Weight signal processing method and system and computer readable medium

Abstract

The invention relates to a weight signal processing method and system, and a computer readable medium. The method comprises the following steps: obtaining an initial weight signal within a weighing duration; screening from the initial weight signals according to a preset screening rule to obtain initial tare weight signals; filtering the initial tare signal according to a filter to obtain a filtered tare signal; calculating an initial tare value according to a weighting method and the filtered tare signal; and performing fitting calculation according to a preset tare weight drift model related to time and the initial tare weight value to obtain a model fitting parameter, and calculating a tare weight true value according to the model fitting parameter and the initial tare weight value. According to the invention, the tare weight value with high precision can be obtained, so that the weighing precision can be improved and the requirement of short-time product weight re-checking can be met in the product weighing re-checking process, and the influence of interference of dust, dust, liquid drops and the like in the weighing process can be avoided.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of weight signal processing, and in particular to a weight signal processing method, system and computer-readable medium. Background Art

[0002] In the process of filling products such as bag packaging machines, the volume weighing method that measures the volume of the product can usually be used to supplement the product with materials. In the case of high-speed packaging of products, such as 50pcs / min (50 products per minute), it is difficult to ensure the uniformity of the distribution of product materials, so the use of the volume weighing method will have problems with poor weighing accuracy and material filling accuracy. For example, the target canned weight of a product is 500g. After the volume weighing method is used for material supplementary filling, the final product canned weight may float between 495g and 500g, and there may still be a weight error of about 5g, which cannot meet the user's requirements for the net content of the material. The manufacturer can adjust the target canned weight of the product to 505g, so that the final product canned weight may float between 500g and 510g. Although it can meet the user's requirements for the net content, it increases the manufacturer's cost.

[0003] In the prior art, a weighing module can also be used to weigh the product to determine whether the product is qualified. If there is a shortage of material, it is necessary to refill the material. The weighing module includes an analog sensor module, but the analog sensor module needs to be matched with a transmitter and other equipment for subsequent signal processing. In the process of processing the weight signal, the weight signal can be converted to obtain the product weight value or the tare value on the weighing module. The manufacturer can remove the tare value to obtain a more accurate product weight value, and then make a difference judgment between the product weight value and the target weight value of the product, so as to choose whether to refill the product with materials. For example, the target canned weight of a product is 500g, and the filling of the product can be divided into two steps. In the first pre-filling step, the filling target is set to 490g. After weighing by the weighing module, the actual product weight may float between 485g and 495g; in the second supplementary filling step, the product needs to be supplemented with about 5g to 15g of material. Weighing and checking products based on existing weighing modules can improve weighing accuracy to a certain extent, but the weighing accuracy may fluctuate around ±3g, which cannot meet the needs of some high-precision customers.

[0004] The existing weighing modules can generally only meet the running speed of the packaging production line below 30pcs / min (30 products per minute), and cannot adapt to a higher work rhythm. Therefore, there is a problem of low packaging efficiency, and it cannot meet the needs of short-time product weight verification in high-speed packaging scenarios. The product production line will include packaging machinery and weighing modules, etc. The simultaneous operation of various mechanical components will cause large vibrations, which may affect the weight signal obtained by the weighing module. For example, the weight signal may include some noise or interference information such as tare signals. For example, when the product of the bag packaging machine is dust, because there is pulling of the packaging bag during the production process, dust may be scattered into the air, and the weight of some of the dust will accumulate on the sensor of the weighing module. The weight of these dust can be considered as the tare value, and the tare value should be eliminated during the product weighing and verification process. However, in the high-speed packaging scenario, the entire weighing time is very short and there is a lot of mechanical interference. There is a problem that the obtained tare value is not accurate, resulting in poor product weighing and verification. Summary of the invention

[0005] The technical problem to be solved by the present application is to provide a weight signal processing method, system and computer-readable medium, which can obtain a tare weight value with high accuracy, thereby improving the weighing accuracy and meeting the needs of short-time product weight review during the product weighing review process, and avoiding the influence of interference such as dust, dirt and droplets during the weighing process.

[0006] The technical solution adopted by the present application to solve the above-mentioned technical problems is a weight signal processing method, including: obtaining an initial weight signal within the weighing time; filtering the initial tare weight signal from the initial weight signal according to preset screening rules; filtering the initial tare weight signal according to a filter to obtain a filtered tare weight signal; calculating the initial tare weight value according to a weighting method and the filtered tare weight signal; and performing fitting calculations based on a preset tare weight drift model related to time and the initial tare weight value to obtain model fitting parameters, and calculating the true tare weight value based on the model fitting parameters and the initial tare weight value.

[0007] In one embodiment of the present application, the step of obtaining the initial weight signal within the weighing time also includes obtaining the temperature signal within the weighing time; and before the step of filtering the initial tare weight signal from the initial weight signal according to a preset filtering rule, it also includes: performing temperature compensation processing according to the initial weight signal and the temperature signal to obtain a temperature compensated weight signal, and using the temperature compensated weight signal as the latest initial weight signal.

[0008] In one embodiment of the present application, the step of performing temperature compensation processing according to the initial weight signal and the temperature signal includes: using the following formula to calculate the temperature compensated weight signal w k :

[0009]

[0010] Among them, the subscript k represents the k moment, weight k Represents the weight value at time k, temp k represents the temperature value at time k, n1 represents the order of the compensation polynomial, α i Represents the i-th order initial weight signal and temperature signal Corresponding to the weighting coefficients, the number of initial weight signals is equal to the number of temperature signals.

[0011] In one embodiment of the present application, the weighting coefficient α i The value of is obtained according to the average weighted method or the triangle wave weighted method.

[0012] In one embodiment of the present application, the step of filtering out the initial tare weight signal from the initial weight signal according to preset filtering rules includes: determining whether the initial weight signal is less than or equal to the signal threshold corresponding to the zero point superimposed on the set weight; if the judgment is yes, filtering out the initial weight signal corresponding to the preset time length, and using the filtered initial weight signal as the initial tare weight signal.

[0013] In an embodiment of the present application, the filtering process includes filtering a specific frequency interference of the initial tare signal.

[0014] In one embodiment of the present application, the step of calculating the initial tare value according to the weighted method and the filtered tare signal includes: calculating the initial tare value T using the following formula:

[0015]

[0016] Where k represents the starting number of the filtered tare signal, k+m represents the ending number of the filtered tare signal, and f(w i ) represents the i-th filtered tare signal, β i Represents the weighted value corresponding to the i-th filtered tare signal.

[0017] In one embodiment of the present application, the time-dependent preset tare drift model is expressed by the following formula:

[0018] g(t)=γ 1 *t+γ 2

[0019] Among them, g(t) represents the estimated tare value at time t, t represents the time, γ 1 represents the first model fitting parameter, γ 2 represents the second model fitting parameter, which includes the first model fitting parameter γ 1 and the second model fitting parameter γ2 .

[0020] In one embodiment of the present application, a fitting calculation is performed according to a preset tare drift model related to time and an initial tare value to obtain a model fitting parameter, and the step of calculating the true tare value according to the model fitting parameter and the initial tare value includes: fitting the preset tare drift model related to time and the initial tare value according to a least squares solution method, and solving to obtain a first model fitting parameter γ 1 and the second model fitting parameter γ 2 ; Use the following formula to calculate the true value of tare weight j :

[0021] O j =γ 1 *t j +γ 2

[0022] Among them, O j represents the true value of the tare weight corresponding to time j, γ 1 represents the first model fitting parameter, γ 2 represents the second model fitting parameter, t j Indicates the time value corresponding to time j.

[0023] In order to solve the above technical problems, the present application also proposes a weight signal processing system, including: a weight signal acquisition module, connected to a processor via a first analog-to-digital converter, the weight signal acquisition module is used to collect weight signals; a temperature signal acquisition module, connected to the processor via a second analog-to-digital converter, the temperature signal acquisition module is used to collect temperature signals; a memory, used to store instructions executable by a processor; a processor, used to execute instructions to implement the above weight signal processing method.

[0024] In order to solve the above technical problems, the present application also proposes a computer-readable medium storing computer program codes, and the computer program codes implement the above weight signal processing method when executed by a processor.

[0025] The technical solution of the present application performs pattern recognition processing on the initial weight signal according to preset screening rules, and can screen out an effective initial tare weight signal from the initial weight signal, so as to facilitate the subsequent further acquisition of the true tare weight value; the initial tare weight signal is filtered according to the filter, so as to filter out obvious clutter signals and noise signals, and minimize the noise interference caused by the vibration of the weighing machine; the initial tare weight value is estimated according to the weighting method and the filtered tare weight signal, and the preset tare weight drift model related to time and the initial tare weight value are fitted, and the true tare weight value is calculated accordingly, so as to obtain more accurate tare weight information, which is convenient for determining whether material replenishment and filling operations are required during the product weighing review process.

[0026] The technical solution of the present application can dynamically process weight signals, obtain tare values ​​with higher precision, improve the compatibility of weighing scales and other packaging machinery operating speeds, improve the response speed and weighing efficiency of the weighing process, improve the overall weighing accuracy and meet the needs of short-time product weight review, and avoid the influence of interference such as dust, dust and droplets during the weighing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below with reference to the accompanying drawings, wherein:

[0028] Figure 1 is an exemplary flow chart of a weight signal processing method according to an embodiment of the present application;

[0029] Figure 2 is an exemplary flow chart for a bag-type product packaging line;

[0030] Figure 3 is a schematic diagram of a weight signal processing system according to an embodiment of the present application;

[0031] Figure 4 It is a system block diagram of a weight signal processing system according to another embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below with reference to the accompanying drawings.

[0033] In the following description, many specific details are set forth to facilitate a full understanding of the present application, but the present application may also be implemented in other ways different from those described herein, and therefore the present application is not limited to the specific embodiments disclosed below.

[0034] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0035] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously. At the same time, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0036] The weight signal processing method, system and computer-readable medium proposed in the present application can be applied in product weighing scenarios, for example, in a scenario of short-time product weight verification. Figure 2 is an exemplary flow chart for a bag-type product packaging line, refer to Figure 2 As shown, the production of bag-feeding products usually needs to go through the processing of the feeding station 201, the weighing station 202, the filling station 203 and the bag sealing station 204. The feeding station 201 is used to perform preliminary material filling of the product, for example, filling the material according to 95% of the canning target; the weighing station 202 is used to check the weight of the product that has been filled with materials, and output the tare information on the weighing scale or the actual product weight information. The weight signal processing method proposed in this application can be applied to the weighing station 202, for example, the weight signal processing method of this application can be integrated into the weighing module of the packaging machinery; the filling station 203 can perform the filling operation appropriately according to the difference between the actual product weight value and the target product weight value to ensure the accuracy of the final product canning; the bag sealing station 204 is used to output the product to the subsequent production process after sealing the product.

[0037] Here, the weight signal processing system of the present application is first introduced, and the weight signal processing method will be described in detail later. Figure 3 is a schematic diagram of a weight signal processing system according to an embodiment of the present application, with reference to Figure 3 As shown, the weight signal processing system 300 of the present application includes: a weight signal acquisition module 310, which is connected to the processor 360 through a first analog-to-digital converter 340, and the weight signal acquisition module 310 is used to collect weight signals; a temperature signal acquisition module 320, which is connected to the processor 360 through a second analog-to-digital converter 350, and the temperature signal acquisition module 320 is used to collect temperature signals and temperature values; a memory (not shown), which is used to store instructions that can be executed by the processor 360; the processor 360 is used to execute the instructions to implement the weight signal processing method of the present application.

[0038] Exemplarily, the weight signal processing system 300 of the present application also includes a strain gauge 330, which is connected to the first analog-to-digital converter 340 through a third operational amplifier 331. The strain gauge is a sensor commonly used to measure physical quantities such as pressure and strain. When the strain gauge senses a change in weighing, the strain gauge will deform, causing the output voltage to change. The first analog-to-digital converter 340 and the second analog-to-digital converter 350 can be set as a high-speed and high-precision analog-to-digital converter (ADC). The weight signal acquisition module 310 includes a weight sensor 311 and a first operational amplifier 312. The weight sensor 311 can be set as a strain type weighing sensor; the temperature signal acquisition module 320 includes a temperature sensor 321 and a second operational amplifier 322. The weight signal acquisition module 310 can be used as a weight signal channel, and the temperature signal acquisition module 320 can be used as a temperature signal channel. The processor 360 can be set as a microcontroller unit (MCU), and the corresponding signals dynamically collected by the weight signal channel and the temperature signal channel can be transmitted to the processor 360 for processing.

[0039] Continue to refer Figure 3 As shown, illustratively, the present application performs lossless amplification of the collected signal through a low-noise first operational amplifier 312, a second operational amplifier 322 and a third operational amplifier 331; performs data conversion through a first analog-to-digital converter 340 and a second analog-to-digital converter 350 with high sampling rate, high precision and low temperature drift; connects the supply voltage of the strain gauge 330 to the reference end of the first analog-to-digital converter 340, so that the conversion voltage of the first analog-to-digital converter 340 and the supply voltage of the strain gauge 330 are synchronized; and performs temperature compensation and other processing on the corresponding signal through a processor 360 that can perform the same-team operation. Such a setting of the present application can further improve the accuracy of product weight verification and the universality of the application of the weight signal processing system 300.

[0040] The weight signal processing system 300 of the present application can be used as a weighing module. In practical applications, the corresponding sensors, amplifiers, analog-to-digital converters, processors and other components can be integrated into a printed circuit board (PCB) that can respond at high speed. The weight signal processing system 300 can conveniently and quickly collect dynamic signals with high precision and high-speed response, and can be applied in scenarios such as bag-feeding packaging machines.

[0041] The weight signal processing method of the present application can be run in the processor of the weighing scale, and can also be run in the cloud platform. When the weight signal processing method is run on the cloud platform, the relevant data of the weighing scale interacts with the cloud platform data through a wireless network. Exemplarily, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an interconnected cloud, and multiple clouds, etc., or any combination thereof. The present application does not limit the operating environment of the weight signal processing method.

[0042] Figure 1 is an exemplary flow chart of a weight signal processing method according to an embodiment of the present application, with reference to Figure 1 As shown, the weight signal processing method of this embodiment includes the following steps.

[0043] Step S110: obtaining an initial weight signal within the weighing time.

[0044] Step S120: Filtering the initial weight signal to obtain the initial tare weight signal according to a preset filtering rule.

[0045] Step S130: filtering the initial tare weight signal using a filter to obtain a filtered tare weight signal.

[0046] Step S140: Calculate an initial tare value according to the weighting method and the filtered tare signal.

[0047] Step S150: performing fitting calculation according to a preset time-related tare weight drift model and an initial tare weight value to obtain model fitting parameters, and calculating a true tare weight value according to the model fitting parameters and the initial tare weight value.

[0048] The above-mentioned steps S110 to S150 are described in detail below.

[0049] In step S110, an initial weight signal within the weighing duration is obtained. In some embodiments, the step of obtaining the initial weight signal within the weighing duration also includes obtaining a temperature signal within the weighing duration. Exemplarily, the weighing duration can be set to 1s (second), the initial weight signal obtained at the current moment is represented as weight, and the temperature signal obtained at the current moment is represented as temperature (hereinafter referred to as temp).

[0050] In some embodiments, before the step S120 mentioned later: the step of filtering the initial tare signal from the initial weight signal according to a preset filtering rule, it also includes a step S111: performing temperature compensation processing according to the initial weight signal and the temperature signal to obtain a temperature compensated weight signal, and using the temperature compensated weight signal as the latest initial weight signal. Exemplarily, the present application designs a corresponding signal processing function F (weight, temp) to perform temperature compensation processing on the initial weight signal weight and the temperature signal temp. After the temperature compensated weight signal is obtained by processing the function F (weight, temp), the original initial weight signal can be replaced by the temperature compensated weight signal as the latest initial weight signal, and the step S120 described later is further executed according to the updated initial weight signal.

[0051] In some embodiments, the step of performing temperature compensation processing according to the initial weight signal and the temperature signal includes: using the following formula (1) to calculate the temperature compensated weight signal w k :

[0052]

[0053] Among them, the subscript k represents the k moment, weight k Represents the weight value at time k, temp k represents the temperature value at time k, n1 represents the order of the compensation polynomial, α i Represents the i-th order initial weight signal and temperature signal The corresponding weighting coefficient, the number of initial weight signals is equal to the number of temperature signals. In some embodiments, the weighting coefficient α i The value of is obtained according to the average weighted method or the triangle wave weighted method.

[0054] Exemplarily, the formula (1) is equivalent to the signal processing function F(weight, temp) mentioned above. According to the calculation of formula (1), the temperature compensated weight signal can be obtained within the preset weighing time: 1 , w 2 , w 3 ,……,w n , n represents the number of temperature compensated weight signals, for example, there are 200 temperature compensated weight signals. In order to facilitate understanding of the technical solution of the present application, the following content will be further developed based on 200 temperature compensated weight signals. In practical applications, the number of temperature compensated weight signals can be set as needed, and the present application does not impose any restrictions. The present application can minimize the impact of temperature changes in the weighing environment on the weight signal by performing temperature compensation processing on the initial weight signal, and can improve the accuracy of subsequent signal processing.

[0055] In step S120, an initial tare weight signal is screened from the initial weight signal according to a preset screening rule. Exemplarily, during the weighing of a product, the signals that may be obtained include the tare weight signal on the weighing scale (the signal when there is no product on the weighing scale), the signal before the product is placed on the scale, the signal when the product is placed on the scale, and the signal after the product is removed from the scale, etc. It is necessary to screen out the effective tare weight signal from various signals for subsequent processing. This step S120 is equivalent to a pattern recognition step, which can screen out the tare weight signal on the weighing scale.

[0056] In some embodiments, the step of screening an initial tare weight signal from the initial weight signal according to a preset screening rule includes: step S121: determining whether the initial weight signal is less than or equal to the signal threshold corresponding to the zero-point superposition set weight; step S122: if the determination is yes, then screening out the initial weight signal corresponding to a preset duration, and using the screened initial weight signal as the initial tare weight signal.

[0057] Exemplarily, in step S121, the zero-point superposition set weight is equivalent to the weight when there is no product on the weighing scale. The zero-point superposition set weight can be set as the floating weight of the current zero-point weight plus 10 g, or can be set as one-third of the target weight value, and the present application does not make any limitations. Through the determination in step S121, when the determination is yes, it indicates that the weight on the weighing scale has changed. For example, there may be weights such as dust, dirt, and droplets. This step S122 is equivalent to an effective signal interception step. The preset duration in step S122 can be set to 100 ms (milliseconds). Through step S122, occasional mechanical interference signals can be filtered out and the initial tare weight signal can be collected, which is convenient for further obtaining the true tare weight value later. After the processing of step S122, the initial tare weight signal f(w k ) is screened out, f(w k+1 ) is screened out, f(w k+2 ) is screened out, ……, f(w k+m ) is screened out, 1 < k < k + m < n, k can represent the starting number value of the initial tare weight signal, k + m can represent the ending number value of the initial tare weight signal. For example, 150 initial tare weight signals are screened out.

[0058] In step S130, the initial tare weight signal is filtered according to a filter to obtain a filtered tare weight signal. Exemplarily, part of the interference in the initial tare weight signal can be filtered out through the filter. The filter can be arbitrarily selected from filters such as a moving average filter, a band-stop filter, a band-pass filter, a high-pass filter, and a low-pass filter, and the present application does not make any limitations. In the present application, the initial tare weight signal is filtered according to the filter, which can filter out obvious clutter signals and noise signals, and minimize the noise interference caused by the weighing mechanical vibration as much as possible.

[0059] In some embodiments, the filtering process includes filtering the specific frequency interference of the initial tare signal. For example, the specific frequency interference is, for example, the power frequency interference of the power supply. In practical applications, the initial tare signal may contain power frequency interference, which is a common interference signal. The power frequency greatly hinders the subsequent detection of the signal, so power frequency filtering is required. For example, after filtering, the number of filtered tare signals obtained is 150.

[0060] In step S140, an initial tare value is calculated according to the weighted method and the filtered tare signal. In some embodiments, the step of calculating the initial tare value according to the weighted method and the filtered tare signal includes: calculating the initial tare value T using the following formula (2):

[0061]

[0062] Where k represents the starting number of the filtered tare signal, k+m represents the ending number of the filtered tare signal, and f(w i ) represents the i-th filtered tare signal, β i Represents the weighted value corresponding to the i-th filtered tare signal. For example, the value of k and the value of k+m in formula (2) can be set to be the same as the corresponding number value of the initial tare signal described above, and this application does not limit it. According to the calculation of formula (2), the initial tare value T corresponding to a certain moment can be obtained. Subsequently, the initial tare values ​​corresponding to different moments within a period of time (for example, 2 minutes) can be counted according to formula (2): T 1 , T 2 , ..., T j , where T j represents the initial tare value corresponding to time j. In some embodiments, the weighted value β i The value of can be obtained according to the average weighted method or the triangle wave weighted method.

[0063] In step S150, a fitting calculation is performed based on a preset time-related tare drift model and an initial tare value to obtain a model fitting parameter, and a true tare value is calculated based on the model fitting parameter and the initial tare value. Exemplarily, step S150 may be equivalent to the identification of a tare drift model and the predicted output of a tare value.

[0064] In some embodiments, the time-dependent preset tare drift model is expressed by the following formula (3):

[0065] g(t)=γ 1 *t+γ 2 (3)

[0066] Among them, g(t) represents the estimated tare value at time t, t represents the time, γ 1represents the first model fitting parameter, γ 2 represents the second model fitting parameter, which includes the first model fitting parameter γ 1 and the second model fitting parameter γ 2 Exemplarily, the time-dependent preset tare drift model of the present application is equivalent to a straight line model.

[0067] In some embodiments, a fitting calculation is performed based on a preset tare drift model related to time and an initial tare value to obtain a model fitting parameter, and the step of calculating the true tare value based on the model fitting parameter and the initial tare value includes: step S151: fitting the preset tare drift model related to time and the initial tare value according to a least square solution method, and solving to obtain a first model fitting parameter γ 1 and the second model fitting parameter γ 2 Step S152: Calculate the true value of tare weight using the following formula (4): j :

[0068] O j =γ 1 *t j +γ 2 (4)

[0069] Among them, O j represents the true value of the tare weight corresponding to time j, γ 1 represents the first model fitting parameter, γ 2 represents the second model fitting parameter, t j Indicates the time value corresponding to time j. For example, the initial tare value in step S151 may be the initial tare value corresponding to different time as described above: 1 , T 2 , ..., T j In the process of fitting calculation based on the preset time-related tare drift model and the initial tare value, the initial tare values ​​at different times can be substituted into g(t) in formula (3) to perform fitting calculation. 1 and γ 2 The value of is the model fitting parameter obtained in the aforementioned step S151.

[0070] Exemplarily, step S152 of the present application is equivalent to the final tare true value output step, refer to Figure 2 As shown, the weighing station 202 can jTransmitted to the refilling station 203, the refilling station 203 automatically determines whether the product needs to be refilled with materials according to the true value of the tare weight and the actual product weight value. In the prior art, in order to ensure the eligibility of the products shipped, some manufacturers may directly reject the corresponding products after the weighing station 202 detects products with unqualified weight. This solution of directly rejecting products will cause waste of materials. When the materials are non-recyclable (such as food), it will increase production costs and reduce product competitiveness. The present application adopts a method of refilling materials for substandard products, which can reduce production costs and meet users' requirements for net content.

[0071] The technical solution of the present application can minimize the impact of temperature changes in the weighing environment on the weight signal and improve the accuracy of subsequent signal processing by performing temperature compensation processing on the initial weight signal; perform pattern recognition processing on the temperature compensated weight signal according to preset screening rules, and obtain an effective initial tare signal from the temperature compensated weight signal, so as to facilitate the subsequent further acquisition of the true tare value; perform filtering processing on the initial tare signal according to the filter, and filter out obvious clutter signals and noise signals, and minimize the noise interference caused by the vibration of the weighing machine; estimate the initial tare value according to the weighted method and the filtered tare signal, and perform fitting processing based on the preset tare drift model related to time and the initial tare value, and calculate the true tare value accordingly, so as to obtain more accurate tare information, which is convenient for determining whether material replenishment and filling operations are required during the product weighing review process.

[0072] The technical solution of the present application can dynamically process weight signals, obtain tare values ​​with higher precision, improve the compatibility of weighing scales and other packaging machinery operating speeds, improve the response speed and weighing efficiency of the weighing process, improve the overall weighing accuracy and meet the needs of short-time product weight review, and avoid the influence of interference such as dust, dust and droplets during the weighing process.

[0073] Here, a weight signal processing system according to another embodiment of the present application is introduced. Figure 4 is a system block diagram of a weight signal processing system according to another embodiment of the present application. Figure 4As shown, the weight signal processing system 400 may include an internal communication bus 401, a processor 402, a read-only memory (ROM) 403, a random access memory (RAM) 404 and a communication port 405, and the weight signal processing system 400 may also include a hard disk 406. The internal communication bus 401 can realize data communication between the components of the weight signal processing system 400. The processor 402 can make judgments and issue prompts. In some embodiments, the processor 402 can be composed of one or more processors. The communication port 405 can realize data communication between the weight signal processing system 400 and the outside. In some embodiments, the weight signal processing system 400 can send and receive information and data from the network through the communication port 405. The weight signal processing system 400 may also include different forms of program storage units and data storage units, such as a hard disk 406, a read-only memory (ROM) 403 and a random access memory (RAM) 404, which can store various data files used for computer processing and / or communication, and possible program instructions executed by the processor 402. The processor executes these instructions to implement the main part of the method. The processing result of the processor is transmitted to the user device through the communication port and displayed on the user interface.

[0074] The weight signal processing method described above may be implemented as a computer program, stored in the hard disk 406 , and loaded into the processor 402 for execution, so as to implement the weight signal processing method of the present application.

[0075] The present application also includes a computer-readable medium storing a computer program code, which implements the weight signal processing method described above when executed by a processor.

[0076] When the weight signal processing method is implemented as a computer program, it can also be stored in a computer-readable storage medium as a product. For example, a computer-readable storage medium may include, but is not limited to, a magnetic storage device (e.g., a hard disk, a floppy disk, a magnetic strip), an optical disk (e.g., a compact disk (CD), a digital versatile disk (DVD)), a smart card, and a flash memory device (e.g., an electrically erasable programmable read-only memory (EPROM), a card, a stick, a key drive). In addition, the various storage media described herein can represent one or more devices and / or other machine-readable media for storing information. The term "machine-readable medium" may include, but is not limited to, wireless channels and various other media (and / or storage media) that can store, contain and / or carry code and / or instructions and / or data.

[0077] It should be understood that the embodiments described above are only illustrative. The embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or any combination thereof. For hardware implementation, the processor may be implemented in one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, and / or other electronic units designed to perform the functions described herein, or a combination thereof.

[0078] Some aspects of the present application may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". The processor may be one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DAPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, or combinations thereof. In addition, various aspects of the present application may be expressed as computer products located in one or more computer-readable media, which include computer-readable program codes. For example, computer-readable media may include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, tapes ...), optical disks (e.g., compact disks CDs, digital versatile disks DVDs ...), smart cards, and flash memory devices (e.g., cards, sticks, key drives ...).

[0079] A computer-readable medium may include a propagated data signal containing computer program code, such as in baseband or as part of a carrier wave. The propagated signal may have a variety of manifestations, including electromagnetic, optical, etc., or a suitable combination. A computer-readable medium may be any computer-readable medium other than a computer-readable storage medium, which may be connected to an instruction execution system, device or apparatus to communicate, propagate or transmit a program for use. The program code on the computer-readable medium may be propagated via any suitable medium, including radio, cable, fiber optic cable, radio frequency signal, or similar medium, or any combination of the above mediums.

[0080] The basic concepts have been described above. Obviously, for those skilled in the art, the above application disclosure is only an example and does not constitute a limitation of the present application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements and amendments to the present application. Such modifications, improvements and amendments are suggested in the present application, so such modifications, improvements and amendments still belong to the spirit and scope of the exemplary embodiments of the present application.

[0081] At the same time, the present application uses specific words to describe the embodiments of the present application. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present application can be appropriately combined.

[0082] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of the present application are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

Claims

1. A weight signal processing method, characterized in that: include: Obtaining the initial weight signal within the weighing time; Filtering the initial weight signal to obtain an initial tare weight signal according to a preset filtering rule; Performing filtering processing on the initial tare weight signal according to the filter to obtain a filtered tare weight signal; Calculating an initial tare value based on a weighted method and the filtered tare signal; as well as A fitting calculation is performed based on a preset time-related tare weight drift model and the initial tare weight value to obtain model fitting parameters, and a true tare weight value is calculated based on the model fitting parameters and the initial tare weight value.

2. The weight signal processing method according to claim 1, characterized in that: The step of obtaining an initial weight signal within the weighing time also includes obtaining a temperature signal within the weighing time; and before the step of filtering out an initial tare weight signal from the initial weight signal according to a preset filtering rule, it also includes: performing temperature compensation processing according to the initial weight signal and the temperature signal to obtain a temperature compensated weight signal, and using the temperature compensated weight signal as the latest initial weight signal.

3. The weight signal processing method according to claim 2, characterized in that: The step of performing temperature compensation processing according to the initial weight signal and the temperature signal comprises: calculating the temperature compensated weight signal w using the following formula: k : Among them, the subscript k represents the k moment, weight k Represents the weight value at time k, temp k represents the temperature value at time k, n1 represents the order of the compensation polynomial, α i Represents the i-th order initial weight signal and temperature signal Corresponding to the weighting coefficient, the number of the initial weight signals is equal to the number of the temperature signals.

4. The weight signal processing method according to claim 3, characterized in that: The weighting coefficient α i The value of is obtained according to the average weighted method or the triangle wave weighted method.

5. The weight signal processing method according to claim 1, characterized in that: The step of filtering the initial tare weight signal from the initial weight signal according to a preset filtering rule comprises: Determine whether the initial weight signal is less than or equal to the signal threshold corresponding to the zero point superimposed set weight; If the judgment is yes, the initial weight signal corresponding to a time length that is less than or equal to the preset time length is screened out, and the screened out initial weight signal is used as the initial tare weight signal.

6. The weight signal processing method according to claim 1, characterized in that: The filtering process includes filtering a specific frequency interference of the initial tare signal.

7. The weight signal processing method according to claim 1, characterized in that: The step of calculating the initial tare value according to the weighting method and the filtered tare signal includes: calculating the initial tare value T using the following formula: Wherein, k represents the starting number value of the filtered tare signal, k+m represents the ending number value of the filtered tare signal, and f(w i ) represents the i-th filtered tare signal, β i represents the weighted value corresponding to the i-th filtered tare signal.

8. The weight signal processing method according to claim 1, characterized in that: The time-dependent preset tare drift model is expressed by the following formula: g(t)=γ1*t+γ2 Among them, g(t) represents the estimated tare value at time t, t represents the time, γ1 represents the first model fitting parameter, γ2 represents the second model fitting parameter, and the model fitting parameters include the first model fitting parameter γ1 and the second model fitting parameter γ2.

9. The weight signal processing method according to claim 8, characterized in that: Performing fitting calculation according to a preset tare weight drift model related to time and the initial tare weight value to obtain model fitting parameters, and calculating the true tare weight value according to the model fitting parameters and the initial tare weight value comprises: Fitting the preset tare weight drift model related to time and the initial tare weight value according to the least square solution method, and solving to obtain the first model fitting parameter γ1 and the second model fitting parameter γ2; Use the following formula to calculate the true value of the tare weight: j : The j =γ1*t j +γ2 Among them, O j represents the true value of the tare weight corresponding to time j, γ1 represents the first model fitting parameter, γ2 represents the second model fitting parameter, t j Indicates the time value corresponding to time j.

10. A weight signal processing system, characterized in that: include: A weight signal acquisition module is connected to the processor via a first analog-to-digital converter, and is used to acquire weight signals; A temperature signal acquisition module is connected to the processor via a second analog-to-digital converter, and the temperature signal acquisition module is used to acquire a temperature signal; a memory for storing instructions executable by a processor; The processor is used to execute the instructions to implement the weight signal processing method according to any one of claims 1-9.

11. A computer readable medium storing computer program code, characterized in that: When the computer program code is executed by a processor, the computer program code implements the weight signal processing method according to any one of claims 1 to 9.

Images