Method, system, equipment and medium for improving manufacturing reliability of complete gateway electric energy meter

Abstract

The invention discloses a method, a system, equipment and a medium for improving the manufacturing reliability of a whole gateway electric energy meter. The method adopted by the invention comprises the steps of deploying a sensor on a production line, recording the defect number and the total production number of each part of the gateway electric energy meter in a process stage, and calculating the initial reliability of each part according to the defect number and the total production number; calculating the initial reliability of the whole gateway electric energy meter based on the initial reliability of each component, and taking the initial reliability as a starting point of dynamic process parameter optimization; analyzing the relationship between the reliability of the parts and the process parameters through a dynamic sensitivity analysis algorithm, and calculating a dynamic sensitivity factor of the process parameters of each part; based on the dynamic sensitivity factor, the component reliability and the process parameters, the process parameters are dynamically adjusted through a dynamic process parameter optimization and reliability iterative improvement algorithm, and the reliability of the component and the whole machine is gradually improved in combination with sensitivity analysis and iterative verification. The manufacturing efficiency and the stability of the product quality are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy meter manufacturing control, and in particular to a method, system, equipment and medium for improving the manufacturing reliability of a gateway electric energy meter. Background Art

[0002] With the widespread adoption of smart grids, gateway energy meters are becoming increasingly complex. They not only perform traditional energy metering functions but also enable power quality analysis, voltage and current monitoring, remote meter reading, load forecasting, and more. The accuracy, stability, and reliability of energy meters directly impact the operational efficiency of the power system, user electricity costs, and power company revenue.

[0003] The overall assembly and debugging of existing gateway electricity meters mainly rely on manual operation and traditional mechanized manufacturing processes. Although the manufacturing process can meet basic production needs at the time, manual operation and mechanical processing can easily lead to product precision errors, especially in the assembly process, which may cause loose connections between electronic components and poor circuit contact, thereby affecting the stability and accuracy of the electricity meter; although the existing control method can ensure a certain product quality, due to its heavy reliance on manual operation, the production efficiency is low and it is difficult to meet the needs of large-scale production; due to the different technical levels and experience of operators, quality differences between batches are prone to occur during the manual manufacturing process, especially in the welding of complex circuits, component installation and other links, inconsistencies are prone to occur, affecting product reliability.

[0004] The above-mentioned existing control methods also rely on experience and manual monitoring, and are easily affected by factors such as changes in the production environment and fluctuations in equipment performance, resulting in unstable component reliability; they ignore the coupling effect between different process parameters, and optimizing a single process parameter may cause other process parameters to fail, affecting the reliability of the entire machine manufacturing. Summary of the Invention

[0005] The present invention provides a method for improving the manufacturing reliability of gateway electric energy meters, so as to solve the problems that the traditional method only relies on static quality assessment, lacks dynamic and real-time data support, and cannot accurately evaluate the reliability of each component at each process stage; in the production process, there is a lack of a systematic and operational method to quantify the impact of process parameters on component reliability, and the reliability improvement of components often relies on empirical judgment rather than scientific and data-driven optimization; it is difficult to achieve dynamic process parameter adjustment, relies on statically set standards, and does not have sufficient flexibility and adaptability when facing different production conditions and equipment changes; there is a lack of real-time monitoring of component reliability and process parameter adjustment feedback mechanism, resulting in insufficient efficiency and accuracy of the optimization process.

[0006] To this end, the present invention adopts the following technical solutions.

[0007] In a first aspect, the present invention provides a method for improving the manufacturing reliability of a gateway electric energy meter, comprising: Sensors are deployed on the production line to record the number of defects and total production quantity of each component of the gateway electricity meter during the process stage. The initial reliability of each component is calculated based on the number of defects and total production quantity. Based on the initial reliability of each component, the initial reliability of the entire gateway electric energy meter is calculated as the starting point for dynamic process parameter optimization; Analyze the relationship between component reliability and process parameters through dynamic sensitivity analysis algorithm, and calculate the dynamic sensitivity factor of each component process parameter; Based on the dynamic sensitivity factor, component reliability and process parameters, the process parameters are dynamically adjusted through dynamic process parameter optimization and reliability iterative improvement algorithm, and combined with sensitivity analysis and iterative verification, the reliability of components and the entire machine is gradually improved.

[0008] Furthermore, the dynamic sensitivity analysis algorithm evaluates the direct impact of process parameter changes on component reliability, takes the derivative of the experimental fitting curve of the control variable, and obtains the partial derivative of component reliability with respect to the process parameter; the partial derivative is multiplied by the ratio of the component process parameter value to the component reliability to standardize the impact and remove the dimension effect, so that the sensitivity of different parameters can be directly compared; a nonlinear influencing factor is then introduced to describe the impact of process parameters when they deviate from the process parameter reference value through an exponential decay function, simulating the nonlinear effect on component reliability when process parameters deviate from the optimal state in actual production; in order to quantify the coupling effect, the interaction between process parameters is considered, and the correlation between each process parameter and other process parameters is calculated by statistical methods, and the covariance and variance between the process parameters are calculated to obtain a standardized interaction weight.

[0009] Furthermore, the calculation formula of the dynamic sensitivity factor is:

[0010] in, Indicates the In the iteration Part No. Dynamic sensitivity factors of process parameters; Indicates the The first iteration Component reliability, initial , Indicates the Iteration No. Initial reliability of each component; Indicates the In the iteration Part No. process parameter values; It represents the partial derivative of component reliability with respect to process parameter value, reflecting the direct impact rate of process parameter change on component reliability. It is obtained by taking the derivative of the fitting curve of the control variable experiment. It represents the relative ratio of process parameters to component reliability and is used to normalize the partial derivatives; It represents the nonlinear influence factor, reflecting the attenuation of the influence when the process parameters deviate from the reference value of the formula parameters. It is reflected by the exponential attenuation function. The greater the deviation, the smaller the influence. Indicates the Part No. Reference values of process parameters; Represents the attenuation coefficient, which controls the attenuation speed; Represents the weighted sum of the interactive effects of process parameters, taking into account the coupling effects between process parameters; Indicates the and The interaction weights between the process parameters.

[0011] Furthermore, the dynamic process parameter optimization and reliability iterative improvement algorithm calculates the adjustment amount of the process parameters based on the dynamic sensitivity factor and component reliability, and reasonably determines the adjustment range of the process parameters; by calculating the unreliability of the current component reliability, that is, 1 minus the current component reliability value, reflecting the improvement space of the component reliability, multiplying the unreliability by the dynamic sensitivity factor, and obtaining a preliminary adjustment range as the numerator. In order to avoid excessive adjustment or instability due to excessive sensitivity, the square of the dynamic sensitivity factor and a very small positive number are introduced into the denominator for normalization processing to keep the process parameter adjustment amount within a controllable range; at the same time, an adjustment coefficient is introduced to allow the adjustment step size to be controlled according to actual needs, such as using a smaller step size in the early stage to ensure stability, and increasing the step size in the later stage to accelerate optimization.

[0012] Furthermore, the calculation formula of the process parameter adjustment amount is as follows:

[0013] in, Indicates the The first iteration Part No. Amount of process parameter adjustment; represents the adjustment coefficient; Indicates the The unreliability of the reliability of each component; Indicates the The first iteration Reliability of individual components; represents the normalized denominator; Represents a very small positive number; Indicates the In the iteration Part No. The dynamic sensitivity factor of each process parameter.

[0014] Furthermore, the dynamic process parameter optimization and iterative reliability improvement algorithm updates the process parameter value based on the current process parameter value and the process parameter adjustment amount. To ensure that the adjusted process parameter is physically feasible, a boundary constraint is introduced. The sum of the current process parameter value and the process parameter adjustment amount is compared with the maximum allowable value of the process parameter, and the smaller value is taken. The sum is then compared with the minimum allowable value of the process parameter, and the larger value is taken as the final updated process parameter value. This ensures that the adjusted parameter does not exceed the limitations of equipment capabilities or material properties, thereby ensuring the practicality of the optimization. further updating the reliability of the component based on the updated process parameter values; The adjusted process parameters directly reflect changes in component reliability. The actual process parameter changes in this iteration are calculated, that is, the updated process parameter value minus the process parameter value before adjustment, divided by the reference value of the process parameter, to obtain a dimensionless relative change amplitude. This is multiplied by the corresponding dynamic sensitivity factor to quantify the contribution of the process parameters to component reliability. To ensure that component reliability does not exceed 1 and to simulate the saturation effect in reality, unreliability is introduced (that is, 1 minus the current component reliability), so that the component reliability improvement amplitude gradually decreases when it approaches perfection.

[0015] Furthermore, the dynamic process parameter optimization and reliability iterative improvement algorithm calculates the reliability of the entire gateway electric energy meter based on the reliability of the components after iteration, and evaluates the error between the reliability of the entire gateway electric energy meter and the target value. If the error is a positive number, it means that the current reliability of the entire gateway electric energy meter has not yet reached the target, that is, the effect of the current process parameter adjustment is insufficient and further iterative improvement is needed. If the error is zero or a negative number, it indicates that the target has been achieved or exceeded.

[0016] In a second aspect, the present invention provides a system for improving the manufacturing reliability of a gateway electric energy meter, comprising: Component initial reliability calculation unit: This unit deploys sensors on the production line to record the number of defects and total production quantity of each component of the gateway electricity meter during the process stage, and calculates the initial reliability of each component based on the number of defects and total production quantity. Energy meter initial reliability calculation unit: Based on the initial reliability of each component, the initial reliability of the entire gateway energy meter is calculated as the starting point for dynamic process parameter optimization; Dynamic sensitivity factor calculation unit: Analyzes the relationship between component reliability and process parameters through dynamic sensitivity analysis algorithm, and calculates the dynamic sensitivity factor of each component process parameter; Process parameter adjustment unit: Based on the dynamic sensitivity factor, component reliability and process parameters, the process parameters are dynamically adjusted through dynamic process parameter optimization and reliability iterative improvement algorithm, and combined with sensitivity analysis and iterative verification, the reliability of components and the entire machine is gradually improved.

[0017] In a third aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for improving the manufacturing reliability of a gateway electric energy meter when executing the computer program.

[0018] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned method for improving the manufacturing reliability of a gateway electric energy meter.

[0019] The present invention has the following beneficial effects: 1. Through sensor deployment and initial reliability calculations, a quantitative benchmark for component and complete machine quality during the production process was established, enabling real-time monitoring of the manufacturing process and data-driven reliability assessment, avoiding the blindness of empirical adjustments, thereby improving manufacturing efficiency and product quality stability.

[0020] 2. By identifying key process parameters through dynamic sensitivity analysis algorithms, the contribution of process parameters to component reliability is clarified, blind adjustments are avoided, and trial and error costs are reduced. By identifying key parameters and quantifying their impact, precise direction is provided for process adjustments, improving the robustness and flexibility of the manufacturing process and adapting to parameter fluctuations under different production conditions.

[0021] 3. Dynamic process parameter optimization and iterative reliability improvement algorithms enable dynamic adaptive adjustment of process parameters and iterative improvement of reliability. Automated iterative optimization gradually improves component and complete machine reliability, reduces the need for manual intervention, and reduces production costs and time. Boundary constraints ensure the feasibility of adjustments, and saturation effect simulation improves the practicality of optimization, enabling it to cope with complex production scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flow chart of a method for improving the manufacturing reliability of a gateway electric energy meter according to the present invention; Figure 2 This is a structural diagram of a system for improving the manufacturing reliability of a gateway electric energy meter according to the present invention; Figure 3A schematic diagram of the logical structure of a computer device provided in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0023] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0024] Example 1 Refer to the attached Figure 1 , which shows a flow chart of a method for improving the manufacturing reliability of a gateway electric energy meter according to the present invention, the method comprising the following steps: S1. Deploy sensors on the production line to record the number of defects and total production quantity of each component of the gateway electricity meter during the process stage, and calculate the initial reliability of each component based on the number of defects and total production quantity. The details are as follows: Deploy sensors (such as optical detectors and electrical testing equipment) on the production line to record the number of defects (such as cracked solder joints or chip short circuits in current transformers) in each component of the gateway electricity meter during the process stage (such as welding and assembly) and total production quantity ; The initial reliability of each component is calculated based on the number of defects and the total production quantity of each component. The value range of initial reliability is between 0 and 1, where 0 represents completely unreliable and 1 represents completely reliable. It reflects the quality level of the component in the initial manufacturing stage. The formula is as follows:

[0025] in, Indicates the The initial reliability of each component ranges from ; Indicates the The number of defects per component; Indicates the The total production quantity of each component.

[0026] S2. Based on the initial reliability of each component, calculate the initial reliability of the entire gateway electric energy meter as the starting point for dynamic process parameter optimization. The details are as follows: The calculation formula for the initial reliability of the gateway electric energy meter is:

[0027] in, Indicates the initial reliability of the gateway electric energy meter, reflects the initial quality level of the gateway electric energy meter as a whole, and serves as the starting point for dynamic process parameter optimization; Indicates the multiplication symbol, from arrive Multiply all the terms of to ensure that failure of any component of the whole machine will lead to failure of the whole machine; Based on data collection and calculation of existing production lines, it is highly feasible and can adapt to different production conditions and parameter fluctuations, thus improving the flexibility and robustness of process control.

[0028] S3. Analyze the relationship between component reliability and process parameters through the dynamic sensitivity analysis algorithm and calculate the dynamic sensitivity factor of each component process parameter. The details are as follows: In order to improve the reliability of components during the manufacturing process, the relationship between component reliability and process parameters is analyzed through a dynamic sensitivity analysis algorithm, and the dynamic sensitivity factor of each component process parameter is calculated; The dynamic sensitivity analysis algorithm evaluates the direct impact of process parameter changes on component reliability and obtains the partial derivative of component reliability with respect to the process parameter by taking the derivative of the curve fitting through a controlled variable experiment. The controlled variable experiment is a technical means well known to those skilled in the art and will not be described in detail here. The influence is further multiplied by the ratio of the current process parameter value to the current component reliability to standardize the effect and remove the influence of dimension, so that the sensitivity of different parameters can be directly compared. A nonlinear influencing factor is further introduced to describe the impact of process parameters deviating from their reference values through an exponential decay function, simulating the nonlinear effect on component reliability when process parameters deviate from their optimal state in actual production. During the manufacturing process, process parameters often do not exist in isolation. For example, welding temperature and pressure may jointly affect solder joint quality. To quantify the coupling effect, the dynamic sensitivity analysis algorithm considers the interaction between process parameters. It uses existing statistical methods to calculate the correlation between each process parameter and other process parameters, and calculates the covariance and variance between the process parameters to obtain a standardized interaction weight. The calculation formula of the dynamic sensitivity factor is:

[0029] in, Indicates the In the iteration Part No. The sensitivity of each process parameter, namely the dynamic sensitivity factor, quantifies the impact of the process parameter on component reliability; Indicates the The first iteration Component reliability, initial , Indicates the Iteration No. Initial reliability of each component; Indicates the In the iteration Part No. process parameter values; Indicates component reliability Process parameters The partial derivative of reflects the direct impact rate of process parameter changes on component reliability, and the derivative can be obtained by fitting the curve through the control variable experiment; It represents the relative ratio of process parameters to component reliability, and is used to normalize partial derivatives, eliminate dimension effects, and make sensitivities comparable; It represents the nonlinear influence factor, reflecting the attenuation of the influence when the process parameters deviate from the reference value of the process parameters. The exponential attenuation function shows that the greater the deviation, the smaller the influence. Indicates the process reference value (such as optimal welding temperature), which comes from the process specification; Indicates the attenuation coefficient, controls the attenuation speed, and can be set according to the specific implementation scenario, and is not limited here; Represents the weighted sum of parameter interactions, taking into account the coupling effects between process parameters, such as the interaction between welding temperature and pressure; Indicates the and The interaction weights between the process parameters are used to quantify the correlation between the process parameters. The larger the interaction weight, the stronger the coupling effect.

[0030] The calculation formula of the interaction weight is:

[0031] in, Indicates the In the iteration Part No. and The covariance of the process parameters; Indicates the In the iteration Part No. The variance of the process parameters; Indicates the In the iteration Part No. The variance of the process parameters; By calculating the dynamic sensitivity factor, the contribution of process parameters to component reliability can be clarified to avoid blind adjustments. By identifying key parameters, a clear direction is provided for subsequent process adjustments, reducing trial and error costs and improving optimization efficiency.

[0032] S4: Based on dynamic sensitivity factors, component reliability, and process parameters, the process parameters are dynamically adjusted through dynamic process parameter optimization and reliability iterative improvement algorithms. Combined with sensitivity analysis and iterative verification, the reliability of components and the entire machine is gradually improved. This is particularly suitable for manufacturing scenarios with high reliability requirements, such as gateway electricity meters. The details are as follows: The dynamic process parameter optimization and reliability iterative improvement algorithm calculates the adjustment amount of the process parameters based on the dynamic sensitivity factor and component reliability, and reasonably determines the adjustment range of the process parameters; The unreliability of the current component reliability is calculated (i.e., 1 minus the current component reliability value) to reflect the room for improvement in component reliability. The unreliability is then multiplied by the dynamic sensitivity factor to obtain a preliminary adjustment range. To avoid overly drastic adjustments or instability due to excessive sensitivity, the square of the dynamic sensitivity factor and a very small positive number are added to the denominator for normalization, keeping the process parameter adjustment within a controllable range. At the same time, an adjustment coefficient is introduced to allow the adjustment step size to be controlled according to actual needs. For example, a smaller step size can be used in the early stages to ensure stability, and a larger step size can be used in the later stages to accelerate optimization. The calculation formula for the process parameter adjustment amount is:

[0033] in, Indicates the The first iteration Part No. Amount of process parameter adjustment; Indicates the adjustment coefficient, controls the adjustment step size, and can be set according to the specific implementation scenario and is not limited here; Represents unreliability. As an optimization target, the greater the unreliability, the greater the room for reliability improvement. Indicates the normalized denominator to prevent the adjustment amount from getting out of control when the sensitivity is too large. Represents a very small positive number, used to avoid the denominator being zero; The dynamic process parameter optimization and reliability iterative improvement algorithm updates the process parameter value based on the current process parameter value and the process parameter adjustment amount. In order to ensure that the adjusted process parameter is physically feasible, a boundary constraint is introduced. The sum of the current process parameter value and the process parameter adjustment amount is compared with the maximum allowable value of the process parameter, and the smaller value is taken. The sum is then compared with the minimum allowable value of the process parameter, and the larger value is taken as the final updated process parameter value to ensure that the adjusted parameter does not exceed the limitations of equipment capabilities or material properties, thereby ensuring the practicality of the optimization; The calculation formula for the updated process parameter value is:

[0034] in, Indicates the The first iteration Part No. process parameter values; Indicates the Part No. The minimum allowable value of a process parameter serves as a physical boundary to prevent the process parameter from exceeding the equipment or material limitations; Indicates the Part No. The maximum allowable value of each process parameter serves as a physical boundary to prevent the process parameter from exceeding the equipment or material limitations; It means taking the maximum value to ensure that the adjusted process parameters are not lower than the lower limit; Indicates taking the minimum value to ensure that the adjusted process parameters do not exceed the upper limit; The dynamic process parameter optimization and reliability iterative improvement algorithm further updates the reliability of the component based on the updated process parameter values; The adjusted process parameters directly reflect changes in component reliability. The actual process parameter change in this iteration is calculated. This is the updated process parameter value minus the pre-adjustment process parameter value, divided by the reference value of the process parameter. This yields a dimensionless relative change amplitude, which is used to normalize the impact of different process parameters. This amplitude is multiplied by the corresponding dynamic sensitivity factor to quantify the contribution of process parameter changes to component reliability. To ensure that component reliability does not exceed 1 and to simulate the saturation effect in reality, an unreliability factor (i.e., 1 minus the current component reliability) is introduced, causing the component reliability improvement to gradually decrease as it approaches perfection. The calculation formula of component reliability after iteration is:

[0035] in, Indicates the The first iteration Reliability of individual components; Represents the summation symbol, from arrive Sum the contributions of all process parameters; Indicates the change in process parameters; The dynamic process parameter optimization and reliability iterative improvement algorithm calculates the reliability of the entire gateway electric energy meter based on the reliability of the components after iteration, and evaluates the error between the reliability of the entire gateway electric energy meter and the target value. If the error is a positive number, it means that the reliability of the entire gateway electric energy meter has not yet reached the target, that is, the effect of the current process parameter adjustment is insufficient and further iterative improvement is needed. If the error is zero or negative, it indicates that the target has been achieved or exceeded. Through the iterative feedback mechanism, precise optimization of process parameters is achieved, the reliability of gateway electricity meters is improved, the failure rate of the entire machine due to component failure is reduced, and the service life of the product is extended. Through automated iterative optimization, the need for manual intervention is reduced, saving production costs and time. It can cope with different production conditions and has strong versatility and practicality.

[0036] Example 2 This embodiment provides a system for improving the manufacturing reliability of gateway electric energy meters, which is used to implement the method described in Example 1. Figure 2 As shown, it consists of a component initial reliability calculation unit, an electric energy meter initial reliability calculation unit, a dynamic sensitivity factor calculation unit and a process parameter adjustment unit.

[0037] The component initial reliability calculation unit is used to deploy sensors on the production line, record the number of defects and the total production quantity of each component of the gateway electricity meter during the process stage, and calculate the initial reliability of each component based on the number of defects and the total production quantity of the component.

[0038] The electric energy meter initial reliability calculation unit calculates the initial reliability of the entire gateway electric energy meter based on the initial reliability of each component, which serves as the starting point of the process parameter optimization process.

[0039] The dynamic sensitivity factor calculation unit analyzes the relationship between component reliability and process parameters through a dynamic sensitivity analysis algorithm, and calculates the dynamic sensitivity factor of each component process parameter.

[0040] The process parameter adjustment unit dynamically adjusts the process parameters based on the dynamic sensitivity factor, component reliability and process parameters through dynamic process parameter optimization and reliability iterative improvement algorithm, and gradually improves the reliability of components and the entire machine by combining sensitivity analysis and iterative verification.

[0041] It should be noted that the various units in the aforementioned system for improving the manufacturing reliability of gateway electric energy meters can be implemented in whole or in part through software, hardware, or a combination thereof. Each of the aforementioned units can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the aforementioned units. For the specific definition of a system for improving the manufacturing reliability of gateway electric energy meters, please refer to the definition of a method for improving the manufacturing reliability of gateway electric energy meters (i.e., Example 1) above. The two have the same functions and effects and will not be repeated here.

[0042] Example 3 This embodiment provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, wherein the computer program, when executed by the at least one processor, causes the electronic device to perform the method according to Embodiment 1 of the present invention.

[0043] Example 4 This embodiment provides a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to perform the method according to embodiment 1 of the present invention.

[0044] refer to Figure 3 , a block diagram of an electronic device 400 that can serve as a server or client of the present invention will now be described, which is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0045] like Figure 3 As shown, electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. Various programs and data required for the operation of electronic device 400 may also be stored in RAM 403. Computing unit 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to bus 404.

[0046] Multiple components within electronic device 400 are connected to I / O interface 405, including an input unit 406, an output unit 407, a storage unit 408, and a communication unit 409. Input unit 406 can be any type of device capable of inputting information into electronic device 400. Input unit 406 can receive input numeric or character information and generate key input signals related to user settings and / or function control of the electronic device. Output unit 407 can be any type of device capable of presenting information and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. Storage unit 408 may include, but is not limited to, a magnetic disk or an optical disk. Communication unit 409 allows electronic device 400 to exchange information / data with other devices via computer networks such as the Internet and / or various telecommunication networks and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0047] The computing unit 401 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above. For example, in some embodiments, the aforementioned method for improving the manufacturing reliability of a gateway electric energy meter can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 400 via the ROM 402 and / or the communication unit 409. In some embodiments, the computing unit 401 can be configured to perform the aforementioned method for improving the manufacturing reliability of a gateway electric energy meter by any other suitable means (e.g., via firmware).

[0048] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. Such program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0049] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or apparatus. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0050] As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., a magnetic disk, an optical disk, a memory, a programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0051] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0052] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0053] Computer systems may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The client and server relationship arises through computer programs running on the respective computers and having a client-server relationship to each other.

[0054] It is obvious that those skilled in the art can easily make various modifications to the above embodiments and apply the general principles described herein to other embodiments without requiring creative effort. Therefore, the present invention is not limited to the above embodiments, and improvements and modifications made by those skilled in the art based on the disclosure of the present invention should fall within the scope of protection of the present invention.

Claims

1. A method for improving the manufacturing reliability of a gateway electric energy meter, characterized in that: include: Sensors are deployed on the production line to record the number of defects and total production quantity of each component of the gateway electricity meter during the process stage. The initial reliability of each component is calculated based on the number of defects and total production quantity. Based on the initial reliability of each component, the initial reliability of the entire gateway electric energy meter is calculated as the starting point for dynamic process parameter optimization; Analyze the relationship between component reliability and process parameters through dynamic sensitivity analysis algorithm, and calculate the dynamic sensitivity factor of each component process parameter; Based on the dynamic sensitivity factor, component reliability and process parameters, the process parameters are dynamically adjusted through dynamic process parameter optimization and reliability iterative improvement algorithm, and combined with sensitivity analysis and iterative verification, the reliability of components and the entire machine is gradually improved.

2. A method for improving the manufacturing reliability of a gateway electric energy meter according to claim 1, characterized in that: The dynamic sensitivity analysis algorithm evaluates the direct impact of process parameter changes on component reliability, takes the derivative of the experimental fitting curve of the control variable, and obtains the partial derivative of component reliability with respect to the process parameter; the partial derivative is multiplied by the ratio of the component process parameter value to the component reliability; A nonlinear influencing factor is introduced to describe the impact of process parameters deviating from their reference values through an exponential decay function, simulating the nonlinear effect on component reliability when process parameters deviate from their optimal state in actual production. Considering the interaction between process parameters, the correlation between each process parameter and other process parameters is calculated by statistical methods, the covariance and variance between the process parameters are calculated, and a standardized interaction weight is obtained.

3. A method for improving the manufacturing reliability of a gateway electric energy meter according to claim 1 or 2, characterized in that: The calculation formula of the dynamic sensitivity factor is: ， in, Indicates the In the iteration Part No. Dynamic sensitivity factors of process parameters; Indicates the The first iteration Component reliability, initial , Indicates the Iteration No. Initial reliability of each component; Indicates the In the iteration Part No. process parameter values; represents the partial derivative of component reliability with respect to the process parameter value; Indicates the relative proportion of process parameters to component reliability; represents the nonlinear impact factor; Indicates the Part No. Reference values of process parameters; represents the attenuation coefficient; represents the weighted sum of the interactive effects of process parameters; Indicates the and The interaction weights between the process parameters.

4. The method for improving the manufacturing reliability of a gateway electric energy meter according to claim 1, characterized in that: The dynamic process parameter optimization and reliability iterative improvement algorithm calculates the adjustment amount of the process parameters based on the dynamic sensitivity factor and component reliability, and reasonably determines the adjustment range of the process parameters; by calculating the unreliability of the current component reliability, the unreliability is multiplied by the dynamic sensitivity factor to obtain a preliminary adjustment range as the numerator, and the sum of the square of the dynamic sensitivity factor and a very small positive number is introduced into the denominator; at the same time, an adjustment coefficient is introduced to allow the adjustment step size to be controlled according to actual needs.

5. A method for improving manufacturing reliability of a gateway electric energy meter according to claim 1 or 4, characterized in that: The calculation formula for the process parameter adjustment amount is as follows: ， in, Indicates the The first iteration Part No. Amount of process parameter adjustment; represents the adjustment coefficient; Indicates the The unreliability of the reliability of each component; Indicates the The first iteration Reliability of individual components; represents the normalized denominator; Represents a very small positive number; Indicates the In the iteration Part No. The dynamic sensitivity factor of each process parameter.

6. The method for improving the manufacturing reliability of a gateway electric energy meter according to claim 4, characterized in that: The dynamic process parameter optimization and reliability iterative improvement algorithm updates the process parameter value based on the current process parameter value and the process parameter adjustment amount. To ensure that the adjusted process parameter is physically feasible, a boundary constraint is introduced. The sum of the current process parameter value and the process parameter adjustment amount is compared with the maximum allowable value of the process parameter, and the smaller value is taken. The sum is then compared with the minimum allowable value of the process parameter, and the larger value is taken as the final updated process parameter value, ensuring that the adjusted parameter does not exceed the limitations of equipment capabilities or material properties. further updating the reliability of the component based on the updated process parameter values; The adjusted process parameters directly reflect the component reliability changes. The actual process parameter changes in this iteration are calculated. This is the updated process parameter value minus the pre-adjustment process parameter value, divided by the reference value of the process parameter. This yields a dimensionless relative change amplitude, which is then multiplied by the corresponding dynamic sensitivity factor. Unreliability is introduced so that the improvement in component reliability gradually decreases as it approaches perfection.

7. The method for improving the manufacturing reliability of a gateway electric energy meter according to claim 1, characterized in that: The dynamic process parameter optimization and reliability iterative improvement algorithm calculates the reliability of the entire gateway electric energy meter based on the reliability of the components after iteration, and evaluates the error between the reliability of the entire gateway electric energy meter and the target value. If the error is a positive number, it means that the current reliability of the entire gateway electric energy meter has not yet reached the target, that is, the effect of the current process parameter adjustment is insufficient and further iterative improvement is needed. If the error is zero or a negative number, it indicates that the target has been achieved or exceeded.

8. A system for improving the manufacturing reliability of gateway electric energy meters, used to implement the method according to any one of claims 1 to 7, characterized in that: include: Component initial reliability calculation unit: This unit deploys sensors on the production line to record the number of defects and total production quantity of each component of the gateway electricity meter during the process stage, and calculates the initial reliability of each component based on the number of defects and total production quantity. Energy meter initial reliability calculation unit: Based on the initial reliability of each component, the initial reliability of the entire gateway energy meter is calculated as the starting point for dynamic process parameter optimization; Dynamic sensitivity factor calculation unit: Analyzes the relationship between component reliability and process parameters through dynamic sensitivity analysis algorithm, and calculates the dynamic sensitivity factor of each component process parameter; Process parameter adjustment unit: Based on the dynamic sensitivity factor, component reliability and process parameters, the process parameters are dynamically adjusted through dynamic process parameter optimization and reliability iterative improvement algorithm, and combined with sensitivity analysis and iterative verification, the reliability of components and the entire machine is gradually improved.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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

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