Electronic product weighing processing method

Abstract

This invention discloses a method for weighing electronic products. It obtains stable and valid values ​​through invalid data filtering and continuous stability determination, calculates the real-time weighing center value of the work order using an intelligent sampling algorithm to remove extreme values, and performs automatic comparison and over-station control in conjunction with a graded differentiated floating tolerance strategy. This achieves mandatory verification of full-volume weighing and dynamically and adaptively updates the baseline gross weight data according to environmental changes. This invention effectively solves the problem of large-scale weighing misjudgments caused by fluctuations in the moisture content of packaging materials, realizing automated, accurate, and closed-loop control of the entire weighing process. Data is fully traceable and adaptable to various large-scale production scenarios for electronic products.

Description

Technical Field

[0001] This invention relates to the field of electronic product manufacturing process control technology, and in particular to a method for weighing electronic products. Background Technology

[0002] In large-scale packaging production lines for electronic products, gross weight weighing is required after the products are fully packaged. This serves two purposes: firstly, to identify quality anomalies such as missing accessories or components, and secondly, to provide accurate weight data for customs import and export declarations, reducing declaration errors and customs clearance risks. As a critical process control node, if the weighing judgment logic is unreasonable, data interference is prevalent, or misjudgments and false alarms occur frequently, it will lead to frequent unpacking and re-inspection on the production line, consuming significant manpower and time, and severely restricting production rhythm. Furthermore, missed or false inspections will directly result in defective products being shipped out, causing rework losses and customer complaints.

[0003] Currently, the electronic product packaging weighing industry mainly consists of two modes: single-machine manual weighing and MES fixed sampling weighing. Among them, traditional single-machine weighing equipment relies entirely on manual operation, requiring operators to manually place products, read weights, record product serial numbers, and manually archive them. This results in high human intervention, low operational efficiency, and a high rate of human error, making it unsuitable for the needs of high-precision weighing and efficient mass production.

[0004] Another MES-assisted sampling and weighing method captures raw data from the weighing machine via serial communication such as RS232. Operators use 10 products per work order as a sample, directly obtaining the center weight value through arithmetic mean, and setting a fixed tolerance of 3%-5% for comparison and judgment. Although this method achieves data system archiving, it has significant technical defects: the raw weighing data is not filtered or preprocessed, and cannot remove abnormal data caused by equipment fluctuations and environmental interference, resulting in poor sampling benchmark accuracy; the fixed percentage tolerance cannot adapt to the accuracy requirements of products of different weight specifications; in high humidity environments such as the rainy season, the moisture content of packaging materials such as cartons and polystyrene fluctuates greatly, causing overall weighing deviations, large-scale misjudgments by the system, forcing the production line to repeat sampling or relax tolerances, resulting in quality control failure and a serious decrease in production efficiency. At the same time, the existing technology lacks a mandatory weighing verification mechanism, which easily leads to the problem of products being shipped with missing weights, and the weighing data lacks closed-loop iteration capability, resulting in poor environmental adaptability and traceability.

[0005] In summary, existing electronic product weighing technologies suffer from drawbacks such as high reliance on manual labor, low sampling accuracy, weak resistance to environmental interference, high misjudgment rate, lack of closed-loop leakage prevention mechanism, and inability to adaptively iterate data. The industry urgently needs a fully automated, high-precision, environmentally adaptive, and fully traceable electronic product weighing method. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a weighing process for electronic products, which solves the technical problems of low efficiency, poor accuracy, weak environmental adaptability, many misjudgments, and easy omissions in the existing weighing process, and realizes the automation, precision, closed-loop, and adaptive control of the electronic product weighing process.

[0007] The technical solution adopted in this invention is:

[0008] A method for weighing electronic products includes the following steps:

[0009] S1. Data Acquisition and Preprocessing: The weighing values ​​of the digital weighing head are collected in real time through IoT devices. The collected data is filtered for invalid data and stable to obtain stable valid values. The stable valid values ​​are then standardized.

[0010] S2. Basic data pre-storage: Construct a product gross weight basic data table and pre-store the standard gross weight center value corresponding to different machine models and material models;

[0011] S3. Work Order Data Matching: The MES system automatically binds to the current production work order, retrieves the product identifiers that have not been weighed under the same work order, and caches the retrieved product identifiers.

[0012] S4. Intelligent Sampling and Real-time Center Value Calculation: Sampling is performed based on the current production work order. The sampled data is compared with the standard gross weight center value to filter out valid sampled data. After accumulating a preset number of valid sampled data, the valid sampled data is processed to remove extreme values, and the arithmetic mean of the remaining data is calculated. The arithmetic mean is used as the real-time weighing center value of the current work order.

[0013] S5. Graded Differentiated Tolerance Configuration: Configure corresponding differentiated floating tolerance values ​​according to the product weight range;

[0014] S6. Automatic comparison and over-station control: The real-time weighing value of the current product is compared with the real-time weighing center value and the differential floating tolerance. Based on the comparison result, the product is controlled to over-station or an alarm is triggered and locked.

[0015] S7. Work order weighing closed-loop control: Statistically calculate the weighing completion rate of work orders. If the weighing completion rate reaches or exceeds the qualified release rate threshold, the work order is allowed to be put into the warehouse and shipped.

[0016] S8. Dynamic adaptive correction of benchmark data: After the work order is completed, the deviation ratio between the real-time weighing center value and the standard gross weight center value is compared. When the deviation ratio is within the preset environmental adaptive range, the standard gross weight center value in the product gross weight base data table is updated using the real-time weighing center value.

[0017] Furthermore, step S1 specifically includes:

[0018] Set an invalid weighing threshold to filter weighing values ​​that do not exceed the invalid weighing threshold;

[0019] Perform continuous multiple data collection and judgment. If the values ​​collected in multiple consecutive data collections are completely consistent, they are judged as stable and valid values. If there are fluctuations, data collection is repeated.

[0020] The stable effective values ​​are standardized, including unifying the data precision and units of measurement.

[0021] Furthermore, in step S3, the product identifier cache duration supports two configuration modes by default: 5 minutes and 2 hours. It also supports custom duration settings to adapt to different production line production cycles.

[0022] Furthermore, in step S4, the process of screening valid sample data includes: setting a tolerance range, comparing the single sample value with the standard gross weight center value, and if it is within the tolerance range, it is determined to be valid sample data;

[0023] The extreme value removal process specifically includes: sorting the accumulated preset number of valid sampled data according to their numerical values, removing the preset number of maximum and minimum values ​​from the sorted data, and calculating the arithmetic mean using the remaining data.

[0024] Furthermore, the tolerance range is ±3% of the standard gross weight center value.

[0025] Furthermore, the preset quantity is 10 groups, and the predetermined elimination quantity is 4 groups, that is, eliminating two groups of maximum values ​​and two groups of minimum values, and using the remaining 6 groups of valid data to calculate the arithmetic mean.

[0026] Furthermore, in step S5, the floating tolerance for the 0-1KG weight range is defined as ±0.01KG, and the floating tolerance for the 1-4KG weight range is ±0.06KG.

[0027] Furthermore, in step S6, if the real-time weighing value exceeds the range of the real-time weighing center value and the differential floating tolerance limit, the system automatically locks the product's passage permission, outputs an alarm signal, and records an anomaly log.

[0028] Furthermore, in step S7, the calculation formula for the work order weighing completion ratio is: the ratio of the number of weighed products to the total production quantity of the work order.

[0029] Furthermore, in step S8, the preset environmental adaptation range is 5%-10%.

[0030] Compared with the prior art, the present invention has the following outstanding advantages:

[0031] 1. High weighing accuracy and strong anti-interference capability. This invention filters out equipment fluctuations and environmental interference through invalid data filtering and continuous stability judgment mechanism, eliminates single-point abnormal data through extreme value removal intelligent sampling algorithm, and is further supplemented by a graded differentiated tolerance system to adapt to the accuracy requirements of products of different weights, effectively solving the technical defects of poor sampling benchmark accuracy and high misjudgment rate of traditional weighing schemes.

[0032] 2. Fully automated, significantly improving production efficiency. This invention achieves fully automated processing of the entire process—barcode matching, data collection, sampling calculation, comparison judgment, and station control—through an automatic barcode retrieval and caching mechanism, automatic comparison, and station control. It eliminates the need for manual barcode reading, manual recording, and manual parameter setting, thus removing the human resource bottleneck of traditional weighing processes.

[0033] 3. Robust closed-loop management ensures extremely low quality risk. This invention achieves mandatory full-volume weighing verification through a work order weighing closed-loop management mechanism, eliminating issues such as missed weighing and missing accessories at the system level, and constructing a complete quality closed loop.

[0034] 4. Excellent environmental adaptability. This invention utilizes a dynamic adaptive correction mechanism based on baseline data to automatically and iteratively update the baseline gross weight according to environmental changes. This eliminates the need for frequent manual parameter adjustments and effectively solves the problem of large-scale weighing misjudgments caused by fluctuations in packaging material moisture content during the rainy season.

[0035] 5. End-to-end data traceability. All raw weighing data, preprocessing records, sampling center values, comparison results, anomaly records, and baseline correction records are stored in the MES system in real time, supporting end-to-end data traceability, customs declaration data query, and process quality anomaly review. Attached Figure Description

[0036] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments;

[0037] Fig. 1 This is a diagram of the IoT architecture involved in this invention;

[0038] Fig. 2 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0040] like Figs. 1-2 As shown, this invention discloses a method for weighing electronic products, comprising the following steps:

[0041] Step S1, Data Acquisition and Preprocessing: The weighing values ​​of the digital weighing meter are collected in real time through IoT devices. The collected data is filtered for invalid data and stable to obtain stable valid values. The stable valid values ​​are then standardized.

[0042] Specifically, the IoT data acquisition terminal connects to the digital weighing head of the production line in real time, collects real-time weight data according to the preset acquisition frequency, and uploads the collected weighing values ​​to the MES system through network protocols.

[0043] The system filters out invalid data from the collected data. The system pre-sets an invalid weighing threshold; weighing values ​​not exceeding this threshold are considered invalid interference data and filtered out. In this embodiment, the invalid weighing threshold is set to 0.3KG, meaning weighing values ​​not exceeding 0.3KG are considered invalid data, automatically discarded by the system, and not proceeding to the subsequent stability determination process. The specific value of the invalid weighing threshold can be customized according to actual needs.

[0044] The system performs stability checks on the filtered data. The system performs multiple consecutive data collection checks; if the values ​​collected consecutively are completely consistent, the value is considered stable and valid. If the data fluctuates, the data is collected again. Specifically, the system checks the consistency of continuously collected weighing values. If the number of consecutive collections reaches a preset N and the values ​​are completely consistent, the value is determined to be stable and valid and output. If any inconsistent data occurs during the collection process, the accumulation process restarts. In this embodiment, N is set to 5 times, meaning that if 5 consecutive collections are completely consistent, the current value is considered stable and valid. It should be noted that the specific value of the number of consecutive collections N is a configurable parameter of the system and can be customized according to the product production scenario and the accuracy of the weighing equipment.

[0045] The system standardizes weighing data that are determined to be stable and valid values: it unifies data precision and units of measurement. Specifically, the system uniformly retains stable and valid values ​​to two decimal places and unifies the unit of measurement to KG.

[0046] Step S2, Pre-storage of basic data: Construct a basic data table of product gross weight and pre-store the standard gross weight center value corresponding to different machine models and material models.

[0047] Specifically, during the development phase of a new model, designers calculate the theoretical weight based on the product structure, component specifications, and packaging material parameters to determine the standard gross weight of the model. Designers then use the basic data maintenance interface of the MES system to enter the corresponding standard gross weight center value into the system according to the model and material type. The system stores this data in the product gross weight basic data table.

[0048] The product gross weight basic data table is stored independently with the material model as the primary key, and each model corresponds to an independent data record. When the same production line switches to produce different models of products at different times, the system can automatically call the corresponding standard gross weight center value according to the product model corresponding to the current work order.

[0049] In this embodiment, the standard gross weight center value serves as the initial comparison benchmark during the new product introduction phase, and will be dynamically updated according to the environmental adaptive correction mechanism in step S8 during subsequent mass production.

[0050] Step S3, Work Order Data Matching: The MES system automatically binds the current production work order, retrieves the product identifiers that have not been weighed under the same work order, and caches the retrieved product identifiers;

[0051] Specifically, when a production line initiates a production work order, the MES weighing interface automatically binds the current work order information. Work order information includes, but is not limited to: work order number, product model, and total production quantity of the work order.

[0052] After the MES system binds to the current production work order, it retrieves the product identifiers from the MES database based on the product model information in the work order. These product identifiers are products that have completed previous production processes but have not yet undergone weighing. The product identifier is the electronic product serial number (SN) barcode. The MES system maintains a process status record table for each product. When a product completes its previous process, its process status changes to "Pending Weighing." The MES weighing module filters out all product identifiers with a "Pending Weighing" status and a matching work order number, sorts them by the time they were completed at the previous weighing station, and then matches them sequentially to form a pending weighing list.

[0053] The system caches the retrieved product identifiers, preloading them into the local cache of the weighing module. The barcode caching duration is a configurable parameter. In this embodiment, the system supports two default configuration modes: 5 minutes and 2 hours, adaptable to different scenarios such as high-speed production lines and small-batch line changeovers. It eliminates the need for manual barcode scanning and entry, achieving automatic barcode information matching. Custom duration settings are also supported to suit different production line rhythms.

[0054] Step S4, Intelligent Sampling and Real-time Center Value Calculation: Sampling is performed based on the current production work order. The sampled data is compared with the standard gross weight center value to filter out valid sampled data. After accumulating a preset number of valid sampled data, extreme value removal is performed on the valid sampled data, and the arithmetic mean of the remaining data is calculated. The arithmetic mean is used as the real-time weighing center value of the current work order.

[0055] Specifically, all sampling data in the sampling phase are stable and valid values ​​obtained after preprocessing in step S1.

[0056] The system compares the sampled value of a single unit with the standard gross weight center value ±3% tolerance range preset in step S2. If the value is within the tolerance range, it is determined to be valid sampled data; if the sampled value of a single unit is outside the tolerance range, it is determined to be invalid sampled data and is not accumulated. In other embodiments of the present invention, the tolerance threshold can be customized according to the product model, packaging material characteristics, and production environment.

[0057] The system collects a predetermined number of valid sample data. In this embodiment, the predetermined number is 10 sets. That is, after the system has accumulated 10 sets of stable valid values ​​that have passed the valid sampling screening, it triggers the extreme value removal operation. The specific method of extreme value removal is as follows: the 10 sets of valid sample data are sorted in ascending order of numerical value, and a predetermined number of maximum and minimum values ​​are removed from the sorted data. In this embodiment, two sets of maximum values ​​and two sets of minimum values ​​are removed, that is, the 1st and 2nd positions and the 9th and 10th positions after sorting are removed, and the remaining 6 sets of valid data from the 3rd to the 8th positions are used in subsequent operations.

[0058] The system calculates the arithmetic mean of the remaining 6 sets of valid data after removing extreme values, and uses this arithmetic mean as the real-time weighing center value for the current work order.

[0059] Step S5, Graded Differentiated Tolerance Configuration: Configure the corresponding differentiated floating tolerance value according to the product weight range.

[0060] Specifically, the system builds a weighing specification maintenance configuration table. The product weight range division standard and the corresponding floating tolerance value of each range are all configurable parameters, supporting the addition and modification of weight ranges and the adaptation of tolerances.

[0061] In this embodiment, the system defines two weight ranges and their corresponding floating tolerances: the floating tolerance for the 0-1KG weight range is ±0.01KG; and the floating tolerance for the 1-4KG weight range is ±0.06KG.

[0062] The above tolerance values ​​mean that for products with a weight range of 0-1KG, the weighing value is allowed to deviate from the real-time weighing center value by ±0.01KG; for products with a weight range of 1-4KG, the weighing value is allowed to deviate from the real-time weighing center value by ±0.06KG.

[0063] In other embodiments of the present invention, more weight ranges and corresponding tolerance values ​​can be configured according to the actual product line to meet the weighing accuracy requirements of products with different weight specifications.

[0064] Step S6, Automatic Comparison and Over-Station Control: Compare the current product's real-time weighing value with the real-time weighing center value and differential floating tolerance, and control the product to over-station or lock the alarm based on the comparison results.

[0065] Specifically, during the mass production process of a work order, after each electronic product completes the stable and valid weight acquisition, the system automatically matches and compares the current product's real-time weight value with the real-time weight center value calculated in step S4 and the differentiated floating tolerance configured in step S5. The comparison logic is as follows: the system first determines the weight range to which the current product belongs based on its real-time weight value, and then determines the applicable floating tolerance value. Then, the system calculates the deviation between the real-time weight value and the real-time weight center value, and compares this deviation with the floating tolerance value. If the deviation is less than or equal to the floating tolerance value, the comparison result is acceptable; if the deviation is greater than the floating tolerance value, the comparison result is unacceptable.

[0066] When the comparison result meets the specifications, the system automatically sends a transit signal to the powered roller conveyor on the production line, controlling the current product to automatically pass through the station. If the real-time weighing value exceeds the range of the real-time weighing center value and the differential floating tolerance limit, the system automatically locks the product's transit permission, that is, sends an interception signal to the powered roller conveyor to prevent the product from entering the next process. At the same time, the system outputs an alarm signal and records an anomaly log.

[0067] Step S7, Closed-loop control of work order weighing: Calculate the weighing completion rate of work orders. If the weighing completion rate reaches or exceeds the qualified release rate threshold, the work order is allowed to be put into storage and shipped.

[0068] Specifically, after a work order is completed, the system automatically calculates the weighing completion status of that work order. The formula for calculating the work order weighing completion rate is: the ratio of the number of weighed products to the total production quantity of the work order, that is, work order weighing completion rate = number of weighed products ÷ total production quantity of the work order.

[0069] The system supports 100% full-volume weighing control configuration. This means the system provides a weighing control mode selection function, configurable as either "full-volume weighing mode" or "sampling inspection mode." In full-volume weighing mode, the pass / fail release ratio threshold is 100%. When the configuration is active, if any product in a work order lacks valid weighing data, the system determines that the work order's weighing completion ratio has not reached 100%, blocking the work order's warehousing and shipment. Specific blocking methods include: locking the product inventory record corresponding to the work order, prohibiting the generation of warehousing slips, and prohibiting the printing of shipping labels or shipping documents. In sampling inspection mode, the pass / fail release ratio threshold can be configured to any value below 100% as needed.

[0070] Step S8, Dynamic Adaptive Correction of Baseline Data: After the work order is completed, the deviation ratio between the real-time weighing center value and the standard gross weight center value is compared. When the deviation ratio is within the preset environmental adaptive range, the standard gross weight center value in the product gross weight base data table is updated using the real-time weighing center value.

[0071] Specifically, after a single work order is completed, the system archives the real-time weighing center value calculated in step S4 and compares it with the original standard gross weight center value stored in step S2. The formula for calculating the deviation ratio is:

[0072] Deviation ratio = |Real-time weighing center value - Standard gross weight center value| ÷ Standard gross weight center value × 100%.

[0073] The system implements differentiated processing strategies based on different deviation percentage ranges. When the deviation percentage is ≤5%, it is determined to be a normal production fluctuation, and the system does not perform a baseline data update operation, retaining the original standard gross weight center value. When the deviation percentage is in the 5%-10% range (i.e., the preset environmental adaptive range), it is determined to be an overall weight shift caused by environmental factors. The system automatically writes back the real-time weighing center value of the current work order to update the product gross weight base data table, replacing the original standard gross weight center value with this value as the comparison benchmark for subsequent work orders.

[0074] In this embodiment, the aforementioned deviation ratio thresholds of 5% and 10% are configurable parameters that can be customized and adjusted according to factors such as changes in the moisture content of packaging materials in different seasons and fluctuations in the temperature and humidity of the production environment.

[0075] All the raw weighing data, preprocessed data, sampling center values, specification comparison results, and benchmark data correction records involved in the above steps are stored in the MES system in real time, supporting full data traceability, customs declaration data query, and production quality anomaly review.

[0076] Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Without conflict, the embodiments and features described and illustrated herein can be combined with each other. The components of the embodiments of the present invention generally described and illustrated in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of the present invention is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

Claims

1. A method for weighing electronic products, characterized in that: Includes the following steps: S1. Data Acquisition and Preprocessing: The weighing values ​​of the digital weighing head are collected in real time through IoT devices. The collected data is filtered for invalid data and stable to obtain stable valid values. The stable valid values ​​are then standardized. S2. Basic Data Pre-storage: Construct a product gross weight basic data table and pre-store the standard gross weight center value corresponding to different machine models and material models; S3. Work Order Data Matching: The MES system automatically binds to the current production work order, retrieves the product identifiers that have not been weighed under the same work order, and caches the retrieved product identifiers. S4. Intelligent Sampling and Real-time Center Value Calculation: Sampling is performed based on the current production work order. The sampled data is compared with the standard gross weight center value to filter out valid sampled data. After accumulating a preset number of valid sampled data, the valid sampled data is processed to remove extreme values, and the arithmetic mean of the remaining data is calculated. The arithmetic mean is used as the real-time weighing center value of the current work order. S5. Graded Differentiated Tolerance Configuration: Configure corresponding differentiated floating tolerance values ​​according to the product weight range; S6. Automatic comparison and over-station control: The real-time weighing value of the current product is compared with the real-time weighing center value and the differential floating tolerance. Based on the comparison result, the product is controlled to over-station or an alarm is triggered and locked. S7. Work order weighing closed-loop control: Statistically calculate the weighing completion rate of work orders. If the weighing completion rate reaches or exceeds the qualified release rate threshold, the work order is allowed to be put into the warehouse and shipped. S8. Dynamic adaptive correction of benchmark data: After the work order is completed, the deviation ratio between the real-time weighing center value and the standard gross weight center value is compared. When the deviation ratio is within the preset environmental adaptive range, the standard gross weight center value in the product gross weight base data table is updated using the real-time weighing center value.

2. The electronic product weighing method according to claim 1, characterized in that: Step S1 specifically includes: Set an invalid weighing threshold to filter weighing values ​​that do not exceed the invalid weighing threshold; Perform continuous multiple data collection and judgment. If the values ​​collected in multiple consecutive data collections are completely consistent, they are judged as stable and valid values. If there are fluctuations, data collection is repeated. The stable effective values ​​are standardized, including unifying the data precision and units of measurement.

3. The electronic product weighing method according to claim 1, characterized in that: In step S3, the product identifier cache duration supports two configuration modes by default: 5 minutes and 2 hours. It also supports custom duration settings to adapt to different production line production cycles.

4. The electronic product weighing method according to claim 1, characterized in that: In step S4, the process of screening valid sample data includes: setting a tolerance range, comparing the single sample value with the standard gross weight center value, and if it is within the tolerance range, it is determined to be valid sample data; The extreme value removal process specifically includes: sorting the accumulated preset number of valid sampled data according to their numerical values, removing the preset number of maximum and minimum values ​​from the sorted data, and calculating the arithmetic mean using the remaining data.

5. The electronic product weighing method according to claim 4, characterized in that: The tolerance range is ±3% of the standard gross weight center value.

6. The electronic product weighing method according to claim 4, characterized in that: The preset quantity is 10 groups, and the predetermined elimination quantity is 4 groups, that is, eliminating two groups of maximum values ​​and two groups of minimum values, and using the remaining 6 groups of valid data to calculate the arithmetic mean.

7. The electronic product weighing method according to claim 1, characterized in that: In step S5, the floating tolerance for the 0-1KG weight range is defined as ±0.01KG, and the floating tolerance for the 1-4KG weight range is ±0.06KG.

8. The electronic product weighing method according to claim 1, characterized in that: In step S6, if the real-time weighing value exceeds the range of the real-time weighing center value and the differential floating tolerance limit, the system automatically locks the product's passage permission, outputs an alarm signal, and records an anomaly log.

9. The electronic product weighing method according to claim 1, characterized in that: In step S7, the formula for calculating the work order weighing completion ratio is: the ratio of the number of weighed products to the total production quantity of the work order.

10. The electronic product weighing method according to claim 1, characterized in that: In step S8, the preset environmental adaptation range is 5%-10%.

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