Intelligent data analysis system and method for grading scale

Abstract

The invention relates to the technical field of grading scale data analysis, and particularly discloses an intelligent data analysis system and method for a grading scale, the system comprises a grading scale self-checking module, a sample analysis module and a grading scale prompting module, the abnormal state of the grading scale can be found in time by obtaining and analyzing the current state information of the grading scale, and the grading scale is more intelligent. The wrong weighing of the product caused by the fault of the grading scale is avoided, so that the grading scale can accurately identify the weight of the product; the weight of each sample is monitored so as to ensure that each product with abnormal weight can be accurately identified, and finally, the weight abnormity of each sample is prompted, so that the consistency of the products is ensured, meanwhile, the samples with abnormal weight are timely found and prompted, the weight deviation of the products in the production process is reduced, and the overall product quality is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis for sorting scales, and in particular to an intelligent data analysis system and method for sorting scales. Background Art

[0002] At present, with the increasing requirements for product quality, it is necessary to strictly control the weight of products. In the existing process of product weight monitoring, most of the weight of products is detected by sorting scales. However, if there is a lack of detailed analysis of the availability of sorting scales, the weight of products identified by the sorting scales will be wrong, thus failing to ensure that the products meet the quality standards.

[0003] For example, the invention patent with announcement number CN113918017B announces a command input method based on the identification scale platform, which obtains the user's operation mode of the identification scale platform by identifying the scale platform and determines the key commands corresponding to different operation modes, realizes information interaction with the identification scale, and can confirm and print the preferred products more quickly, greatly reducing the number of times the operator raises his hand.

[0004] For example, the invention patent with publication number CN114741013A provides a method and storage medium for intelligent identification scales and realizing fast fixed services in scales, the method comprising: in response to a fast fixed service setting event, displaying a list of options that can be mapped to a fast fixed service button on a display screen; in response to a selection event of at least one option in the option list, displaying a fast fixed service button corresponding to the selected option in a designated area of ​​the display screen, and mapping the service corresponding to the selected option to the fast fixed service button; wherein the service includes at least one of the functions realized by the application provided by the host. With the above technical solution, frequent repetitive operations can be replaced by setting a fast fixed service and using the fast fixed service button for one-click operation in the required scenario, thereby improving work efficiency.

[0005] Based on the above solution, it is found that in the process of product identification, most of the current devices directly identify the products. Direct identification of products may lead to the possibility of identification deviation between the identification results and the actual product status. That is, the device does not accurately obtain the identification results of the product, which is not conducive to the precise identification and analysis of the equipment. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides an intelligent data analysis system and method for selecting scales, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: In a first aspect, the present invention provides an intelligent data analysis system for a sorting scale, including: a sorting scale self-check module, which is used to obtain and analyze the current state information of the sorting scale, comprehensively determine the functional index of the sorting scale, and compare it with the functional threshold preset in the sorting scale sample database. If the functional index of the sorting scale is less than the functional threshold preset in the sorting scale sample database, a functional reminder is given to the sorting scale; if the functional index of the sorting scale is greater than or equal to the functional threshold preset in the sorting scale sample database, the sample analysis module is entered; the sample analysis module is used to record the sorting scale whose functional index is greater than or equal to the functional threshold preset in the sorting scale sample database as a self-check qualified sorting scale, and the weight of each sample is monitored by the self-check qualified sorting scale to obtain the monitored weight of each sample; the sorting scale prompt module is used to compare the monitored weight of each sample with the sample preset weight preset in the sorting scale sample database, and finally give a weight abnormality prompt to each sample.

[0008] As a further solution, the current status information of the sorting scale specifically includes the startup response time period of the sorting scale, the maximum transmission speed of the conveyor belt of the sorting scale within the detection cycle, the signal stability information of the built-in weight sensor of the sorting scale, and the display information corresponding to the display platform to which the sorting scale belongs.

[0009] The signal stability information of the built-in weight sensor of the sorting scale specifically includes the minimum signal power, maximum noise power, average signal transmission rate and minimum signal strength of the built-in weight sensor of the sorting scale within a detection period.

[0010] As a further solution, the display information corresponding to the display platform to which the selection scale belongs specifically includes the horizontal pixel count and vertical pixel count of the selection scale display platform, the average display brightness of the selection scale display platform, and the average ambient brightness of the area to which the selection scale display platform belongs.

[0011] As a further solution, the signal impact index corresponding to the built-in weight sensor of the scale is selected. The specific analysis formula is:

[0012]

[0013] In the formula, S is the signal impact index corresponding to the built-in weight sensor of the sorting scale, SNR is the minimum signal-to-noise ratio of the built-in weight sensor of the sorting scale within the detection period, ΔSNR is the reference signal-to-noise ratio preset in the sorting scale sample database, R is the average signal transmission rate of the built-in weight sensor of the sorting scale within the detection period, P is the minimum signal strength of the built-in weight sensor of the sorting scale within the detection period, a1 is the impact factor corresponding to the unit value of the average signal transmission rate preset in the sorting scale sample database, and a2 is the impact factor corresponding to the unit value of the minimum signal strength preset in the sorting scale sample database.

[0014] As a further solution, the functional indicators of the scale are selected. The specific analysis process is as follows:

[0015] The power-on response time period of the sorting scale, the signal impact index corresponding to the built-in weight sensor of the sorting scale, the resolution of the sorting scale display platform and the visibility of the sorting scale display platform are comprehensively analyzed to obtain the functional index of the sorting scale.

[0016] As a further solution, the functional indicators of the sorting scale are compared with the functional thresholds preset in the sorting scale sample database. The specific comparison process is as follows:

[0017] The functional index of the sorting scale is compared with the functional threshold preset in the sorting scale sample database. If the functional index of the sorting scale is greater than or equal to the functional threshold preset in the sorting scale sample database, the sorting scale can monitor the weight of each sample.

[0018] If the functional index of the sorting scale is less than the functional threshold preset in the sorting scale sample database, the sorting scale cannot monitor the weight of each sample.

[0019] As a further solution, the monitored weight of each sample is compared with the preset weight of the sample preset in the sorting scale sample database. The specific comparison process is as follows:

[0020] The monitored weight of each sample is compared with the preset weight of the sample in the sample database of the sorting scale to obtain the weight deviation ratio of each sample.

[0021] As a further solution, each sample is given an abnormal weight prompt. The specific analysis process is as follows:

[0022] The weight deviation ratio of each sample is compared with the allowable weight deviation range preset in the sorting scale sample database. If the weight deviation ratio of a sample does not fall within the allowable weight deviation range preset in the sorting scale sample database, the sample is recorded as an abnormal weight sample. Thus, the samples whose weight deviation ratios do not fall within the allowable weight deviation range preset in the sorting scale sample database are counted and marked as abnormal weight samples. Finally, a weight abnormality prompt is given for each abnormal weight sample.

[0023] The second aspect of the present invention provides a method for an intelligent data analysis system for a sorting scale, including: S1. Sorting scale self-check: obtain and analyze the current status information of the sorting scale, comprehensively determine the functional index of the sorting scale, and compare it with the functional threshold preset in the sorting scale sample database. If the functional index of the sorting scale is less than the functional threshold preset in the sorting scale sample database, the sorting scale is given a functional reminder. If the functional index of the sorting scale is greater than or equal to the functional threshold preset in the sorting scale sample database, enter S2.

[0024] S2. Sample analysis: The sorting scales whose functional index is greater than or equal to the functional threshold preset in the sorting scale sample database are recorded as self-test qualified sorting scales. The weight of each sample is monitored by the self-test qualified sorting scale to obtain the monitored weight of each sample.

[0025] S3. Prompt for sorting scale: Compare the monitored weight of each sample with the preset weight of the sample in the sorting scale sample database, and finally give a weight abnormality prompt for each sample.

[0026] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0027] (1) The present invention can timely discover abnormal status of the sorting scale, such as signal transmission error of the built-in weight sensor of the sorting scale, display error, and transmission belt failure of the sorting scale, by acquiring and analyzing the current status information of the sorting scale, so as to avoid incorrect weighing of the product due to the failure of the sorting scale, so that the sorting scale can accurately identify the weight of the product.

[0028] (2) By monitoring the weight of each sample, the present invention can ensure that each product with abnormal weight can be accurately identified and compared with the preset sample weight, and finally an abnormal weight prompt is given to each sample, thereby ensuring product consistency. At the same time, the present invention can timely discover and prompt samples with abnormal weight, which helps to reduce weight deviation of products in the production process and thus improve the overall product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0030] Figure 1 It is a schematic diagram of system module connection of the present invention.

[0031] Figure 2 The figure is a schematic flow chart of the method steps of the present invention.

[0032] Figure 3 It is a simulation curve diagram of the signal-to-noise ratio and the signal transmission rate involved in the present invention. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0034] Reference Figure 1 As shown, the first aspect of the present invention provides an intelligent data analysis system for a sorting scale, comprising: a sorting scale self-checking module, a sample analysis module, a sorting scale prompting module and a sorting scale sample database.

[0035] The sorting scale self-checking module is connected to the sample analysis module, the sample analysis module is connected to the sorting scale prompting module, and the sorting scale self-checking module, the sample analysis module and the sorting scale prompting module are all connected to the sorting scale sample database.

[0036] The self-check module of the sorting scale is used to obtain and analyze the environmental information corresponding to the signal domain to which the sorting scale belongs and the display information corresponding to the display platform to which the sorting scale belongs, comprehensively determine the functional index of the sorting scale, and compare it with the functional threshold preset in the sorting scale sample database. If the functional index of the sorting scale is less than the functional threshold preset in the sorting scale sample database, the sorting scale is given a functional reminder; if the functional index of the sorting scale is greater than or equal to the functional threshold preset in the sorting scale sample database, the sample analysis module is entered.

[0037] In this embodiment, the self-check module of the sorting scale mentioned above refers to the self-check analysis of a sorting scale. The sorting scale mainly works through the built-in sorting weight sensor. These sensors usually adopt the principle of strain gauge or capacitance. When detecting whether the sorting scale can be used for accurate weighing, it is necessary to place the standard weight on the scale platform of the sorting scale. The sensor is subjected to pressure and the capacitance changes, and then the mechanical change is converted into a signal, and the signal is transmitted to the data processing unit of the sorting scale for the next step of analysis. For the signal domain to which the sorting scale belongs, considering environmental information, optimizing the signal-to-noise ratio and signal strength are important means to improve the accuracy and stability of weighing. In practical applications, if the sorting scale is exposed to a high temperature, humid or dusty environment for a long time, it will cause the sensor to short-circuit or poor contact, thereby affecting the transmission and stability of the signal. At the same time, the strong magnetic field environment will interfere with the normal operation of the sensor, resulting in distortion or instability of the measurement signal. Therefore, it is necessary to self-check the function of the sorting scale according to the signal stability information of the built-in weight sensor of the sorting scale when the sorting scale is turned on to ensure that the sample will not be weighed inaccurately due to errors in the sorting scale.

[0038] The sample analysis module is used to record the sorting scales whose functional indicators are greater than or equal to the functional threshold preset in the sorting scale sample database as self-test qualified sorting scales, and monitor the weight of each sample through the self-test qualified sorting scale to obtain the monitored weight of each sample.

[0039] The sorting scale prompt module is used to compare the monitored weight of each sample with the preset weight of the sample preset in the sorting scale sample database, and finally to prompt the weight abnormality of each sample.

[0040] The sorting scale sample database is used to store preset function thresholds, preset sample preset weights, preset reference signal-to-noise ratios, influence factors corresponding to preset signal average transmission rate unit values, influence factors corresponding to preset signal minimum intensity unit values, correction factors corresponding to preset signal influence index deviation values ​​corresponding to built-in weight sensors of sorting scales, preset signal influence index thresholds, influence factors corresponding to preset resolution unit values, influence factors corresponding to preset visibility unit values, preset weight allowable deviation intervals, preset sorting scale startup response reference time periods, correction factors corresponding to preset startup efficiency, preset conveyor belt reference transmission speeds, and correction factors corresponding to preset maximum transmission speeds.

[0041] Specifically, the current status information of the sorting scale includes the startup response time period of the sorting scale, the maximum transmission speed of the conveyor belt of the sorting scale within the detection cycle, the signal stability information of the built-in weight sensor of the sorting scale, and the display information corresponding to the display platform to which the sorting scale belongs.

[0042] The signal stability information of the built-in weight sensor of the sorting scale specifically includes the minimum signal power, maximum noise power, average signal transmission rate and minimum signal strength of the built-in weight sensor of the sorting scale within a detection period.

[0043] It should be explained that the above-mentioned power-on response time period of the sorting scale can be obtained through the built-in timer of the sorting scale. The maximum transmission speed of the conveyor belt of the sorting scale within the detection period can use an external speed measurement tool, such as a photoelectric encoder or a radar Doppler speed sensor, to monitor the actual running speed of the conveyor belt. The detection period of the sorting scale is randomly assigned according to the actual operation of the sorting scale. In this embodiment, the signal of the built-in weight sensor of the sorting scale refers to the conversion of the weight of the object into a signal through the built-in weight sensor of the sorting scale. The sensor of the sorting scale usually adopts the principle of strain gauge or capacitance. The standard weight is placed on the scale platform of the sorting scale. The sensor is deformed or the capacitance changes due to pressure, and then the mechanical change is converted into a signal. The minimum signal power of the built-in weight sensor of the sorting scale within the detection period can be obtained by using a series of standard weights of known weights, starting from the minimum weight and gradually increasing. Standard weights of different weights are placed on the sorting scale in sequence. The signal analyzer obtains the electrical signal voltage transmitted by the standard weight sensors of different weights of the built-in weight sensor of the sorting scale within the detection period, and finds the electrical signal voltage corresponding to the minimum weight that the weight sensor can stably output and is accurately recognized by the sorting scale. The minimum stable voltage value is passed through the power formula to obtain the minimum signal power. The maximum noise power of the built-in weight sensor of the sorting scale during the detection period is obtained by using the noise coefficient analyzer to obtain the noise power data, and the noise power data is processed and analyzed to obtain the spectrum distribution diagram of the noise power. In the spectrum distribution diagram of the noise power, the maximum power point is found, which is the maximum noise power of the built-in weight sensor of the sorting scale during the detection period. The average signal transmission rate of the built-in weight sensor of the sorting scale during the detection period refers to the data output end of the weight sensor of the sorting scale and the data input end of the data processing unit. At the beginning of the measurement, a certain number of bytes of test signals are sent from the weight sensor, and the time when the test signal starts to be sent and the time when the data processing unit completely receives the test signal are recorded respectively. The time when the data processing unit receives the signal minus the time when the signal starts to be sent is used to obtain the total signal transmission time. The known amount of data is divided by the total signal transmission time to obtain the average signal transmission rate. The minimum signal strength of the built-in weight sensor of the sorting scale during the detection period is that during the detection period of the sorting scale, the signal strength is continuously monitored using a signal analyzer, and the minimum signal strength is recorded.

[0044] Specifically, the display information corresponding to the display platform to which the selection scale belongs includes the horizontal pixel count and vertical pixel count of the selection scale display platform, the average display brightness of the selection scale display platform, and the average ambient brightness of the area to which the selection scale display platform belongs.

[0045] It should be explained that the horizontal pixel count and vertical pixel count of the above-mentioned sorting scale display platform can be measured using professional screen testing software to accurately display the resolution of the current screen. The average display brightness of the sorting scale display platform can be measured using a professional measuring instrument, a color analyzer, to accurately measure the average brightness of the display. The average ambient brightness of the area to which the sorting scale display platform belongs can be indirectly measured using a illuminance meter. When determining the brightness of the environment surrounding the display, the photometer needs to be aimed in different directions for measurement, and then the results are averaged to obtain the average brightness of the environment.

[0046] Furthermore, the analysis and selection of the current status information of the scale includes the following specific analysis processes:

[0047] The minimum signal power and the maximum noise power of the built-in weight sensor of the sorting scale within the detection period are processed by ratio, so as to obtain the minimum signal-to-noise ratio of the built-in weight sensor of the sorting scale within the detection period.

[0048] The minimum signal-to-noise ratio, average signal transmission rate and minimum signal strength of the built-in weight sensor of the sorting scale within the detection period are comprehensively analyzed to obtain the signal impact index corresponding to the built-in weight sensor of the sorting scale.

[0049] Multiply the horizontal pixel count of the scale display platform by the vertical pixel count to get the resolution of the scale display platform.

[0050] The average display brightness of the sorting scale display platform is processed by ratio with the average ambient brightness of the area to which the sorting scale display platform belongs, so as to obtain the visibility of the sorting scale display platform.

[0051] Specifically, the signal impact index corresponding to the built-in weight sensor of the sorting scale is analyzed by the following formula:

[0052]

[0053] Where S is the signal impact index corresponding to the built-in weight sensor of the sorting scale. When the minimum signal-to-noise ratio of the built-in weight sensor of the sorting scale during the detection period is lower than the reference signal-to-noise ratio preset in the sorting scale sample database, it means that the proportion of noise in the signal increases. The interference of noise will reduce the accuracy of the measurement result, which may lead to misjudgment of the sample, misjudging qualified samples as unqualified, or misjudging unqualified samples as qualified. When the average signal transmission rate is low, it means that the time required for data transmission increases, which leads to slower measurement and response speed of the sorting scale to the sample weight. At the same time, the low transmission rate may also increase the error rate in the data transmission process. Since the signal may be subject to various interferences and attenuation during the transmission process, the lower the transmission rate, the more easily the signal is affected, thus causing data errors. The lower the minimum signal strength means that the sensitivity of the sorting scale when receiving the signal is reduced, which will cause the sorting scale to be unable to accurately and stably receive signals from sensors or other devices. Therefore, by analyzing the various data of the signal influencing indicators corresponding to the built-in weight sensor of the sorting scale, the key factors affecting the measurement accuracy and stability of the sorting scale can be identified, such as signal strength, signal-to-noise ratio, transmission rate, etc. At the same time, by optimizing these factors, the measurement accuracy and stability of the sorting scale can be improved, and measurement errors and fluctuations can be reduced.

[0054] In this embodiment, the minimum signal-to-noise ratio, average signal transmission rate and minimum signal strength of the built-in weight sensor of the sorting scale within the detection period are analyzed by nonlinear analysis to obtain the signal influence index corresponding to the built-in weight sensor of the sorting scale, which can capture the complex and nonlinear relationship that may exist between these parameters, thereby more accurately reflecting the true situation of the signal. At the same time, based on the results of nonlinear analysis, the key factors affecting the performance of the sorting scale can be found out in a targeted manner, and corresponding measures can be taken to optimize them, thereby improving the measurement accuracy and stability of the sorting scale and reducing measurement errors and fluctuations.

[0055] SNR is the minimum signal-to-noise ratio of the built-in weight sensor of the sorting scale during the detection cycle, which refers to the lowest signal-to-noise ratio that can be achieved during the measurement and analysis of the sorting scale.

[0056] ΔSNR is the reference signal-to-noise ratio preset in the sorting scale sample database. It refers to a pre-set standard value of the signal-to-noise ratio, which is used to evaluate the performance and accuracy of the sorting scale.

[0057] R is the average signal transmission rate of the built-in weight sensor of the sorting scale during the detection cycle, which refers to the average speed at which the signal is transmitted from the sensor to the processing system.

[0058] P is the minimum signal strength of the built-in weight sensor of the sorting scale during the detection cycle, which refers to the lowest signal level that can be achieved during the measurement and analysis of the sorting scale.

[0059] a1 is the influence factor corresponding to the unit value of the signal average transmission rate preset in the sorting scale sample database. According to the relationship between the historical average transmission rate and the signal influence index, a mapping set of the average transmission rate and its corresponding influence factor is constructed, and the real-time average transmission rate is input into the mapping set to obtain the influence factor corresponding to the unit value of the average transmission rate. At the same time, its value range in this example is [0, 1].

[0060] a2 is the influence factor corresponding to the unit value of the signal minimum intensity preset in the sorting scale sample database. According to the relationship between the historical signal minimum intensity and the signal influence index, a mapping set of the signal minimum intensity and its corresponding influence factor is constructed, and the real-time signal minimum intensity is input into the mapping set to obtain the influence factor corresponding to the unit value of the signal minimum intensity. At the same time, its value range in this example is [0, 1].

[0061] In this embodiment, there is a close relationship between the minimum signal-to-noise ratio, the average signal transmission rate and the minimum signal strength of the built-in weight sensor of the above-mentioned sorting scale within the detection period. These relationships jointly affect the performance and accuracy of the sorting scale. There is a direct connection between the signal-to-noise ratio and the signal strength, because the increase in signal strength usually increases the signal-to-noise ratio, making the signal more recognizable in the noise. The signal-to-noise ratio also has an important influence on the average transmission rate of the signal. According to Shannon's law, the maximum transmission rate is proportional to the signal-to-noise ratio, which means that the higher the signal-to-noise ratio, the higher the maximum transmission rate. Therefore, improving the signal-to-noise ratio can increase the amount of data transmitted by the sorting scale per unit time, thereby improving its detection efficiency. The signal strength also has a direct influence on the transmission rate. Too low signal strength may cause unreliable data transmission or even interruption. In the application of the sorting scale, a higher signal strength helps to maintain a stable data transmission rate and avoid errors or data loss caused by signal weakening. Therefore, a simulation curve between the signal-to-noise ratio (decibel) of the built-in weight sensor of the sorting scale within the detection period and the average signal transmission rate (megabits per second) of the built-in weight sensor of the sorting scale within the detection period can be constructed, as shown in detail. Figure 3 The simulation curve of signal noise ratio and signal transmission rate is shown in the figure, where the horizontal axis is the minimum signal noise ratio, unit: decibel, and the vertical axis is the average signal transmission rate, unit: megabits per second. Figure 3 The simulation curve diagram of signal-to-noise ratio and signal transmission rate shows that as the signal-to-noise ratio increases, the average signal transmission rate also shows an upward trend, which indicates that more data can be transmitted in the same time.

[0062] In this embodiment, the reference signal-to-noise ratio is set to 60 decibels, the impact factor corresponding to the unit value of the average signal transmission rate is 0.6, and the impact factor corresponding to the unit value of the minimum signal intensity is 0.4. From this, a table of the data relationship between the signal impact index corresponding to the built-in weight sensor of the sorting scale and each parameter can be obtained, as shown in Table 1 Signal Impact Index Table.

[0063] Table 1 Signal impact index table

[0064]

[0065] By observing the signal impact index table in Table 1 above, it is found that the signal impact index corresponding to the built-in weight sensor of the sorting scale will change with the changes in the minimum signal-to-noise ratio, average signal transmission rate, and minimum signal strength of the built-in weight sensor of the sorting scale during the detection period. When the minimum signal-to-noise ratio, average signal transmission rate, and minimum signal strength of the built-in weight sensor of the sorting scale during the detection period are larger, the signal impact index corresponding to the built-in weight sensor of the sorting scale is smaller, which means that the signal domain to which the sorting scale belongs is better.

[0066] Specifically, the functional indicators of the selection scale are analyzed in the following steps:

[0067] The function index of the sorting scale is obtained by comprehensively analyzing the startup response time period of the sorting scale, the maximum conveying speed of the conveyor belt of the sorting scale within the detection cycle, the signal impact index corresponding to the built-in weight sensor of the sorting scale, the resolution of the sorting scale display platform and the visibility of the sorting scale display platform. The specific analysis formula is:

[0068]

[0069] Wherein, P is the functional index of the sorting scale. If the actual startup response time period deviates greatly from the preset reference time period, it indicates that the actual startup response speed is slower than expected. The deviation of the startup response time may cause the sorting scale to fail to reach the optimal state during the initialization or calibration process, thereby affecting the accuracy and precision of its subsequent measurements. When the signal influence index corresponding to the built-in weight sensor of the sorting scale deviates greatly from the signal influence index threshold preset in the sorting scale sample database, the sorting scale will introduce more errors when measuring the sample weight. These errors may come from multiple factors such as signal noise, signal attenuation, and transmission delay, which directly affect the accuracy of the measurement results. Lower resolution may cause the sorting scale to fluctuate when displaying weight. Or unstable phenomenon, when the visibility of the display platform of the sorting scale is low, it means that the information on the display platform cannot be clearly seen, which may cause the operator to read the data incorrectly, and then lead to incorrect operation of the sample. If the maximum transmission speed of the conveyor belt of the sorting scale during the detection cycle is lower than the reference transmission speed of the conveyor belt, it means that the processing capacity of the sorting scale will decrease, which will lead to a decrease in the overall efficiency of the production line, and it will not be able to meet the needs of high-speed production lines. The low transmission rate may cause congestion and prolonged waiting time in the sorting scale link of the production line, thus becoming a bottleneck in the entire production process. Therefore, by analyzing the various data in the functional indicators of the sorting scale, the high performance and high reliability of the sorting scale are ensured, thereby ensuring the reliability of the measurement results.

[0070] In this embodiment, by performing nonlinear analysis on the signal impact index corresponding to the built-in weight sensor of the sorting scale, the resolution of the sorting scale display platform, and the visibility of the sorting scale display platform, it is helpful to more accurately understand the impact of the signal on the performance of the sorting scale, so as to take corresponding measures to reduce external interference and improve measurement accuracy.

[0071] e is a natural constant.

[0072] k1 is the correction factor corresponding to the signal influence index deviation value corresponding to the built-in weight sensor of the sorting scale preset in the sorting scale sample database. According to the relationship between the historical signal influence index deviation value and the functional index, a mapping set of the signal influence index deviation value and its corresponding correction factor is constructed, and the real-time signal influence index deviation value is input into the mapping set to obtain the correction factor corresponding to the signal influence index deviation value. At the same time, its value range in this example is [0, 1].

[0073] S is the signal impact index corresponding to the built-in weight sensor of the sorting scale, and ΔS is the signal impact index threshold preset in the sorting scale sample database, which refers to the allowable limit of the impact of a series of environmental factors on the signal that is pre-set in order to ensure measurement accuracy during the use and testing of the sorting scale.

[0074] k2 is the impact factor corresponding to the preset resolution unit value in the sorting scale sample database. A mapping set of resolution and its corresponding impact factor is constructed based on the relationship between historical resolution and functional indicators, and the real-time resolution is input into the mapping set to obtain the impact factor corresponding to the resolution unit value. In this example, its value range is [0, 1].

[0075] F is the resolution of the scale display platform, which refers to the number of pixels that the scale display platform can currently display.

[0076] k3 is the impact factor corresponding to the visibility unit value preset in the sorting scale sample database. A mapping set of visibility and its corresponding impact factor is constructed based on the relationship between historical visibility and functional indicators, and the real-time visibility is input into the mapping set to obtain the impact factor corresponding to the visibility unit value. At the same time, its value range in this example is [0, 1].

[0077] T is the visibility of the display platform of the sorting scale, which refers to the ability of the display platform of the sorting scale to clearly display the information on the display screen.

[0078] b is the power-on response time period of the selection scale, which refers to the time period from pressing the power button to the scale being fully started, the system being ready, and being able to perform normal weighing operations.

[0079] Δb is the reference time period for the startup response of the sorting scale preset in the sorting scale sample database, which refers to the maximum time period of an expected startup response time.

[0080] k4 is the correction factor corresponding to the startup efficiency preset in the sorting scale sample database. According to the relationship between the historical startup response time period and the functional index, a mapping set between the startup corresponding time period and its corresponding correction factor is constructed, and the real-time startup response time period is input into the mapping set to obtain the correction factor corresponding to the startup efficiency. At the same time, its value range in this example is [0, 1].

[0081] v is the maximum conveying speed of the conveyor belt of the sorting scale during the detection cycle, which refers to the highest speed that the conveyor belt can reach during the detection cycle.

[0082] Δv is the reference conveyor speed preset in the sorting scale sample database, which refers to the ideal conveyor speed set according to different product characteristics and production requirements.

[0083] k5 is the correction factor corresponding to the maximum transmission speed preset in the sorting scale sample database. According to the relationship between the historical transmission rate and the functional index, a mapping set of the transmission rate and its corresponding correction factor is constructed, and the real-time transmission rate is input into the mapping set to obtain the correction factor corresponding to the startup efficiency. At the same time, its value range in this example is [0, 1].

[0084] In this embodiment, there is a close relationship between the maximum transmission speed of the conveyor belt of the above-mentioned sorting scale within the detection cycle, the signal impact index corresponding to the built-in weight sensor, the resolution of the display platform, and the visibility of the display platform. The maximum transmission speed of the conveyor belt will directly affect the signal acquisition and processing of the weight sensor. High-speed transmission may cause the sensor to be unable to accurately capture and convert the weight signal in a short period of time, thereby affecting the measurement accuracy. For example, if the transmission rate is too fast, the sensor may not respond in time, resulting in an increase in dynamic weighing errors. The weight sensor needs to stably collect signals within a certain period of time and convert them into electrical signals that can be processed. If the conveyor belt speed exceeds the response and conversion rate of the sensor, it will result in incomplete signal acquisition. , which in turn affects the subsequent weight calculation and display. Therefore, ensuring that the transmission rate matches the sensor performance is the key to ensuring detection accuracy; the maximum transmission speed of the conveyor belt determines the frequency of data updates. High transmission rates require the display platform to have high resolution to accurately display the continuously updated weight data in real time; if the transmission rate is high and the display resolution is low, it may cause data display to lag or be inaccurate. The signal accuracy of the weight sensor directly affects the reliability of the data on the display platform. If the sensor output signal is unstable or has errors, even if the display platform has high visibility, it will display incorrect weighing results. Ensuring the accuracy of the sensor is a key step. Therefore, the above factors jointly affect the performance and accuracy of the sorting scale.

[0085] Furthermore, the functional index of the sorting scale is compared with the functional threshold preset in the sorting scale sample database. The specific comparison process is as follows:

[0086] The functional index of the sorting scale is compared with the functional threshold preset in the sorting scale sample database. If the functional index of the sorting scale is greater than or equal to the functional threshold preset in the sorting scale sample database, the sorting scale can monitor the weight of each sample without the need for an early warning reminder for the sorting scale.

[0087] In one embodiment, the sorting scale is suitable for standard systems for food, personal care or pharmaceutical applications, enjoying high accuracy and advanced functions, and is suitable for various packaged products such as cartons, bags, cups, bottles, cans, bags, boxes, etc.

[0088] It should be explained that when the functional index of the above-mentioned sorting scale is greater than or equal to the functional threshold preset in the sorting scale sample database, it means that the current performance status of the sorting scale has met or exceeded the basic requirements of design and use, and has the ability to accurately and reliably monitor the weight of each sample. Therefore, the sorting scale can be used to monitor the weight of each sample, and there is no need to issue an early warning reminder for the sorting scale. In this embodiment, the product that needs to be monitored for weight using a sorting scale is called a sample.

[0089] If the function index of the sorting scale is less than the function threshold preset in the sorting scale sample database, the sorting scale cannot monitor the weight of each sample and issue an early warning reminder for the function of the sorting scale.

[0090] It needs to be explained that when the functional index of the above-mentioned sorting scale is less than the functional threshold preset in the sorting scale sample database, it means that the current performance status of the sorting scale does not meet the basic requirements for use. Therefore, the sorting scale cannot be used to monitor the weight of each sample, and an early warning reminder of the function of the sorting scale is required. In this embodiment, the above-mentioned early warning reminder of the function of the sorting scale is specifically: displaying the sorting scale fault through the display platform of the sorting scale.

[0091] Specifically, the monitored weight of each sample is compared with the preset weight of the sample preset in the sorting scale sample database. The specific comparison process is:

[0092] The monitored weight of each sample is analyzed with the preset weight of the sample in the sorting scale sample database to obtain the weight deviation ratio of each sample. The specific analysis formula is:

[0093]

[0094] In the formula, G i is the weight deviation ratio of the ith sample. When the monitored weight of the sample deviates greatly from the preset weight of the sample in the sample database of the sorting scale, it means that the actual weight of the sample is higher or lower than the standard weight of the sample, which leads to the inconsistency of the sample weight. Therefore, by analyzing the weight deviation ratio of each sample, the weight consistency of the sample can be guaranteed. At the same time, samples with abnormal weight can be discovered and prompted in time, which helps to reduce the weight deviation of the product in the production process and thus improve the overall product quality.

[0095] yg i is the monitored weight of the ith sample, which refers to the actual monitored weight value of the sample by the sorting scale, and is obtained by actually monitoring the sample by the sorting scale.

[0096] Δyg is the preset weight of the sample preset in the sample database of the sorting scale, which refers to the preset standard weight value that the sample should reach.

[0097] i is the number of each sample, i=1,2,3...j, j is the total number of samples.

[0098] In this embodiment, the preset weight of the sample is set to 100 grams, thereby obtaining the weight deviation ratio of each sample, as shown in Table 2:

[0099] Table 2 Sample weight deviation ratio table

[0100] serial number Monitoring weight of sample (g) Sample weight deviation ratio (%) 1 95 -4.98 2 96 -3.99 3 98 -2.0 4 100 0.00 5 101 0.1

[0101] By observing the data in the weight deviation ratio table of the samples in Table 2 above, it is found that the closer the monitored weight of the sample is to the set sample preset weight, the closer the weight deviation ratio of the obtained sample is to 0. For example, when the monitored weight of the sample is 95 grams, it differs from the set sample preset weight by 5 grams, and the weight deviation ratio of the obtained sample is -4.98%; when the monitored weight of the sample is 101 grams, it differs from the set sample preset weight by 1 gram, and the weight deviation ratio of the obtained sample is 0.1%; when the monitored weight of the sample is 100 grams, it is equal to the set sample preset weight, and the weight deviation ratio of the obtained sample is 0%.

[0102] Specifically, the weight abnormality prompt is given to each sample, and the specific analysis process is as follows:

[0103] The weight deviation ratio of each sample is compared with the allowable weight deviation range preset in the sorting scale sample database. If the weight deviation ratio of a sample does not fall within the allowable weight deviation range preset in the sorting scale sample database, the sample is recorded as an abnormal weight sample. Thus, the samples whose weight deviation ratios do not fall within the allowable weight deviation range preset in the sorting scale sample database are counted and marked as abnormal weight samples. Finally, a weight abnormality prompt is given for each abnormal weight sample.

[0104] In this embodiment, the allowable weight deviation interval is set to [-3%, 3%]. When the weight deviation ratio of a sample belongs to the allowable weight deviation interval preset in the database of other scale samples, it means that the weight of the sample meets the production quality standards and can be recorded as a qualified weight sample. The qualified weight sample will pass the test directly without any additional prompts and diversion processing. When the weight deviation ratio of a sample does not belong to the allowable weight deviation interval preset in the database of other scale samples, it means that the sample is an abnormal weight sample, that is, the value of the weight deviation ratio of the sample is less than -3% or greater than 3%, and the sample needs to be prompted for abnormal weight. Specifically, if the weight deviation value ratio of the sample is less than -3%, it means that the sample is too light, and the sample needs to be prompted for too light weight and transferred to the sample underweight area. If the weight deviation value ratio of the sample is greater than 3%, it means that the sample is too heavy, and the sample needs to be prompted for too heavy weight and transferred to the sample overweight area.

[0105] Reference Figure 2 As shown, the second aspect of the present invention provides a method for an intelligent data analysis system for a sorting scale, including: S1. Sorting scale self-check: acquiring and analyzing the current state information of the sorting scale, comprehensively determining the functional index of the sorting scale, and comparing it with the functional threshold preset in the sorting scale sample database; if the functional index of the sorting scale is greater than or equal to the functional threshold preset in the sorting scale sample database, enter S2.

[0106] S2. Sample analysis: The sorting scales whose functional index is greater than or equal to the functional threshold preset in the sorting scale sample database are recorded as self-test qualified sorting scales. The weight of each sample is monitored by the self-test qualified sorting scale to obtain the monitored weight of each sample.

[0107] S3. Prompt for sorting scale: Compare the monitored weight of each sample with the preset weight of the sample in the sorting scale sample database, and finally give a weight abnormality prompt for each sample.

[0108] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined in this specification, they should all fall within the protection scope of the present invention.

Claims

1. An intelligent data analysis system for selecting scales, characterized in that: include: The self-check module of the sorting scale is used to obtain and analyze the current status information of the sorting scale, comprehensively determine the functional index of the sorting scale, and compare it with the functional threshold preset in the sorting scale sample database. If the functional index of the sorting scale is less than the functional threshold preset in the sorting scale sample database, an early warning reminder will be issued for the function of the sorting scale. If the functional index of the sorting scale is greater than or equal to the functional threshold preset in the sorting scale sample database, the sample analysis module will be entered; The sample analysis module is used to record the sorting scales whose functional indicators are greater than or equal to the functional thresholds preset in the sorting scale sample database as self-test qualified sorting scales, and monitor the weight of each sample through the self-test qualified sorting scale to obtain the monitored weight of each sample; The sorting scale prompt module is used to compare the monitored weight of each sample with the preset sample weight preset in the sorting scale sample database, and finally to prompt each sample for abnormal weight.

2. The intelligent data analysis system for selecting scales according to claim 1, characterized in that: The current status information of the sorting scale specifically includes the startup response time period of the sorting scale, the maximum conveying speed of the conveyor belt of the sorting scale within the detection cycle, the signal stability information of the built-in weight sensor of the sorting scale, and the display information corresponding to the display platform to which the sorting scale belongs; The signal stability information of the built-in weight sensor of the sorting scale specifically includes the minimum signal power, maximum noise power, average signal transmission rate and minimum signal strength of the built-in weight sensor of the sorting scale within a detection period.

3. The intelligent data analysis system for selecting scales according to claim 2 is characterized in that: The display information corresponding to the display platform to which the selection scale belongs specifically includes the horizontal pixel count and vertical pixel count of the selection scale display platform, the average display brightness of the selection scale display platform, and the average ambient brightness of the area to which the selection scale display platform belongs.

4. The intelligent data analysis system for selecting scales according to claim 1 is characterized in that: The analysis and selection of the current status information of the scale includes the following specific analysis processes: The minimum signal power of the built-in weight sensor of the sorting scale in the detection period is processed with the maximum noise power to obtain the minimum signal-to-noise ratio of the built-in weight sensor of the sorting scale in the detection period; Comprehensively analyze the minimum signal-to-noise ratio, average signal transmission rate and minimum signal strength of the built-in weight sensor of the sorting scale within the detection period to obtain the signal impact index corresponding to the built-in weight sensor of the sorting scale; Multiply the horizontal pixel number of the display platform of the sorting scale by the vertical pixel number to obtain the resolution of the display platform of the sorting scale; The average display brightness of the sorting scale display platform is processed by ratio with the average ambient brightness of the area to which the sorting scale display platform belongs, so as to obtain the visibility of the sorting scale display platform.

5. The intelligent data analysis system for selecting scales according to claim 4 is characterized in that: The signal impact index of the built-in weight sensor of the sorting scale is analyzed by the following formula: In the formula, S is the signal impact index corresponding to the built-in weight sensor of the sorting scale, SNR is the minimum signal-to-noise ratio of the built-in weight sensor of the sorting scale within the detection period, ΔSNR is the reference signal-to-noise ratio preset in the sorting scale sample database, R is the average signal transmission rate of the built-in weight sensor of the sorting scale within the detection period, P is the minimum signal strength of the built-in weight sensor of the sorting scale within the detection period, a1 is the impact factor corresponding to the unit value of the average signal transmission rate preset in the sorting scale sample database, and a2 is the impact factor corresponding to the unit value of the minimum signal strength preset in the sorting scale sample database.

6. The intelligent data analysis system for selecting scales according to claim 1, characterized in that: The functional indicators of the selection scale are analyzed in detail as follows: The function index of the sorting scale is obtained by comprehensively analyzing the startup response time period of the sorting scale, the maximum transmission speed of the conveyor belt of the sorting scale within the detection cycle, the signal impact index corresponding to the built-in weight sensor of the sorting scale, the resolution of the sorting scale display platform and the visibility of the sorting scale display platform.

7. The intelligent data analysis system for selecting scales according to claim 6, characterized in that: The functional index of the sorting scale is compared with the functional threshold preset in the sorting scale sample database. The specific comparison process is as follows: Compare the functional index of the sorting scale with the functional threshold preset in the sorting scale sample database. If the functional index of the sorting scale is greater than or equal to the functional threshold preset in the sorting scale sample database, the sorting scale can monitor the weight of each sample without the need for early warning of the sorting scale. If the function index of the sorting scale is less than the function threshold preset in the sorting scale sample database, the sorting scale cannot monitor the weight of each sample and issue an early warning reminder for the function of the sorting scale.

8. The intelligent data analysis system for selecting scales according to claim 7, characterized in that: The monitored weight of each sample is compared with the preset weight of the sample preset in the sorting scale sample database. The specific comparison process is as follows: The monitored weight of each sample is compared with the preset weight of the sample in the sample database of the sorting scale to obtain the weight deviation ratio of each sample.

9. The intelligent data analysis system for selecting scales according to claim 8, characterized in that: The specific analysis process of the abnormal weight prompt for each sample is as follows: The weight deviation ratio of each sample is compared with the allowable weight deviation range preset in the sorting scale sample database. If the weight deviation ratio of a sample does not fall within the allowable weight deviation range preset in the sorting scale sample database, the sample is recorded as an abnormal weight sample. Thus, the samples whose weight deviation ratios do not fall within the allowable weight deviation range preset in the sorting scale sample database are counted and marked as abnormal weight samples. Finally, a weight abnormality prompt is given for each abnormal weight sample.

10. A method for applying the intelligent data analysis system for selecting scales according to any one of claims 1 to 9, characterized in that: include: S1. Self-check of the sorting scale: obtain and analyze the current status information of the sorting scale, comprehensively determine the functional index of the sorting scale, and compare it with the functional threshold preset in the sorting scale sample database. If the functional index of the sorting scale is less than the functional threshold preset in the sorting scale sample database, the function of the sorting scale is warned. If the functional index of the sorting scale is greater than or equal to the functional threshold preset in the sorting scale sample database, enter S2; S2. Sample analysis: The scales with functional indicators greater than or equal to the preset functional threshold in the scale sample database are recorded as self-test qualified scales, and the weight of each sample is monitored by the self-test qualified scale to obtain the monitored weight of each sample; S3. Prompt for sorting scale: Compare the monitored weight of each sample with the preset weight of the sample in the sorting scale sample database, and finally give a weight abnormality prompt for each sample.

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