Automatic sample weighing, bagging, packaging, laser coding and conveying system
By using real-time weighing, dynamic decision-making, and laser marking technologies to achieve data linkage in automated production, the problem of data disconnect in automated production has been solved, enabling precise traceability of product quality and stable control of the production process.
Patent Information
- Application Number
- CN202610087720.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-22
- Publication Date
- 2026-03-06
AI Technical Summary
In automated production scenarios involving automatic sample weighing, sealing, packaging, laser coding, and conveying, the lack of data linkage between various functional links makes it difficult to trace production decision information when product quality issues arise. This creates blind spots in quality control, makes it impossible to identify production process anomalies, and makes it difficult to meet the needs of refined production.
The system employs a real-time weighing module for continuous weighing, a dynamic decision-making module for dynamic material cutting decisions, and generates decision process feature identifiers. A strategy traceability code is generated through a laser marking module, and the packaging bags are transported to the designated area by a conveying and sorting module, thus achieving accurate data binding and traceability.
This ensures that each bag of product is accurately linked to production decision data, enabling quality traceability and production review, improving the accuracy and stability of production control, identifying production process anomalies, and enhancing the level of precision in quality management.
Smart Images
Figure CN121608952A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of automated production control technology, and in particular to an automatic sample weighing, sealing, packaging, laser coding, and conveying system. Background Technology
[0002] In automated production scenarios involving automatic sample weighing, sealing, packaging, laser coding, and conveying, existing technologies often operate independently, lacking effective data linkage design. The weighing stage only collects material weight data and outputs it to the control unit; the cutting decision relies solely on a single weight signal triggering the action; the sealing stage mechanically responds to the cutting command to complete the sealing; the laser coding only records basic information such as batch number and timestamp; and the sorting stage only sorts based on the final weight detection result. The core data of each stage is stored separately and has no direct correspondence.
[0003] This disconnect between data at each stage means that key data such as material cutting decisions and material weight changes during the production of each bag of product cannot be precisely linked to the product itself. When product quality issues arise, it is difficult to trace the specific production decision information corresponding to that product, making it impossible to quickly pinpoint the root cause of the problem. Simultaneously, the sorting stage, lacking connection to upstream production process data, can only determine pass / fail status based on the final weight result, failing to identify products that pass but exhibit abnormalities in the production process. This creates blind spots in quality control, making it difficult to meet the demands of refined production. Summary of the Invention
[0004] This invention provides an automatic sample weighing, sealing, packaging, laser coding, and conveying system to solve the problems mentioned in the background art.
[0005] To achieve the above objectives, the present invention provides an automatic sample weighing, sealing, packaging, laser coding, and conveying system, the system comprising a real-time weighing module, a dynamic decision-making module, an execution sealing module, a laser coding module, and a conveying and sorting module, wherein: The real-time weighing module is used to continuously weigh the weight of the material inside the packaging bag in real time during the process of the sample material falling into the packaging bag, and to obtain real-time cumulative weight data and weight growth rate data. The dynamic decision-making module is used to execute dynamic material cutting decision logic based on real-time cumulative weight data, weight growth rate data, and preset nominal weight and tolerance range, and generate material cutting and bag sealing instructions and decision process feature identifiers; the decision process feature identifiers shall at least include the key control parameters on which this decision is based; The bag sealing module is used to stop the material from falling and seal the current packaging bag based on the material cutting and sealing command, so as to obtain a sealed packaging bag and then start a new packaging bag. The laser marking module is used to laser mark sealed packaging bags, wherein the marking content includes at least a strategy traceability code generated based on the decision process feature identifier, resulting in a packaging bag with a strategy traceability code; The conveying and sorting module is used to transport packaging bags with policy traceability codes to designated areas.
[0006] Preferably, when the real-time weighing module performs real-time continuous weighing of the material inside the packaging bag to obtain real-time cumulative weight data and weight growth rate data, it is specifically used for: Continuous measurement is performed using a weighing sensor that carries the packaging bag to obtain a raw electrical signal reflecting the cumulative weight of the material. The original electrical signal is conditioned, and the signal conditioning includes at least gain adjustment and zero drift compensation based on a preset reference to obtain a conditioned signal that characterizes the change in net weight. The conditioned signal is digitally filtered to obtain the filtered signal, and the real-time cumulative weight data and weight growth rate data are extracted based on the filtered signal.
[0007] Preferably, when the dynamic decision-making module executes dynamic material cutting decision logic based on real-time cumulative weight data, weight growth rate data, and preset nominal weight and tolerance range, it is specifically used for: Based on the weight growth rate data and the difference between the nominal weight and the real-time cumulative weight data, the remaining time is predicted to obtain the predicted remaining time. Based on the current material flow rate and tolerance range characterized by the weight growth rate data, the latest material cutting time is analyzed to obtain the theoretical latest material cutting time. When the predicted remaining time is less than or equal to the theoretical latest cutting time, the material cutting decision is made based on the closeness between the real-time cumulative weight data and the nominal weight, combined with the preset lead time correction factor, and the material cutting and sealing instruction and the decision process feature identifier containing the predicted remaining time and the theoretical latest cutting time are obtained.
[0008] Preferably, when the dynamic decision-making module executes the preset lead time correction factor, it is specifically used for: Collect the actual cut weight data, real-time cumulative weight data, and historical lead time correction factors used in the historical bagging process corresponding to the cut-and-seal instructions to obtain the historical decision dataset; Based on historical decision datasets, the correlation between historical lead time correction factors and the deviation of the actual cut weight of the corresponding batch from the nominal weight is analyzed to obtain the adjustment amount of the correction factor. Based on the adjustment amount of the correction factor, the preset advance correction factor is updated to obtain the advance correction factor used for the bagging decision of the next batch.
[0009] Preferably, when the bag-sealing module executes a material-stopping and bag-sealing command to stop the material from falling and seals the current packaging bag to obtain a sealed packaging bag, and then uses a new packaging bag, it is specifically used for: In response to the material cutting and sealing command, the action of cutting off the material flow and recording the current decision process feature identifier are executed simultaneously to obtain the packaging bags that have stopped feeding and the identification data to be associated. After confirming that the material flow has been cut off, the heat sealing operation for the stopped packaging bags is triggered and executed based on the identification data to be associated, so as to obtain the sealed packaging bags; After the heat sealing operation is completed, the action of activating the new packaging bag is executed, and the real-time cumulative weight data corresponding to the previous packaging bag is cleared, and the bagging status is reset.
[0010] Preferably, when the laser marking module performs laser marking on the sealed packaging bag, it is specifically used for: The key control parameters contained in the characteristic identifier of the decision-making process are formatted and encoded to obtain structured parameter codes; The structured parameter code is combined and serialized with the current packaging batch number and coding timestamp to obtain the strategy traceability code; The laser marking machine, controlled by the strategy traceability code, performs marking operations on a designated area of a sealed packaging bag to obtain a packaging bag with a strategy traceability code.
[0011] Preferably, when the conveying and sorting module conveys packaging bags with policy traceability codes to a designated area, it is specifically used for: During the transportation process, the strategy traceability code on the packaging bag is read and decoded online to obtain the restored decision-making process feature identifier; Based on the key control parameters in the restored decision process feature identification, the stability assessment of the bagging process corresponding to the packaging bag is carried out, and the process stability assessment results are obtained. Based on the process stability assessment results, the diversion mechanism of the conveying path is controlled to guide the packaging bags to the designated area corresponding to the assessment results.
[0012] Preferably, when the conveying and sorting module performs stability assessment of the bagging process corresponding to the packaging bag by executing key control parameters in the feature identification of the decision-making process based on restoration, it is specifically used for: Extract the weight growth rate data, which characterizes the material flow rate, from the key control parameters and represent a numerical sequence over a continuous period of time before material cutting. Smoothness analysis is performed on the numerical sequence to calculate its local fluctuation intensity, and the local fluctuation intensity is used as the result of process stability assessment.
[0013] Preferably, after performing online reading and decoding of the strategy traceability code on the packaging bag to obtain the restored decision process feature identifier, the conveying and sorting module is further used for: The restored decision process feature identifiers are compared with the decision process feature identifiers recorded synchronously when the material cutting and sealing instruction is executed. When the comparison results are inconsistent, the packaging bag will be directed to the abnormal processing area as its corresponding designated area.
[0014] Preferably, the system further includes an adaptive optimization module, specifically used for: Based on the process stability assessment results of multiple consecutive packaging bags, determine whether there is a systematic deviation trend in the bagging process; When a systematic deviation trend is determined, the control parameters in the dynamic material cutting decision logic are adaptively adjusted based on the characteristics of the deviation trend.
[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention generates decision process feature identifiers containing key control parameters for decision-making, and converts them into strategy traceability codes to establish a unique association with packaging bags. This enables each bag of product to be precisely anchored to the corresponding core production decision data, ensuring that key information such as material cutting basis and material flow rate are completely retained and directly traceable throughout the entire process. This provides accurate and reliable data support for quality traceability and production review, and ensures the accuracy and completeness of production information transmission.
[0016] 2. The historical data-driven advance correction factor iterative update technology, in conjunction with the bagging process stability assessment and systematic offset trend adaptive adjustment technology based on traceability code decoding data, enables dynamic optimization of production control parameters, continuously improving the accuracy of bag weight and product consistency. At the same time, the sorting control linked with traceability code verification and process stability assessment can accurately identify abnormal situations in the production process, avoid batch quality risks in advance, and further enhance the stability of production line operation and the level of quality control refinement. Attached Figure Description
[0017] Figure 1 This is a system architecture diagram of an automatic sample weighing, sealing, packaging, laser coding, and conveying system provided in an embodiment of the present invention; Figure 2 This is a system architecture diagram of an automatic sample weighing, sealing, packaging, laser marking, and conveying system provided in an embodiment of the present invention.
[0018] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings.
[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments belong to some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0020] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “said” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.
[0021] Depending on the context, the word "if" or "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."
[0022] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.
[0023] In practice, the server-side equipment deployed in an automated sample weighing, sealing, packaging, laser marking, and conveying system may consist of one or more devices. This automated sample weighing, sealing, packaging, laser marking, and conveying system can be implemented as a business instance, a virtual machine, or hardware equipment. For example, this automated sample weighing, sealing, packaging, laser marking, and conveying system can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, this automated sample weighing, sealing, packaging, laser marking, and conveying system can be understood as software deployed on a cloud node, used to provide automated sample weighing, sealing, packaging, laser marking, and conveying services to various user terminals. Alternatively, this automated sample weighing, sealing, packaging, laser marking, and conveying system can also be implemented as a virtual machine deployed on one or more devices in a cloud node. This virtual machine contains application software for managing various user terminals. Alternatively, this automated sample weighing, sealing, packaging, laser marking, and conveying system can also be implemented as a server composed of numerous identical or different types of hardware devices, with one or more hardware devices configured to provide automated sample weighing, sealing, packaging, laser marking, and conveying services to various user terminals.
[0024] In terms of implementation, the automatic sample weighing, sealing, packaging, laser coding, and conveying system and the user terminal are mutually compatible. That is, if the automatic sample weighing, sealing, packaging, laser coding, and conveying system is implemented as an application installed on a cloud service platform, then the user terminal is implemented as a client that establishes a communication connection with the application; or if the automatic sample weighing, sealing, packaging, laser coding, and conveying system is implemented as a website, then the user terminal is implemented as a webpage; or if the automatic sample weighing, sealing, packaging, laser coding, and conveying system is implemented as a cloud service platform, then the user terminal is implemented as a mini-program in an instant messaging application.
[0025] Example 1, such as Figure 1 The figure shown is a system architecture diagram of an automatic sample weighing, sealing, packaging, laser marking, and conveying system provided in an embodiment of the present invention.
[0026] The automatic sample weighing, sealing, packaging, laser coding, and conveying system of this invention can be set up in a cloud server. In terms of implementation, it can be used as one or more service devices, or as an application installed in the cloud (e.g., a mobile service operator's server, server cluster, etc.), or it can be developed into a website. Depending on the functions implemented, the automatic sample weighing, sealing, packaging, laser coding, and conveying system can include a real-time weighing module, a dynamic decision-making module, a sealing execution module, a laser coding module, and a conveying and sorting module. The modules of this invention can also be called units, referring to a series of computer program segments that can be executed by an electronic device's processor and perform a fixed function, stored in the electronic device's memory.
[0027] In this embodiment of the invention, in the automatic sample weighing, sealing, packaging, laser marking, and conveying system, each of the above-mentioned modules can be implemented independently and can be called upon with other modules. Here, "calling upon" can be understood as one module connecting to multiple modules of another type and providing corresponding services to those connected modules. In the automatic sample weighing, sealing, packaging, laser marking, and conveying system provided by this embodiment of the invention, the applicable scope of the system architecture can be adjusted by adding modules and directly calling them without modifying the program code, achieving cluster-based horizontal expansion to quickly and flexibly expand the automatic sample weighing, sealing, packaging, laser marking, and conveying system. In practical applications, the above-mentioned modules can be set in the same device or different devices, or they can be set in a virtual device, such as a service instance in a cloud server.
[0028] The following describes the components and workflow of the automatic sample weighing, sealing, packaging, laser coding, and conveying system, using specific embodiments as examples: The real-time weighing module is used to continuously weigh the weight of the material inside the packaging bag in real time during the process of the sample material falling into the packaging bag, and to obtain real-time cumulative weight data and weight growth rate data. The dynamic decision-making module is used to execute dynamic material cutting decision logic based on real-time cumulative weight data, weight growth rate data, and preset nominal weight and tolerance range, and generate material cutting and bag sealing instructions and decision process feature identifiers; the decision process feature identifiers shall at least include the key control parameters on which this decision is based; The bag sealing module is used to stop the material from falling and seal the current packaging bag based on the material cutting and sealing command, so as to obtain a sealed packaging bag and then start a new packaging bag. The laser marking module is used to laser mark sealed packaging bags, wherein the marking content includes at least a strategy traceability code generated based on the decision process feature identifier, resulting in a packaging bag with a strategy traceability code; The conveying and sorting module is used to transport packaging bags with policy traceability codes to designated areas.
[0029] In this embodiment, when the real-time weighing module performs real-time continuous weighing of the material inside the packaging bag to obtain real-time cumulative weight data and weight growth rate data, it is specifically used for: Continuous measurement is performed using a weighing sensor that carries the packaging bag to obtain a raw electrical signal reflecting the cumulative weight of the material. The original electrical signal is conditioned, and the signal conditioning includes at least gain adjustment and zero drift compensation based on a preset reference to obtain a conditioned signal that characterizes the change in net weight. The conditioned signal is digitally filtered to obtain the filtered signal, and the real-time cumulative weight data and weight growth rate data are extracted based on the filtered signal.
[0030] In practice, the strain gauge load cell is attached and fixed to the bottom of the packaging bag carrying platform. When the material falls continuously into the packaging bag, the weight of the material acts on the carrying platform, causing the strain gauge inside the sensor to deform continuously. The resistance value of the strain gauge changes continuously in accordance with the degree of deformation. The sensor converts the continuous change in resistance value into a continuous voltage signal that is linearly related to the cumulative weight of the material. This continuous voltage signal is the original electrical signal that reflects the cumulative weight of the material.
[0031] Furthermore, the original electrical signal is connected to a dedicated signal conditioning circuit, and the gain adjustment stage determines the appropriate signal amplification factor based on the overall measurement range of the material weight.
[0032] The original electrical signal is amplified to a signal amplitude that perfectly matches the input range of the subsequent data acquisition equipment by the variable gain amplifier in the conditioning circuit. The zero drift compensation stage cancels out the tiny offset voltage output by the sensor in real time when there is no material load through the precision zero adjustment circuit in the conditioning circuit, completely eliminating the signal baseline drift caused by ambient temperature fluctuations and the characteristics of the sensor itself. The signal after the two processes of gain adjustment and zero drift compensation is the conditioned signal that represents the change in net weight.
[0033] Furthermore, the conditioned signal is transmitted to the data acquisition unit to complete analog-to-digital conversion to obtain the corresponding digital signal. The digital signal is then processed using a moving average filtering method, and the arithmetic mean of multiple consecutive digital signal sampling points is calculated sequentially.
[0034] Finally, the calculated average value is used to replace the original digital signal value at the corresponding sampling position. The digital signal after the moving average filtering process is the filtered signal. The real-time cumulative weight data is directly determined based on the real-time sampled value of the filtered signal. The weight growth rate data is obtained by calculating the difference between the sampled values of the filtered signal in two adjacent sampling periods and dividing it by the fixed duration of the sampling period.
[0035] It should be noted that this solution is a preferred method. Those skilled in the art can also collect the cumulative weight of the packaging bag at fixed intervals, calculate the ratio of the weight difference between two adjacent collections to the time interval, and use this as the average weight growth rate within that time period; or set a flow sensor in the material falling channel to collect the instantaneous flow rate of the material, and convert it into the weight growth rate by combining the density parameter of the material.
[0036] Overall, this solution is based directly on real-time continuous weighing signals and ensures data accuracy and timeliness through a complete signal processing flow.
[0037] In this embodiment, when the dynamic decision module executes dynamic material cutting decision logic based on real-time cumulative weight data, weight growth rate data, and preset nominal weight and tolerance range, it is specifically used for: Based on the weight growth rate data and the difference between the nominal weight and the real-time cumulative weight data, the remaining time is predicted to obtain the predicted remaining time. Based on the current material flow rate and tolerance range characterized by the weight growth rate data, the latest material cutting time is analyzed to obtain the theoretical latest material cutting time. When the predicted remaining time is less than or equal to the theoretical latest cutting time, the material cutting decision is made based on the closeness between the real-time cumulative weight data and the nominal weight, combined with the preset lead time correction factor, and the material cutting and sealing instruction and the decision process feature identifier containing the predicted remaining time and the theoretical latest cutting time are obtained.
[0038] The formula for calculating the predicted remaining time is as follows: In the formula, Indicates the predicted remaining time. Indicates the nominal weight. This indicates real-time cumulative weight data. This indicates the current material flow rate.
[0039] The theoretical latest material cutting time is calculated using the following formula: In the formula, This indicates the theoretical latest material cutting time. This indicates the maximum allowable deviation, which can be taken as the absolute value of half the width of the tolerance range between the preset nominal weight and the tolerance range.
[0040] This represents the safety factor. It is a constant greater than 1 used to compensate for the system response delay from the issuance of the command to the complete cutoff of the material flow; its value is determined by the characteristics of the specific actuator. This indicates the current material flow rate.
[0041] In this embodiment, when the dynamic decision-making module executes the preset lead time correction factor, it is specifically used for: Collect the actual cut weight data, real-time cumulative weight data, and historical lead time correction factors used in the historical bagging process corresponding to the cut-and-seal instructions to obtain the historical decision dataset; Based on historical decision datasets, the correlation between historical lead time correction factors and the deviation of the actual cut weight of the corresponding batch from the nominal weight is analyzed to obtain the adjustment amount of the correction factor. Based on the adjustment amount of the correction factor, the preset advance correction factor is updated to obtain the advance correction factor used for the bagging decision of the next batch.
[0042] In practice, the nominal weight is determined based on the product's preset standard bag weight and is obtained by retrieving pre-stored product specification parameters. The real-time cumulative weight data is taken from the filtered signal output by the real-time weighing module. The current material flow rate is the weight growth rate data extracted by the real-time weighing module. The nominal weight is subtracted from the real-time cumulative weight data to obtain the weight difference. The weight difference is then divided by the current material flow rate, and the result is the predicted remaining time.
[0043] In general, this time reflects the time required to reach the nominal weight by continuing bagging at the current material flow rate. The larger the weight difference, the longer the predicted remaining time; the higher the current material flow rate, the shorter the predicted remaining time. It can accurately predict the time node of the bagging completion stage.
[0044] Furthermore, the maximum allowable deviation is calculated using a preset nominal weight and tolerance range. First, the tolerance range is set according to the product quality standard, and then the absolute value of half the width of the tolerance range is taken as the maximum allowable deviation to ensure that the bagged weight does not exceed the preset tolerance. It should be noted that the safety factor is determined by repeatedly testing the response characteristics of the actuator. The system response delay time from the issuance of the material cut-off command to the complete cut-off of the material flow is repeatedly recorded. Combined with the falling weight of the material during the delay period, a safety factor greater than 1 is determined and preset to compensate for the weight deviation caused by the response delay.
[0045] Specifically, the theoretical latest material cutting time is calculated by dividing the maximum allowable deviation by the product of the safety factor and the current material flow rate. This time is the last material cutting time to ensure that the bagged weight does not exceed the tolerance. The higher the current material flow rate, the shorter the theoretical latest material cutting time. The higher the safety factor, the shorter the theoretical latest material cutting time, which can avoid the problem of overweight caused by delay.
[0046] Furthermore, the predicted remaining time is compared with the theoretical latest cutting time in real time. When the predicted remaining time is less than or equal to the theoretical latest cutting time, the cutting decision is executed. The degree of closeness between the two is judged by calculating the ratio of the difference between the real-time cumulative weight data and the nominal weight to the nominal weight.
[0047] It should be noted that the smaller the proportion, the closer it is to the nominal weight. The lead time correction factor is preset based on the initial trial assembly data. During the trial assembly, the actual cutting weight under different lead times is recorded. The value that makes the weight fall within the tolerance center area is selected as the initial preset value. The cutting time is adjusted in combination with the lead time correction factor. The closer the weight is, the earlier the cutting command is issued according to the correction factor. Finally, the cutting and sealing command is generated. At the same time, the predicted remaining time and the theoretical latest cutting time obtained in this calculation are included in the decision-making process feature identifier to ensure that the decision-making process is traceable.
[0048] Furthermore, after each bagging is completed, the actual cut weight data corresponding to the cut-and-seal instruction is recorded. This data is obtained by performing a second precise weighing of the sealed packaging bag, and the real-time cumulative weight data at the time of bagging and the historical lead correction factor used are retrieved simultaneously.
[0049] Specifically, the three sets of data can be correlated and stored in the storage unit to gradually accumulate and form a historical decision dataset. The historical decision dataset can be compared and analyzed one by one to observe the correlation between the changes in the historical lead time correction factor and the deviation between the actual cut weight and the nominal weight of the corresponding batch. If the deviation decreases when the correction factor increases, the adjustment amount of the correction factor can be calculated according to this rule to ensure that the adjustment amount is compatible with the trend of deviation change.
[0050] Finally, the calculated adjustment amount of the correction factor is added to the current preset lead time correction factor to complete the update of the lead time correction factor. The updated lead time correction factor is used for the next batch bagging decision, continuously optimizing the material cutting time and improving the bagging weight accuracy.
[0051] It should also be emphasized that those skilled in the art can also preset a fixed weight lead time, and when the real-time accumulated weight reaches the difference between the nominal weight and the lead time, the cutting command is directly triggered; or based on human experience, multiple weight ranges are divided, and a corresponding cutting trigger threshold is set for each range. When the real-time weight falls into the target range, the cutting is executed, and the decision is based solely on the static weight value.
[0052] Overall, this solution is a preferred implementation method. By combining real-time weight growth rate prediction of remaining time, it can adapt to different material flow rate scenarios, avoid material cutting deviation caused by flow rate fluctuations, and compensate for system response delay by using the theoretical latest material cutting time, thereby avoiding the risk of overloading from the root.
[0053] More importantly, the lead time correction factor can be iteratively updated based on historical data to continuously optimize decision-making accuracy, ensuring that the bagged weight remains within the tolerance range, and providing reliable support for subsequent traceability and quality control.
[0054] In this embodiment, when the sealing module executes the material-stopping sealing command to stop the material from falling and seals the current packaging bag to obtain a sealed packaging bag, and then uses a new packaging bag, it is specifically used for: In response to the material cutting and sealing command, the action of cutting off the material flow and recording the current decision process feature identifier are executed simultaneously to obtain the packaging bags that have stopped feeding and the identification data to be associated. After confirming that the material flow has been cut off, the heat sealing operation for the stopped packaging bags is triggered and executed based on the identification data to be associated, so as to obtain the sealed packaging bags; After the heat sealing operation is completed, the action of activating the new packaging bag is executed, and the real-time cumulative weight data corresponding to the previous packaging bag is cleared, and the bagging status is reset.
[0055] In practice, upon receiving the instruction to cut off the material and seal the bag, the pneumatic baffle at the material falling channel is immediately driven to close. The baffle squeezes against the inner wall of the channel to quickly cut off the material flow. At the same time, the storage unit is triggered to write the characteristic identifier of the current decision process into a record, and the identifier information is bound to the bagging process of the corresponding packaging bag. Finally, the packaging bag that has stopped feeding and the identifier data to be associated with the packaging bag are obtained.
[0056] Furthermore, by monitoring the changes in the weighing signal in real time, it is confirmed whether the material flow has been interrupted. When the weighing signal remains stable without fluctuation within a set time, it is determined that the material flow has been completely interrupted. At this time, the heat sealing mechanism is triggered based on the identification data to be associated. The heat sealing mechanism drives the upper and lower sets of heat sealing blades to clamp the bag opening of the stopped packaging bag. The heat sealing blades are powered on to heat up and maintain a set pressure for a period of time, so that the plastic layer of the bag opening melts and adheres. After the heat sealing operation is completed, the heat sealing blades are reset and released, and a sealed packaging bag is obtained.
[0057] Furthermore, after the heat sealing operation is completed, the drive conveyor belt moves the sealed packaging bag out of the heat sealing station. At the same time, the bag feeding mechanism transports the new blank packaging bag to the designated bagging position and fixes it in place. Then, the real-time cumulative weight data corresponding to the previous packaging bag in the storage unit is cleared to zero, and the weighing equipment, heat sealing mechanism and bag feeding mechanism are all reset to the initial bagging state to prepare for the next bagging process.
[0058] It should be emphasized that those skilled in the art can also set up conventional sealing control methods, such as first performing the action of cutting off the material flow, and then recording the decision process feature identifier after the sealing is completed. The two are not performed synchronously, and the association between the identifier and the packaging bag depends on subsequent manual or equipment matching; or after receiving the material cutting instruction, the sealing operation is started directly without confirming whether the material flow has been cut off, and the material cutting status is judged only by the theoretical response efficiency of the material cutting mechanism.
[0059] Overall, this solution simultaneously executes material cutting and labeling records, directly achieving real-time binding of labels to packaging bags, avoiding potential label misalignment or omission issues during subsequent matching, and providing accurate data support for product traceability.
[0060] This solution simultaneously completes new bag feeding, data clearing, and equipment reset after heat sealing, forming a complete closed-loop process of material cutting, sealing, and reset. This avoids data from the previous batch interfering with the bagging of the next batch, ensuring the continuity and consistency of continuous production, and significantly improving the operational stability and product qualification rate of the automated production line.
[0061] Furthermore, this solution constructs a status-confirmation-based safety execution logic, using material flow cutoff signals and identification data as dual triggering conditions. This not only avoids sealing defects caused by material residue, but also upgrades sealing from a mechanical action to a controlled link in an intelligent decision-making process, enhancing the traceability of the entire process.
[0062] In this embodiment, when the laser marking module performs laser marking on the sealed packaging bag, it is specifically used for: The key control parameters contained in the characteristic identifier of the decision-making process are formatted and encoded to obtain structured parameter codes; The structured parameter code is combined and serialized with the current packaging batch number and coding timestamp to obtain the strategy traceability code; The laser marking machine, controlled by the strategy traceability code, performs marking operations on a designated area of a sealed packaging bag to obtain a packaging bag with a strategy traceability code.
[0063] In practice, key control parameters are extracted from the feature identifiers of the decision-making process, arranged in a pre-defined parameter type order, and different types of key control parameters are converted into a unified character format.
[0064] The character length of the parameters is standardized. If the length is less than the set length, placeholder characters are added. If the length exceeds the set length, the core effective part is truncated. In this way, the formatted encoding of key control parameters is completed, and finally a structured parameter code with a unified structure that is easy to combine later is obtained.
[0065] Furthermore, the pre-stored current packaging batch number is retrieved. This batch number is preset and entered into the storage unit according to the production plan before production.
[0066] Simultaneously, the timing module obtains the time information of the current coding moment, converts it into character form in the order of year, day, month, hour, minute, and second to form a coding timestamp, and concatenates the structured parameter code, the current packaging batch number, and the coding timestamp in a fixed order. The concatenated character sequence is then processed to make it continuous, and redundant intervening characters are removed to form a coherent string, which is the strategy traceability code.
[0067] Furthermore, the designated coding area of the sealed packaging bag is determined by the positioning sensor, the string of the strategy traceability code is converted into a control signal that can be recognized by the laser marking machine, and transmitted to the laser marking machine to control the laser head of the laser marking machine to move to the designated area.
[0068] Finally, the laser head emits a laser beam according to the control signal to perform high-speed scanning and etching on the surface of the packaging bag, so that clear and durable marking patterns are formed in the designated area. After the marking operation is completed, the laser head is reset, and a packaging bag with a strategy traceability code is obtained.
[0069] It should be emphasized that those skilled in the art can also encode the key control parameters separately first, and then stamp the batch number and timestamp in different positions on the packaging bag without combining and serializing them; or directly concatenate the key control parameters, batch number, and timestamp in random order without formatting, resulting in a chaotic encoding format and no fixed parsing rules.
[0070] It is important to emphasize that this solution provides a preferred implementation method. It combines multiple types of information to generate a unique strategy traceability code, which is then centrally affixed to a designated area. This avoids information misalignment and omissions caused by multiple coding locations, while also reducing the number of coding attempts and improving coding efficiency and the integrity of the packaging appearance. Compared to unstructured coding methods, this method uses formatted coding to unify the presentation of key control parameters, ensuring a standardized coding structure and convenient parsing, thus avoiding subsequent inaccurate decoding and traceability issues caused by format confusion.
[0071] More importantly, this solution generates traceability codes based on the characteristic identifiers of the decision-making process, achieving a strong correlation between the coding information and the upstream material cutting decision data. This ensures that the traceability code of each bag of product can accurately correspond to the basis for its generation decision, providing accurate data support for subsequent sorting evaluation and quality traceability, and building a complete data chain of decision-making-execution-traceability. This is a system-level collaborative effect that other coding methods cannot achieve.
[0072] In this embodiment, when the conveying and sorting module conveys the packaging bags with policy traceability codes to the designated area, it is specifically used for: During the transportation process, the strategy traceability code on the packaging bag is read and decoded online to obtain the restored decision-making process feature identifier; Based on the key control parameters in the restored decision process feature identification, the stability assessment of the bagging process corresponding to the packaging bag is carried out, and the process stability assessment results are obtained. Based on the process stability assessment results, the diversion mechanism of the conveying path is controlled to guide the packaging bags to the designated area corresponding to the assessment results.
[0073] In this embodiment, when the conveying and sorting module executes key control parameters based on the characteristic identifiers of the restoration-based decision-making process to perform a stability assessment of the bagging process corresponding to the packaging bag, it is specifically used for: Extract the weight growth rate data, which characterizes the material flow rate, from the key control parameters and represent a numerical sequence over a continuous period of time before material cutting. Smoothness analysis is performed on the numerical sequence to calculate its local fluctuation intensity, and the local fluctuation intensity is used as the result of process stability assessment.
[0074] The formula for calculating the intensity of local fluctuations is as follows: In the formula, Indicates the intensity of local fluctuations. This represents the sequence sampling point, that is, the first point in the extracted numerical sequence. Individual weight growth rate data, Represents the average value of the sequence. This indicates the sequence length, which is the total number of sampling points in the extracted numerical sequence.
[0075] In practice, the barcode scanner is fixed next to the conveyor path and aligned with the coding area of the packaging bag. When the packaging bag passes by the barcode scanner at a constant speed with the conveyor belt, the barcode scanner emits light to scan the tracking code and receives the reflected signal. The reflected signal is then converted into an electrical signal and transmitted to the decoding unit.
[0076] Then, the decoding unit reverses the serialization rules of laser marking, first separating the structured parameter code, the current packaging batch number and the marking timestamp, and then reverses the structured parameter code according to the preset format to restore the decision process feature identifier containing key control parameters, and finally obtains the restored decision process feature identifier.
[0077] Furthermore, key control parameters are extracted from the restored decision-making process feature identifiers, the weight growth rate data representing the material flow rate is located, and all weight growth rate data in a continuous period before material cutting is selected.
[0078] Then, the values are arranged sequentially in chronological order to form an ordered numerical sequence, ensuring that the numerical sequence fully covers the change process of material flow rate before the material is cut off and accurately reflects the flow rate state of the material falling before the material is cut off.
[0079] Furthermore, the sequence sampling point is each weight growth rate data in the numerical sequence. The sequence length is determined by the pre-test apparatus. The weight growth rate data within different time periods are repeatedly recorded, and the number of sampling points corresponding to the time period that can reflect the trend of flow rate change before material cutting is selected as the preset value.
[0080] Then, the local fluctuation intensity is determined using the above formula. The greater the local fluctuation intensity, the more violent the material flow rate fluctuation before cutting, and the worse the stability of the bagging process. Conversely, the lower the local fluctuation intensity, the better the stability.
[0081] Furthermore, the process stability assessment results are determined by comparing the local fluctuation intensity with the preset stability threshold. The preset stability threshold is set through multiple bagging experiments, and the pass rate of bag weight under different fluctuation intensities is recorded to classify the process into three levels: stable, general, and unstable, and their corresponding threshold ranges.
[0082] Finally, based on the evaluation results, a control signal is sent to the diversion mechanism. Packaging bags of a stable level trigger the diversion mechanism to maintain the original path and be guided to the qualified product area. Packaging bags of an unstable level trigger the diversion mechanism to switch the conveying path and be guided to the re-inspection area, so as to convey the packaging bags to the corresponding designated area according to the evaluation results.
[0083] Of course, those skilled in the art may also simply perform a final weight check on the packaging bags, compare them with the nominal weight to determine whether they are qualified and then divert them; or divide the conveying path according to the packaging batch, with all packaging bags in the same batch being directed to the same area, without taking into account the differences in the individual bag packing process.
[0084] The proposed solution is a preferred implementation method that uses bagging process data as the core to drive sorting. By decoding the traceability code to restore the characteristic identifiers of the decision-making process, extracting the weight growth rate data before cutting the material to assess the intensity of local fluctuations, and accurately judging the stability of the single bag bagging process, it can not only sort out potentially risky products that are qualified but have unstable processes, but also provide data support for subsequent optimization of cutting decisions.
[0085] Furthermore, the sorting process forms a closed loop with upstream decision-making data and coding information. The diversion of each package corresponds to a clear process evaluation basis, avoiding the blind spots of conventional sorting. This refined sorting based on process data can significantly improve the accuracy of product quality control and provide precise direction for production optimization, achieving a system-level control effect that conventional sorting methods cannot achieve.
[0086] In this embodiment, after the conveying and sorting module performs online reading and decoding of the strategy traceability code on the packaging bag to obtain the restored decision process feature identifier, it is also used for: The restored decision process feature identifiers are compared with the decision process feature identifiers recorded synchronously when the material cutting and sealing instruction is executed. When the comparison results are inconsistent, the packaging bag will be directed to the abnormal processing area as its corresponding designated area.
[0087] In practice, after decoding and obtaining the restored decision process feature identifier, the decision process feature identifier recorded synchronously when the corresponding packaging bag was executed with the cutting and sealing instruction is immediately retrieved from the storage unit. The corresponding fields of the two sets of identifiers are aligned and verified bit by bit in a fixed order of key control parameters, predicted remaining time, and theoretical latest cutting time.
[0088] First, compare whether the character sequences of the key control parameters are completely consistent. Then, check whether the representation information of the predicted remaining time matches the theoretical latest material cutting time. The entire process is checked field by field without omission, and finally the consistency comparison results of the two sets of identifiers are obtained.
[0089] When the comparison results are inconsistent, an abnormal control signal is immediately sent to the diversion mechanism on the conveying path. After receiving the signal, the diversion mechanism starts to operate, driving the steering baffle to rotate to a preset angle, changing the conveying trajectory of the packaging bag, so that the packaging bag is separated from the normal conveying path and conveyed along the dedicated abnormal channel.
[0090] Simultaneously, an anomaly recording action is triggered, which associates the policy traceability code of the packaging bag, the inconsistent field information, and the current timestamp with it and stores them in the anomaly database to facilitate subsequent investigation of the cause. Finally, the packaging bag is directed to the preset anomaly handling area to complete the anomaly diversion operation.
[0091] This solution, as a preferred implementation method, compares the restored decision-making process feature identifier with the original identifier recorded simultaneously during material cutting and sealing. It directly verifies the matching between the strategy traceability code on the packaging bag and the upstream bagging decision data, eliminating the problem of code-to-product discrepancy from the data level. This ensures that the traceability code of each bag of product can accurately correspond to its true material cutting decision basis, providing reliable data support for subsequent quality traceability and production review, and strengthening the integrity and credibility of the entire process data chain.
[0092] Example 2, as Figure 2 This is a system architecture diagram of an automatic sample weighing, sealing, packaging, laser marking, and conveying system provided in an embodiment of the present invention.
[0093] In this embodiment, the automatic sample weighing, sealing, packaging, laser coding, and conveying system further includes an adaptive optimization module, specifically used for: Based on the process stability assessment results of multiple consecutive packaging bags, determine whether there is a systematic deviation trend in the bagging process; When a systematic deviation trend is determined, the control parameters in the dynamic material cutting decision logic are adaptively adjusted based on the characteristics of the deviation trend.
[0094] In practice, the process stability assessment results of multiple consecutive packaging bags are collected and arranged in chronological order of bagging time to form a trend sequence. The values in the sequence are compared one by one to observe the overall direction of change. At the same time, individual abnormal values caused by sudden failures are removed from the sequence to avoid interference from accidental factors in the judgment.
[0095] If the remaining values show a uniform pattern of continuous increase or decrease, and this pattern continuously covers multiple bagging cycles without reverse fluctuations, it can be determined that there is a systematic deviation trend in the bagging process. This allows for accurate differentiation between systematic deviations and random fluctuations, ensuring the reliability of the judgment results.
[0096] When a systematic deviation trend is determined, the characteristics of the deviation trend are first analyzed. If the trend is that the intensity of local fluctuations continues to increase, it indicates that the stability of the material flow rate is gradually decreasing. The safety factor in the dynamic material cutting decision logic should be adjusted accordingly. The safety factor should be appropriately increased to extend the advance cutting time and compensate for the deviation caused by the flow rate fluctuation.
[0097] If the trend is that the intensity of local fluctuations continues to decrease, it indicates that the stability of material flow rate is improving. The safety factor can be appropriately reduced to improve bagging efficiency while ensuring bagging accuracy.
[0098] After the adjustment is completed, the new control parameters are synchronously written into the decision logic and applied to the next batch of bagging process. At the same time, the stability assessment results of the subsequent packaging process are continuously monitored to verify the effect of parameter adjustment, ensuring that the adjusted control parameters are accurately matched with the offset trend characteristics, and realizing dynamic optimization of the bagging process.
[0099] It is important to emphasize that this solution utilizes stability assessment results. Based on the process stability assessment results of multiple consecutive packaging bags, it accurately identifies systematic deviation trends during the bagging process, thus breaking away from the lagging mode of "manually adjusting parameters after a batch of non-conforming products appear" in traditional production.
[0100] By capturing trend changes in real time and proactively intervening, the response cycle for parameter adjustments is significantly shortened, reducing the time and labor costs of manual monitoring and debugging, and improving the level of automation in the production line.
[0101] Furthermore, this solution addresses systemic deviation trends such as increased material flow rate fluctuations and changes in system response delays. The module can adjust the control parameters in the dynamic material cutting decision logic based on the deviation characteristics. By timely correcting key parameters such as the safety factor and lead time correction factor, the risk of overweight or underweight bagging caused by deviations is suppressed at the source, avoiding batch quality problems and ensuring that the product weight remains stable within the tolerance range at different production stages, thus improving product consistency.
[0102] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.
[0103] The embodiments described above can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.
[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims
1. An automatic sample weighing, bag sealing, packing, laser coding, and conveying system, characterized in that, The system comprises a real-time weighing module, a dynamic decision module, an execution sealing module, a laser coding module and a conveying and sorting module, wherein: The real-time weighing module is configured to continuously weigh the weight of the material in the packaging bag in real time during the process of the sample material falling into the packaging bag, to obtain real-time cumulative weight data and weight growth rate data. The dynamic decision module is configured to execute a dynamic cutting decision logic based on the real-time cumulative weight data, the weight growth rate data and the preset nominal weight and tolerance range, to generate a cutting and sealing instruction and a decision process characteristic identifier. The execution sealing module is configured to stop the material from falling and seal the current packaging bag based on the cutting and sealing instruction, to obtain a sealed packaging bag, and then enable a new packaging bag. The laser coding module is configured to code the sealed packaging bag by laser, wherein the coding content at least includes a strategy traceability code generated based on the decision process characteristic identifier, to obtain a packaging bag with a strategy traceability code. The conveying and sorting module is configured to convey the packaging bag with the strategy traceability code to a designated area.
2. The automatic sample weighing, bag sealing, packing, laser code printing and conveying system of claim 1, wherein, When the real-time weighing module is configured to continuously weigh the weight of the material in the packaging bag in real time, to obtain real-time cumulative weight data and weight growth rate data, it is specifically configured to: Based on the weighing sensor carrying the packaging bag, continuously measure to obtain an original electrical signal reflecting the cumulative weight of the material. Signal conditioning is performed on the original electrical signal, which at least includes gain adjustment and zero drift compensation based on a preset reference, to obtain a conditioned signal representing the change in net weight. Digital filtering is performed on the conditioned signal to obtain a filtered signal, and the real-time cumulative weight data and weight growth rate data are extracted based on the filtered signal.
3. The automatic sample weighing, bag sealing, packing, and laser code printing conveyor system of claim 1, wherein, When the dynamic decision module is configured to execute a dynamic cutting decision logic based on the real-time cumulative weight data, the weight growth rate data and the preset nominal weight and tolerance range, it is specifically configured to: Based on the weight growth rate data and the difference between the nominal weight and the real-time cumulative weight data, the remaining time is predicted to obtain a predicted remaining time. Based on the current material flow rate represented by the weight growth rate data and the tolerance range, the latest cutting time is analyzed to obtain a theoretical latest cutting time. When the predicted remaining time is less than or equal to the theoretical latest cutting time, the cutting decision is made based on the closeness of the real-time cumulative weight data to the nominal weight, combined with the preset advance correction factor, to obtain the cutting and sealing instruction and the decision process characteristic identifier including the predicted remaining time and the theoretical latest cutting time.
4. The automatic sample weighing, bag sealing, packing, and laser code printing conveyor system of claim 3, wherein, When the dynamic decision module is configured to execute the preset advance correction factor, it is specifically configured to: Collect actual cutting weight data, real-time cumulative weight data and historical advance correction factors used in the historical bagging process corresponding to the cutting and sealing instruction, to obtain a historical decision data set. Based on the historical decision data set, analyze the correlation between the historical advance correction factor and the deviation of the actual cutting weight of the corresponding batch relative to the nominal weight, to obtain a correction factor adjustment amount. The advance quantity correction factor is updated based on the correction factor adjustment amount, and an advance quantity correction factor for the next batch of bagging decision is obtained.
5. The automatic sample weighing bag sealing, packing, laser code marking and conveying system of claim 1, wherein, The execution bag sealing module is used for stopping the material falling and sealing the current packaging bag based on the material cutting and bag sealing instruction, obtaining a sealed packaging bag, and then enabling a new packaging bag, and specifically used for: In response to the material cutting and bag sealing instruction, the action of cutting off the material flow and the action of recording the current decision process characteristic identifier are performed synchronously, obtaining the packaging bag stopped from feeding and the identifier data to be associated; After confirming that the material flow is cut off, the hot sealing operation for the packaging bag stopped from feeding is triggered and performed based on the identifier data to be associated, obtaining the packaging bag with completed sealing; After the hot sealing operation is completed, the action of enabling the new packaging bag is performed, and the real-time cumulative weight data corresponding to the previous packaging bag is emptied, and the bagging state is reset.
6. The automatic sample weighing bag sealing, packing, laser code marking and conveying system of claim 1, wherein, The laser coding module is used for performing laser coding on the sealed packaging bag, and specifically used for: The key control parameters contained in the decision process characteristic identifier are formatted and encoded to obtain structured parameter encoding; The structured parameter encoding, the current packaging batch number and the coding time stamp are combined and serialized to obtain a strategy trace code; The strategy trace code is used to control the laser marking machine to perform marking operation on the specified area of the sealed packaging bag, obtaining the packaging bag with the strategy trace code.
7. The automatic sample weighing bag sealing, packing, laser code marking and conveying system of claim 1, wherein, The conveying and sorting module is used for conveying the packaging bag with the strategy trace code to the specified area, and specifically used for: During the conveying process, the strategy trace code on the packaging bag is read and decoded online to obtain the restored decision process characteristic identifier; Based on the key control parameters in the restored decision process characteristic identifier, the stability of the bagging process corresponding to the packaging bag is evaluated to obtain a process stability evaluation result; According to the process stability evaluation result, the shunt mechanism of the conveying path is controlled to guide the packaging bag to the specified area corresponding to the evaluation result.
8. The automatic sample weighing, bag sealing, packing, and laser code printing conveyor system of claim 7, wherein, The conveying and sorting module is used for evaluating the stability of the bagging process corresponding to the packaging bag based on the key control parameters in the restored decision process characteristic identifier, and specifically used for: The weight growth rate data representing the material flow rate in the key control parameters is extracted to obtain a numerical sequence within a continuous time period before the material cutting; The numerical sequence is subjected to smoothness analysis, the local fluctuation intensity is calculated, and the local fluctuation intensity is taken as the process stability evaluation result.
9. The automatic sample weighing, bag sealing, packing, and laser code printing conveyor system of claim 7, wherein, After the conveying and sorting module reads and decodes the strategy trace code on the packaging bag to obtain the restored decision process characteristic identifier, the module is further used for: The restored decision process characteristic identifier is compared with the decision process characteristic identifier recorded synchronously when the material cutting and bag sealing instruction is executed for consistency; When the comparison result is inconsistent, the packaging bag is guided to an abnormal processing area as the corresponding specified area.
10. The automatic sample weighing, bag sealing, packing, and laser code printing conveyor system of claim 7, wherein, The system further includes an adaptive optimization module, which is specifically used for: Based on the process stability evaluation results of a plurality of consecutive packaging bags, it is determined whether there is a systematic deviation trend in the bagging process; When it is determined that there is a systematic deviation trend, the control parameters in the dynamic material cutting decision logic are adaptively adjusted based on the characteristics of the deviation trend.