WO2020024872A1 - 一种基于称重的货品数量计算方法 - Google Patents

一种基于称重的货品数量计算方法 Download PDF

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WO2020024872A1
WO2020024872A1 PCT/CN2019/097853 CN2019097853W WO2020024872A1 WO 2020024872 A1 WO2020024872 A1 WO 2020024872A1 CN 2019097853 W CN2019097853 W CN 2019097853W WO 2020024872 A1 WO2020024872 A1 WO 2020024872A1
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weight
goods
value
weighing
cumulative error
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PCT/CN2019/097853
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English (en)
French (fr)
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白杨
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白杨
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Priority to US17/263,400 priority Critical patent/US20210156727A1/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01G—WEIGHING
    • G01G19/00—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01G—WEIGHING
    • G01G23/00—Auxiliary devices for weighing apparatus
    • G01G23/01—Testing or calibrating of weighing apparatus
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01G—WEIGHING
    • G01G23/00—Auxiliary devices for weighing apparatus
    • G01G23/14—Devices for determining tare weight or for cancelling out the tare by zeroising, e.g. mechanically operated

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  • the invention relates to a method for calculating the quantity of goods, in particular to a method for calculating the quantity of goods based on weighing.
  • Such solutions usually consist of load cells, digital-to-analog conversion (ADC) modules, and industrial computers.
  • the load cell is usually installed under the cargo tray to convert the weight into the corresponding voltage or current value;
  • the digital-to-analog conversion module measures the voltage change of the load cell in real time and converts it into digital signals;
  • the conversion module reads the latest weighing bare readings (ADC bare readings), converts them into weight units, and further completes the quantity statistics.
  • Weight scaling problem The unit after weight conversion may not be convenient for personnel to operate (for example: "gram” is used as the unit for weight conversion calibration, but it is more convenient to use "ton”).
  • the load cell may have permanent or semi-permanent mechanical deformation (creep) due to long-term load, or its zero measurement may be offset due to environmental factors such as condensation, temperature change, falling dust, sand, etc.
  • creep permanent or semi-permanent mechanical deformation
  • environmental factors such as condensation, temperature change, falling dust, sand, etc.
  • measurement readings are out of alignment.
  • the existing manual calibration technology requires manual operation and measurement, wastes a lot of labor costs, and causes business interruption.
  • the calibration interval is large, it is difficult to maintain long-term stable and accurate weighing readings, and manual operations are prone to errors and cause larger errors.
  • Peeling problem The system needs to be able to sense and automatically remove the weight of the container such as the blue frame used to hold the goods.
  • each shelf can also contain multiple cargo spaces
  • scene full of WIFI, mobile phone signals (3G / 4G / 5G), Lora, Radio frequency interference such as Zigbee, and electromagnetic interference such as power frequency interference generated by various mechanical and electrical equipment and AC transmission lines.
  • AGV vehicle commonly used in modern storage will produce inertia (elevator effect) when lifting or lowering the shelf, and when it is carrying the shelf, it will also cause changes in inertia and centrifugal force due to bumps, steering and other reasons. These will affect the readings of the load cells at the corresponding cargo positions.
  • the purpose of the present invention is to provide a method for calculating the quantity of goods based on weighing, which significantly improves the accuracy and stability of the weighing-based management technology in actual working scenarios.
  • a method for calculating the quantity of goods based on weighing including the following steps:
  • Step one weight conversion; accurately convert the weighing reading to the corresponding weight value W;
  • Step 2 Weight scaling; that is, conversion of symmetrical weight units
  • Step three zero correction; symmetric weight readings for calibration;
  • Step 4 Peel; remove the weight of non-goods objects from the weight value
  • Step 5 Anti-mutation filtering; anti-mutation filtering of weight value;
  • Step 6 Quantity conversion; convert the weight value after the above steps 1 to 5 into the general quantity of the goods;
  • Step 7 Quantitative calibration; calibrate the general quantity of the above goods.
  • SW WS * W (WU); where SW is the weight after scaling, WS is a variable coefficient specified in advance, and WU is a unit.
  • the zero point correction is zero automatic calibration.
  • a continuous sampling reading with at least a preset value falls into the scale Within the range of the re-reading threshold ZPT, the average number of consecutive samples of ZPCS is calculated in a weighted form, and the value is stored in the zero offset compensation amount ZPO as a new zero offset compensation amount.
  • the zero point correction is zero and the automatic follow-up calibration is performed.
  • the number of consecutive sampling (ZPCS) mathematical averages or weighted averages is accumulated into the zero offset compensation (ZPO), where ZPTMAX is the maximum following step value and ZPTMIN is the minimum following step value.
  • the zero point correction is zero and the automatic follow-up calibration is performed.
  • the normal weight changes caused by the business of goods shipment and purchase are automatically recorded and adjusted. Weight loss, the purchase will lead to normal weight gain of the corresponding space).
  • the sampling mathematical average or weighting of ZPCS consecutive samples The average value is accumulated into the zero offset compensation amount ZPO, where ZPTMAX is the maximum following step value and ZPTMIN is the minimum following step value.
  • the fourth step is to remove the weight of non-goods objects such as basket containers from the weight value.
  • the tare operation is realized by subtracting a pre-specified tare parameter BW from the current weight value.
  • the value after subtracting the BW parameter is the pure product weight value at the current location.
  • the fifth step excludes from the sampling the problem of sudden changes in the weighing reading caused by electromagnetic interference, inertia, centrifugal force and other phenomena.
  • the anti-mutation filtering process is mainly controlled by parameters such as the maximum range of anti-mutation AMMAX, the minimum range of maximum-mutation resistance AMMIN, the ratio range of anti-mutation AMR, and the number of samples of anti-mutation AMS.
  • step seven the range of the cumulative error tolerance CAT is calculated by limiting the full storage capacity CAP, the standard gross weight of the product GSW, the tolerance range APU, the maximum cumulative error CAMAX, the minimum cumulative error CAMIN, and the error tolerance ATF; Furthermore, the total quantity of the calculated goods is further calibrated using the cumulative error tolerance CAT.
  • the present invention has the following advantages:
  • the invention significantly improves the accuracy and stability of weighing-based cargo space management technology in actual working scenarios.
  • FIG. 1 is a schematic flowchart of the present invention.
  • FIG. 2 is an example of a linear interpolation algorithm for weight conversion according to the present invention.
  • FIG. 3 is an example of a non-linear interpolation algorithm for weight conversion according to the present invention.
  • FIG. 4 is a schematic diagram of the position of the standard gross weight GSW in the weight distribution in the quantity conversion of the present invention.
  • FIG. 5 is a schematic diagram of a smooth adjustment curve of an “inverse sigmoid” in a preferred embodiment of the quantity calibration of the present invention.
  • a method for calculating the quantity of goods based on weighing including the following steps:
  • Step one weight conversion; accurately convert the weighing reading to the corresponding weight value W;
  • Step 2 Weight scaling; that is, conversion of symmetrical weight units
  • Step three zero correction; symmetric weight readings for calibration;
  • Step 4 Peel; remove the weight of non-goods objects from the weight value
  • Step 5 Anti-mutation filtering; anti-mutation filtering of weight value;
  • Step 6 Quantity conversion; convert the weight value after the above steps 1 to 5 into the general quantity of the goods;
  • Step 7 Calibration of quantity; calibration of the above-mentioned general quantity of goods;
  • the traditional two-point calibration linear conversion cannot truly reflect the physical characteristics of the load cell, resulting in insufficient accuracy of the weight conversion result.
  • the weight scaling in step 2 is essentially a conversion of a symmetric weight unit.
  • the zero point correction in step 3 is the zero point automatic calibration.
  • the zero point automatic calibration means that the system detects that the current cargo position has been emptied, but the weight readings after the steps such as weight conversion and weight scaling have not returned to zero, and it is automatically restarted. Calibrate the zero offset offset of the weighing readings to help the weighing system return to zero.
  • the specific automatic zero point calibration is mainly controlled by two parameters: the weight reading threshold ZPT and the continuous sampling number ZPCS.
  • the weighing reading threshold ZPT is used to ensure the maximum range of the weighing reading when the cargo position is emptied (absolute value, ZPT can also be separately designated as ZPT + and ZPT-: that is, the maximum range of positive values and the maximum range of negative values); and
  • the number of continuous sampling ZPCS specifies at least how many continuous sampling readings completely fall within the range specified by the ZPT parameter before the zero point automatic calibration process is performed (can be 1, which means that continuous sampling is not required).
  • the system detects that the continuous sampling reading with at least a preset value (such as the number of consecutive samples ZPCS) falls within the range of the weighing reading threshold ZPT (indicating that the current cargo position has been emptied), but the weighing reading does not return to zero .
  • the average number of consecutive samples of ZPCS samples is calculated in a weighted form, and the value is stored as a new zero offset compensation amount in the zero offset compensation amount ZPO, and applied in the future weight calculation process, apply This offset value is used to compensate the zero drift (creep) of the sensor.
  • the specific automatic calibration process can be: weighting the most recent 20 samples (for example: newer samples have higher weights; or simple mathematical averages, that is, all samples have a weight of 1) or the first sample Whichever prevails, that is, the weight of the first sample is 1, the weight of subsequent samples is 0, etc.) to obtain the average, and save this value as a new zero offset compensation amount in the ZPO. After each new sampling in the future, after completing the steps of weight conversion and scaling, a ZPO value will be added to compensate for the zero drift.
  • the ZPT value should be much smaller than the weight of the container or single item (without container) on the current position of the basket, otherwise it may lead to misjudgment (the weight of the basket or the product is incorrectly determined as zero drift).
  • the recommended ZPT value should be less than 50% of the weight of the basket or single item. Large ZPCS value can protect the system from sudden interference caused by electromagnetic interference, inertia, centrifugal force and other conditions.
  • the zero point correction is the zero point automatic follow-up calibration.
  • the zero point follow-up function can be automatically activated without emptying the cargo space.
  • the zero-point auto-following function can be performed under the premise that the position is stable (for example, the quantity of goods has not changed) and non-steady state (dynamic, such as: continuous operations such as purchases and shipments, and frequent changes in the quantity of goods). Real-time and dynamic calibration of the zero drift caused by creep, environmental changes and other reasons on the current cargo position.
  • the zero automatic following process is controlled by parameters such as the maximum following step value ZPTMAX, the minimum following step value ZPTMIN, and the continuous sampling number ZPTCS.
  • the maximum following step value ZPTMAX determines the maximum acceptable weight change value (Delta, absolute value, the change value refers to the current weight value and the last time it entered a steady state or the last time the zero point was completed automatically) Following the difference between subsequent weight values, ZPTMAX can also be separately designated as ZPTMAX + and ZPTMAX-: that is, the maximum range of positive values and the maximum range of negative values).
  • the minimum following step value ZPTMIN determines the minimum weight change range required to activate a zero-point auto-following process (absolute value, "variation" is defined above; it can be 0, which means that there is no limit on the minimum fluctuation range, ZPTMIN It can also be specified separately as ZPTMIN + and ZPTMIN-: that is, the minimum range of positive values and the minimum range of negative values).
  • the continuous sampling number ZPTCS specifies at least how many continuous sampling readings the absolute value of the change (difference) value (Delta) falls completely within [ZPTMIN..ZPTMAX] (when absolute values are not used, then: [ ZPTMIN + .. ZPTMAX +] or [ZPTMAX-.. ZPTMIN-]) parameters, then the zero-point auto-tracking process is performed (can be 1, which means that continuous sampling is not required).
  • the specific calibration method is: when the cargo position is stable, and the continuous sampling readings of at least a preset value (such as the number of consecutive samples ZPCS) fall within the range of [ZPTMIN, ZPTMAX], the number of consecutive samples is ZPCS
  • the sampling mathematical average or weighted average is accumulated into the zero offset compensation amount ZPO, and in the future weight calculation process, the offset value is applied to compensate the zero drift (creep) of the sensor.
  • the change in state must first be compensated.
  • the method is: accumulate the difference between the current state and the previous state one by one in each successive sample currently saved, or accumulate it into the currently saved reference value.
  • the zero-point automatic follow-up process is started, and there are already 10 valid continuous samples.
  • the weighing reading of the current position (after cleaning in steps 1 to 5) is 200.5g, and the quantity of goods is 10. From the 11th sampling, the weighing reading was changed to 240.75g, and the number of goods was 12. Then the state has changed (the number of goods has changed from 10 to 12 and the weight has increased by 40.25g).
  • the value of the first 10 samples should be increased by 40.25 one by one, that is: To compensate for non-offset weighing differences in the first 10 samples.
  • the algorithm retains only one reference weight value (instead of retaining a complete weighing reading for each sample), an additional 40.25 should be added to that reference weight value.
  • the ZPTMAX value can usually be set to a very small value relative to the effective range of the current sensor, which also helps prevent misjudgment of the system.
  • Setting ZPTMIN to a non-zero value not only prevents the system from starting the zero-point auto-following process too frequently, but also protects the process from interferences such as signals or other random fluctuations.
  • Large ZPTCS value can protect the system from sudden interference caused by electromagnetic interference, inertia, centrifugal force and other conditions.
  • the fourth step is to remove the weight of non-goods objects such as basket containers from the weight value.
  • the tare operation is realized by subtracting a pre-specified tare parameter BW from the current weight value.
  • the value after subtracting the BW parameter is the pure product weight value at the current location.
  • the step 5 excludes the problem of abrupt weighing readings caused by electromagnetic interference, inertia, centrifugal force and other phenomena from the sampling.
  • the anti-mutation filtering process is mainly controlled by parameters such as the maximum range of anti-mutation AMMAX, the minimum range of maximum-mutation resistance AMMIN, the ratio range of anti-mutation AMR, and the number of samples of anti-mutation AMS.
  • Anti-mutation ratio range AMR is the range of maximum fluctuation ratio between a set of consecutively arrived stable samples.
  • the same sample belongs to the same sampling group, and when the proportion of variation between different samples is within the range specified by AMR, it is considered that the sampling of the group is stable and no mutation occurs.
  • the maximum range of anti-mutation AMMAX determines the absolute upper limit of AMR. If the product of the current weight value and AMR is greater than this value, this value will prevail (absolute value, AMMAX can also be separately designated as AMMAX + and AMMAX-: that is positive
  • AMMAX + and AMMAX- that is positive
  • the minimum range of anti-mutation AMMIN determines the absolute lower limit of AMR. If the product of the current weight value and AMR is less than this value, this value will prevail (absolute value, AMMIN can also be separately designated as AMMIN + and AMMIN-: namely positive Minimum range of values and minimum range of negative values; can be 0, which means the minimum range is unlimited).
  • the number of anti-mutation samples AMS stipulates that the absolute value of at least several consecutive sampling readings completely falls within the range of the AMR ratio constrained by AMMAX and AMMIN before it is considered that no mutation has occurred (can be 1, indicating that continuous sampling is not required) .
  • the specific filtering method is: output the mathematical average or weighted average of the AMS sub-sampling as the final weight value of the set of samples to the next step.
  • AMR 0.1
  • AMMAX helps to limit the maximum allowable reading fluctuation range under the condition of heavy load (large quantity of goods and large weight value reading), preventing the absolute value from being too large (too slow) after proportional limitation; instead AMMIN helps to limit the minimum allowable reading fluctuation range under the condition of light load (small quantity of goods and small weight value reading), preventing the absolute value from being too small (too sensitive) after proportional limitation.
  • a large AMR value can protect the system from sudden interference caused by electromagnetic interference, inertia, centrifugal force and other conditions.
  • the process of converting the quantity in step 6 is specifically: converting the weight value after the calibration and correction in steps 1 to 5 to a general quantity.
  • the conversion process is:
  • the general quantity of the goods in the current position P the weight value of the current space / the standard gross weight of the goods in the current position GSW.
  • the standard gross weight of the product GSW refers to the standard value of the gross weight of the product obtained through actual weighing tests or through trusted channels. It should be able to meet the median under normal distribution after taking into account the effects of tolerances. This means that if one item is randomly selected from N pieces of the model, the item: 1.
  • the probability of weight above GSW and the probability of weight below GSW should be substantially equal (ie: both are 50%); 2 At the same time, the magnitude of its weight above GSW should also be probabilistically equivalent to its magnitude below GSW.
  • the GSW of a product is 100g
  • the standard tolerance is 2%.
  • One randomly selected from 1000 products of this model then: 1.
  • the probability that the product weighs more than 100g or less than 100g is 50%; 2.
  • the probability that the weight is 99g should be equivalent to the probability that it weighs 101g.
  • the approximate value of GSW can be obtained by randomly selecting M pieces from one or more batches of the same model and weighing them, and finding the average value, that is:
  • GSW is one of the key indicators to ensure accurate quantity conversion. The closer it is to the median of the normal distribution described in the previous article, the more the same number of products of the same model are contained in one location, eliminating its cumulative error, making the quantity calculation more accurate.
  • GSW usually needs to be measured by itself or obtained from other trusted authoritative channels.
  • the main parameters involved in the quantity calibration process include:
  • Full storage capacity CAP The full storage capacity of the current location (the number of goods, 0 means no limit).
  • Tolerance range APU The maximum acceptable tolerance range (percent or absolute weight) of each item relative to its median.
  • Maximum cumulative error CAMAX The maximum acceptable cumulative error upper limit for all goods in the current position (can be a percentage relative to GSW, or an absolute weight value, 0 means no maximum limit).
  • Minimum cumulative error CAMIN the minimum acceptable cumulative error lower limit for all goods in the current position (can be a percentage relative to GSW, or an absolute weight value, 0 means no minimum limit).
  • Error tolerance ATF the tolerance of the current cargo position to errors. Geometrically, this parameter will determine the steepness of the curve or straight line that gradually limits the cumulative error together with the current full capacity of the cargo position. The larger the value, the higher the tolerance of the error (the smoother the curve, or the smaller the absolute value of the straight line slope, 1 means no adjustment).
  • this process can integrate multiple parameters and data to give a smooth adjustment curve of "inverse sigmoid” as shown in Figure 5 for the acceptable cumulative error of the current cargo position.
  • "inverse sigmoid” Smooth curve
  • ISF 1 / (CAP * ATF).
  • ISITF (1 / (1 + exp (P * ISF)) * ISC.
  • ISWCT P * APU.
  • ISCWCT ISITF * ISWCT.
  • CAMIN CAMIN / ISWCT. This ensures that the cumulative tolerance range meets the minimum range constraints specified by CAMIN.
  • ISRPA CAT * APU.
  • ISWCO weight value after the above steps 1-5 after calibration and correction-P * GSW.
  • CAP and ATF are mainly used to control the smoothness of the tolerance curve (or the absolute value of the slope of the tolerance line).
  • CAMIN and CAMAX are used to properly scale the curve to ensure that the cargo space can maintain a reasonable CAT value under light and heavy loads.
  • the GSW and APU determine the actual value of the final error range.
  • the present invention has accurate calculation, which significantly improves the accuracy and stability of weighing-based cargo space management technology in actual working scenarios.

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Abstract

本发明公开一种基于称重的货品数量计算方法，包括以下步骤：步骤一、重量转换；将称重读数准确地转换为对应的重量值W；步骤二、重量缩放；即对称重单位的转换；步骤三、零点纠偏；对称重读数进行校准；步骤四、去皮；去除重量值中非货品物体的重量；步骤五、抗突变滤波；对重量值进行抗突变滤波；步骤六、数量转换；将经过了上述步骤一至五校准和纠偏后的重量值转换为货品大体数量；步骤七、数量校准；对上述货品大体数量进行校准。本发明计算精准，显著提升了基于称重的货位管理技术在实际工作场景中的精度和稳定性。

Description

一种基于称重的货品数量计算方法 技术领域
本发明涉及一种货品数量计算方法，尤其涉及一种基于称重的货品数量计算方法。
背景技术
随着物联网(IoT)技术和智能系统的逐步普及，为每个货位加装称重传感器，并通过重量来实时统计该货位中的货品数量的方法，正成为近年来较流行的发展方向。此类方案通常由称重传感器、数模转换(ADC)模块，以及工控机等部分组成。其中称重传感器通常安装在货位托盘下方，将重量转换为相应的电压或电流值；数模转换模块则实时测量称重传感器的电压变化，并将其转换为数字信号；工控机从数模转换模块读取最新的称重裸读数(ADC裸读数)，将其换算为重量单位，并进一步完成数量统计。
上述方案具备成本低、可靠性高、环境耐受性好、维护简单等优点。但与此同时，该方案也存在如下不足：
1.重量转换的精度问题：由ADC裸读数转换为重量时，通常仅采用两点线性标定算法，转换精度不足。
2.重量缩放问题：重量转换后的单位可能不便于人员实际操作(如：重量转换标定时使用“克”为单位，但实际操作是可能使用“吨”更为方便)。
3.零点偏移问题：称重传感器可能由于长期负重而产生永久性或半永久性机械形变(蠕变)，或由于结露、温度变化、落尘、沙土等环境因素导致其零点测量发生偏移，致使测量读数失准。现有的手动校准技术需要人工操作和测量，浪费大量人工成本，并使得业务中断。同时还有校准间隔大，难以保持 称重读数长期稳定和准确，以及人工操作容易失误引起更大误差等问题。
4.去皮问题：系统需要能够感应并自动去除货位上用于盛放货品的蓝框等容器的重量。
5.突变问题：现代称重传感器由于制作材质和工艺的原因，其满量程电压通常也只有几个毫伏(mV)的范围，非常微弱。因此极易受到外部环境的电磁干扰而产生较高的抖动和误差；另一方面，货位本身也可能被移动，而移动过程中产生的惯性和离心力等因素也会导致称重读数瞬间发生变化。这些突变因素均可导致称重读数以及货品数量计量的不准确。
例如：在一个典型的现代仓库(一个仓库内可同时容纳多个货架，每个货架内又可包含多个货位)场景中，充斥着WIFI、手机信号(3G/4G/5G)、Lora、Zigbee等射频干扰，以及各类机电设备和交流输电线路产生的工频干扰等电磁干扰。同时，现代仓储中普遍使用的AGV车在托举或放下货架时，会产生惯性(电梯效应)，并且其在背负货架运动时，也会由于颠簸，转向等原因产生惯性和离心力上的变化。这些都会对相应货位上称重传感器的读数产生影响。
6.公差问题：由于一个货位中可同时容纳多件货品，因此其中每件货品的公差将可能产生累积，导致最终的货品数量计算错误。
例如：考虑一种货品A的重量为100g，其公差范围为正负2％。即：出厂合格品的单件重量范围在98g到102g之间。假如一个货位同时堆放了200件此类A货品，则计入公差后，其合法的重量范围在19600g到20400g之间。意即：即使我们克服了之前的5点问题，获得了精确且稳定的称重读数，以此最终计算出的商品数量仍然可能有高达8件(正负4件)的偏差。
显然，上述问题严重阻碍了基于称重的货位管理技术在实际应用场景中的效果。
发明内容
本发明的目的是提供一种基于称重的货品数量计算方法，显著提升了基于称重的货位管理技术在实际工作场景中的精度和稳定性。
为了实现上述目的，本发明的技术方案是：
一种基于称重的货品数量计算方法，包括以下步骤：
步骤一、重量转换；将称重读数准确地转换为对应的重量值W；
步骤二、重量缩放；即对称重单位的转换；
步骤三、零点纠偏；对称重读数进行校准；
步骤四、去皮；去除重量值中非货品物体的重量；
步骤五、抗突变滤波；对重量值进行抗突变滤波；
步骤六、数量转换；将经过了上述步骤一至五校准和纠偏后的重量值转换为货品大体数量；
步骤七、数量校准；对上述货品大体数量进行校准。
进一步的，所述步骤一中的转化公式为：W＝f({S},ADC)，其中W为重量值，f表示要使用的线性或非线性插值算法，{S}表示事先经标定的采样集合，ADC表示的称重数据。
进一步的，所述步骤二的计算公式为：SW＝WS*W(WU)；其中SW为缩放后重量，WS为事先指定的可变系数，WU为单位。
进一步的，所述步骤三中零点纠偏为零点自动校准，当系统检测到当前货位已被清空，但称重读数却并未归零时，在至少有预设数值的连续采样读数落入称重读数阈值ZPT的范围内后，将连续采样数ZPCS个的采样按照加权的形式求取平均数，并将该数值作为新的零点偏移补偿量保存到零点偏移补偿量ZPO 中。
进一步的，所述步骤三中零点纠偏为零点自动追随校准，在货位处于稳定状态时，且至少有预设数值的连续采样读数的变化量落入了[ZPTMIN，ZPTMAX]范围内后，将连续采样数(ZPCS)个的采样数学平均数或加权平均值累加到零点偏移补偿量(ZPO)中，其中ZPTMAX为最大跟随步进值，ZPTMIN为最小跟随步进值。
进一步的，所述步骤三中零点纠偏为零点自动追随校准，在货位处于不稳定状态时，自动记录并调整货品出货、进货等业务引起的正常重量变化，(出货导致对应货位正常减重，进货导致对应货位正常增重)。在此基础上，若检测到至少预设数值的连续采样读数，其正常变化之外的差量落入了[ZPTMIN，ZPTMAX]范围内后，将连续采样数ZPCS个的采样数学平均数或加权平均值累加到零点偏移补偿量ZPO中，其中ZPTMAX为最大跟随步进值，ZPTMIN为最小跟随步进值。
进一步的，所述步骤四旨在去除重量值中篮筐容器等非货品物体的重量。去皮操作通过在当前重量值上，减去一个事先指定的皮重参数BW来实现。减去BW参数后的值即为当前货位上的纯货品重量值。
进一步的，所述步骤五从采样中排除由电磁干扰、惯性、离心力等现象带来的称重读数突变问题。抗突变滤波过程主要由：抗突变最大范围AMMAX、抗突变最大范围最小范围AMMIN、抗突变比例范围AMR以及抗突变采样数AMS等参数控制。
进一步的，所述步骤六中的数量转换公式为：货品大体数量P＝经过步骤五后的重量值/货品的标准毛重GSW
进一步的，所述步骤七中，通过对满仓容量CAP、货品标准毛重GSW、公差 范围APU、最大累计误差CAMAX、最小累计误差CAMIN以及误差容限ATF的限定，计算累计误差容忍度CAT的范围；进而使用累计误差容忍度CAT对计算出的货品大体数量进一步校准。
本发明相对于现有技术具有以下优点：
本发明显著提升了基于称重的货位管理技术在实际工作场景中的精度和稳定性。
附图说明
图1是本发明流程示意图。
图2是本发明重量转换的线性插值算法示例。
图3是本发明重量转换的非线性插值算法示例。
图4是本发明数量转换中标准毛重GSW在重量分布中的位置示意图。
图5是本发明数量校准中优选实施例“倒S型”(inverse sigmoid)平滑调整曲线的示意图。
具体实施方式
以下结合附图进一步说明本发明的实施例。
实施例:请参见图1所示，
一种基于称重的货品数量计算方法，包括以下步骤：
步骤一、重量转换；将称重读数准确地转换为对应的重量值W；
步骤二、重量缩放；即对称重单位的转换；
步骤三、零点纠偏；对称重读数进行校准；
步骤四、去皮；去除重量值中非货品物体的重量；
步骤五、抗突变滤波；对重量值进行抗突变滤波；
步骤六、数量转换；将经过了上述步骤一至五校准和纠偏后的重量值转换为货品大体数量；
步骤七、数量校准；对上述货品大体数量进行校准；
其中，所述步骤六中的数量转换公式为：货品大体数量P＝经过步骤一至五后的重量值/货品的标准毛重GSW。
优选的，由于传统的两点标定线性转换无法真实反映称重传感器的物理特性，导致重量转换结果准确度不足。为此，步骤一中的转化公式为：W＝f({S},ADC)，其中W为重量值，f表示要使用的线性或非线性插值算法，{S}表示事先经标定的采样集合，ADC表示的称重数据(裸读数)。请参见图2和图3所示，该转换方法支持任意多个称重数据采样S(S1、S2、S3等)，其中每个采样均包含ADC裸读数X及与之对应的实际重量值Y(S1＝[X1,Y1]、S2＝[X2,Y2]、S3＝[X3,Y3]……)，并结合线性插值(分段函数)或非线性插值(平滑曲线)算法，可将ADC裸读数准确地转换为对应重量值。
优选的，步骤二中重量缩放本质上是对称重单位的转换，可通过将当前称重值乘以一个事先指定的可变系数WS来实现的计算公式为：SW＝WS*W(WU)；其中SW为缩放后重量，WS为事先指定的可变系数，WU为单位(如：吨、公斤、市斤、克等等)。
优选的，步骤三中零点纠偏为零点自动校准，零点自动校准指系统检测到当前货位已被清空，但经过重量转换和重量缩放等步骤后的称重读数却并未归零时，自动重新校准称重读数的零点补偿偏移值，以帮助称重系统回零的过程。
具体的零点自动校准主要由两个参数进行控制：称重读数阈值ZPT和连续采样数ZPCS。其中称重读数阈值ZPT用来保证当货位已清空时的称重读数最大 范围(绝对值，ZPT也可被分开指定为ZPT+和ZPT-：即正值最大范围和负值最大范围)；而连续采样数ZPCS则规定了至少有多少个连续采样读数完全落入ZPT参数所规定的范围内后，才执行零点自动校准过程(可以为1，表示不需要执行连续采样)。
当系统检测到至少有预设数值(如连续采样数ZPCS)的连续采样读数落入称重读数阈值ZPT的范围内(说明当前货位已被清空)，但称重读数却并未归零时，将连续采样数ZPCS个的采样按照加权的形式求取平均数，并将该数值作为新的零点偏移补偿量保存到零点偏移补偿量ZPO中，并在未来的重量计算过程中，应用该偏移值，以对传感器的零点漂移(蠕变)进行补偿。
例如：ZPT＝30、ZPCS＝20表示至少有连续20个采样的重量值落入±30的范围内时，才开始一次零点自动校准过程。具体的自动校准过程可以为：将最近20次的采样按照加权(例如：越新的采样权重越高；或简单的数学平均数，即：所有采样的权重均为1；或以第一个采样为准，即：第一个采样的权重为1，后续采样的权重均为0，等等)的形式求取平均数，并将该数值作为新的零点偏移补偿量保存到ZPO中。未来的每次新采样在完成重量转换和缩放等步骤后，均会追加ZPO值以补偿零点漂移。
显然，ZPT值应该远小于当前货位上篮筐等容器或单件货品(无容器情况下)的重量，否则可能导致误判(将篮筐或货品的重量错误地判定为零点漂移)。推荐的ZPT值应小于篮筐或单件货品重量的50％。较大的ZPCS值则可保护系统不会受到由电磁干扰、惯性、离心力等情况引起的突变干扰。
优选的，步骤三中零点纠偏为零点自动追随校准，与零点自动校准不同，零点自动追随功能无需清空货位即可被自动激活。零点自动追随功能可以在货位处于稳定状态(例如：货品数量未发生变化)以及非稳定状态(动态，例如： 持续进行进货、出货等操作，货品数量频繁发生变化等情况)的前提下，实时、动态地对当前货位上，由蠕变、环境变化等原因引起的零点漂移现象进行实时校准。
零点自动跟随过程由最大跟随步进值ZPTMAX、最小跟随步进值ZPTMIN、以及连续采样数ZPTCS等参数控制。
其中最大跟随步进值ZPTMAX决定了每次发生零点自动追随过程时，可接受的最大重量变化值(Delta，绝对值，变化值是指当前重量值与上次进入稳态或上次完成零点自动追随后的重量值之差，ZPTMAX也可被分开指定为ZPTMAX+和ZPTMAX-：即正值最大范围和负值最大范围)。
而最小跟随步进值ZPTMIN则决定了要激活一次零点自动追随过程需要的最低重量变化值范围(绝对值，“变化值”的定义见上文；可以为0，表示不限制最小波动范围，ZPTMIN也可被分开指定为ZPTMIN+和ZPTMIN-：即正值最小范围和负值最小范围)。
最后，连续采样数ZPTCS规定了至少有多少个连续采样读数的变化(差量)值(Delta)的绝对值完全落入了[ZPTMIN..ZPTMAX](在不使用绝对值时，则是：[ZPTMIN+..ZPTMAX+]或[ZPTMAX-..ZPTMIN-])参数所规定的范围内后，才执行零点自动追踪过程(可以为1，表示不需要执行连续采样)。
具体的校准方式为：在货位处于稳定状态时，且至少有预设数值(如连续采样数ZPCS)的连续采样读数落入了[ZPTMIN，ZPTMAX]范围内后，将连续采样数ZPCS个的采样数学平均数或加权平均值累加到零点偏移补偿量ZPO中，并在未来的重量计算过程中，应用该偏移值，以对传感器的零点漂移(蠕变)进行补偿。
例如：假设ZPTMIN＝1.5、ZPTMAX＝5、ZPTCS＝100、ZPO＝10，此时若在上次货 品数量发生变化以后，连续100次采样的变化值(Delta)范围在[1.5..5]以内，则将这100个变化值的加权平均值(假设为2.5)累加到ZPO(新的ZPO＝12.5)参数上。未来的每次新采样在完成重量转换和缩放等步骤后，均会追加该ZPO值以补偿零点漂移。
反之，在货位处于非稳定状态时，则首先要对状态的变化进行补偿。方法是：将当前状态与上一状态的差值逐一累加到当前已保存的每一个连续采样中，或将其累加到当前已保存的基准值中。
例如：假设开始零点自动追随过程，并且已存在10次有效的连续采样时，当前货位的称重读数(经过步骤一到步骤五清洗)为200.5g，货品数量为10。而从第11次采样开始，其称重读数变更为240.75g，货品数量为12。则此时状态发生了变化(货品数量由10变为了12，同时重量增加了40.25g)。为了能够动态地继续跟踪零点偏移值，此时应对前10个采样的数值逐一增加40.25，即：
Figure PCTCN2019097853-appb-000001
Figure PCTCN2019097853-appb-000002
以补偿其前10个采样中的非偏移性称重差异。或者，若算法仅保留一个基准重量值(而不是为每个采样保留完整的称重读数)，则应对该基准重量值追加40.25。
由于蠕变或环境变化导致的零点漂移通常是以微小步进量逐步发生，因此ZPTMAX值通常可设置为一个相对当前传感器有效量程来说非常小的值，这也有助于防止系统发生误判。将ZPTMIN设置为一个非零数值既可以防止系统过于频繁地启动零点自动跟随过程，也可以保护本过程免受信号等干扰，或其它随机波动等因素的影响。较大的ZPTCS值则可保护系统不会受到由电磁干扰、惯性、离心力等情况引起的突变干扰。
优选的，所述步骤四旨在去除重量值中篮筐容器等非货品物体的重量。去 皮操作通过在当前重量值上，减去一个事先指定的皮重参数BW来实现。减去BW参数后的值即为当前货位上的纯货品重量值。
优选的，所述步骤五从采样中排除由电磁干扰、惯性、离心力等现象带来的称重读数突变问题。
抗突变滤波过程主要由：抗突变最大范围AMMAX、抗突变最大范围最小范围AMMIN、抗突变比例范围AMR以及抗突变采样数AMS等参数控制。
抗突变比例范围AMR是一组连续到达的稳定采样之间的最大波动比例范围。同属于一个采样组内，不同采样之间的变化波动比例在AMR指定范围内时，才被认为该组采样是稳定的，没有发生突变。
抗突变最大范围AMMAX决定了AMR的绝对上限值，若当前重量值与AMR的积大于此值，则以此值为准(绝对值，AMMAX也可被分开指定为AMMAX+和AMMAX-：即正值最大范围和负值最大范围；可以为0，表示最大范围不限)。
抗突变最小范围AMMIN决定了AMR的绝对下限值，若当前重量值与AMR的积小于此值，则以此值为准(绝对值，AMMIN也可被分开指定为AMMIN+和AMMIN-：即正值最小范围和负值最小范围；可以为0，表示最小范围不限)。
抗突变采样数AMS规定了至少有多少个连续采样读数的绝对值完全落入了受AMMAX和AMMIN约束的AMR比例范围内后，才认为没有发生突变(可以为1，表示不需要执行连续采样)。
具体滤波方式为：将AMS次采样的数学平均数或加权平均值作为该组采样的最终重量值输出至下一步。
例如：AMR＝0.1、AMMAX＝30、AMMIN＝1.5、AMS＝5表示每5个连续采样为一组，若这5个采样的波动范围不超过±10％(同时，对于该组采样的重量值来说，其与0.1的积若小于1.5则强制设置为1.5、若大于30则强制设定为30)，则认 为该组采样稳定，该组采样的最终重量值为上述5个重量值的加权平均数。
显然，AMR限制的波动比例范围保证了数据的相对稳定。AMMAX则在货位重载(货品数量多，重量值读数大)的情况下帮助限制了可允许的读数最大波动范围，防止按比例限制后绝对值仍然过大(过于迟钝)的现象发生；相反AMMIN在货位轻载(货品数量少，重量值读数小)的情况下帮助限制了可允许的读数最小波动范围，防止按比例限制后绝对值过小(过于敏感)的现象发生。较大的AMR值则可保护系统不会受到由电磁干扰、惯性、离心力等情况引起的突变干扰。
优选的，步骤六数量转换过程具体为：将经过了上述步骤一至五步骤校准和纠偏后的重量值转换为大体数量。其转换过程为：
当前货位货品大体数量P＝当前货位重量值/当前货位上货品的标准毛重GSW。
请参见图4所示，其中货品的标准毛重GSW是指，通过实际称量测试或通过可信渠道获得的，货品毛重标准值。它应该在计入公差影响后，能够满足正态分布条件下的中位数。意即：若随机从N件该型号产品中抽取一件，则该件货品：1.重量高于GSW的概率与重量低于GSW的概率上应当基本相等(即：均为50％)；2.同时，其重量高于GSW的幅度在概率上也应与其低于GSW的幅度相当。
例如：若一件产品的GSW为100g，其标准公差为2％。从1000件该型号产品中随机抽取一件，则：1.该件产品重量大于100g或小于100g的概率均为50％；2.并且其重量为99g的概率应与其重量为101g的概率相当。
GSW的近似值可以通过从一批或多批相同型号的货品中，随机抽取M件进行称重，并求其平均值来得出，即：
GSW≈M件随机货品的总重/M
显而易见，GSW是保证数量转换准确的关键指标之一。它越接近前文中描述的正态分布中位数，就在“一个货位内，同时盛放多件相同型号货品”的情况下，消除其累积误差，使得数量计算更为精确。
可以想象，若某品牌方便面，其包装标称保重为100g，标准公差3％。但由于厂商追逐利益，实际测量后其所有产品重量均分布在97g到98g之间。此时若将该货品的GSW值设置为厂方标称的100g，则在该货位盛放100包方便面时，实际测得的结果可能只有97包。
因此GSW通常需要通过自行实际测量或从其它可信的权威渠道获取。
虽然在步骤六中引入了GSW后，已能做到比较准确地完成数量计算。但仍无法避免如±1件之类的微小偏差。为了进一步增强数量统计的准确度，在执行数量转换后，仍需进行进一步的数量精细校准过程。数量校准过程较为复杂，大体上来讲，该过程的主要动作是对当前货位中所有货品的可接受累计公差(重量值)进行合理的控制，并据此对上一步产生的数量转换结果进行必要的修正。
优选的，步骤七中，数量校准过程中涉及到的主要参数包括：
满仓容量CAP：当前货位的满仓容量(货品件数，为0表示无件数限制)。
公差范围APU：每件货品相对于其中位数的最大可接受公差范围(可以为百分比或重量绝对值)。
最大累计误差CAMAX：当前货位所有货品的最大可接受累计误差上限(可以为相对于GSW的百分比，或重量绝对值，为0表示无最大限制)。
最小累计误差CAMIN：当前货位所有货品的最小可接受累计误差下限(可以为相对于GSW的百分比，或重量绝对值，为0表示无最小限制)。
误差容限ATF：当前货位对误差的容忍度。从几何意义上说，此参数将与当 前货位满仓容量一起决定逐步限制累计误差的曲线或直线陡峭程度。此值越大，对误差的宽容度越高(曲线越平滑、或直线斜率的绝对值越小，为1表示无调整)。
以及在步骤6中得到的大体数量P和标准毛重GSW。
通过对上述几个方面施加合理限制，即可计算出合理的累计误差容忍度CAT范围。进而使用CAT对实际货品数量执行进一步校准，显著提升系统准确度。
举例来说，此过程可通过综合多中参数和数据，对当前货位的可接受累积误差给出如图5所示的“倒S型”(inverse sigmoid)平滑调整曲线，基于“倒S型平滑曲线”的CAT计算方法：
1.计算“倒S型曲线”平滑下降因子ISF：ISF＝1/(CAP*ATF)。
2.计算“倒S型曲线”缩放补偿值ISC：ISC＝1/(1/(1+exp(ISF)))。
3.将由步骤六中得到的货品大体数量值P带入，计算其误差容忍中间系数ISITF：ISITF＝(1/(1+exp(P*ISF)))*ISC。
以及其在最差情况下，可能造成的累积误差最大单位(件数)比例ISWCT：ISWCT＝P*APU。
4.计算其受限后的最大累计误差单位比例ISCWCT：ISCWCT＝ISITF*ISWCT。
5.计算累计公差容忍度CAT：
a)若CAMIN不为零，且ISCWCT<CAMIN(此时CAMIN应转换为相对于GSW的百分比)，则：CAT＝CAMIN/ISWCT。此举确保累计公差范围满足CAMIN指定的最小范围约束。
b)若CAMAX不为零，且ISCWCT>CAMAX(此时CAMAX应转换为相对于GSW的百分比)，则：CAT＝CAMAX/ISWCT。此举确保累计公差范围满足CAMAX 指定的最大范围约束。
c)否则说明ISCWCT介于[CAMIN..CAMAX]之间，此时CAT受上述“倒S型曲线”控制：CAT＝ISITF。
6.计算实际允许的货位总公差(百分比)ISRPA：ISRPA＝CAT*APU。
7.计算实际误差容忍范围(重量值)ISAAR：ISAAR＝经过了上述步骤一至五校准和纠偏后的重量值*ISRPA。
8.计算步骤六中得到的货品大体数量值P(P为整数)后的称重值余数ISWCO：ISWCO＝经过了上述步骤一至五校准和纠偏后的重量值–P*GSW。
9.若ISWCO+ISAAR≥GSW，则执行补偿：P＝P+1，为货品数量值加1。
注：本文中用到的数学算式与C语言中的数学表达式语法基本相同，即：“+”表示加法、“-”表示减法、“*”表示乘法、“/”表示除法、exp表示“以自然数e为底的幂运算”，例如：exp(10)表示e的10次方、exp(N)表示e的N次方。
应当指出的是，上例中仅列出了对包括P、GSW、CAP、APU、CAMAX、CAMIN、ATF在内的各项校准方法的一种合理应用。实际上，我们可以有任意多中方式对上述限制方法进行合理应用。例如：将上述算式中的自然数e替换为圆周率π或任意其它实数、将上述算式中的幂运算替换为对数运算、将上述非线性(曲线)计算替换为线性(直线)计算等等。
因此上例中提到的所有算式以及“倒S型曲线”算法均仅为举例，并不对本发明的范围有任何限制作用。
可以看出，CAP和ATF从几何意义上来说，主要用来控制容忍度曲线的平滑程度(或容忍度直线的斜率绝对值)。而CAMIN和CAMAX则用于对曲线进行适当缩放，保证货位在轻载和重载条件下也能保持合理的CAT值。而GSW和APU则 决定了最终误差范围的实际值。
综上，本发明计算精准，显著提升了基于称重的货位管理技术在实际工作场景中的精度和稳定性。

Claims (4)

  1. 一种基于称重的货品数量计算方法，其特征在于，包括：数量校准方法，对上述货品大体数量进行校准；通过对满仓容量(CAP)、货品的标准毛重(GSW)、公差范围(APU)、最大累计误差(CAMAX)、最小累计误差(CAMIN)以及误差容限(ATF)的限定，计算累计误差容忍度(CAT)的范围；进而使用累计误差容忍度(CAT)对计算出的货品大体数量进一步校准；其中最大累计误差(CAMAX)、最小累计误差(CAMIN)用于控制累计误差容忍度(CAT)的最大和最小取值范围；通过对满仓容量(CAP)、公差范围(APU)和误差容限(ATF)则决定了累计误差容忍度(CAT)未超出最大累计误差(CAMAX)和最小累计误差(CAMIN)范围时的值；货品的标准毛重(GSW)和累计误差容忍度(CAT)计算出实际的累计误差容忍重量范围。
  2. 根据权利要求1所述的一种基于称重的货品数量计算方法，其特征在于：包括数量转化方法，用于计算所述货品大体数量，所述数量转换的公式为：货品大体数量(P)＝重量值(W)/货品的标准毛重(GSW)。
  3. 根据权利要求1所述的一种基于称重的货品数量计算方法，其特征在于：还包括重量缩放方法，对称重单位进行转换；所述转换公式为：SW＝WS*W(WU)；其中SW为缩放后重量，WS为事先指定的可变系数，WU为单位。
  4. 根据权利要求1所述的一种基于称重的货品数量计算方法，其特征在于：还包括去皮方法，去除重量值中非货品物体的重量，将称得的重量减去预设的皮重参数BW。
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