A method, system, device and medium for improving the reliability of the whole machine manufacturing of a gateway electric energy meter

Abstract

The application discloses a kind of methods, systems, equipment and media for improving the reliability of gateway electric energy meter whole machine manufacturing.The method used in the application comprises: deploying sensors on the production line, recording the number of defects and the total production quantity of each component of the gateway electric energy meter at the process stage, calculating the initial reliability of each component based on the number of defects and the 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 of dynamic process parameter optimization;The relationship between component reliability and process parameters is analyzed by dynamic sensitivity analysis algorithm, and the dynamic sensitivity factor of each component process parameter is calculated;Based on the dynamic sensitivity factor, component reliability and process parameters, the process parameters are dynamically adjusted by dynamic process parameter optimization and reliability iterative improvement algorithm, and combined with sensitivity analysis and iterative verification, the reliability of components and whole machine is gradually improved.The application improves the manufacturing efficiency and the stability of product quality.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric energy meter manufacturing control, and in particular to a method, system, device and medium for improving the reliability of a whole electric energy meter. BACKGROUND

[0002] With the popularization of smart grids, the functions of electric energy meters are increasingly complex, not only having traditional electric energy metering functions, but also being capable of performing electric energy quality analysis, voltage and current monitoring, remote meter reading, load forecasting and other functions. The accuracy, stability and reliability of electric energy meters directly affect the operation efficiency of power systems, the electricity cost of users and the income of power companies.

[0003] The overall assembly and debugging of existing electric energy meters mainly rely on manual operation and traditional mechanical manufacturing processes. Although the manufacturing process can meet the basic production needs at the time, manual operation and mechanical processing are prone to cause precision errors of products, especially in the assembly link, which may cause the connection of electronic components to be not firm, the line contact to be poor, and thus affect the stability and accuracy of electric energy meters. The existing control method can ensure a certain product quality, but due to a large amount of reliance on manual operation, the production efficiency is low and it is difficult to meet the large-scale production needs. Due to different technical levels and experiences of operators, quality differences between batches are prone to occur in the manual manufacturing process, especially in the welding of complex circuits and the installation of components, which is prone to inconsistency and affects the reliability of products.

[0004] The above-mentioned existing control method also has the problems of relying on experience and manual monitoring, being easily affected by production environment changes, equipment performance fluctuations and other factors, leading to unstable component reliability; ignoring the coupling effect between different process parameters, and optimizing a single process parameter may cause other process parameters to fail and affect the reliability of the whole machine manufacturing. SUMMARY

[0005] The present application provides a method for improving the reliability of whole electric energy meter manufacturing to solve the technical problems that the traditional method only relies on static quality evaluation and 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 operable method to quantify the influence of process parameters on component reliability, and the improvement of component reliability often relies on experience judgment rather than scientific and data-driven optimization; it is difficult to realize dynamic process parameter adjustment, and relying on static set standards does not have enough flexibility and adaptability in the face of different production conditions and equipment changes; there is a lack of real-time monitoring of component reliability and process parameter adjustment feedback mechanism, leading to insufficient efficiency and accuracy of the optimization process.

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

[0007] In a first aspect, the present invention provides a method for improving the manufacturing reliability of a gated energy meter, comprising:

[0008] Sensors are deployed on the production line to record the number of defects and the total production quantity of each component of the gate electricity meter at each stage of the process. The initial reliability of each component is calculated based on the number of defects and the total production quantity.

[0009] Based on the initial reliability of each component, the initial reliability of the entire gate energy meter is calculated, serving as the starting point for dynamic process parameter optimization.

[0010] The relationship between component reliability and process parameters is analyzed using a dynamic sensitivity analysis algorithm, and the dynamic sensitivity factor of each component's process parameters is calculated.

[0011] Based on dynamic sensitivity factors, component reliability, and process parameters, 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 whole machine is gradually improved.

[0012] Furthermore, the dynamic sensitivity analysis algorithm evaluates the direct impact of process parameter changes on component reliability. By taking the derivative of the curve fitted to the control variable experiment, the partial derivative of component reliability with respect to process parameters is obtained. The partial derivative is multiplied by the ratio of the component process parameter value to the component reliability to standardize the impact, remove the influence of dimensions, and make the sensitivity of different parameters directly comparable. A nonlinear impact factor is then introduced, which describes the impact of process parameters deviating from the reference value through an exponential decay function, simulating the nonlinear effect of process parameters deviating from the optimal state on component reliability in actual production. To quantify the coupling effect, the interaction between process parameters is considered. The correlation between each process parameter and other process parameters is calculated using statistical methods, and the covariance and variance between process parameters are calculated to obtain a standardized interaction weight.

[0013] Furthermore, the formula for calculating the dynamic sensitivity factor is as follows:

[0014]

[0015] in, Indicates the first In the nth iteration The first component Dynamic sensitivity factor of each process parameter; Indicates the first The iteration of the ... The reliability of each component, initially , Indicates the first The second iteration initial reliability of a component; represents the first component in the first process parameter value of the first component in the first represents the partial derivative of component reliability with respect to process parameter value, reflecting the direct influence rate of process parameter change on component reliability, obtained by taking the derivative of the curve fitted by the control variable experiment; represents the relative proportion of process parameters and component reliability, used to normalize the partial derivative; represents a nonlinear influence factor, reflecting the influence attenuation when the process parameter deviates from the formula parameter reference value, reflected by an exponential decay function, the greater the deviation, the smaller the influence; represents the first process parameter reference value of the first component; represents the decay coefficient, controlling the decay rate; represents the weighted sum of process parameter interaction, considering the coupling effect between process parameters; represents the interaction weight between the first and the first process parameters.

[0016] Further, the dynamic process parameter optimization and reliability iteration promotion algorithm calculates the adjustment amount of the process parameter according to the dynamic sensitivity factor and the component reliability, reasonably determines the adjustment range of the process parameter; by calculating the unreliability of the current component reliability, i.e. 1 minus the current component reliability value, reflecting the promotion space of the component reliability, multiplying the unreliability by the dynamic sensitivity factor, obtaining a preliminary adjustment range as the numerator, in order to avoid excessive adjustment or instability caused by excessive sensitivity, introducing the square of the dynamic sensitivity factor and a small positive number in the denominator for normalization processing, so that the process parameter adjustment amount is kept within a controllable range; at the same time, an adjustment coefficient is introduced, allowing the step size of the adjustment 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 speed up the optimization.

[0017] Further, the calculation formula of the process parameter adjustment amount is as follows:

[0018]

[0019] wherein, represents the first component in the first process parameter adjustment amount of the first component in the first iteration; represents the adjustment coefficient; represents the unreliability of the first represents the first represents the first component reliability; represents the normalization denominator; represents a very small positive number; represents the first represents the first represents the first dynamic sensitivity factor of the first process parameter of the first component in the first iteration.

[0020] Further, the dynamic process parameter optimization and reliability iteration promotion 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 allowed value of the process parameter, the smaller value of the two is taken, and then compared with the minimum allowed value of the process parameter, the larger value of the two is taken as the final updated process parameter value, to ensure that the adjusted parameter will not exceed the limit of equipment capacity or material characteristics, thereby ensuring the practicability of optimization;

[0021] The reliability of the component is further updated based on the updated process parameter value;

[0022] The actual process parameter change amount in the current iteration is calculated by directly reflecting the change of the component reliability through the adjusted process parameter, i.e. 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, which is multiplied by the corresponding dynamic sensitivity factor to quantify the contribution of the process parameter to the component reliability; in order to ensure that the component reliability will not exceed 1 and simulate the saturation effect in practice, an unreliability (i.e. 1 minus the current component reliability) is introduced, so that the component reliability promotion amplitude gradually decreases when it approaches perfection.

[0023] Further, the dynamic process parameter optimization and reliability iteration promotion algorithm calculates the reliability of the entire gateway electric energy meter based on the component reliability after iteration, and evaluates the error between the entire gateway electric energy meter reliability and the target value, if the error is positive, it means that the current entire gateway electric energy meter reliability has not reached the target, i.e. the effect of the current process parameter adjustment is insufficient, and it needs to be iterated to improve, if the error is zero or negative, it means that the target has been reached or exceeded.

[0024] In the second aspect, the application provides a system for improving the manufacturing reliability of the entire gateway electric energy meter, which comprises:

[0025] A 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 electric energy meter at the process stage, and calculate the initial reliability of each component according to the number of defects and the total production quantity of the component;

[0026] An initial reliability calculation unit of the electric energy meter calculates the initial reliability of the entire gateway electric energy meter based on the initial reliability of each component as a starting point of dynamic process parameter optimization.

[0027] A dynamic sensitivity factor calculation unit calculates the dynamic sensitivity factor of the process parameter of each component by analyzing the relationship between the component reliability and the process parameter through a dynamic sensitivity analysis algorithm.

[0028] A process parameter adjustment unit dynamically adjusts the process parameter based on the dynamic sensitivity factor, the component reliability and the process parameter through a dynamic process parameter optimization and reliability iterative improvement algorithm, and gradually improves the reliability of the component and the entire machine by combining sensitivity analysis and iterative verification.

[0029] In a third aspect, the present application provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method for improving the manufacturing reliability of the entire gateway electric energy meter when executing the computer program.

[0030] In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the steps of the method for improving the manufacturing reliability of the entire gateway electric energy meter.

[0031] The present application has the following beneficial effects:

[0032] 1. The establishment of a quantitative benchmark for the quality of components and the entire machine in the production process through sensor deployment and initial reliability calculation realizes real-time monitoring of the manufacturing process and data-driven reliability evaluation, avoids the blindness of empirical adjustment, and thus improves the manufacturing efficiency and the stability of product quality.

[0033] 2. The dynamic sensitivity analysis algorithm identifies the key process parameters, determines the contribution of the process parameters to the reliability of the components, avoids blind adjustment, reduces trial-and-error costs, provides a precise direction for process adjustment by identifying key parameters and quantifying the impact, and improves the robustness and flexibility of the manufacturing process to adapt to parameter fluctuations under different production conditions.

[0034] 3. The dynamic process parameter optimization and reliability iterative improvement algorithm realizes the dynamic adaptive adjustment of the process parameter and the iterative improvement of the reliability, gradually improves the reliability of the components and the entire machine through automated iterative optimization, reduces the need for manual intervention and production costs and time, and ensures the feasibility of adjustment through boundary constraints and improves the practicality of optimization through saturation effect simulation, which can cope with complex production scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1A flow chart of a method for improving the manufacturing reliability of a gateway electric energy meter according to the present application;

[0036] Figure 2 A configuration diagram of a system for improving the manufacturing reliability of a gateway electric energy meter according to the present application;

[0037] Figure 3 A logical structure diagram of a computer device provided in the specific embodiment of the present application. DETAILED DESCRIPTION

[0038] The technical solutions of the present application will be described in detail below in combination with the accompanying drawings and specific embodiments.

[0039] Embodiment 1

[0040] Referring to the accompanying drawings Figure 1 , a flow chart of a method for improving the manufacturing reliability of a gateway electric energy meter according to the present application is shown, which comprises the following steps:

[0041] S1, deploying sensors on the production line, recording the number of defects and the total production number of each component of the gateway electric energy meter at the process stage, and calculating the initial reliability of each component according to the number of defects and the total production number of the component. Specifically as follows:

[0042] Deploy sensors (such as optical detectors, electrical test equipment) on the production line, record the number of defects (such as detecting the cracking of the welding point of the current transformer or the short circuit of the chip, etc.) and the total production number of each component of the gateway electric energy meter at the process stage (such as welding, assembly) .

[0043] According to the number of defects and the total production number of each component, the initial reliability of each component is calculated, the value range of the initial reliability is between 0 and 1, 0 represents complete unreliability, 1 represents complete reliability, which reflects the quality level of the component at the initial manufacturing stage, and the formula is as follows:

[0044]

[0045] Wherein, represents the initial reliability of the i-th component, the value range is . represents the number of defects of the i-th component; represents the total production number of the i-th component. S2, based on the initial reliability of each component, the initial reliability of the entire gateway electric energy meter is calculated as the starting point of dynamic process parameter optimization. Specifically as follows:

[0046] S2, based on the initial reliability of each component, the initial reliability of the entire gateway electric energy meter is calculated as the starting point of dynamic process parameter optimization. Specifically as follows: ​​​

[0047] The calculation formula of the initial reliability of the gateway electric energy meter is:

[0048]

[0049] wherein, represents the initial reliability of the gateway electric energy meter, reflecting the initial quality level of the gateway electric energy meter as a whole, serving as the starting point of dynamic process parameter optimization; represents a continuous multiplication symbol, multiplying all terms from to ensures that the failure of any component of the whole machine leads to the failure of the whole machine;

[0050] Based on the data collection and calculation of the existing production line, it has high implementability, can adapt to different production conditions and parameter fluctuations, and improves the flexibility and robustness of process control.

[0051] S3, analyze the relationship between component reliability and process parameters through a dynamic sensitivity analysis algorithm, and calculate the dynamic sensitivity factor of each component process parameter. Specifically as follows:

[0052] In order to improve the component reliability in 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;

[0053] The dynamic sensitivity analysis algorithm evaluates the direct impact of process parameter changes on component reliability, takes the derivative of the curve fitted by the control variable experiment, obtains the partial derivative of component reliability with respect to process parameters, and the control variable experiment is a technical means familiar to those skilled in the art, which will not be repeated here; Further multiplied by the ratio of the current process parameter value and the current component reliability, used to standardize the impact, remove the influence of dimension, so that the sensitivity of different parameters can be directly compared;

[0054] A nonlinear influence factor is further introduced, which describes the influence of process parameter deviation from the process parameter reference value through an exponential decay function, simulating the nonlinear effect of process parameter deviation from the optimal state on component reliability in actual production;

[0055] In the manufacturing process, process parameters are not isolated, such as welding temperature and pressure, which may jointly affect the quality of the welding point. In order to quantify the coupling effect, the dynamic sensitivity analysis algorithm considers the interaction between process parameters, calculates the correlation between each process parameter and other process parameters through existing statistical methods, calculates the covariance and variance between process parameters, and obtains a standardized interaction weight;

[0056] The calculation formula of the dynamic sensitivity factor is:

[0057]

[0058] in, Indicates the first In the nth iteration The first component The sensitivity of a process parameter, i.e., the dynamic sensitivity factor, quantifies the impact of process parameters on component reliability; Indicates the first The iteration of the ... The reliability of each component, initially , Indicates the first The second iteration Initial reliability of each component; Indicates the first In the nth iteration The first component Each process parameter value; Indicating component reliability For process parameters The partial derivatives reflect the rate at which changes in process parameters directly affect the reliability of components, and can be obtained by controlling variables and fitting the curve to obtain the derivatives. It represents the relative proportion between process parameters and component reliability, and is used to normalize partial derivatives, eliminate the influence of dimensions, and make the sensitivity comparable; This represents the nonlinear influence factor, reflecting the attenuation of the impact when process parameters deviate from the reference value. The exponential decay function reflects that the greater the deviation, the smaller the impact. This indicates a process reference value (such as the optimal welding temperature), which comes from the process specification. This represents the attenuation coefficient, which controls the attenuation rate. It can be set according to the specific implementation scenario and is not limited here. This represents the weighted sum of the interaction effects of parameters, taking into account the coupling effects between process parameters, such as the interaction effects between welding temperature and pressure. Indicates the first The and the first The interaction weights between process parameters quantify the correlation between them; the larger the interaction weight, the stronger the coupling effect.

[0059] The formula for calculating the interaction weight is:

[0060]

[0061] in, Indicates the first In the nth iteration The first component The and the first Covariance of each process parameter; Indicates the first the variance of the i th process parameter of the j th component in the n th iteration the variance of the i th process parameter of the j th component in the n th iteration the variance of the i th process parameter of the j th component in the n th iteration the variance of the i th process parameter of the j th component in the n th iteration the variance of the i th process parameter of the j th component in the n th iteration the variance of the i th process parameter of the j th component in the n th iteration the variance of the i th process parameter of the j th component in the n th iteration

[0062] By calculating the dynamic sensitivity factor, the contribution of the process parameter to the component reliability is determined, blind adjustment is avoided, the subsequent process adjustment is provided with a clear direction by identifying the key parameters, the trial and error cost is reduced, and the optimization efficiency is improved.

[0063] S4, based on the dynamic sensitivity factor, the component reliability and the process parameter, the process parameter is dynamically adjusted through the dynamic process parameter optimization and reliability iteration improvement algorithm, and combined with the sensitivity analysis and iteration verification, the reliability of the component and the whole machine is gradually improved, which is especially suitable for manufacturing scenes with high reliability requirements such as key energy meters. Specifically as follows:

[0064] The dynamic process parameter optimization and reliability iteration improvement algorithm calculates the adjustment amount of the process parameter according to the dynamic sensitivity factor and the component reliability, and reasonably determines the adjustment range of the process parameter;

[0065] By calculating the unreliability of the current component reliability, i.e. 1 minus the current component reliability value, the improvement space of the component reliability is reflected, the unreliability is multiplied by the dynamic sensitivity factor to obtain a preliminary adjustment range. In order to avoid excessive adjustment or instability caused by excessive sensitivity, the square of the dynamic sensitivity factor and a small positive number are introduced in the denominator for normalization processing, so that the process parameter adjustment amount is kept within a controllable range. At the same time, an adjustment coefficient is introduced to allow the adjustment step to be controlled according to actual needs, such as using a smaller step in the early stage to ensure stability, and increasing the step in the later stage to speed up optimization;

[0066] The calculation formula of the process parameter adjustment amount is:

[0067]

[0068] Wherein, the adjustment amount of the i th process parameter of the j th component in the n th iteration the adjustment amount of the i th process parameter of the j th component in the n th iteration the adjustment amount of the i th process parameter of the j th component in the n th iteration the adjustment amount of the i th process parameter of the j th component in the n th iteration the adjustment coefficient controls the adjustment step, which can be set according to specific implementation scenarios, and is not limited here; the unreliability, which is the optimization target, the larger the unreliability, the larger the reliability improvement space; the normalization denominator prevents the adjustment amount from being out of control when the sensitivity is too large, It represents a very small positive number and is used to avoid the denominator being zero;

[0069] The dynamic process parameter optimization and reliability iterative improvement algorithm updates the process parameter values ​​based on the current process parameter values ​​and the process parameter adjustment amount. To ensure that the adjusted process parameters are physically feasible, boundary constraints are 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. Then, it is 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 parameters do not exceed the limitations of equipment capacity or material properties, thereby ensuring the practicality of the optimization.

[0070] The updated formula for calculating process parameter values ​​is as follows:

[0071]

[0072] in, Indicates the first The iteration of the ... The first component Each process parameter value; Indicates the first The first component The minimum allowable value of each process parameter serves as a physical boundary to prevent the process parameter from exceeding equipment or material limitations. Indicates the first The first component The maximum allowable value of each process parameter serves as a physical boundary to prevent the process parameter from exceeding equipment or material limitations; This indicates that the maximum value is taken, ensuring that the adjusted process parameters are not lower than the lower limit. This indicates that the minimum value is taken to ensure that the adjusted process parameters do not exceed the upper limit.

[0073] The dynamic process parameter optimization and reliability iterative improvement algorithm further updates the reliability of the component based on the updated process parameter values;

[0074] The adjusted process parameters directly reflect changes in component reliability. The actual change in process parameters in this iteration is calculated by subtracting the original process parameter value from the updated process parameter value and dividing by the reference value of the process parameter. This yields a dimensionless relative change magnitude, which is used to standardize the impact of different process parameters. This magnitude is then 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, thereby gradually reducing the rate of improvement in component reliability as it approaches perfection.

[0075] The formula for calculating the reliability of the component after iteration is:

[0076]

[0077] wherein, represents the iteration of the component reliability; represents a summation symbol, summing up the contribution of all process parameters from to ; represents the variation of the process parameter;

[0078] The dynamic process parameter optimization and reliability iteration promotion algorithm calculates the reliability of the entire gateway electric energy meter based on the component reliability after iteration, and evaluates the error between the entire gateway electric energy meter reliability and the target value. If the error is positive, it means that the current entire gateway electric energy meter reliability has not reached the target, that is, the effect of the current process parameter adjustment is insufficient, and iteration needs to be continued to improve. If the error is zero or negative, it means that the target has been reached or exceeded.

[0079] Through the iteration feedback mechanism, the process parameters are accurately optimized, the reliability of the gateway electric energy meter is improved, the whole machine failure rate caused by component failure is reduced, the product service life is prolonged, and the demand for manual intervention is reduced through automatic iteration optimization, production cost and time are saved, different production conditions can be coped with, and the system has strong universality and practicality.

[0080] Embodiment 2

[0081] The embodiment provides a system for improving the reliability of the entire gateway electric energy meter, which is used to realize the method of embodiment 1, as shown in Figure 2 , which is composed 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.

[0082] The component initial reliability calculation unit is used to deploy sensors on the production line, record the number of defects and the total production number of each component of the gateway electric energy meter at the process stage, and calculate the initial reliability of each component according to the number of defects and the total production number of the component.

[0083] 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 is the starting point of the process parameter optimization process.

[0084] The dynamic sensitivity factor calculation unit calculates the dynamic sensitivity factor of each component process parameter by analyzing the relationship between component reliability and process parameter through a dynamic sensitivity analysis algorithm.

[0085] The process parameter adjustment unit: based on the dynamic sensitivity factor, the component reliability and the process parameter, the process parameter is adjusted dynamically through the dynamic process parameter optimization and reliability iteration promotion algorithm, and the sensitivity analysis and iteration verification are combined, and the reliability of the component and the whole machine is gradually improved.

[0086] It should be noted that each unit in the above system for improving the manufacturing reliability of the gateway electric energy meter can be realized by software, hardware and combinations thereof, in whole or in part. The above units can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above units. For specific limitations of the system for improving the manufacturing reliability of the gateway electric energy meter, see the limitations of the method for improving the manufacturing reliability of the gateway electric energy meter (i.e. embodiment 1) in the above, both of which have the same functions and effects, and will not be described here.

[0087] Embodiment 3

[0088] The embodiment provides an electronic device, comprising: at least one processor; and a memory connected with the at least one processor in communication. The memory stores a computer program capable of being executed by the at least one processor, and the computer program is used for causing the electronic device to execute the method according to the embodiment 1 of the application when executed by the at least one processor.

[0089] Embodiment 4

[0090] The embodiment provides a non-transitory computer readable storage medium storing a computer program, wherein the computer program is used for causing a computer to execute the method according to the embodiment 1 of the application when executed by a processor of the computer.

[0091] Reference Figure 3 The structure block diagram of the electronic device 400 which can be the server or the client of the present application will be described, which is an example of the hardware device which can be applied to each aspect of the present application. 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 realizations of the present application described and / or claimed herein.

[0092] As Figure 3As shown, the electronic device 400 includes a computing unit 401 that can perform various appropriate actions and processes in accordance with a computer program stored in a read-only memory (ROM) 402 or a computer program loaded into a random access memory (RAM) 403 from a storage unit 408. In the RAM 403, various programs and data required for the operation of the electronic device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other through a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0093] A plurality of components in the electronic device 400 are connected to the I / O interface 405, including an input unit 406, an output unit 407, a storage unit 408, and a communication unit 409. The input unit 406 can be any type of device that can input information to the electronic device 400, and can receive inputted digital or character information, and generate key signal inputs related to user settings and / or function controls of the electronic device. The output unit 407 can be any type of device that can present information, and can include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 408 can include, but is not limited to, a magnetic disk, an optical disk. The communication unit 409 allows the electronic device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can 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.

[0094] The computing unit 401 can be various general and / or special purpose processing components 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 special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 401 performs various methods and processes described above. For example, in some embodiments, the aforementioned method of improving the manufacturing reliability of a whole meter 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 onto 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 of improving the manufacturing reliability of a whole meter of a gateway electric energy meter by any other appropriate means, such as by means of firmware.

[0095] Program code for carrying out operations of the methods of the present application can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces the functions / operations specified in the flowcharts and / or block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine as part of a separate software package, and partially on a remote machine or entirely on a remote machine or server.

[0096] In the context of the present application, a machine-readable medium can be a tangible medium that can contain or store program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable storage media can include, without limitation, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media can 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), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0097] As used in the present application, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus and / or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal that can be used to provide machine instructions and / or data to a programmable processor.

[0098] To provide for interaction with a user, the systems and techniques described here 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 a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, 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, speech, or tactile input.

[0099] The systems and techniques described here can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here, or any combination of such back end, middleware, 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.

[0100] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

[0101] Those skilled in the art will readily observe that numerous modifications and alterations of the device and process can be made without departing from the scope of the present application. Accordingly, the above disclosure is intended to be illustrative only and not limiting of the scope of the present application. The present application is limited only as defined in the following claims and the equivalents thereto.

Claims

1. A method for improving the reliability of the whole machine manufacturing of a gateway electric energy meter, characterized in that, The application relates to a method for improving the reliability of a metering device, comprising the following steps: deploying sensors on the production line to record the number of defects and the total production quantity of each component of the metering device at the process stage, and calculating the initial reliability of each component according to the number of defects and the total production quantity; calculating the initial reliability of the entire metering device based on the initial reliability of each component, as the starting point of dynamic process parameter optimization; analyzing the relationship between the component reliability and the process parameters through a dynamic sensitivity analysis algorithm, and calculating the dynamic sensitivity factor of the process parameters of each component; based on the dynamic sensitivity factor, the component reliability and the process parameters, dynamically adjusting the process parameters through a dynamic process parameter optimization and reliability iterative improvement algorithm, and gradually improving the reliability of the components and the entire metering device by combining sensitivity analysis and iterative verification; the dynamic sensitivity analysis algorithm obtains the partial derivative of the component reliability with respect to the process parameters by evaluating the direct influence of the process parameter change on the component reliability, fitting a curve through a control variable experiment and taking the derivative; the partial derivative is multiplied by the ratio of the component process parameter value to the component reliability; a nonlinear influence factor is introduced to describe the influence of the process parameter deviating from the process parameter reference value through an exponential decay function, and the nonlinear effect of the process parameter deviating from the optimal state on the component reliability in actual production is simulated; the interaction between the process parameters is considered, the correlation between each process parameter and other process parameters is calculated through a statistical method, the covariance and variance between the process parameters are calculated, and a standardized interaction weight is obtained; the calculation formula of the dynamic sensitivity factor is as follows: ， in, Indicates the first In the nth iteration The first component Dynamic sensitivity factor of each process parameter; Indicates the first The iteration of the ... The reliability of each component, initially , Indicates the first The second iteration Initial reliability of each component; Indicates the first In the nth iteration The first component Each process parameter value; This represents the partial derivative of component reliability with respect to process parameter values; This indicates the relative proportion between process parameters and component reliability. Indicates the nonlinear influence factor; Indicates the first The first component Reference values ​​for each process parameter; Indicates the attenuation coefficient; This represents the weighted sum of the interactive effects of process parameters; Indicates the first The and the first Interaction weights between process parameters.

2. The method for improving the reliability of the whole machine manufacturing of the 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 according to the dynamic sensitivity factor and the component reliability, and reasonably determines the adjustment range of the process parameters; the unreliable degree of the current component reliability is multiplied by the dynamic sensitivity factor to obtain a preliminary adjustment range as the numerator, and the square of the dynamic sensitivity factor and a minimum positive number are introduced into the denominator; meanwhile, an adjustment coefficient is introduced to allow the step length of the adjustment to be controlled according to actual requirements.

3. The method for improving the reliability of the whole machine manufacturing of the gateway electric energy meter according to claim 1 or 2, characterized in that, the calculation formula of the process parameter adjustment amount is as follows: ， in, Indicates the first The iteration of the ... The first component Adjustment amount of each process parameter; Indicates the adjustment coefficient; Indicates the first The unreliability of individual components; Indicates the first The iteration of the ... Reliability of individual components; Indicates the normalized denominator; Represents a very small positive number; Indicates the first In the nth iteration The first component The dynamic sensitivity factor of each process parameter.

4. The method for improving the reliability of the whole machine manufacturing of the gateway electric energy meter according to claim 2, 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; 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, the smaller value of the two is taken, and then the larger value of the two is compared with the minimum allowable value of the process parameter, the larger value of the two is taken as the final updated process parameter value, so that the adjusted parameter cannot exceed the limitation of the equipment capacity or the material characteristics; the reliability of the component is further updated based on the updated process parameter value; the actual process parameter change amount in this iteration is calculated by directly reflecting the change of the component reliability through the adjusted process parameter, that is, the updated process parameter value is subtracted from the process parameter value before adjustment, and then divided by the reference value of the process parameter to obtain a dimensionless relative change range, which is multiplied by the corresponding dynamic sensitivity factor; the unreliable degree is introduced to gradually reduce the component reliability improvement range when it approaches perfection.

5. The method for improving the reliability of the whole machine manufacturing of the 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 positive, 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 it needs to be iteratively improved. If the error is zero or negative, it means that the target has been reached or exceeded.

6. A system for improving the reliability of the whole machine manufacturing of a gateway electric energy meter, for implementing the method of any one of claims 1 to 5, characterized in that, Comprise: A component initial reliability calculation unit is configured to deploy sensors on a production line, record the number of defects of each component of the gateway electric energy meter at the process stage and the total production quantity, and calculate the initial reliability of each component based on the number of defects and the total production quantity of the component; An electric energy meter initial reliability calculation unit is configured to calculate the initial reliability of the entire gateway electric energy meter based on the initial reliability of each component, as the starting point of dynamic process parameter optimization; A dynamic sensitivity factor calculation unit is configured to analyze the relationship between the reliability of the components and the process parameters by a dynamic sensitivity analysis algorithm, and calculate the dynamic sensitivity factor of each component process parameter; A process parameter adjustment unit is configured to dynamically adjust the process parameters based on the dynamic sensitivity factor, the reliability of the components and the process parameters, and gradually improve the reliability of the components and the entire machine through the dynamic process parameter optimization and reliability iterative improvement algorithm combined with sensitivity analysis and iterative verification.

7. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the steps of the method of any one of claims 1 to 5.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the method of any one of claims 1 to 5.

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