Weighing signal processing method and system and computer readable medium

Abstract

The invention relates to a weighing 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; performing first filtering processing on the initial weight signal according to a first filter to obtain a first filtering weight signal; screening from the first filtering weight signal according to a preset screening rule to obtain an effective weighing signal; filtering out an out-of-tolerance signal in the effective on-scale signal according to a preset filtering rule to obtain an effective intercepted on-scale signal; performing second filtering processing on the effectively intercepted weighing signal according to a second filter to obtain a second filtering weight signal; and performing fitting calculation according to a preset weight calculation model and the second filtering weight signal to obtain a model parameter, and calculating a weight statistical value according to the model parameter and the second filtering weight signal. According to the invention, the weighing efficiency and the weighing precision can be improved, and the requirement of short-time product weight re-checking can be met.

Description

Technical Field

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

[0002] In the process of product weight verification, weighing modules are usually used to weigh the product. The weighing modules include analog sensor modules that can output analog signals. The weighing modules need to be used with transmitters and other equipment for subsequent signal processing. The product production line includes packaging machinery and weighing modules. The simultaneous operation of various mechanical components will cause large vibrations, which may affect the weighing data, and the weighing signal will include some noise.

[0003] In the process of processing weighing signals, a deep filter is usually used to filter out some noise in the weight signal to obtain a filtered weight signal, and then the filtered weight signal is converted to obtain the product weight value. The manufacturer of the product can make a difference judgment between the obtained product weight value and the target weight value of the product, and then choose whether to refill the product.

[0004] During the product weight verification process, a higher work rhythm is required. For example, when the speed of the packaging production line runs to 60pcs / min (60 products per minute), the time used for weighing may be only about 400ms (milliseconds). Filters with deeper depths include more orders and longer filter response times. In scenarios with faster weighing speeds, considering interference from mechanical devices and vibrations, the current method of directly using filters with deeper depths for filtering will reduce weighing accuracy, increase filtering time, require more weighing time, and reduce the overall speed of the weighing process, thereby affecting the time for subsequent signal processing and the time for outputting product weight values. Generally, it can only meet the operating speed of the packaging production line of 30pcs / min (30 products per minute), and cannot adapt to a higher work rhythm. Therefore, there is a problem of low weighing efficiency and it cannot meet the needs of short-time product weight verification. Summary of the invention

[0005] The technical problem to be solved by the present application is to provide a weighing signal processing method, system and computer-readable medium, which can improve weighing efficiency and weighing accuracy and meet the needs of product weight review in a short time.

[0006] The technical solution adopted by the present application to solve the above-mentioned technical problems is a weighing signal processing method, including: obtaining an initial weight signal within the weighing time; performing a first filtering process on the initial weight signal according to a first filter to obtain a first filtered weight signal; filtering out an effective weighing signal from the first filtered weight signal according to a preset filtering rule; filtering out the out-of-tolerance signal in the effective weighing signal according to a preset filtering rule to obtain an effective intercepted weighing signal; performing a second filtering process on the effective intercepted weighing signal according to a second filter to obtain a second filtered weight signal; and performing fitting calculation according to a preset weight calculation model and the second filtered weight signal to obtain model parameters, and calculating weight statistics according to the model parameters and the second filtered weight signal.

[0007] In an embodiment of the present application, the first filtering process includes filtering the power frequency.

[0008] In one embodiment of the present application, the step of filtering out a valid weighing signal from the first filtered weight signal according to preset filtering rules includes: determining whether the first filtered weight signal is greater than or equal to a signal threshold corresponding to the zero point superimposed target weight; if the judgment is yes, filtering out the first filtered weight signal corresponding to a time length that is greater than or equal to a preset time length, and using the filtered first filtered weight signal as a valid weighing signal.

[0009] In one embodiment of the present application, the step of filtering out the out-of-tolerance signal in the effective weighing signal according to a preset filtering rule includes: selecting the effective weighing signal in the middle section from the effective weighing signal; calculating the mean value m and variance σ of the effective weighing signal in the middle section; according to the mean value m and variance σ, filtering out the out-of-tolerance signals in the effective weighing signal that are greater than or equal to m+pσ and less than or equal to m-pσ, and taking the remaining effective weighing signal as the effective intercepted weighing signal, wherein p represents the adjustment coefficient and p is a constant.

[0010] In one embodiment of the present application, the step of selecting the effective weighing signal in the middle segment from the effective weighing signal includes: dividing the effective weighing signal into three array segments in sequence, and intercepting the effective weighing signal in the second array segment as the effective weighing signal in the middle segment.

[0011] In one embodiment of the present application, the step of performing a second filtering process on the effectively intercepted scale signal according to the second filter includes: configuring the cutoff frequency and order of the second filter according to the weighing object and the operating speed of the weighing scale; performing a second filtering process on the effectively intercepted scale signal according to the cutoff frequency and order.

[0012] In one embodiment of the present application, the preset weight calculation model is expressed by the following formula:

[0013] g(t)=A1+A2*sin(A3*t+A4)

[0014] Among them, g(t) represents the estimated product weight value at time t, A1 represents the weight value of the product packaging bag, A2 represents the amplitude of the trigonometric function, A3 represents the frequency of the trigonometric function, A4 represents the phase of the trigonometric function, and t represents the time corresponding to the current signal.

[0015] In one embodiment of the present application, fitting calculation is performed according to the preset weight calculation model and the second filtered weight signal to obtain model parameters, and the step of calculating the weight statistics according to the model parameters and the second filtered weight signal includes: fitting calculation is performed on the preset weight calculation model and the second filtered weight signal according to the least square solution method, and the model parameters A1, A2, A3 and A4 are obtained by solving the calculation; the weight statistics are calculated using the following formula: k :

[0016] O k =f(w j+k )-A2*sin(A3*t k +A4)

[0017] Among them, O k represents the kth weight statistic, f(w j+k ) represents the second filtering weight signal, k=1,…,n3, n3 represents the number of the second filtering weight signals, and j represents the starting number value of the second filtering weight signal.

[0018] In one embodiment of the present application, after the step of obtaining the weight statistics, the method further includes: k Use the following formula to calculate the true value of the product weight output:

[0019]

[0020] Among them, α k represents the weighting coefficient of the kth weight statistic, α k The value of is obtained according to the average weighted method or the triangle wave weighted method.

[0021] In one embodiment of the present application, the first filter includes a sliding average filter.

[0022] In order to solve the above technical problems, the present application also proposes a weighing signal processing system, comprising: a memory for storing instructions executable by a processor; a processor for executing the instructions to implement the above weighing signal processing method.

[0023] 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 weighing signal processing method when executed by a processor.

[0024] The technical solution of the present application performs a first filtering process on the initial weight signal according to the first filter, which can filter out obvious clutter signals; performs pattern recognition processing on the first filtered weight signal according to the preset screening rules, and can filter out the effective weighing signal from the first filtered weight signal, thereby confirming that the weighed product has been weighed; according to the preset filtering rules, the out-of-tolerance signal in the effective weighing signal is filtered out, and some strong interference signals can be judged and filtered out from the time domain; according to the second filter, the effective intercepted weighing signal is subjected to a second filtering process, which can filter out noise signals and minimize the noise interference caused by the vibration of the weighing machine; according to the preset weight calculation model and the second filtered weight signal, the fitting process is performed and the weight statistics are calculated accordingly, so that high-quality product weight information can be obtained. The technical solution of the present application can shorten the time of the entire signal processing, so that the product weight verification process can meet a higher work rhythm, improve the compatibility of the weighing scale and other packaging machinery running speeds, improve the response speed, weighing accuracy and weighing efficiency of the weighing process, and meet the needs of short-time product weight verification. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] 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:

[0026] Figure 1 is an exemplary flow chart of a weighing signal processing method according to an embodiment of the present application;

[0027] Figure 2 is an exemplary flow chart for a bag-type product packaging line;

[0028] Figure 3 It is a system block diagram of a weighing signal processing system according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] This application proposes a weighing signal processing method, which can be applied in the 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 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 the material and output the actual product weight information. The weighing signal processing method proposed in this application can be applied to the weighing station 202; 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; the bag sealing station 204 is used to output the product to the subsequent production process after sealing the product.

[0034] The weighing 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 weighing 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 weighing signal processing method.

[0035] Figure 1 is an exemplary flow chart of a weighing signal processing method according to an embodiment of the present application, Figure 1 As shown, the weighing signal processing method of this embodiment includes the following steps:

[0036] Step S110: obtaining an initial weight signal within the weighing time.

[0037] Step S120: performing a first filtering process on the initial weight signal according to the first filter to obtain a first filtered weight signal.

[0038] Step S130: Filtering the first filtered weight signal to obtain a valid weighing signal according to a preset filtering rule.

[0039] Step S140: filtering out the out-of-tolerance signal in the effective weighing signal according to a preset filtering rule to obtain an effective intercepted weighing signal.

[0040] Step S150: performing a second filtering process on the effectively intercepted weighing signal according to the second filter to obtain a second filtered weight signal.

[0041] Step S160: Perform fitting calculation according to the preset weight calculation model and the second filtered weight signal to obtain model parameters, and calculate weight statistics according to the model parameters and the second filtered weight signal.

[0042] The above-mentioned steps S110 to S160 are described in detail below.

[0043] In step S110, the initial weight signal within the weighing time is obtained. For example, taking the packaging speed of 60pcs / min as an example, the corresponding weighing time can be set to 1s (second). The initial weight signals obtained within the weighing time are w1, w2, w3, ..., w n1 , n1 represents the number of initial weight signals, for example, there are 200 initial 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 initial weight signals. In practical applications, the number of initial weight signals can be set as needed, and the present application does not impose any limitation.

[0044] In step S120, the initial weight signal is subjected to a first filtering process according to the first filter to obtain a first filtered weight signal. In some embodiments, the first filter includes a sliding average filter, and the first filtering process includes filtering the power frequency. For example, in practical applications, there may be power frequency interference in the weighing signal. The power frequency is the most common interference signal, and the power frequency greatly hinders the subsequent detection of the weighing signal. Therefore, power frequency filtering is required. This step S120 is equivalent to preprocessing the initial weight signal, and the initial weight signal is filtered using the first filter to filter the initial weight signal for power frequency interference. The first filtered weight signals obtained after filtering out the power frequency are f(w1), f(w2), ..., f(w n1 ), for example, the first filtered weight signal is 200.

[0045] In step S130, a valid weighing signal is obtained by filtering the first filtered weight signal according to a preset filtering rule. For example, in the process of weighing a product, the signals that may be obtained include the signal before the product is weighed, the signal when the product is weighed, and the signal after the product is weighed, etc. It is necessary to filter out the signal when the product is weighed from the multiple signals for subsequent processing. This step S130 is equivalent to a pattern recognition step, which can filter out the signal when the product is weighed.

[0046] In some embodiments, the step of screening an effective weighing signal from the first filtered weight signal according to a preset screening rule includes: Step S131: Determine whether the first filtered weight signal is greater than or equal to the signal threshold corresponding to the zero superposition target weight; Step S132: If the determination is yes, then screen out the first filtered weight signals corresponding to a preset duration, and use the screened first filtered weight signals as effective weighing signals.

[0047] Exemplarily, in Step S131, the zero superposition target weight is equivalent to the weight when there is no product on the weighing scale. Through the determination in Step S131, when the determination is yes, it indicates that the weight on the weighing scale has changed. In Step S132, it can also be set to screen out the first filtered weight signals that are greater than or equal to the preset duration and greater than or equal to the threshold weight value, and use the screened first filtered weight signals as effective weighing signals. In practical applications, the preset duration can be set accordingly according to the packaging operation speed of the product. The preset duration is, for example, set to 20 ms (milliseconds), and the threshold weight value can be set to 50% of the target product weight. After the processing in Step S132, the effective weighing signals f(w k ) are screened out, f(w k+1 ) are screened out, f(w k+2 ) are screened out, ……, f(w k+n2 ) are screened out, 1 < k < k + n2 < n1. For example, 150 effective weighing signals are screened out.

[0048] In Step S140, according to a preset filtering rule, the out-of-tolerance signals in the effective weighing signals are filtered to obtain effective intercepted weighing signals. Exemplarily, this Step S140 is equivalent to the step of intercepting effective signals.

[0049] In some embodiments, the step of filtering the out-of-tolerance signals in the effective weighing signals according to a preset filtering rule includes:

[0050] Step S141: Select the middle-segment effective weighing signals from the effective weighing signals;

[0051] Step S142: Calculate the average value m and variance σ of the middle-segment effective weighing signals;

[0052] Step S143: According to the average value m and variance σ, filter out the out-of-tolerance signals in the effective weighing signals that are greater than or equal to m + pσ and less than or equal to m - pσ, and use the remaining effective weighing signals as effective intercepted weighing signals, where p represents an adjustment coefficient and p is a constant. Exemplarily, the out-of-tolerance signals refer to the signals that exceed the set upper and lower limits. The out-of-tolerance signals in this application include the signals in the effective weighing signals that are greater than or equal to m + pσ and less than or equal to m - pσ.

[0053] In some embodiments, the effective weighing signal can be represented by an array, and the step of selecting the effective weighing signal of the middle segment from the effective weighing signal includes: dividing the effective weighing signal into three array segments in sequence, and intercepting the effective weighing signal of the second array segment as the effective weighing signal of the middle segment. Exemplarily, for example, there are 150 effective weighing signals, and these 150 effective weighing signals are divided into three array segments in sequence, the first array segment, the second array segment, and the third array segment respectively include 50 effective weighing signals, and the 50 effective weighing signals of the second array segment are intercepted as the effective weighing signals of the middle segment. The effective weighing signals of the middle segment can be represented as:

[0054] In other embodiments, for example, there are 100 valid weighing signals, and in the process of dividing the valid weighing signals into three array segments in sequence, there will be a situation where the number cannot be divided evenly. The method of rounding up can be adopted, that is, the first array segment after division includes 34 valid weighing signals, the second array segment includes 34 valid weighing signals, and the third array segment includes 32 valid weighing signals. In practical applications, 30% to 50% of the signals in the middle of the valid weighing signal array can also be intercepted as valid weighing signals in the middle segment; the corresponding signals can also be intercepted from one quarter of the valid weighing signals, and the position of intercepting the middle segment can be selected according to the number of valid weighing signals, and this application does not make any restrictions.

[0055] In some embodiments, in the above step S143, the adjustment coefficient p can be taken from constants 1 to 5. For example, the adjustment coefficient p can be set to p=2 or p=2.5 or p=3, etc. Exemplarily, when p=2 is set, the out-of-tolerance signals greater than or equal to m+2σ and less than or equal to m-2σ in the effective weighing signal can be filtered out; when p=2.5 is set, the out-of-tolerance signals greater than or equal to m+2.5σ and less than or equal to m-2.5σ in the effective weighing signal can be filtered out; when p=3 is set, the out-of-tolerance signals greater than or equal to m+3σ and less than or equal to m-3σ in the effective weighing signal can be filtered out. The present application does not impose any restrictions on the value of p. The processing of step S143 is equivalent to removing some strong interference signals. The effective intercepted weighing signal obtained after step S143 can be expressed as f(w j ), f(w j+1 ), f(w j+2 ), ..., f(w j+n3 ), wherein j represents the number value of the remaining valid weighing signals after filtering out the out-of-tolerance signals in the valid weighing signals, for example, the number of valid intercepted weighing signals is 40.

[0056] In step S150, the second filter is used to perform a second filtering process on the effectively intercepted weighing signal according to the second filter to obtain a second filtered weight signal. Exemplarily, the filter coefficient of the second filter can be configured so that the corresponding signal is filtered according to the second filter. The first filter and the second filter can be arbitrarily selected from filters such as band-stop filters, band-pass filters, high-pass filters, and low-pass filters, and this application does not limit this.

[0057] In some embodiments, the step of performing a second filtering process on the effectively intercepted signal on the scale according to the second filter includes: step S151: configuring the cutoff frequency and order of the second filter according to the weighing object and the operating speed of the weighing scale; step S152: performing a second filtering process on the effectively intercepted signal on the scale according to the cutoff frequency and order. Exemplarily, the weighing object in step S151 is a product that needs to be weighed and rechecked. After step S152, for example, 40 second filtered weight signals are obtained.

[0058] In step S160, a fitting calculation is performed according to the preset weight calculation model and the second filtered weight signal to obtain model parameters, and a weight statistical value is calculated according to the model parameters and the second filtered weight signal. Exemplarily, a weight statistical value can be obtained after the processing of step S160, and the weight statistical value is not the real weight value of the product that can be directly output by the weighing scale. In practical applications, the weight statistical value can be further processed to obtain the real weight value of the product.

[0059] In some embodiments, the preset weight calculation model is expressed by the following formula (1):

[0060] g(t)=A1+A2*sin (A3*t+A4) (1)

[0061] Wherein, g(t) represents the estimated product weight value at time t, A1 represents the weight value of the product packaging bag, A2 represents the amplitude of the trigonometric function, A3 represents the frequency of the trigonometric function, A4 represents the phase of the trigonometric function, and t represents the time corresponding to the current signal. Exemplarily, the above formula (1) is equivalent to the superposition of the trigonometric function and the fixed true value.

[0062] In some embodiments, fitting calculation is performed according to a preset weight calculation model and the second filtered weight signal to obtain model parameters, and the step of calculating weight statistics according to the model parameters and the second filtered weight signal includes:

[0063] Step S161: fitting the preset weight calculation model and the second filtered weight signal according to the least square solution method, and solving to obtain model parameters A1, A2, A3 and A4;

[0064] Step S162: Calculate the weight statistics value O using the following formula (2):k :

[0065] O k =f(w j+k )- A2*sin (A3*t k +A4) (2)

[0066] Among them, O k represents the kth weight statistic, f(w j+k ) represents the second filtered weight signal, k=1, ..., n3, n3 represents the number of the second filtered weight signals, j represents the starting number value of the second filtered weight signal, and the starting number value can be the number value of the valid weighing signal remaining after filtering out the out-of-tolerance signal in the valid weighing signal in step S143 described above, A2 represents the amplitude of the trigonometric function, A3 represents the frequency of the trigonometric function, A4 represents the phase of the trigonometric function, and the values ​​of A2, A3 and A4 are the model parameters obtained in the aforementioned step S161. Exemplarily, the aforementioned formula (2) can represent the true value of the second filtered weight signal after subtracting the low-frequency interference, and the weight statistical value O obtained according to formula (2) k Can be an array that fluctuates around the package weight.

[0067] In some embodiments, after the step of obtaining the weight statistics, the method further includes: step S170: calculating the weight statistics according to the weighting method and the weight statistics. k Use the following formula (3) to calculate the true value of product weight output:

[0068]

[0069] Among them, α k represents the weighting coefficient of the kth weight statistic, α k The value of is obtained according to the average weighting method or the triangle wave weighting method. Exemplarily, step S170 is equivalent to the final product weight output step, and the product weight true value output is equivalent to the actual weight value of the product. Figure 2 As shown, the weighing station 202 can transmit the true value output of the product weight to the refilling station 203, and the refilling station 203 automatically determines whether it is necessary to refill the product.

[0070] The technical solution of the present application performs a first filtering process on the initial weight signal according to the first filter, which can filter out obvious clutter signals; performs pattern recognition processing on the first filtered weight signal according to the preset screening rules, and can filter out the effective weighing signal from the first filtered weight signal, thereby confirming that the weighed product has been weighed; according to the preset filtering rules, the out-of-tolerance signal in the effective weighing signal is filtered out, and some strong interference signals can be judged and filtered out from the time domain; according to the second filter, the effective intercepted weighing signal is subjected to a second filtering process, which can filter out noise signals and minimize the noise interference caused by the vibration of the weighing machine; according to the preset weight calculation model and the second filtered weight signal, the fitting process is performed and the weight statistics are calculated accordingly, so that high-quality product weight information can be obtained. The technical solution of the present application can shorten the time of the entire signal processing, so that the product weight verification process can meet a higher work rhythm, improve the compatibility of the weighing scale and other packaging machinery running speeds, improve the response speed, weighing accuracy and weighing efficiency of the weighing process, and meet the needs of short-time product weight verification.

[0071] The present application also includes a weighing signal processing system, including a memory and a processor, wherein the memory is used to store instructions executable by the processor; and the processor is used to execute the instructions to implement the weighing signal processing method described above.

[0072] Figure 3 is a system block diagram of a weighing signal processing system according to an embodiment of the present application. Figure 3 As shown, the weighing signal processing system 300 may include an internal communication bus 301, a processor 302, a read-only memory (ROM) 303, a random access memory (RAM) 304, and a communication port 305. The weighing signal processing system 300 may also include a hard disk 306. The internal communication bus 301 can realize data communication between the components of the weighing signal processing system 300. The processor 302 can make judgments and issue prompts. In some embodiments, the processor 302 can be composed of one or more processors. The communication port 305 can realize data communication between the weighing signal processing system 300 and the outside. In some embodiments, the weighing signal processing system 300 can send and receive information and data from the network through the communication port 305. The weighing signal processing system 300 may also include different forms of program storage units and data storage units, such as a hard disk 306, a read-only memory (ROM) 303 and a random access memory (RAM) 304, which can store various data files used for computer processing and / or communication, and possible program instructions executed by the processor 302. The processor executes these instructions to implement the main part of the method. The result processed by the processor is transmitted to the user device through the communication port and displayed on the user interface.

[0073] The weighing signal processing method described above may be implemented as a computer program, stored in the hard disk 306 , and loaded into the processor 302 for execution, so as to implement the weighing signal processing method of the present application.

[0074] The present application also includes a computer-readable medium storing a computer program code, which, when executed by a processor, implements the weighing signal processing method described above.

[0075] When the weighing 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.

[0076] 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.

[0077] 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 ...).

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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 weighing signal processing method, characterized in that: include: Obtaining the initial weight signal within the weighing time; Performing a first filtering process on the initial weight signal according to a first filter to obtain a first filtered weight signal; Filtering the first filtered weight signal to obtain a valid weighing signal according to a preset screening rule; Filter out the out-of-tolerance signal in the effective weighing signal according to a preset filtering rule to obtain an effective intercepted weighing signal; Performing a second filtering process on the effectively intercepted weighing signal according to a second filter to obtain a second filtered weight signal; as well as A fitting calculation is performed according to a preset weight calculation model and the second filtered weight signal to obtain model parameters, and weight statistics are calculated according to the model parameters and the second filtered weight signal.

2. The weighing signal processing method according to claim 1, characterized in that: The first filtering process includes filtering the power frequency.

3. The weighing signal processing method according to claim 1, characterized in that: The step of filtering the first filtered weight signal to obtain a valid weighing signal according to a preset filtering rule comprises: Determining whether the first filtered weight signal is greater than or equal to a signal threshold corresponding to the zero-point superimposed target weight; If the judgment is yes, the first filtered weight signal corresponding to a time length greater than or equal to a preset time length is screened out, and the screened out first filtered weight signal is used as the effective weighing signal.

4. The weighing signal processing method according to claim 1, characterized in that: The step of filtering out the out-of-tolerance signal in the effective weighing signal according to the preset filtering rule comprises: Selecting the effective weighing signal in the middle section from the effective weighing signals; Calculate the mean value m and variance σ of the effective weighing signal of the middle section; According to the mean value m and the variance σ, the out-of-tolerance signals greater than or equal to m+pσ and less than or equal to m-pσ in the effective weighing signal are filtered out, and the remaining effective weighing signal is used as the effective intercepted weighing signal, where p represents the adjustment coefficient and p is a constant.

5. The weighing signal processing method according to claim 4, characterized in that: The step of selecting the effective weighing signal of the middle segment from the effective weighing signal comprises: dividing the effective weighing signal into three array segments in sequence, and intercepting the effective weighing signal of the second array segment as the effective weighing signal of the middle segment.

6. The weighing signal processing method according to claim 1, characterized in that: The step of performing a second filtering process on the effectively intercepted weighing signal according to the second filter comprises: configuring the cutoff frequency and order of the second filter according to the weighing object and the operating speed of the weighing scale; The second filtering process is performed on the effective intercepted scale signal according to the cutoff frequency and the order.

7. The weighing signal processing method according to claim 1, characterized in that: The preset weight calculation model is expressed by the following formula: g(t)=A1+A2*sin(A3*t+A4) Among them, g(t) represents the estimated product weight value at time t, A1 represents the weight value of the product packaging bag, A2 represents the amplitude of the trigonometric function, A3 represents the frequency of the trigonometric function, A4 represents the phase of the trigonometric function, and t represents the time corresponding to the current signal.

8. The weighing signal processing method according to claim 7, characterized in that: Performing fitting calculation according to a preset weight calculation model and the second filtered weight signal to obtain model parameters, and calculating weight statistics according to the model parameters and the second filtered weight signal comprises: Fitting the preset weight calculation model and the second filtered weight signal according to the least square solution method, and solving to obtain model parameters A1, A2, A3 and A4; The weight statistics are calculated using the following formula: k : <h2 style=";text-align:left;direction:ltr">O<h2 style=";text-align:left;direction:ltr"> k <h2 style=";text-align:left;direction:ltr"> =f(w<h2 style=";text-align:left;direction:ltr"> j+k <h2 style=";text-align:left;direction:ltr"> A2*sin(A3*t)<h2 style=";text-align:left;direction:ltr"> k <h2 style=";text-align:left;direction:ltr"> +A4) Among them, O k represents the kth weight statistic, f(w j+k ) represents the second filtering weight signal, k=1,…,n3, n3 represents the number of the second filtering weight signals, and j represents the starting number value of the second filtering weight signal.

9. The weighing signal processing method according to claim 8, characterized in that: After the step of obtaining the weight statistics, the method further includes: calculating the weight statistics according to the weighting method and the weight statistics. k Use the following formula to calculate the true value of the product weight output: Among them, α k represents the weighting coefficient of the kth weight statistic, α k The value of is obtained according to the average weighted method or the triangle wave weighted method.

10. The weighing signal processing method according to claim 1, characterized in that: The first filter comprises a moving average filter.

11. A weighing signal processing system, characterized in that: include: a memory for storing instructions executable by a processor; A processor, used to execute the instructions to implement the weighing signal processing method as described in any one of claims 1-10.

12. A computer readable medium storing computer program code, characterized in that: When the computer program code is executed by a processor, the weighing signal processing method according to any one of claims 1 to 10 is implemented.

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