the debug program 101, input the debug program 101 into the program segmentation model, and obtain a number of corresponding debug program segments 101; Step 310) Generate a debugging strategy based on the response of the strategy generation model to the debugging program segment 101.

[0036] The sample debugging strategy records the processing strategies for the loop body, constant range processing strategy, and performance testing processing strategy in the debugging program 101. The loop body processing strategy involves splitting the loop condition into several segments, with each segment executed on a smart water meter 300, thereby accelerating the execution efficiency of the loop body. The constant range processing strategy involves dividing the constant range into several range segments, with each range segment executed on a smart water meter 300, thereby accelerating the debugging efficiency of the constant range. Within each range segment, several sample points are collected at equal intervals to set the constant value, allowing the debugger to quickly obtain the impact of the constant value on the smart water meter 300. The performance testing processing strategy includes strategies for handling communication latency, communication packet loss rate, and inversion. The communication latency processing strategy involves multiple communications to the target address, calculating the average communication latency as the communication latency. The communication packet loss rate processing strategy involves multiple communications to the target address, calculating the average packet loss rate as the communication packet loss rate. The strategy for handling the reversal is to repeatedly reverse the water flow through the smart water meter 300, causing the smart water meter 300 to reverse, read the state changes of the smart water meter 300, and determine whether the smart water meter 300 can correctly make the state changes under reversal.

[0037] On the other hand, this manual provides an auxiliary debugging method for the smart water meter 300. Please refer to the appendix. Figure 8 The steps include: Step 402) Establish a communication connection with the smart water meter 300 to be debugged; Step 404) Connect the smart water meter 300 via the connecting device 200 and run the debugging program 101. The debugging program 101 includes a program auxiliary subroutine 1011, a strategy auxiliary subroutine 1012, and an operation auxiliary subroutine 1013. The program-assistant subroutine 101 monitors the writing or receiving of natural language tasks by the debugging program 101, and provides reference program snippets. The strategy assistance subroutine 1012 generates a debugging strategy based on the debugging program 101. The operation assistance subroutine 1013 interacts with the smart water meter 300 according to the debugging strategy and obtains the debugging results based on the interactive data.

[0038] On the other hand, in another implementation, when the program helper subroutine 1011 provides a reference program fragment, it performs the following steps: Input the program segment already written in the debug program 101 or the received natural language task into the pre-generated program generation model to obtain the response of the program generation model; A reference program fragment is generated based on the response, and the reference program fragment is displayed.

[0039] On the other hand, in another embodiment, the program generation model is generated by the program auxiliary subroutine 1011. When generating the program generation model, the program auxiliary subroutine 1011 performs the following steps: Receive a sample debugging program, and build a program to generate a learning task based on the sample debugging program; The program generates a learning task and submits it to a pre-connected PLM. The output of the PLM is monitored until the output of the PLM converges. The program generation model is obtained based on the PLM.

[0040] Please see Figure 9 The diagram shown is a structural schematic of an electronic device provided in an embodiment of this specification.

[0041] like Figure 9 As shown, the electronic device 1100 may include: at least one processor 1101, at least one network interface 1104, a user interface 1103, a memory 1105, and at least one communication bus 1102. The communication bus 1102 can be used to connect and communicate with the various components mentioned above. The user interface 1103 may include buttons, and optionally, may also include standard wired or wireless interfaces. The network interface 1104 may include, but is not limited to, a Bluetooth module, an NFC module, or a Wi-Fi module. The processor 1101 may include one or more processing cores. The processor 1101 connects to various parts within the electronic device 1100 using various interfaces and lines, and performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1105, and by calling data stored in the memory 1105. Optionally, the processor 1101 may be implemented using at least one hardware form of DSP, FPGA, or PLA. The processor 1101 may integrate one or more combinations of CPU, GPU, and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content that the display screen needs to show; and the modem is used for wireless communication.

[0042] It is understandable that the aforementioned modem may not be integrated into the processor 1101, but may be implemented using a separate chip.

[0043] The memory 1105 may include RAM or ROM. Optionally, the memory 1105 may include a non-transitory computer-readable medium. The memory 1105 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 1105 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 1105 may also be at least one storage device located remotely from the aforementioned processor 1101. As a computer storage medium, the memory 1105 may include an operating system, a network communication module, a user interface module, and application programs. The processor 1101 may be used to call the application programs stored in the memory 1105 and execute the methods in one or more of the above embodiments.

[0044] This specification also provides a computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform one or more steps in the above embodiments. If the constituent modules of the above-described electronic device are implemented as software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium.

[0045] Where there is no conflict, the technical features in this embodiment and implementation scheme can be combined arbitrarily.

[0046] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this specification are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., Digital Versatile Discs (DVDs)), or semiconductor media (e.g., Solid State Disks (SSDs)).

[0047] When implemented through hardware or firmware, the aforementioned method flow is programmed into the hardware circuit to obtain the corresponding hardware circuit structure and achieve the corresponding function. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit, whose logic function is determined by the user programming the device. Designers can program a digital system onto a PLD themselves, eliminating the need for chip manufacturers to design and fabricate dedicated integrated circuit chips. Furthermore, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly implemented using "logic compiler" software, similar to the software compiler used in program development. The original code before compilation must also be written in a specific programming language, called a Hardware Description Language (HDL). There is not just one HDL, but many. Those skilled in the art should understand that by simply performing some logic programming on the method flow using one of the aforementioned hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0048] The embodiments described above are merely preferred embodiments of this specification and are not intended to limit the scope of this specification. Any modifications and improvements made by those skilled in the art to the technical solutions of this specification without departing from the spirit of this specification should fall within the protection scope defined by the claims of this specification.

Claims

1. A smart water meter auxiliary debugging device, characterized in that, include: A connection device for establishing a communication connection with the smart water meter to be debugged; An auxiliary debugging host is used to connect to the smart water meter via the connection device and run the debugging program. The debugging program includes program auxiliary subroutines, strategy auxiliary subroutines, and operation auxiliary subroutines. The program-aided subroutine monitors the writing of debugging programs or receives natural language tasks for writing and debugging programs, and provides reference program snippets. The strategy-assistance subroutine generates a debugging strategy based on the debugging program. The operation assistance subroutine interacts with the smart water meter according to the debugging strategy and obtains the debugging results based on the interactive data. When the strategy-assistance subroutine generates a debugging strategy based on the debugging program, it performs the following steps: Receive sample debugging strategy, the sample debugging strategy includes a debugging program segment and an associated debugging strategy; A neural network model is established, the debugging strategy is used as a label, the neural network model is trained and tested using the sample debugging strategy, and a strategy generation model is obtained based on the neural network model. A program segmentation model is established, which divides the debug program into several debug program segments; Read the debugger, input the debugger into the program segmentation model, and obtain several corresponding debugger segments; Based on the strategy, a debugging strategy is generated to respond to the debugging program segment.

2. The intelligent water meter auxiliary debugging device according to claim 1, characterized in that, The connection device includes a power supply module, a tap module, and several wireless communication modules. The tap module has a first communication interface for connecting to the auxiliary debugging host and several second communication interfaces for connecting to the wireless communication modules. The wireless communication modules are used to establish a communication connection with the smart water meter.

3. The intelligent water meter auxiliary debugging device according to claim 1 or 2, characterized in that, When the program-assistant subroutine provides a reference program fragment, it performs the following steps: Input the read debug program pre-written program segment or the received natural language task into the pre-generated program generation model and obtain the response from the program generation model; A reference program fragment is generated based on the response, and the reference program fragment is displayed.

4. The intelligent water meter auxiliary debugging device according to claim 3, characterized in that, The program generation model is generated by the program-aided subroutine. When generating the program generation model, the program-aided subroutine performs the following steps: Receive a sample debugging program, and build a program to generate a learning task based on the sample debugging program; The program generates a learning task and submits it to a pre-connected PLM. The output of the PLM is monitored until the output of the PLM converges. The program generation model is obtained based on the PLM.

5. A method for auxiliary debugging of smart water meters, based on the auxiliary debugging device for smart water meters as described in claim 1, characterized in that, Including the following steps: Establish a communication connection with the smart water meter to be debugged; Connect the smart water meter via a connection device and run the debugging program. The debugging program includes program auxiliary subroutines, strategy auxiliary subroutines, and operation auxiliary subroutines. The program-aided subroutine monitors the writing of debugging programs or receives natural language tasks for writing and debugging programs, and provides reference program snippets. The strategy-assistance subroutine generates a debugging strategy based on the debugging program. The operation assistance subroutine interacts with the smart water meter according to the debugging strategy and obtains the debugging results based on the interacting data.

6. The auxiliary debugging method for smart water meters according to claim 5, characterized in that, When the program-assistant subroutine provides a reference program fragment, it performs the following steps: Input the read debug program pre-written program segment or the received natural language task into the pre-generated program generation model and obtain the response from the program generation model; A reference program fragment is generated based on the response, and the reference program fragment is displayed.

7. The auxiliary debugging method for smart water meters according to claim 6, characterized in that, The program generation model is generated by the program-aided subroutine. When generating the program generation model, the program-aided subroutine performs the following steps: Receive a sample debugging program, and build a program to generate a learning task based on the sample debugging program; The program generates a learning task and submits it to a pre-connected PLM. The output of the PLM is monitored until the output of the PLM converges. The program generation model is obtained based on the PLM.

8. An electronic device, characterized in that, Including the processor and memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading executable program code stored in the memory, for performing the method as described in any one of claims 5 to 7.

9. A computer-readable storage medium, characterized in that, It stores a computer program thereon, which, when executed by a processor, implements the method as described in any one of claims 5 to 7.

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