Production management method and system for single-phase cost control intelligent electric energy meter

Abstract

The invention discloses a production management method and system for a single-phase charge-control intelligent electric energy meter, and relates to the technical field of electric variable measurement. The production management method for the single-phase cost control intelligent electric energy meter comprises the following steps: S1, evaluating process parameters; s2, evaluating image quality; and S3, evaluating the electric energy meter. According to the method, the technological parameter evaluation value is obtained through the obtained technological parameter data, whether technological parameter optimization is carried out or not is judged based on the technological parameter evaluation value, then the image quality evaluation value is obtained according to the obtained image acquisition data, and whether image acquisition optimization is carried out or not is judged based on the image quality evaluation value. And finally, obtaining an electric energy meter evaluation value according to the obtained electric energy meter data, and judging whether to perform electric energy meter parameter optimization or not based on the electric energy meter evaluation value, thereby achieving an effect of more accurately performing production quality management of the single-phase cost control intelligent electric energy meter. The problem that in the prior art, production quality management of a single-phase cost-control intelligent electric energy meter is not comprehensive is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of measuring electric variables, and in particular to a production management method and system for a single-phase fee-controlled intelligent electric energy meter. Background Art

[0002] With the in-depth development and popularization of smart grids, the demand for single-phase fee-controlled smart energy meters will continue to grow. At the same time, as users' requirements for the quality and performance of energy meters continue to increase, the requirements for production management methods and systems are also getting higher and higher. Single-phase fee-controlled smart energy meters are an important part of smart grid construction, and they play an irreplaceable role in the metering and management of residential electricity consumption. Single-phase fee-controlled smart energy meters improve the power company's ability to control users' electricity consumption behavior and reduce the cost of electricity recovery by combining remote fee control and local fee control.

[0003] The existing technology realizes comprehensive monitoring and management of the production process of the electric energy meter by reading the preset operation information corresponding to the internal serial number of the microcontroller unit (MCU) of the single-phase fee-controlled smart electric energy meter and comparing it with the recorded actual operation information.

[0004] For example, a single-phase fee-controlled smart energy meter production management method is announced in the invention patent with announcement number: CN106124813B, including: an MCU internal serial number generation module; the MCU internal serial number is generated when the MCU of the single-phase fee-controlled smart energy meter is burned with a built-in program; a process recording module; the process recording module records the operation information of each process of the single-phase fee-controlled smart energy meter; the operation information corresponds to the MCU internal serial number; a detection module; at the last process of the single-phase fee-controlled smart energy meter, it is detected whether the operation information preset corresponding to the MCU internal serial number corresponds to the operation information recorded by the process recording module.

[0005] For example, a dual-core electric energy meter safety management method announced in the invention patent with announcement number: CN108318721B includes: if it is detected that the current dual-core electric energy meter is in the operating mode, the metering core is controlled to only perform clock setting and only perform read operations to the outside; if it is determined that the dual-core electric energy meter has successively obtained the first combined state, the second combined state, and the third combined state, the dual-core electric energy meter is controlled to switch to the laboratory mode, allowing the metering core to perform data clearing operations; at the same time, the safety management method also includes, before the dual-core electric energy meter obtains the corresponding combined state, it also detects whether the external input signal meets the corresponding combined state condition. If so, the corresponding combined state acquisition operation is performed, otherwise, the operation is exited.

[0006] However, in the process of implementing the technical solution of the invention in the embodiment of the present application, it is found that the above technology has at least the following technical problems:

[0007] In the prior art, during the production process of single-phase fee-controlled smart energy meters, interference from various external and internal factors may lead to unstable product quality, thereby affecting the stability of the energy meter. There is a problem of incomplete production quality management of single-phase fee-controlled smart energy meters. Summary of the invention

[0008] The embodiments of the present application solve the problem of incomplete production quality management of single-phase cost-controlled smart electricity meters in the prior art by providing a production management method and system for single-phase cost-controlled smart electricity meters, and achieve more accurate production quality management of single-phase cost-controlled smart electricity meters.

[0009] The embodiment of the present application provides a production management method for a single-phase cost-controlled smart energy meter, comprising the following steps: S1, obtaining a process parameter evaluation value based on the acquired process parameter data, and judging whether to perform process parameter optimization based on the process parameter evaluation value, wherein the process parameter evaluation value is used to evaluate the process quality in the production process of the single-phase cost-controlled smart energy meter; S2, obtaining an image quality evaluation value based on the acquired image acquisition data, and judging whether to perform image acquisition optimization based on the image quality evaluation value, wherein the image quality evaluation value is used to evaluate the surface quality of the single-phase cost-controlled smart energy meter; S3, obtaining an energy meter evaluation value based on the acquired energy meter data, and judging whether to perform energy meter parameter optimization based on the energy meter evaluation value, wherein the energy meter evaluation value is used to evaluate the use quality of the single-phase cost-controlled smart energy meter.

[0010] Furthermore, the process parameter data includes welding temperature, patch pressure, welding time and X-ray resolution; the image acquisition data includes image resolution, illumination uniformity, contrast and image noise.

[0011] Furthermore, the specific method for obtaining the process parameter evaluation value according to the acquired process parameter data is as follows: obtaining the welding temperature deviation according to the relative relationship between the welding temperature and the welding temperature extreme value in the database; obtaining the patch pressure deviation according to the relative relationship between the patch pressure and the patch pressure extreme value in the database; obtaining the X-ray resolution deviation according to the relative relationship between the X-ray resolution and the preset X-ray resolution in the database, and the X-ray resolution deviation is obtained by processing the ratio operation of the X-ray resolution and the preset X-ray resolution; processing the welding temperature deviation, the patch pressure deviation and the X-ray resolution deviation to obtain the process parameter evaluation value; the method for obtaining the welding temperature deviation is: if If the welding temperature is less than the minimum welding temperature, the ratio of the welding temperature to the minimum welding temperature is processed to obtain the welding temperature deviation; if the welding temperature is within the extreme value range of the welding temperature, the welding temperature deviation is recorded as 1, otherwise the ratio of the welding temperature to the maximum welding temperature is processed to obtain the welding temperature deviation; the method for obtaining the patch pressure deviation is as follows: if the patch pressure is less than the minimum patch pressure, the ratio of the patch pressure to the minimum patch pressure is processed to obtain the patch pressure deviation; if the patch pressure is within the extreme value range of the patch pressure, the patch pressure deviation is recorded as 1, otherwise the ratio of the patch pressure to the maximum patch pressure is processed to obtain the patch pressure deviation.

[0012] Furthermore, the specific method for obtaining the image quality evaluation value based on the acquired image acquisition data is as follows: obtaining the image resolution deviation based on the relative relationship between the image resolution and the preset image resolution in the database, and the image resolution deviation is obtained by processing the ratio operation of the image resolution and the preset image resolution; obtaining the illumination uniformity deviation based on the relative relationship between the illumination uniformity and the preset illumination uniformity in the database, and the illumination uniformity deviation is obtained by processing the ratio operation of the illumination uniformity and the preset illumination uniformity; obtaining the image noise deviation based on the relative relationship between the image noise and the preset image noise in the database, and the image noise deviation is obtained by processing the ratio operation of the image noise and the preset image noise; obtaining the contrast deviation based on the relative relationship between the contrast and the preset contrast in the database, and the contrast deviation is obtained by processing the ratio operation of the contrast and the preset contrast; processing the optimized process parameter evaluation value, image resolution deviation, illumination uniformity deviation, image noise deviation and contrast deviation to obtain the image quality evaluation value.

[0013] Furthermore, the electric energy meter data includes load error, temperature error and frequency error; the load error is obtained by processing the relative relationship between the electric energy measured by the single-phase fee-controlled smart electric energy meter to be monitored under different load conditions within a first preset time period and the first reference electric energy; the temperature error is obtained by processing the relative relationship between the electric energy measured by the single-phase fee-controlled smart electric energy meter to be monitored under different temperature conditions within a second preset time period and the second reference electric energy; the frequency error is obtained by processing the relative relationship between the electric energy measured by the single-phase fee-controlled smart electric energy meter to be monitored under different frequency conditions within a third preset time period and the third reference electric energy.

[0014] Furthermore, the specific method for obtaining the electric energy meter evaluation value based on the acquired electric energy meter data is as follows:

[0015] ;

[0016] In the formula, DNB represents the meter evaluation value of the single-phase fee-controlled smart energy meter to be monitored, JBWC represents the load error of the single-phase fee-controlled smart energy meter to be monitored, WDWC represents the temperature error of the single-phase fee-controlled smart energy meter to be monitored, PLWC represents the frequency error of the single-phase fee-controlled smart energy meter to be monitored, Indicates the image quality assessment value after image acquisition optimization.

[0017] An embodiment of the present application provides a production management system for a single-phase cost-controlled smart energy meter, including: a process parameter evaluation module, an image quality evaluation module and an energy meter evaluation module; wherein the process parameter evaluation module is used to obtain a process parameter evaluation value based on the acquired process parameter data, and judge whether to perform process parameter optimization based on the process parameter evaluation value, and the process parameter evaluation value is used to evaluate the process quality in the production process of the single-phase cost-controlled smart energy meter; the image quality evaluation module is used to obtain an image quality evaluation value based on the acquired image acquisition data, and judge whether to perform image acquisition optimization based on the image quality evaluation value, and the image quality evaluation value is used to evaluate the surface quality of the single-phase cost-controlled smart energy meter; the energy meter evaluation module is used to obtain an energy meter evaluation value based on the acquired energy meter data, and judge whether to perform energy meter parameter optimization based on the energy meter evaluation value, and the energy meter evaluation value is used to evaluate the use quality of the single-phase cost-controlled smart energy meter.

[0018] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0019] 1. Determine whether to perform process parameter optimization based on the process parameter evaluation value obtained through the acquired process parameter data, then determine whether to perform image acquisition optimization based on the image quality evaluation value obtained based on the acquired image acquisition data, and finally determine whether to perform meter parameter optimization based on the electric energy meter evaluation value obtained based on the acquired electric energy meter data, thereby improving the product quality of single-phase cost-controlled smart electric energy meters, and further achieving more accurate production quality management of single-phase cost-controlled smart electric energy meters, effectively solving the problem of incomplete production quality management of single-phase cost-controlled smart electric energy meters in the prior art.

[0020] 2. The welding temperature deviation is obtained by the welding temperature and the welding temperature extreme value in the database, and then the patch pressure deviation is obtained according to the patch pressure and the patch pressure extreme value in the database. Then, the X-ray resolution deviation is obtained according to the X-ray resolution and the preset X-ray resolution in the database. Finally, the welding temperature deviation, patch pressure deviation and X-ray resolution deviation are used to obtain the process parameter evaluation value, thereby quantitatively evaluating the degree of deviation between the process parameters and the standard parameters, and then realizing the identification and processing of potential problems in the process of single-phase cost-controlled smart electricity meters.

[0021] 3. The image resolution deviation is obtained through the image resolution and the preset image resolution, and then the illumination uniformity deviation is obtained according to the illumination uniformity and the preset illumination uniformity. Then the image noise deviation is obtained according to the image noise and the preset image noise. Then the contrast deviation is obtained according to the contrast and the preset contrast. Finally, the image quality evaluation value is obtained by the optimized process parameter evaluation value, image resolution deviation, illumination uniformity deviation, image noise deviation and contrast deviation, thereby quantitatively evaluating the quality of the surface image of the single-phase fee-controlled smart electricity meter, thereby achieving more timely identification and solving of image acquisition problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flowchart of a production management method for a single-phase fee-controlled smart energy meter provided in an embodiment of the present application;

[0023] Figure 2 A schematic diagram showing changes in the process parameter evaluation values ​​provided in the embodiment of the present application as the welding temperature changes;

[0024] Figure 3 A schematic diagram showing the change of the process parameter evaluation value provided in the embodiment of the present application with the change of the patch pressure;

[0025] Figure 4 A schematic diagram showing the change of the process parameter evaluation value provided in the embodiment of the present application with the change of the X-ray resolution;

[0026] Figure 5 A schematic diagram of the structure of a production management system for a single-phase cost-controlled smart energy meter provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The embodiments of the present application solve the problem of incomplete production quality management of single-phase cost-controlled smart electricity meters in the prior art by providing a production management method and system for single-phase cost-controlled smart electricity meters. A process parameter evaluation value is obtained by acquiring process parameter data, and whether to perform process parameter optimization is determined based on the process parameter evaluation value. Then, an image quality evaluation value is obtained based on the acquired image acquisition data, and whether to perform image acquisition optimization is determined based on the image quality evaluation value. Finally, an electricity meter evaluation value is obtained based on the acquired electricity meter data, and whether to perform electricity meter parameter optimization is determined based on the electricity meter evaluation value, thereby achieving more accurate production quality management of single-phase cost-controlled smart electricity meters.

[0028] The technical solution in the embodiment of the present application is to solve the problem of incomplete production quality management of the above-mentioned single-phase fee-controlled smart energy meter. The overall idea is as follows:

[0029] Whether to perform process parameter optimization is determined based on the obtained process parameter evaluation value, and then whether to perform image acquisition optimization is determined based on the obtained image quality evaluation value. Finally, whether to perform electric energy meter parameter optimization is determined based on the obtained electric energy meter evaluation value, thereby achieving a more accurate production quality management effect for single-phase cost-controlled intelligent electric energy meters.

[0030] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0031] like Figure 1 As shown, it is a flowchart of a production management method for a single-phase cost-controlled smart energy meter provided in an embodiment of the present application, the method comprising the following steps: S1, process parameter evaluation: obtaining a process parameter evaluation value based on the acquired process parameter data, judging whether to perform process parameter optimization based on the process parameter evaluation value, and the process parameter evaluation value is used to evaluate the process quality in the production process of the single-phase cost-controlled smart energy meter; S2, image quality evaluation: obtaining an image quality evaluation value based on the acquired image acquisition data, judging whether to perform image acquisition optimization based on the image quality evaluation value, and the image quality evaluation value is used to evaluate the surface quality of the single-phase cost-controlled smart energy meter; S3, energy meter evaluation: obtaining an energy meter evaluation value based on the acquired energy meter data, judging whether to perform energy meter parameter optimization based on the energy meter evaluation value, and the energy meter evaluation value is used to evaluate the use quality of the single-phase cost-controlled smart energy meter.

[0032] It should be added that the process parameter data include welding temperature, placement pressure, welding time and X-ray resolution; the image acquisition data include image resolution, illumination uniformity, contrast and image noise.

[0033] In this embodiment, the welding temperature is obtained by an infrared temperature sensor, the patch pressure is obtained by a force sensor, and the welding time is obtained by a timer.

[0034] The pixel size of the smallest discernible defect in the X-ray image is quantified by image analysis software (such as OpenCV), and the physical size of the smallest discernible defect is obtained by converting pixels to actual physical sizes. The X-ray resolution is obtained by performing a ratio operation based on the physical size of the smallest discernible defect and the pixel size of the smallest discernible defect. Similarly, the image resolution is obtained by performing a ratio operation based on the physical size of the smallest discernible detail and the pixel size of the smallest discernible detail.

[0035] The surface image of the single-phase fee-controlled smart energy meter is read by image analysis software (such as OpenCV), and the surface image of the single-phase fee-controlled smart energy meter is converted into a grayscale image. The grayscale values ​​obtained are used for standard deviation calculation to obtain the illumination uniformity.

[0036] The surface image of the single-phase fee-controlled smart energy meter is read by image analysis software (such as OpenCV), and the surface image of the single-phase fee-controlled smart energy meter is converted into a grayscale image to obtain the grayscale value for contrast calculation. The contrast calculation formula is: , where DB represents contrast, represents the maximum gray value, Indicates the minimum grayscale value.

[0037] The noise level in the surface image of the single-phase fee-controlled smart energy meter is quantified by an image noise analyzer, and the image noise is obtained by calculating the standard deviation of pixels in the surface image of the single-phase fee-controlled smart energy meter.

[0038] Through the above steps, fine control of each production link is achieved, thereby improving the product quality and production efficiency of the single-phase cost-controlled smart energy meter, and further realizing more accurate production quality management of the single-phase cost-controlled smart energy meter.

[0039] Furthermore, a specific method for obtaining a process parameter evaluation value based on the acquired process parameter data is as follows: obtaining a welding temperature extreme value, a patch pressure extreme value, and a preset X-ray resolution from a preset database, wherein the welding temperature extreme value includes a welding temperature maximum value and a welding temperature minimum value, and the patch pressure extreme value includes a maximum value and a minimum value; obtaining a welding temperature deviation (i.e., the value in the specific limiting expression of the process parameter evaluation value) based on the relative relationship between the welding temperature and the welding temperature extreme value in the database ); According to the relative relationship between the patch pressure and the extreme value of the patch pressure in the database, the patch pressure deviation (i.e., the specific limit expression of the process parameter evaluation value) is obtained. ); The X-ray resolution deviation (i.e., the specific limiting expression of the process parameter evaluation value) is obtained according to the relative relationship between the X-ray resolution and the preset X-ray resolution in the database. ), the X-ray resolution deviation is obtained by processing the ratio operation of the X-ray resolution and the preset X-ray resolution; the welding temperature deviation, the patch pressure deviation and the X-ray resolution deviation are processed to obtain the process parameter evaluation value; the method for obtaining the welding temperature deviation is as follows: if the welding temperature is less than the minimum welding temperature, the ratio operation of the welding temperature and the minimum welding temperature is processed to obtain the welding temperature deviation; if the welding temperature is within the extreme value range of the welding temperature, the welding temperature deviation is recorded as 1, otherwise the ratio operation of the welding temperature and the maximum welding temperature is processed to obtain the welding temperature deviation; the method for obtaining the patch pressure deviation is as follows: if the patch pressure is less than the minimum patch pressure, the ratio operation of the patch pressure and the minimum patch pressure is processed to obtain the patch pressure deviation; if the patch pressure is within the extreme value range of the patch pressure, the patch pressure deviation is recorded as 1, otherwise the ratio operation of the patch pressure and the maximum patch pressure is processed to obtain the patch pressure deviation.

[0040] Among them, the specific limiting expression of the process parameter evaluation value is as follows:

[0041] ;

[0042] ;

[0043] ;

[0044] In the formula, t represents the number of the production time point, , T represents the total quantity at the production time point, represents the process parameter evaluation value at the t-th production time point, represents the welding temperature deviation at the t-th production time point, represents the welding temperature at the t-th production time point, Indicates the minimum welding temperature. Indicates the maximum welding temperature. It represents the chip pressure deviation of the tth single-phase fee-controlled smart energy meter to be monitored, represents the patch pressure at the t-th production time point, Indicates the minimum patch pressure. Indicates the maximum pressure of the patch, HJSJ indicates the welding time, represents the X-ray resolution at the t-th production time point, Indicates the preset X-ray resolution.

[0045] In this embodiment, the preset X-ray resolution is set to 0.1mm, and the welding temperature is set according to the type of solder. For example, if the standard range of welding temperature is 250℃-400℃, the maximum welding temperature is set to 400℃, and the minimum welding temperature is set to 250℃; the patch pressure is set according to the size, type, substrate material and production process requirements of the component. For example, if the standard range of patch pressure is 0.5MPa-3MPa, the maximum patch pressure is set to 3MPa, and the minimum patch pressure is set to 0.5MPa; through the above steps, the degree of deviation between the process parameters and the standard parameters is quantitatively evaluated, thereby realizing the identification and processing of potential problems in the process of single-phase cost-controlled smart energy meters.

[0046] The algorithm of this embodiment combines the process parameter data for comprehensive analysis to obtain the process parameter evaluation value. The process parameter data in the algorithm of this embodiment do not exist independently, but are interrelated. Generally speaking, the change of welding temperature may affect the heat conduction and heat dissipation between the patch component and the substrate, thereby affecting the selection of patch pressure. Therefore, the greater the welding temperature deviation, the greater the patch pressure deviation; the change of patch pressure may also affect the distribution of thermal stress in the welding process, thereby affecting the control of welding temperature, so the greater the patch pressure deviation, the greater the welding temperature deviation; the level of X-ray resolution may also affect the evaluation and judgment of welding defects, thereby affecting the adjustment and optimization of welding temperature, therefore, the greater the X-ray resolution deviation, the greater the welding temperature deviation; the level of X-ray resolution may also affect the evaluation and judgment of patch quality, thereby affecting the adjustment and optimization of patch pressure, therefore, the smaller the X-ray resolution, the greater the patch pressure deviation; in summary, the greater the welding temperature deviation, the greater the patch pressure deviation, and the smaller the X-ray resolution, the smaller the process parameter evaluation value, which means that the degree of deviation between the process parameters and the standard parameters is greater.

[0047] Assumptions: The minimum welding temperature is 100°C, the maximum welding temperature is 300°C, the minimum patch pressure is 50MPa, the maximum patch pressure is 200MPa, and the preset X-ray resolution is 1mm. Figure 2 A schematic diagram of the change of the process parameter evaluation value with the welding temperature provided in the embodiment of the present application, when the patch pressure deviation and the X-ray resolution deviation are 1, and the welding temperature is less than the minimum welding temperature, the process parameter evaluation value increases with the increase of the welding temperature; when the patch pressure deviation and the X-ray resolution deviation are 1, and the welding temperature is greater than the maximum welding temperature, the process parameter evaluation value decreases with the increase of the welding temperature; otherwise, the process parameter evaluation value does not change.

[0048] like Figure 3The figure shows a schematic diagram of the change of the process parameter evaluation value provided in the embodiment of the present application with the change of the patch pressure. When the welding temperature deviation and the X-ray resolution deviation are 1, and the patch pressure is less than the minimum patch pressure, the process parameter evaluation value increases with the increase of the patch pressure. When the welding temperature deviation and the X-ray resolution deviation are 1, and the patch pressure is greater than the maximum patch pressure, the process parameter evaluation value decreases with the increase of the patch pressure. Otherwise, the process parameter evaluation value does not change.

[0049] like Figure 4 The figure shows a schematic diagram of the change of the process parameter evaluation value provided in the embodiment of the present application with the change of X-ray resolution. When the welding temperature deviation and the patch pressure deviation are 1, the process parameter evaluation value increases with the increase of X-ray resolution.

[0050] Furthermore, the specific process of judging whether to optimize the process parameters based on the process parameter evaluation value is as follows: A1, judging whether the process parameter evaluation value is less than the preset process threshold in the database. If the process parameter evaluation value is less than the preset process threshold, execute A2, otherwise the process parameter optimization is not performed; A2, adjusting the welding parameters, judging whether the process parameter evaluation value obtained after adjusting the welding parameters is less than the preset process threshold in the database, if the process parameter evaluation value is less than the preset process threshold, execute A3, otherwise the process parameter optimization is terminated, and the welding parameters include welding current, voltage, welding speed and electrode diameter; A3, adjusting the welding process, judging whether the process parameter evaluation value obtained after adjusting the welding process is less than the preset process threshold in the database, if the process parameter evaluation value is less than the preset process threshold, execute A4, otherwise the process parameter optimization is terminated, and adjusting the welding process includes reducing the number of welding times and adjusting the welding sequence; A4, optimizing the X-ray resolution, judging whether the process parameter evaluation value obtained after optimizing the X-ray resolution is less than the preset process threshold in the database, if the process parameter evaluation value is less than the preset process threshold, feedback is performed, otherwise the process parameter optimization is terminated, and optimizing the X-ray resolution includes adjusting the voltage and adjusting the current.

[0051] In this embodiment, the preset process threshold is represented by the maximum value of the process parameter evaluation value in the historical time period; the welding current is gradually increased or decreased by the current adjustment knob on the electric welder according to the thickness and type of the welding material. For example, for common manual arc welding, the current is usually controlled within a certain range. For example, when welding thin steel plates (1mm-5mm), the current is set to 60-100A, and when welding thick steel plates (above 10mm), more than 250A may be required; the voltage is adjusted by the voltage adjustment knob of the electric welder. The voltage setting usually depends on the wire diameter, material type and welding speed. The appropriate range of the voltage setting is set according to the specific welding requirements. For example, for common manual arc welding, the current is usually controlled within a certain range, such as when welding thin steel plates (1mm-5mm ) is set to 16-22V, while 28-35V may be required when welding thick steel plates (over 10mm); adjust the welding speed according to the thickness and type of the welding workpiece, for example, the welding rate is 4cm / min-8cm / min when welding thin steel plates (1mm-5mm), and it is set to 1.5cm / min-3cm / min when welding thick steel plates (over 10mm); set the electrode diameter according to the thickness of the welding workpiece, welding current and the selected welding method, for example, the welding diameter is 2.0mm-2.5mm when welding thin steel plates (1mm-5mm), and it is set to 3.2mm-4.0mm when welding thick steel plates (over 10mm); feedback the process parameter evaluation values ​​obtained after the implementation of each process parameter optimization measure to the preset personnel.

[0052] By evaluating the welding requirements of the single-phase fee-controlled smart energy meter, merging multiple joints or adjusting the distribution of the welding sections, the number of welding times can be reduced. For example, if a workpiece needs to weld 4 joints, the two adjacent joints can be merged into one joint; welding can be performed from the middle to both ends of the single-phase fee-controlled smart energy meter to adjust the welding sequence.

[0053] The voltage and current are set according to the thickness of the material of the single-phase fee-controlled smart energy meter. For example, thin material (1mm-5mm): the voltage setting is usually between 70kV-100kV, and the current setting is usually between 1mA-5mA.

[0054] Through the above steps, the process optimization of single-phase fee-controlled smart electricity meter is achieved.

[0055] Furthermore, a specific method for obtaining an image quality evaluation value based on the acquired image acquisition data is as follows: obtaining preset image acquisition data from a preset database, the preset image acquisition data including preset image resolution, preset illumination uniformity, preset image noise, and preset contrast; obtaining an image resolution deviation (i.e., the value of the image quality evaluation value in the method for obtaining the image quality evaluation value) based on a relative relationship between the image resolution and the preset image resolution in the database; ), the image resolution deviation is obtained by processing the ratio of the image resolution to the preset image resolution; the illumination uniformity deviation is obtained according to the relative relationship between the illumination uniformity and the preset illumination uniformity in the database (i.e., the method for obtaining the image quality evaluation value). ), the illumination uniformity deviation is obtained by processing the ratio of the illumination uniformity and the preset illumination uniformity; the image noise deviation is obtained according to the relative relationship between the image noise and the preset image noise in the database (i.e., the method for obtaining the image quality evaluation value). ), the image noise deviation is obtained by processing the ratio of the image noise and the preset image noise; the contrast deviation is obtained according to the relative relationship between the contrast and the preset contrast in the database (i.e., the method for obtaining the image quality evaluation value). ), the contrast deviation is obtained by processing the ratio of the contrast to the preset contrast; the optimized process parameter evaluation value, image resolution deviation, illumination uniformity deviation, image noise deviation and contrast deviation are processed to obtain the image quality evaluation value.

[0056] The method for obtaining the image quality evaluation value is as follows:

[0057] ;

[0058] In the formula, t represents the number of the production time point, , T represents the total quantity at the production time point, represents the optimized process parameter evaluation value at the T-th production time point, TUX represents the image quality evaluation value of the surface image corresponding to the single-phase fee-controlled intelligent energy meter to be monitored, TFEN represents the image resolution of the surface image corresponding to the single-phase fee-controlled intelligent energy meter to be monitored, GZJY represents the illumination uniformity of the surface image corresponding to the single-phase fee-controlled intelligent energy meter to be monitored, TXZS represents the image noise of the surface image corresponding to the single-phase fee-controlled intelligent energy meter to be monitored, DBDU represents the contrast of the surface image corresponding to the single-phase fee-controlled intelligent energy meter to be monitored, Indicates the preset image resolution. Indicates the preset illumination uniformity. represents the preset image noise, Indicates preset contrast.

[0059] In this embodiment, the preset image resolution is set to 12MP; the preset illumination uniformity is set to 95%; the preset contrast is set to 50%; and the preset image noise is set to 5%. Through the above steps, the quality of the surface image of the single-phase fee-controlled smart electricity meter is quantitatively evaluated, thereby achieving more timely identification and solving of image acquisition problems.

[0060] The algorithm of this embodiment combines the image acquisition data with a comprehensive analysis to obtain an image quality evaluation value. The image acquisition data in the algorithm of this embodiment does not exist independently, but is interrelated. Generally speaking, the larger the optimized process parameter evaluation value, the larger the image quality evaluation value; the higher the image resolution usually makes the image clearer, but may also increase image noise; the higher the illumination uniformity can reduce shadows and reflections in the image, making the image look smoother, which may lead to reduced noise and improved contrast; in summary, the larger the optimized process parameter evaluation value, image resolution, illumination uniformity and contrast, the larger the image quality evaluation value; the larger the image noise, the smaller the image quality evaluation value, which means the worse the image quality.

[0061] Furthermore, the specific process of judging whether to perform image acquisition optimization based on the image quality evaluation value is as follows: B1, judging whether the image quality evaluation value is less than the preset image quality threshold in the database. If the image quality evaluation value is less than the preset image quality threshold in the database, execute B2, otherwise do not perform image acquisition optimization; B2, prompt the preset personnel to adjust the camera parameters, and judge whether the image quality evaluation value obtained after adjusting the camera parameters is less than the preset image quality threshold in the database. If the image quality evaluation value is less than the preset image quality threshold in the database, execute B3, otherwise end the image acquisition optimization. Adjusting the camera parameters includes adjusting the focal length, exposure time, and white balance; B3, prompting the preset personnel to improve the lighting conditions, and judge the image quality obtained after improving the lighting conditions Whether the evaluation value is less than the preset image quality threshold in the database. If the image quality evaluation value is less than the preset image quality threshold in the database, execute B4, otherwise end the image acquisition optimization. Improving the lighting conditions includes adding light sources and adjusting the position of the light sources; B4, reducing image noise, judging whether the image quality evaluation value obtained after reducing the image noise is less than the preset image quality threshold in the database, if the image quality evaluation value is less than the preset image quality threshold in the database, execute B5, otherwise end the image acquisition optimization; B5, adjusting the contrast, judging whether the image quality evaluation value obtained after adjusting the contrast is less than the preset image quality threshold in the database, if the image quality evaluation value is less than the preset image quality threshold in the database, provide feedback, otherwise end the image acquisition optimization.

[0062] In this embodiment, the preset image quality threshold is represented by the maximum value of the image quality evaluation value in the historical time period; in different areas of the single-phase cost-controlled smart energy meter production line, light-emitting diode (LED) lamps are reasonably arranged according to work requirements to ensure that different areas of the production line obtain uniform light, which helps to improve the product quality and production efficiency of the single-phase cost-controlled smart energy meter and reduce errors caused by insufficient or improper lighting. For example, in the assembly area, LED lights are evenly distributed on the ceiling to ensure that the light covers the entire working area; by adjusting the angle and height of the LED lamp, ensure that the light is vertically irradiated on the energy meter assembly; according to the size of the working area and the height of the energy meter assembly, adjust the installation height of the LED lamp. Generally, LED lights should be installed on the ceiling 2-3 meters away from the working area. For example, assuming that the height of the workbench is 1.2 meters and the ceiling height is 3 meters, the LED lights can be installed about 2.5 meters above the ceiling. Generally, the angle between the LED lights and the working surface should be close to 90 degrees. For example, if an adjustable-angle LED light is installed, assuming that the default angle of each lamp is 45 degrees, it can be adjusted to 70-80 degrees to ensure that the light beam is more vertically irradiated to the surface of the electric energy meter. The mean filtering algorithm is used to smooth the image and reduce the impact of image noise. The contrast is improved through image processing software (such as Photoshop), or the image contrast is enhanced through a histogram equalization algorithm. The image quality evaluation value obtained after the implementation of each image acquisition optimization measure is fed back to the preset personnel. Through the above steps, the surface quality of the single-phase fee-controlled smart electric energy meter is improved.

[0063] Furthermore, the electric energy meter data includes load error, temperature error and frequency error; the load error (i.e., JBWC in the specific method of the electric energy meter evaluation value) is obtained by processing the relative relationship between the electric energy measured by the single-phase fee-controlled smart electric energy meter to be monitored under different load conditions within the first preset time period and the first reference electric energy; the temperature error (i.e., WDWC in the specific method of the electric energy meter evaluation value) is obtained by processing the relative relationship between the electric energy measured by the single-phase fee-controlled smart electric energy meter to be monitored under different temperature conditions within the second preset time period and the second reference electric energy; the frequency error (i.e., PLWC in the specific method of the electric energy meter evaluation value) is obtained by processing the relative relationship between the electric energy measured by the single-phase fee-controlled smart electric energy meter to be monitored under different frequency conditions within the third preset time period and the third reference electric energy.

[0064] Specifically, the specific method for obtaining the electric energy meter evaluation value according to the acquired electric energy meter data is as follows:

[0065] ;

[0066] ;

[0067] ;

[0068] ;

[0069] Wherein, a represents the number of the first preset time period, , A represents the total number of the first preset time period, b represents the number of the second preset time period, , B represents the total number of the second preset time period, d represents the number of the third preset time period, , D represents the total number of the third preset time period, DNB represents the meter evaluation value of the single-phase fee-controlled smart energy meter to be monitored, JBWC represents the load error of the single-phase fee-controlled smart energy meter to be monitored, WDWC represents the temperature error of the single-phase fee-controlled smart energy meter to be monitored, PLWC represents the frequency error of the single-phase fee-controlled smart energy meter to be monitored, represents the image quality evaluation value after image acquisition optimization, represents the electric energy of the single-phase fee-controlled smart electric energy meter to be monitored in the a-th first preset time period, represents the first reference electric energy in the ath first preset time period, represents the electric energy of the single-phase fee-controlled smart electric energy meter to be monitored in the bth first preset time period, represents the second reference electric energy in the bth second preset time period, represents the electric energy of the single-phase fee-controlled smart electric energy meter to be monitored in the dth third preset time period, represents the third reference electric energy in the dth third preset time period.

[0070] In this embodiment, different load conditions are represented by a load range of 10%-150% of the rated current, and the rated current is 5A; different temperature conditions are represented by a range of -25℃-55℃; different frequency conditions are represented by a range of 50-60Hz; the first reference electric energy represents the electric energy measured by a standard single-phase fee-controlled smart electricity meter (a single-phase fee-controlled smart electricity meter that has passed the performance test) under different load conditions within a first preset time period; the second reference electric energy represents the electric energy measured by a standard single-phase fee-controlled smart electricity meter under different temperature conditions within a second preset time period; the third reference electric energy represents the electric energy measured by a standard single-phase fee-controlled smart electricity meter under different frequency conditions within a third preset time period.

[0071] The algorithm of this embodiment combines the comprehensive analysis of the electric energy meter data to obtain the electric energy meter evaluation value. The electric energy meter data in the algorithm of this embodiment does not exist independently, but is interrelated. Generally speaking, the larger the image quality evaluation value, the better the image quality. Therefore, the greater the probability of identifying and solving the problem of the single-phase fee-controlled intelligent electric energy meter, thereby improving the quality of the single-phase fee-controlled intelligent electric energy meter, which may lead to smaller load error, temperature error and frequency error of the single-phase fee-controlled intelligent electric energy meter; the change of load may cause the effective resistance, capacitance and other parameters of the circuit to change, thereby affecting the frequency stability of the system. Therefore, the larger the load error, the greater the frequency error; the temperature change may cause the parameters of the electronic components to change, thereby affecting the frequency stability of the system. Therefore, the larger the temperature error, the greater the frequency error; the change of load may also cause the temperature distribution of the components to change, thereby affecting the temperature error. Therefore, the larger the load error, the greater the temperature error; in summary, the larger the load error, the larger the frequency error and the larger the temperature error, the smaller the electric energy meter evaluation value; the larger the image quality evaluation value, the larger the electric energy meter evaluation value, which means the better the performance of the electric energy meter.

[0072] Through the above steps, the performance of the electric energy meter is quantitatively evaluated, thereby realizing the identification and resolution of the quality problems of the electric energy meter.

[0073] The statistical table of changes in the evaluation value of the electric energy meter is shown in Table 1:

[0074] Table 1 Statistics of changes in the evaluation value of electric energy meters Load error JBWC Temperature error WDWC Frequency Error PLWC Optimized image quality evaluation value TUX' Energy meter evaluation value DNB 0.453 0.564 0.385 0.434 0.306 0.798 0.564 0.385 0.434 0.298 0.798 0.134 0.385 0.434 0.317 0.798 0.134 0.296 0.434 0.322 0.798 0.134 0.296 1.357 0.690

[0075] It can be seen from the first and second groups of data in the table that when the image quality evaluation value, frequency error and temperature error remain unchanged, the larger the load error, the smaller the electric energy meter evaluation value; it can be seen from the second and third groups of data that when the image quality evaluation value, load error and temperature error after image acquisition optimization remain unchanged, the larger the frequency error, the smaller the electric energy meter evaluation value; it can be seen from the third and fourth groups of data that when the image quality evaluation value, load error and frequency error after image acquisition optimization remain unchanged, the larger the temperature error, the smaller the electric energy meter evaluation value; it can be seen from the fourth and fifth groups of data that when the temperature error, load error and frequency error remain unchanged, the larger the image quality evaluation value, the larger the electric energy meter evaluation value.

[0076] Furthermore, the specific process of judging whether to optimize the parameters of the energy meter based on the energy meter evaluation value is as follows: judge whether the energy meter evaluation value is less than the preset energy meter threshold in the database. If the energy meter evaluation value is less than the preset energy meter threshold, calibrate and adjust, otherwise the energy meter parameters will not be optimized; if the energy meter evaluation value obtained after calibration and adjustment is less than the preset energy meter threshold in the database, feedback will be given, otherwise the energy meter parameter optimization will be terminated. The calibration adjustment indicates that the preset personnel will be prompted to replace the hardware and optimize the code.

[0077] In this embodiment, the preset threshold value of the electric energy meter is represented by the maximum value of the electric energy meter evaluation value in the historical time period; calibration adjustment represents the adjustment of the parameters of the electric energy meter; hardware calibration adjustment includes sensor calibration and circuit board adjustment, for example, sensor calibration: if an electric energy meter is in a high temperature environment for a long time, it may cause the sensor performance to deteriorate, thereby affecting the reading of the electric energy meter. The staff recalibrates the sensor by adjusting the calibration value inside the electric energy meter (such as adjusting the zero offset or gain value of the current sensor) or replaces the damaged sensor and recalibrates the electric energy meter; circuit board adjustment: if the output of the current amplifier of the electric energy meter is unstable due to aging, the staff adjusts the gain of the amplifier or replaces the damaged amplifier component; software calibration Accuracy means algorithm optimization and software update. For example, algorithm optimization: if there is a deviation in the current calculation of the electric energy meter, the lighting personnel may adjust the current calculation algorithm (such as modifying the sampling period or adding a compensation algorithm) according to the standard value of the current measurement to correct the error; software update: if there is a calculation defect in the firmware of the electric energy meter, resulting in the failure to accurately record some electric energy data, the staff will solve this problem through the firmware update released by the manufacturer. For example, the electric energy meter may release a patch to fix certain problems in the power calculation algorithm, thereby improving the accuracy of the electric energy meter; the electric energy meter evaluation value obtained after the implementation of the optimization measures for each electric energy meter parameter is fed back to the preset personnel; through the above steps, the accuracy of the single-phase fee-controlled smart electric energy meter is improved.

[0078] like Figure 5As shown, it is a structural schematic diagram of a production management system for a single-phase cost-controlled intelligent energy meter provided in an embodiment of the present application. The production management system for a single-phase cost-controlled intelligent energy meter provided in an embodiment of the present application includes: a process parameter evaluation module, an image quality evaluation module and an energy meter evaluation module; wherein the process parameter evaluation module is used to obtain a process parameter evaluation value according to the acquired process parameter data, and judge whether to optimize the process parameters based on the process parameter evaluation value, and the process parameter evaluation value is used to evaluate the process quality in the production process of the single-phase cost-controlled intelligent energy meter; the image quality evaluation module is used to obtain an image quality evaluation value according to the acquired image acquisition data, and judge whether to optimize the image acquisition based on the image quality evaluation value, and the image quality evaluation value is used to evaluate the surface quality of the single-phase cost-controlled intelligent energy meter; the energy meter evaluation module is used to obtain an energy meter evaluation value according to the acquired energy meter data, and judge whether to optimize the energy meter parameters based on the energy meter evaluation value, and the energy meter evaluation value is used to evaluate the use quality of the single-phase cost-controlled intelligent energy meter.

[0079] In this embodiment, the process parameter evaluation module improves the production quality of the single-phase cost-controlled smart energy meter by evaluating the process quality of the single-phase cost-controlled smart energy meter during the production process and optimizing the process parameters; the image quality evaluation module improves the surface image quality of the single-phase cost-controlled smart energy meter by evaluating the surface quality of the energy meter and optimizing the image acquisition; the energy meter evaluation module improves the use quality and performance of the energy meter by evaluating the use quality and performance of the energy meter and optimizing the energy meter parameters, thereby achieving more accurate production quality management of the single-phase cost-controlled smart energy meter.

[0080] To summarize, the embodiment of the present application determines whether to perform process parameter optimization by using the process parameter evaluation value obtained through the acquired process parameter data, and then determines whether to perform image acquisition optimization based on the image quality evaluation value obtained based on the acquired image acquisition data. Finally, it determines whether to perform meter parameter optimization based on the electric energy meter evaluation value obtained based on the acquired electric energy meter data, thereby improving the product quality of single-phase cost-controlled smart electric energy meters, and further achieving more accurate production quality management of single-phase cost-controlled smart electric energy meters, effectively solving the problem of incomplete production quality management of single-phase cost-controlled smart electric energy meters in the prior art.

[0081] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0082] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0083] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0084] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0085] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0086] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A production management method for a single-phase fee-controlled smart energy meter, characterized in that: The following steps are involved: S1, obtaining a process parameter evaluation value according to the acquired process parameter data, and judging whether to perform process parameter optimization based on the process parameter evaluation value, wherein the process parameter evaluation value is used to evaluate the process quality in the production process of the single-phase cost-controlled smart energy meter; S2, obtaining an image quality evaluation value according to the acquired image acquisition data, and judging whether to perform image acquisition optimization based on the image quality evaluation value, wherein the image quality evaluation value is used to evaluate the surface quality of the single-phase fee-controlled smart energy meter; S3, obtaining an electric energy meter evaluation value according to the acquired electric energy meter data, and judging whether to optimize the electric energy meter parameters based on the electric energy meter evaluation value, wherein the electric energy meter evaluation value is used to evaluate the use quality of the single-phase fee-controlled smart electric energy meter.

2. The production management method for a single-phase fee-controlled smart energy meter according to claim 1, characterized in that: The process parameter data include welding temperature, patch pressure, welding time and X-ray resolution; The image acquisition data includes image resolution, illumination uniformity, contrast and image noise.

3. The production management method for a single-phase fee-controlled smart energy meter according to claim 2, characterized in that: The specific method for obtaining the process parameter evaluation value according to the acquired process parameter data is as follows: The welding temperature deviation is obtained according to the relative relationship between the welding temperature and the welding temperature extreme value in the database; The patch pressure deviation is obtained according to the relative relationship between the patch pressure and the extreme patch pressure value in the database; Obtaining an X-ray resolution deviation according to a relative relationship between the X-ray resolution and a preset X-ray resolution in a database, wherein the X-ray resolution deviation is obtained by processing a ratio operation of the X-ray resolution and the preset X-ray resolution; The welding temperature deviation, the patch pressure deviation and the X-ray resolution deviation are processed to obtain the process parameter evaluation value; The method for obtaining the welding temperature deviation is: If the welding temperature is less than the minimum welding temperature, the ratio of the welding temperature to the minimum welding temperature is processed to obtain the welding temperature deviation; If the welding temperature is within the extreme value range of the welding temperature, the welding temperature deviation is recorded as 1, otherwise the ratio of the welding temperature to the maximum welding temperature is processed to obtain the welding temperature deviation; The method for obtaining the patch pressure deviation is as follows: If the patch pressure is less than the minimum patch pressure value, the ratio of the patch pressure to the minimum patch pressure value is processed to obtain the patch pressure deviation; If the patch pressure is within the extreme value range of the patch pressure, the patch pressure deviation is recorded as 1, otherwise the ratio of the patch pressure and the maximum value of the patch pressure is processed to obtain the patch pressure deviation.

4. The production management method for a single-phase fee-controlled smart energy meter according to claim 3, characterized in that: The specific process of determining whether to optimize the process parameters based on the process parameter evaluation value is as follows: A1, judging whether the process parameter evaluation value is less than the preset process threshold in the database, if the process parameter evaluation value is less than the preset process threshold, executing A2, otherwise, not performing process parameter optimization; A2, adjusting welding parameters, judging whether the process parameter evaluation value obtained after adjusting the welding parameters is less than the preset process threshold in the database, if the process parameter evaluation value is less than the preset process threshold, executing A3, otherwise ending the process parameter optimization; A3, adjusting the welding process, judging whether the process parameter evaluation value obtained after adjusting the welding process is less than the preset process threshold in the database, if the process parameter evaluation value is less than the preset process threshold, executing A4, otherwise ending the process parameter optimization; A4, optimize the X-ray resolution, determine whether the process parameter evaluation value obtained after optimizing the X-ray resolution is less than the preset process threshold in the database, if the process parameter evaluation value is less than the preset process threshold, feedback is given, otherwise the process parameter optimization is terminated.

5. The production management method for a single-phase fee-controlled smart energy meter according to claim 2, characterized in that: The specific method for obtaining the image quality evaluation value according to the acquired image acquisition data is as follows: Obtaining an image resolution deviation according to a relative relationship between the image resolution and a preset image resolution in a database, wherein the image resolution deviation is obtained by processing a ratio operation of the image resolution and the preset image resolution; Obtaining a light uniformity deviation according to a relative relationship between the light uniformity and a preset light uniformity in a database, wherein the light uniformity deviation is obtained by processing a ratio operation of the light uniformity and the preset light uniformity; Obtaining an image noise deviation according to a relative relationship between the image noise and a preset image noise in a database, wherein the image noise deviation is obtained by processing a ratio operation of the image noise and the preset image noise; Obtaining a contrast deviation according to a relative relationship between the contrast and a preset contrast in a database, wherein the contrast deviation is obtained by performing a ratio operation on the contrast and the preset contrast; The optimized process parameter evaluation value, image resolution deviation, illumination uniformity deviation, image noise deviation and contrast deviation are processed to obtain the image quality evaluation value.

6. The production management method for a single-phase fee-controlled smart energy meter according to claim 5, characterized in that: The specific process of determining whether to perform image acquisition optimization based on the image quality evaluation value is as follows: B1, judging whether the image quality evaluation value is less than the preset image quality threshold in the database, if the image quality evaluation value is less than the preset image quality threshold in the database, executing B2, otherwise, not performing image acquisition optimization; B2, prompting the preset personnel to adjust the camera parameters, and judging whether the image quality evaluation value obtained after adjusting the camera parameters is less than the preset image quality threshold in the database. If the image quality evaluation value is less than the preset image quality threshold in the database, execute B3, otherwise end the image acquisition optimization; B3, prompting the preset personnel to improve the lighting conditions, and judging whether the image quality evaluation value obtained after the lighting conditions are improved is less than the preset image quality threshold in the database. If the image quality evaluation value is less than the preset image quality threshold in the database, executing B4, otherwise ending the image acquisition optimization; B4, reducing image noise, determining whether the image quality evaluation value obtained after reducing image noise is less than a preset image quality threshold in the database, if the image quality evaluation value is less than the preset image quality threshold in the database, executing B5, otherwise ending the image acquisition optimization; B5, adjust the contrast, and determine whether the image quality evaluation value obtained after adjusting the contrast is less than the preset image quality threshold in the database. If the image quality evaluation value is less than the preset image quality threshold in the database, feedback is given, otherwise the image acquisition optimization is terminated.

7. The production management method for a single-phase fee-controlled smart energy meter according to claim 1, characterized in that: The electric energy meter data includes load error, temperature error and frequency error; The load error is obtained by processing the relative relationship between the electric energy measured by the monitored single-phase fee-controlled smart electric energy meter under different load conditions within the first preset time period and the first reference electric energy; The temperature error is obtained by processing the relative relationship between the electric energy measured by the monitored single-phase fee-controlled smart electric energy meter under different temperature conditions within the second preset time period and the second reference electric energy; The frequency error is obtained by processing the relative relationship between the electric energy measured by the monitored single-phase fee-controlled smart electric energy meter under different frequency conditions within the third preset time period and the third reference electric energy.

8. The production management method for a single-phase fee-controlled smart energy meter according to claim 7, characterized in that: The specific method for obtaining the electric energy meter evaluation value based on the acquired electric energy meter data is as follows: ； In the formula, DNB represents the meter evaluation value of the single-phase fee-controlled smart energy meter to be monitored, JBWC represents the load error of the single-phase fee-controlled smart energy meter to be monitored, WDWC represents the temperature error of the single-phase fee-controlled smart energy meter to be monitored, PLWC represents the frequency error of the single-phase fee-controlled smart energy meter to be monitored, Indicates the image quality assessment value after image acquisition optimization.

9. The production management method for a single-phase fee-controlled smart energy meter according to claim 8, characterized in that: The specific process of determining whether to optimize the electric energy meter parameters based on the electric energy meter evaluation value is as follows: Determine whether the electric energy meter evaluation value is less than the preset electric energy meter threshold in the database. If the electric energy meter evaluation value is less than the preset electric energy meter threshold, then calibrate and adjust; otherwise, do not optimize the electric energy meter parameters; If the electric energy meter evaluation value obtained after calibration and adjustment is less than the preset electric energy meter threshold in the database, feedback is given, otherwise the electric energy meter parameter optimization is terminated.

10. Production management system for single-phase fee-controlled smart energy meters, characterized in that: include: Process parameter evaluation module, image quality evaluation module and electric energy meter evaluation module; The process parameter evaluation module is used to obtain a process parameter evaluation value according to the acquired process parameter data, and determine whether to perform process parameter optimization based on the process parameter evaluation value, and the process parameter evaluation value is used to evaluate the process quality in the production process of the single-phase cost-controlled smart energy meter; The image quality assessment module is used to obtain an image quality assessment value according to the acquired image acquisition data, and determine whether to perform image acquisition optimization based on the image quality assessment value, and the image quality assessment value is used to evaluate the surface quality of the single-phase fee-controlled smart energy meter; The electric energy meter evaluation module is used to obtain an electric energy meter evaluation value based on the acquired electric energy meter data, and determine whether to optimize the electric energy meter parameters based on the electric energy meter evaluation value. The electric energy meter evaluation value is used to evaluate the use quality of the single-phase fee-controlled smart electric energy meter.

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