Method and device for regulating and controlling abnormal discharging amount of intelligent liquid discharging machine and computer equipment

Abstract

The invention relates to a discharging amount abnormity regulation and control method and device of an intelligent liquid discharging machine and computer equipment. The method comprises the steps that weight sensing fluctuation data of all discharging pipes of the intelligent liquid discharging machine, product demand information of a current product and all material types are obtained, and the target liquid discharging amount of all the material types is recognized; according to a liquid outlet amount analysis strategy, the current actual liquid outlet amount of each discharging pipe is recognized, and abnormal discharging information of each abnormal discharging pipe is recognized based on the material type corresponding to each discharging pipe, the target liquid outlet amount of each material type and the current actual liquid outlet amount of each discharging pipe; and on the basis of the abnormal discharging information of the abnormal discharging pipes, abnormal regulation and control processing schemes of the intelligent liquid discharging machine are generated, and on the basis of the abnormal regulation and control processing schemes of the intelligent liquid discharging machine, abnormal regulation and control processing is carried out on the current product through selection operation of a worker, and a target product is obtained. By adopting the method, the intelligent regulation and control efficiency of the abnormal discharging amount of the intelligent liquid discharging machine can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent control and human-computer interaction, and in particular to a method, device and computer equipment for regulating abnormal discharge volume of an intelligent liquid discharging machine. Background Art

[0002] Discharge volume monitoring in intelligent liquid dispensing machines is a crucial tool for ensuring normal production, precise resource control, product optimization, anomaly warnings, efficiency improvements, and risk management. Traditional liquid dispensing machines often rely on manual monitoring and adjustment, which has numerous drawbacks. These include long delays in dispensing volume display, an inability to reflect actual discharge conditions in real time, and a lack of timely and effective alerts and remedial measures in the event of anomalies. Therefore, improving the accuracy and timeliness of discharge volume monitoring is a current research priority.

[0003] The existing technology is to monitor the discharge volume in real time through sensors installed on the discharge pipe, and transmit the data to the terminal for processing and analysis. The terminal adjusts the operating parameters of the liquid discharge machine in real time based on the received data to maintain the stability of the discharge volume. At the same time, the discharge volume information is also fed back to the terminal so that the staff can understand the discharge situation in real time. However, although the existing technology can monitor abnormal discharge volume, the accuracy of detection of excessive discharge from the discharge pipe, liquid level fluctuations, etc. is poor. Moreover, this method can only detect the discharge volume, and manual control of abnormal discharge volume is still required, resulting in poor efficiency of intelligent regulation of abnormal discharge volume of the intelligent liquid discharge machine. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for abnormal control of the discharge volume of an intelligent liquid discharge machine in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for regulating abnormal discharge volume of an intelligent liquid dispensing machine, comprising:

[0006] Obtain weight sensor fluctuation data of each discharge pipe of the intelligent liquid dispensing machine, product demand information of the current product of the intelligent liquid dispensing machine, and the material type corresponding to each discharge pipe, and identify the target liquid discharge volume of each material type based on the product demand information of the current product;

[0007] Based on the weight sensor fluctuation data, the current actual liquid discharge of each discharge pipe is identified through a liquid discharge analysis strategy, and based on the material type corresponding to each discharge pipe, the target liquid discharge of each material type, and the current actual liquid discharge of each discharge pipe, the abnormal discharge information of each abnormal discharge pipe is identified;

[0008] Based on the abnormal discharge information of each abnormal discharge pipe, each abnormal control and processing scheme of the intelligent liquid discharge machine is generated, and based on the abnormal control and processing scheme of the intelligent liquid discharge machine, the current product is subjected to abnormal control processing through the selection operation of the staff to obtain the target product.

[0009] Optionally, the identifying a target liquid output of each material type based on the product demand information of the current product includes:

[0010] Based on the product demand information, identifying the material types required for the current product, the product demand ratio value of each material type, and the product specification requirement information of the current product;

[0011] Based on the product specification requirement information, the target product capacity of the current product is identified, and based on the target product capacity and the product demand ratio value of each material type, the theoretical liquid output of each material type is calculated, and based on the theoretical liquid output of each material type, a preset material deviation adjustment strategy is used to generate a liquid output range for each material type;

[0012] The liquid output range of each material type is used as the target liquid output of each material type.

[0013] Optionally, the identifying the current actual liquid discharge of each of the discharge pipes through a liquid discharge analysis strategy based on each of the weight sensor fluctuation data includes:

[0014] For each discharge pipe, generating weight fluctuation distribution information of the discharge pipe based on weight sensor fluctuation data of the discharge pipe;

[0015] Performing linear fitting processing on the weight fluctuation distribution information through a linear fitting strategy to obtain target weight change distribution information of the discharge pipe;

[0016] Based on the target weight change distribution information, the current liquid discharge range of the discharge pipe is identified, and the current liquid discharge range of the discharge pipe is used as the current actual liquid discharge of the discharge pipe.

[0017] Optionally, the identifying abnormal discharge information of each abnormal discharge pipe based on the material type corresponding to each discharge pipe, the target liquid discharge volume of each material type, and the current actual liquid discharge volume of each discharge pipe includes:

[0018] Based on the target liquid discharge volume of each material type and the current actual liquid discharge volume of each material type, identifying the liquid discharge deviation of each material type and the liquid discharge deviation type of each material type, and screening the material type corresponding to the liquid discharge deviation greater than the liquid discharge deviation range of each material type as the abnormal material type;

[0019] The discharge pipes corresponding to the abnormal material types are used as the abnormal discharge pipes, and the discharge deviation amount and the discharge deviation type of the abnormal material types are used as the abnormal discharge information of the abnormal discharge pipes.

[0020] Optionally, generating each abnormal control processing scheme of the intelligent liquid discharging machine based on the abnormal discharge information of each abnormal discharge pipe includes:

[0021] When there is no liquid discharge deviation type of excessive liquid discharge, based on the liquid discharge deviation corresponding to each abnormal discharge pipe, the material replenishment range of each abnormal discharge pipe is generated, and the material replenishment range of all abnormal discharge pipes is used as the abnormal control processing plan of the intelligent liquid discharge machine;

[0022] When there is a liquid discharge deviation type of excessive liquid discharge, based on the liquid discharge deviation of each abnormal discharge pipe of the excessive liquid discharge type and the product demand ratio value of the material type corresponding to each abnormal discharge pipe of the excessive liquid discharge type, the target abnormal discharge pipe with the largest actual proportion of discharge volume is selected from each abnormal discharge pipe;

[0023] Based on the target abnormal discharge pipe and the product demand ratio value of the material type corresponding to the target abnormal discharge pipe, the new product capacity of the current product is calculated, and when the new product capacity is greater than the product capacity upper limit corresponding to the product specification requirement information, the abnormal control processing scheme of the intelligent liquid dispensing machine is determined to be a product order placement scheme;

[0024] When the capacity of the new product is not greater than the upper limit of the product capacity corresponding to the product specification requirement information, calculating the new liquid output of each material type based on the capacity of the new product and the product demand ratio value of each material type;

[0025] Based on the new liquid discharge volume of the material type corresponding to each discharge pipe and the liquid discharge deviation of the material type corresponding to each discharge pipe, the actual material replenishment volume of each discharge pipe is calculated, and the actual material replenishment volume of all discharge pipes is used as the abnormal control processing plan of the intelligent liquid discharge machine.

[0026] Optionally, the abnormal control processing schemes based on the intelligent liquid dispensing machine are used to perform abnormal control processing on the current product through the selection operation of the staff to obtain the target product, including:

[0027] When the abnormal control solution selected by the staff is a product order solution, the product progress information of the current product is updated, and a new product generation task for the current product is generated;

[0028] Based on the product types corresponding to the respective sequence positions in the current product sequence and the product type of the current product, a new sequence position for the current product is generated through a product optimization generation method. When the production time point of the new sequence position of the current product arrives, the current product is regenerated based on the new product generation task for the current product.

[0029] Return to the task of obtaining the weight sensor fluctuation data of each discharging pipe of the intelligent liquid discharging machine, the product demand information of the current product of the intelligent liquid discharging machine, and the material type corresponding to each discharging pipe. This continues until no abnormal discharging pipe exists. The current product generated by the last iteration is used as the target product.

[0030] When the abnormal control processing scheme selected by the staff is not the product order scheme, identifying the current feeding amount of each of the discharge pipes, and controlling each of the discharge pipes to perform material replenishment processing based on the current feeding amount of each of the discharge pipes;

[0031] Return to execute the task of obtaining the weight sensor fluctuation data of each discharge pipe of the intelligent liquid dispensing machine, the product demand information of the current product of the intelligent liquid dispensing machine, and the material type corresponding to each discharge pipe, until there are no abnormal discharge pipes, and the current product generated by the last iteration will be used as the target product.

[0032] In a second aspect, the present application further provides a device for regulating abnormal discharge volume of an intelligent liquid dispensing machine, comprising:

[0033] an acquisition module, configured to acquire weight sensor fluctuation data of each discharge pipe of the intelligent liquid dispensing machine, product demand information of the current product of the intelligent liquid dispensing machine, and the material type corresponding to each of the discharge pipes, and identify a target liquid discharge volume for each material type based on the product demand information of the current product;

[0034] an identification module for identifying the current actual liquid discharge of each of the discharge pipes based on the weight sensor fluctuation data and a liquid discharge analysis strategy, and identifying abnormal discharge information of each abnormal discharge pipe based on the material type corresponding to each of the discharge pipes, the target liquid discharge of each material type, and the current actual liquid discharge of each of the discharge pipes;

[0035] The control module is used to generate various abnormal control and processing schemes for the intelligent liquid discharging machine based on the abnormal discharge information of each abnormal discharge pipe, and based on the various abnormal control and processing schemes for the intelligent liquid discharging machine, through the selection operation of the staff, perform abnormal control processing on the current product to obtain the target product.

[0036] Optionally, the acquisition module is specifically configured to:

[0037] Based on the product demand information, identifying the material types required for the current product, the product demand ratio value of each material type, and the product specification requirement information of the current product;

[0038] Based on the product specification requirement information, the target product capacity of the current product is identified, and based on the target product capacity and the product demand ratio value of each material type, the theoretical liquid output of each material type is calculated, and based on the theoretical liquid output of each material type, a preset material deviation adjustment strategy is used to generate a liquid output range for each material type;

[0039] The liquid output range of each material type is used as the target liquid output of each material type.

[0040] Optionally, the identification module is specifically configured to:

[0041] For each discharge pipe, generating weight fluctuation distribution information of the discharge pipe based on weight sensor fluctuation data of the discharge pipe;

[0042] Performing linear fitting processing on the weight fluctuation distribution information through a linear fitting strategy to obtain target weight change distribution information of the discharge pipe;

[0043] Based on the target weight change distribution information, the current liquid discharge range of the discharge pipe is identified, and the current liquid discharge range of the discharge pipe is used as the current actual liquid discharge of the discharge pipe.

[0044] Optionally, the identification module is specifically configured to:

[0045] Based on the target liquid discharge volume of each material type and the current actual liquid discharge volume of each material type, identifying the liquid discharge deviation of each material type and the liquid discharge deviation type of each material type, and screening the material type corresponding to the liquid discharge deviation greater than the liquid discharge deviation range of each material type as the abnormal material type;

[0046] The discharge pipes corresponding to the abnormal material types are used as the abnormal discharge pipes, and the discharge deviation amount and the discharge deviation type of the abnormal material types are used as the abnormal discharge information of the abnormal discharge pipes.

[0047] Optionally, the control module is specifically used to:

[0048] When there is no liquid discharge deviation type of excessive liquid discharge, based on the liquid discharge deviation corresponding to each abnormal discharge pipe, the material replenishment range of each abnormal discharge pipe is generated, and the material replenishment range of all abnormal discharge pipes is used as the abnormal control processing plan of the intelligent liquid discharge machine;

[0049] When there is a liquid discharge deviation type of excessive liquid discharge, based on the liquid discharge deviation of each abnormal discharge pipe of the excessive liquid discharge type and the product demand ratio value of the material type corresponding to each abnormal discharge pipe of the excessive liquid discharge type, the target abnormal discharge pipe with the largest actual proportion of discharge volume is selected from each abnormal discharge pipe;

[0050] Based on the target abnormal discharge pipe and the product demand ratio value of the material type corresponding to the target abnormal discharge pipe, the new product capacity of the current product is calculated, and when the new product capacity is greater than the product capacity upper limit corresponding to the product specification requirement information, the abnormal control processing scheme of the intelligent liquid dispensing machine is determined to be a product order placement scheme;

[0051] When the capacity of the new product is not greater than the upper limit of the product capacity corresponding to the product specification requirement information, calculating the new liquid output of each material type based on the capacity of the new product and the product demand ratio value of each material type;

[0052] Based on the new liquid discharge volume of the material type corresponding to each discharge pipe and the liquid discharge deviation of the material type corresponding to each discharge pipe, the actual material replenishment volume of each discharge pipe is calculated, and the actual material replenishment volume of all discharge pipes is used as the abnormal control processing plan of the intelligent liquid discharge machine.

[0053] Optionally, the control module is specifically used to:

[0054] When the abnormal control solution selected by the staff is a product order solution, the product progress information of the current product is updated, and a new product generation task for the current product is generated;

[0055] Based on the product types corresponding to the respective sequence positions in the current product sequence and the product type of the current product, a new sequence position for the current product is generated through a product optimization generation method. When the production time point of the new sequence position of the current product arrives, the current product is regenerated based on the new product generation task for the current product.

[0056] Return to the task of obtaining the weight sensor fluctuation data of each discharging pipe of the intelligent liquid discharging machine, the product demand information of the current product of the intelligent liquid discharging machine, and the material type corresponding to each discharging pipe. This continues until no abnormal discharging pipe exists. The current product generated by the last iteration is used as the target product.

[0057] When the abnormal control processing scheme selected by the staff is not the product order scheme, identifying the current feeding amount of each of the discharge pipes, and controlling each of the discharge pipes to perform material replenishment processing based on the current feeding amount of each of the discharge pipes;

[0058] Return to execute the task of obtaining the weight sensor fluctuation data of each discharge pipe of the intelligent liquid dispensing machine, the product demand information of the current product of the intelligent liquid dispensing machine, and the material type corresponding to each discharge pipe, until there are no abnormal discharge pipes, and the current product generated by the last iteration will be used as the target product.

[0059] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the methods described in the first aspect when executing the computer program.

[0060] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any one of the methods in the first aspect.

[0061] In a fifth aspect, the present application provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0062] The above-mentioned method, device and computer equipment for abnormal discharge volume control of the intelligent liquid discharging machine obtain the weight sensor fluctuation data of each discharge pipe of the intelligent liquid discharging machine, the product demand information of the current product of the intelligent liquid discharging machine, and the material type corresponding to each discharge pipe, and identify the target liquid discharge volume of each material type based on the product demand information of the current product; based on each weight sensor fluctuation data, identify the current actual liquid discharge volume of each discharge pipe through the liquid discharge volume analysis strategy, and based on the material type corresponding to each discharge pipe, the target liquid discharge volume of each material type, and the current actual liquid discharge volume of each discharge pipe, identify the abnormal discharge information of each abnormal discharge pipe; based on the abnormal discharge information of each abnormal discharge pipe, generate each abnormal control processing scheme of the intelligent liquid discharging machine, and based on the each abnormal control processing scheme of the intelligent liquid discharging machine, perform abnormal control processing on the current product through the selection operation of the staff to obtain the target product. This solution, after collecting weight sensor fluctuation data from each discharge pipe through sensors, analyzes the actual liquid output of each material type by material type, avoiding the low-precision problems of manual detection or direct upload of liquid output from sensors. This solution then calculates the abnormal discharge information of each abnormal discharge pipe to generate various abnormal control and treatment solutions for the intelligent liquid discharging machine. This eliminates the need for manual selection or manual generation of treatment solutions, improving the efficient and accurate generation of liquid output solutions for the current product when the liquid output is abnormal. This improves the efficiency of abnormal discharge control. After the staff only needs to select the abnormal control and treatment solution, this solution can intelligently take corresponding remedial measures for the current product, thereby not only achieving abnormal monitoring of liquid output and high-precision identification of abnormalities, but also timely and efficiently generating remedial solutions for liquid output, avoiding the low-precision and low remedial efficiency problems of manual monitoring and manual identification, thereby comprehensively improving the efficiency of intelligent control of abnormal discharge output of the intelligent liquid discharging machine. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0064] Figure 1 Schematic diagram of a flow chart of a method for regulating abnormal discharge volume of an intelligent liquid discharging machine in one embodiment;

[0065] Figure 2 A schematic diagram of a flow chart of an example of abnormal discharge volume control of an intelligent liquid dispensing machine in one embodiment;

[0066] Figure 3It is a structural block diagram of a device for controlling abnormal discharge volume of an intelligent liquid discharging machine in one embodiment;

[0067] Figure 4 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0069] The method for regulating the abnormal discharge volume of an intelligent liquid discharging machine provided in the embodiment of the present application can be applied to the control module of the intelligent liquid discharging machine. The control module can be a terminal, which can be, but is not limited to, various personal computers, laptop computers, mid-range computers, etc. After collecting the weight sensor fluctuation data of each discharge pipe through the sensor, the terminal analyzes the actual discharge volume of each material type by dividing the material type, thereby avoiding the low-precision problem of manual detection or direct upload of the discharge volume by the sensor. Then, this solution calculates the abnormal discharge information of each abnormal discharge pipe, thereby generating various abnormal regulation and processing schemes for the intelligent liquid discharging machine, without the need for manual selection or manual generation of processing schemes, thereby improving the efficient and accurate generation of discharge schemes when the current product discharges abnormally. The efficiency of abnormal discharge volume regulation is improved. The staff only needs to select the abnormal control and processing plan, and this plan can intelligently take corresponding remedial measures for the current product, thereby not only realizing abnormal monitoring of liquid discharge volume and high-precision identification of abnormalities, but also being able to generate remedial plans for liquid discharge volume in a timely and efficient manner, avoiding the problems of low precision and low remedial efficiency of manual monitoring and manual identification, thereby comprehensively improving the intelligent control efficiency of abnormal discharge volume of the intelligent liquid discharge machine.

[0070] In an exemplary embodiment, Figure 1 As shown, a method for controlling the abnormal discharge volume of an intelligent liquid dispensing machine is provided, which is described by taking the application of the method to a terminal as an example, and includes the following steps S101 to S103.

[0071] Step S101, obtain the weight sensor fluctuation data of each discharge pipe of the intelligent liquid discharging machine, the product demand information of the current product of the intelligent liquid discharging machine, and the material type corresponding to each discharge pipe, and identify the target liquid discharge volume of each material type based on the product demand information of the current product.

[0072] In this embodiment, the terminal uses weight sensors installed on each dispensing tube of the intelligent liquid dispenser to collect weight change data from the intelligent liquid dispenser during dispensing. The total weight change data for each dispensing tube is used as weight sensor fluctuation data. The terminal then collects the recorded information from the intelligent liquid dispenser to obtain product demand information for the current product. Acquiring the weight sensor fluctuation data for each dispensing tube of the intelligent liquid dispenser is a step performed after the current product has completed the task of generating the current product. After receiving the product information generated by the intelligent liquid dispenser and the dispensing tube information for each dispensing tube, the terminal identifies the product demand information for the current product and the material type corresponding to each dispensing tube. The product demand information includes the product demand ratio for each material type and the product specification requirement information for the current product. The product specification requirement information includes, for example, small cup specification requirement information, medium cup specification requirement information, large cup specification requirement information, and extra large cup specification requirement information. The flattened specification requirement information is used to display the product capacity value for the product. For example, the small cup specification requirement information indicates a product capacity of 300ml, the medium cup specification requirement information indicates a product capacity of 500ml, the large cup specification requirement information indicates a product capacity of 800ml, and the extra large cup specification requirement information indicates a product capacity of 1000ml. The material type is the type of each component material of each product, for example, black tea type (including the black tea type of each product name), green tea type (including the green tea type of each product name), dairy type (including the dairy type of each product name and the types of each dairy product), and auxiliary material type (including the types of various auxiliary materials, such as glycogen, acid essence, food coloring, alcohol, solid ingredients, etc.).

[0073] When each intelligent liquid dispensing machine performs the task of generating the current product, the terminal generates a dynamic display graph for each dispensing pipe based on the theoretical discharge volume of each material type corresponding to each dispensing pipe in the product demand information for the current product. It also calculates the ratio of weight change data collected for each dispensing pipe to the theoretical discharge volume, and displays a distribution graph of each dispensing pipe's discharge volume in the dynamic display graph for each dispensing pipe. This allows staff to promptly understand the discharge status of each dispensing pipe, allowing them to dynamically and promptly monitor the discharge volume of each dispensing pipe during the actual dispensing process, allowing them to quickly take action if an anomaly occurs. The terminal identifies the target total capacity of the current product based on the product specification requirements in the product demand information and generates a dynamic display graph corresponding to this target total capacity. It also calculates the total capacity change data corresponding to each dynamically collected weight change data for each material type. Based on this total capacity change data and the target total capacity, the dynamic display graph displays the product production progress value for the current product. Finally, the terminal identifies the target liquid discharge volume for each material type based on the product demand information for the current product. The specific identification process will be described in detail later.

[0074] Step S102, based on the fluctuation data of each weight sensor, the current actual liquid discharge of each discharge pipe is identified through the liquid discharge analysis strategy, and based on the material type corresponding to each discharge pipe, the target liquid discharge of each material type, and the current actual liquid discharge of each discharge pipe, the abnormal discharge information of each abnormal discharge pipe is identified.

[0075] In this embodiment, the terminal identifies the current actual liquid discharge of each discharge pipe based on the fluctuation data of each weight sensor through the liquid discharge analysis strategy, and identifies the abnormal discharge information of each abnormal discharge pipe based on the material type corresponding to each discharge pipe, the target liquid discharge of each material type, and the current actual liquid discharge of each discharge pipe. Among them, the liquid discharge analysis strategy is used to eliminate the accuracy problem caused by the deviation of sensor data caused by excessive discharge of the discharge pipe, liquid level fluctuation, liquid reflux, liquid changes in the pipe, etc., and then identify the analysis strategy of the actual liquid discharge of each discharge pipe. The specific analysis process will be described in detail later. Based on the material type corresponding to each discharge pipe, the target liquid discharge of each material type, and the current actual liquid discharge of each discharge pipe, the abnormal discharge information of each abnormal discharge pipe is identified. Among them, the abnormal discharge information includes the liquid discharge deviation value and the liquid discharge deviation type. Among them, the liquid discharge deviation type includes excessive liquid discharge type and insufficient liquid discharge type. The specific identification process will be described in detail later.

[0076] Step S103, based on the abnormal discharge information of each abnormal discharge pipe, generate each abnormal control and processing scheme of the intelligent liquid discharge machine, and based on each abnormal control and processing scheme of the intelligent liquid discharge machine, through the selection operation of the staff, perform abnormal control processing on the current product to obtain the target product.

[0077] In this embodiment, the terminal generates various abnormal control and handling solutions for the intelligent liquid dispensing machine based on the abnormal discharge information from each abnormal discharge pipe. Based on these abnormal control and handling solutions, the operator selects and operates to perform abnormal control and handling on the current product, resulting in the target product. These abnormal control and handling solutions include product ordering solutions and non-product ordering solutions. The product ordering solution involves regenerating the current product, while the non-product ordering solution involves refilling the current product through each discharge pipe. The specific generation process will be described in detail later.

[0078] Based on the above solution, after collecting weight sensor fluctuation data from each discharge pipe through sensors, the actual liquid output of each material type is analyzed by material type, avoiding the low precision of manual detection or direct upload of liquid output from sensors. This solution then calculates the abnormal discharge information of each abnormal discharge pipe to generate various abnormal control and treatment plans for the intelligent liquid dispensing machine. This eliminates the need for manual selection or manual generation of treatment plans, improving the efficient and accurate generation of liquid output plans for the current product when the liquid output is abnormal. This improves the efficiency of abnormal discharge control. After the staff only needs to select the abnormal control and treatment plan, this solution can intelligently take appropriate remedial measures for the current product. This not only achieves abnormal monitoring of liquid output and high-precision identification of abnormalities, but also enables timely and efficient generation of remedial plans for liquid output, avoiding the low precision and low remedial efficiency of manual monitoring and identification, thereby comprehensively improving the efficiency of intelligent control of abnormal discharge output of the intelligent liquid dispensing machine.

[0079] Optionally, based on the product demand information of the current product, the target liquid output of each material type is identified, including: based on the product demand information, identifying the material types required for the current product, the product demand ratio value of each material type, and the product specification requirement information of the current product; based on the product specification requirement information, identifying the target product capacity of the current product, and based on the target product capacity and the product demand ratio value of each material type, calculating the theoretical liquid output of each material type, and based on the theoretical liquid output of each material type, generating the liquid output range of each material type through a preset material deviation adjustment strategy; using the liquid output range of each material type as the target liquid output of each material type.

[0080] In this embodiment, based on product demand information, the terminal identifies the material types required for the current product, the product demand ratio for each material type, and the product specification requirement information for the current product. The product demand information includes the specification type and product type of the current product. The terminal then queries the cloud platform database for the product specification requirement information corresponding to that specification type. Based on the product type, the terminal then queries the material types required for that product type and the capacity ratio for each material type to obtain the product demand ratio for each material type. The product type can include product names and specification types, such as small, medium, large, and extra large.

[0081] Based on product specification requirements, the terminal identifies the target capacity of the current product and calculates the theoretical output of each material type based on the target capacity and the product demand ratio for each material type. Since the directly calculated theoretical output is only the target output and may have several decimal places, directly using this theoretical output as the target output for each material type can easily lead to misjudgments of the output. In actual output, as long as the output is within the tolerance range, the output is not considered abnormal. Therefore, based on the theoretical output of each material type, the terminal uses a preset material deviation adjustment strategy to generate an output range for each material type. This material deviation adjustment strategy includes output tolerance values for different material types. The terminal then adds the output tolerance value to the theoretical output of each material type and rounds off the output value after the decimal point to obtain the output range for each material type. Finally, the terminal uses the output range for each material type as the target output for each material type.

[0082] Based on the above solution, by calculating the target liquid output of each material type, the error recognition accuracy of the current product is improved, and the error tolerance of the liquid output of the current product is improved.

[0083] Optionally, based on the weight sensor fluctuation data, the current actual liquid discharge of each discharge pipe is identified through a liquid discharge analysis strategy, including: for each discharge pipe, based on the weight sensor fluctuation data of the discharge pipe, generating the weight fluctuation distribution information of the discharge pipe; through a linear fitting strategy, performing linear fitting processing on the weight fluctuation distribution information to obtain the target weight change distribution information of the discharge pipe; based on the target weight change distribution information, identifying the current liquid discharge range of the discharge pipe, and using the current liquid discharge range of the discharge pipe as the current actual liquid discharge of the discharge pipe.

[0084] In this embodiment, the terminal arranges the weight sensor fluctuation data of each discharge pipe in the order of collection time to obtain the weight fluctuation distribution information of the discharge pipe.

[0085] Then, the terminal performs linear fitting processing on the weight fluctuation distribution information through the linear fitting strategy corresponding to the two-dimensional linear fitting method to obtain the target weight change distribution information of the discharge pipe. This makes the obtained target weight change distribution information more accurate and eliminates a large amount of fluctuation factors and interference data of interference factors. Then, based on the target weight change distribution information, the terminal identifies the current liquid discharge range of the discharge pipe. Among them, the current liquid discharge range is the liquid discharge data collected by the discharge pipe at the last moment of the target weight change distribution information, and the preset liquid discharge error value is added to obtain the liquid discharge range. Finally, the terminal uses the current liquid discharge range of the discharge pipe as the current actual liquid discharge of the discharge pipe.

[0086] Based on the above scheme, the liquid volume range is identified after linear fitting of the distribution information, which reduces the influence of interference data and improves the accuracy of identifying the liquid volume range.

[0087] Optionally, based on the material type corresponding to each discharge pipe, the target liquid discharge volume of each material type, and the current actual liquid discharge volume of each discharge pipe, the abnormal discharge information of each abnormal discharge pipe is identified, including: based on the target liquid discharge volume of each material type and the current actual liquid discharge volume of each material type, identifying the liquid discharge deviation amount of each material type and the liquid discharge deviation type of each material type, and screening the material type corresponding to the liquid discharge deviation amount greater than the liquid discharge deviation amount range of each material type as the abnormal material type; using the discharge pipe corresponding to each abnormal material type as each abnormal discharge pipe, and using the liquid discharge deviation amount of each abnormal material type and the liquid discharge deviation type of each abnormal material type as the abnormal discharge information of each abnormal discharge pipe.

[0088] In this embodiment, the terminal identifies the discharge deviation of each material type and the discharge deviation type of each material type based on the target discharge volume of each material type and the current actual discharge volume of each material type, and selects the material type corresponding to the discharge deviation that is greater than the discharge deviation range of each material type as the abnormal material type. Among them, each material type is preset with a different discharge deviation range, and then the terminal selects the material type corresponding to the discharge deviation that is greater than the discharge deviation range as the abnormal material type. Each discharge pipe corresponds to a material type. For example, the terminal sets the initial weight display of the scale as V0, the weight after placing the cup as V1, the theoretical discharge weight of a certain product is V2, and after the liquid dispensing machine discharges the material, the scale displays the weight as V3, then the actual discharge V4 = V3-V1. If V2 is within the range of (0-100] g and the difference between V2-V4 is within the range of ±2g, the liquid discharge deviation is abnormal; if V2 is greater than 100g and the difference between V4-V2 is within the range of V2*(±2%), the liquid discharge deviation is abnormal.

[0089] Then, the terminal uses the discharge pipe corresponding to each abnormal material type as each abnormal discharge pipe, and uses the discharge deviation amount of each abnormal material type and the discharge deviation type of each abnormal material type as the abnormal discharge information of each abnormal discharge pipe. The discharge deviation type includes but is not limited to an excessive discharge type and an insufficient discharge type.

[0090] Based on the above solution, after calculating the discharge deviation of each material type, the abnormal liquid deviation is identified through the deviation range to determine the abnormal material type, thereby improving the accuracy of identifying abnormal material types and abnormal discharge pipes.

[0091] Optionally, based on the abnormal discharge information of each abnormal discharge pipe, each abnormal control and processing scheme of the intelligent liquid discharge machine is generated, including: when there is no liquid discharge deviation type of excessive discharge, based on the liquid discharge deviation amount corresponding to each abnormal discharge pipe, a material replenishment range of each abnormal discharge pipe is generated, and the material replenishment range of all abnormal discharge pipes is used as the abnormal control and processing scheme of the intelligent liquid discharge machine; when there is a liquid discharge deviation type of excessive discharge, based on the liquid discharge deviation amount of each abnormal discharge pipe of excessive discharge type and the product demand ratio value of the material type corresponding to each abnormal discharge pipe of excessive discharge type, among each abnormal discharge pipe, the target abnormal discharge pipe with the largest actual proportion of discharge volume is screened; based on the target abnormal discharge pipe, and the product demand ratio value of the material type corresponding to the target abnormal discharge pipe, calculate the new product capacity of the current product, and when the new product capacity is greater than the product capacity upper limit value corresponding to the product specification requirement information, determine the abnormal control and processing plan of the intelligent liquid discharge machine as the product order plan; when the new product capacity is not greater than the product capacity upper limit value corresponding to the product specification requirement information, based on the new product capacity and the product demand ratio value of each material type, calculate the new liquid discharge volume of each material type; based on the new liquid discharge volume of the material type corresponding to each discharge pipe and the liquid discharge deviation of the material type corresponding to each discharge pipe, calculate the actual feeding volume of each discharge pipe, and use the actual feeding volume of all discharge pipes as the abnormal control and processing plan of the intelligent liquid discharge machine.

[0092] In this embodiment, when there is no liquid discharge deviation type of excessive liquid discharge, the terminal generates a material replenishment range for each abnormal discharge pipe based on the liquid discharge deviation amount corresponding to each abnormal discharge pipe, and uses the material replenishment range of all abnormal discharge pipes as the abnormal control and processing solution for the intelligent liquid discharge machine.

[0093] When there is a liquid discharge deviation type of excessive liquid discharge, the terminal selects the target abnormal discharge pipe with the largest actual proportion of discharge volume from each abnormal discharge pipe based on the liquid discharge deviation of each abnormal discharge pipe of excessive liquid discharge and the product demand ratio value of the material type corresponding to each abnormal discharge pipe of excessive liquid discharge. Specifically, the terminal presets the target discharge pipe, and based on the product demand ratio value of the material type corresponding to each abnormal discharge pipe, the product demand ratio value of the material type corresponding to the preset target discharge pipe, and the liquid discharge deviation of each discharge pipe, respectively calculates the comparative liquid discharge deviation of the preset target discharge pipe corresponding to the liquid discharge deviation of each abnormal discharge pipe. Then, the terminal selects the abnormal discharge pipe corresponding to the largest comparative liquid discharge deviation as the target abnormal discharge pipe.

[0094] Based on the target abnormal discharge pipe and the product demand ratio of the material type corresponding to the target abnormal discharge pipe, the terminal calculates the new product capacity of the current product. When the new product capacity exceeds the product capacity upper limit corresponding to the product specification requirement information, the terminal determines that the abnormal control and handling solution of the intelligent liquid dispensing machine is a product order solution. Among them, the terminal presets the product capacity upper limit of each product specification requirement information. This product capacity upper limit is used to improve the product fault tolerance rate of the current product. For example, the capacity upper limit of a 300ml product is 380ml, and the capacity upper limit of a 500ml product is 600ml.

[0095] If the new product capacity is no greater than the upper capacity limit specified in the product specification, the terminal calculates the new discharge volume for each material type based on the new product capacity and the product demand ratio for each material type. The terminal then calculates the actual refill volume for each dispensing pipe based on the new discharge volume and the discharge deviation for each material type. The actual refill volume for all dispensing pipes is used as the intelligent dispensing machine's abnormal control and handling solution.

[0096] Based on the above scheme, different abnormal control and treatment schemes are generated for different types of liquid discharge deviations, thereby improving the comprehensiveness, pertinence, and accuracy of the generated abnormal control and treatment schemes.

[0097] Optionally, based on the various abnormal control and processing schemes of the intelligent liquid dispensing machine, the current product is subjected to abnormal control and processing through the selection operation of the staff to obtain the target product, including: when the abnormal control and processing scheme selected by the staff is the product hanging order scheme, the product progress information of the current product is updated, and a new product generation task for the current product is generated; based on the product types corresponding to each sequence position in the current product sequence and the product type of the current product, a new sequence position of the current product is generated through the product optimization generation method, and at the production time point of the new sequence position of the current product, the current product is regenerated based on the new product generation task of the current product; return to execute to obtain the weight of each discharge pipe of the intelligent liquid dispensing machine The sensor fluctuation data, the product demand information of the current product of the intelligent liquid dispensing machine, and the material type tasks corresponding to each dispensing pipe are obtained until there are no abnormal dispensing pipes, and the current product generated by the last iteration is used as the target product; when the abnormal control processing plan selected by the staff is not the product order plan, the current feed quantity of each dispensing pipe is identified, and based on the current feed quantity of each dispensing pipe, each dispensing pipe is controlled to perform material replenishment processing; return to execute to obtain the weight sensor fluctuation data of each dispensing pipe of the intelligent liquid dispensing machine, the product demand information of the current product of the intelligent liquid dispensing machine, and the material type tasks corresponding to each dispensing pipe, until there are no abnormal dispensing pipes, and the current product generated by the last iteration is used as the target product.

[0098] In this embodiment, when the staff member selects the product order placement solution as the abnormal control solution, the terminal updates the product progress information of the current product and generates a new product creation task for the current product. The new product creation task is the creation task for the current product, i.e., the terminal re-adds the production task for the current product to the production tasks.

[0099] The terminal then generates a new sequence position for the current product based on the product types corresponding to each sequence position in the current product sequence and the product type of the current product using a product optimization generation method. This product optimization generation method places the current product's sequence position before the sequence positions of all other products if no products are currently in production. If a product is currently in production, the terminal places the current product's sequence position after the product currently in production.

[0100] When the production time point of the new serial position of the current product is reached, the terminal regenerates the current product based on the new product generation task of the current product, and returns to execute the task of obtaining the weight sensor fluctuation data of each discharge pipe of the intelligent liquid dispensing machine, the product demand information of the current product of the intelligent liquid dispensing machine, and the material type corresponding to each discharge pipe, until there are no abnormal discharge pipes, and the current product generated by the last iteration is used as the target product.

[0101] If the staff member selects a different abnormal control solution than the product order solution, the terminal identifies the current refill amount for each dispensing pipe and, based on that amount, controls the refill process for each pipe. The terminal then returns to the task of acquiring weight sensor fluctuation data from each dispensing pipe of the intelligent liquid dispenser, product demand information for the current product of the intelligent liquid dispenser, and the material type corresponding to each dispensing pipe. This process continues until no abnormal dispensing pipes remain. At this point, the current product generated by the last iteration is used as the target product.

[0102] Based on the above solution, when the staff selects different abnormal adjustment strategies, the terminal can intelligently replenish each discharge pipe, avoiding the inefficiency of manual replenishment, thereby comprehensively improving the intelligent control efficiency of abnormal discharge volume of the intelligent liquid discharge machine.

[0103] This application also provides an example of abnormal control of the discharge volume of an intelligent liquid discharging machine, such as Figure 2 As shown, the specific processing process includes the following steps:

[0104] Step S201, obtaining weight sensor fluctuation data of each discharge pipe of the intelligent liquid dispensing machine, product demand information of the current product of the intelligent liquid dispensing machine, and the material type corresponding to each discharge pipe.

[0105] Step S202 : Based on the product demand information, identify the material types required for the current product, the product demand ratio value of each material type, and the product specification demand information of the current product.

[0106] Step S203: Based on the product specification requirement information, the target product capacity of the current product is identified, and based on the target product capacity and the product demand ratio value of each material type, the theoretical liquid output of each material type is calculated. Based on the theoretical liquid output of each material type, a preset material deviation adjustment strategy is used to generate the liquid output range of each material type.

[0107] Step S204: taking the liquid output range of each material type as the target liquid output of each material type.

[0108] Step S205 , for each discharge pipe, generating weight fluctuation distribution information of the discharge pipe based on the weight sensor fluctuation data of the discharge pipe.

[0109] Step S206: Perform linear fitting processing on the weight fluctuation distribution information through a linear fitting strategy to obtain target weight change distribution information of the discharge pipe.

[0110] Step S207: Based on the target weight change distribution information, the current liquid discharge range of the discharge pipe is identified, and the current liquid discharge range of the discharge pipe is used as the current actual liquid discharge of the discharge pipe.

[0111] Step S208, based on the target liquid output of each material type and the current actual liquid output of each material type, identify the liquid output deviation of each material type and the liquid output deviation type of each material type, and screen the material type corresponding to the liquid output deviation that is greater than the liquid output deviation range of each material type as the abnormal material type.

[0112] In step S209, the discharge pipe corresponding to each abnormal material type is used as each abnormal discharge pipe, and the discharge deviation amount of each abnormal material type and the discharge deviation type of each abnormal material type are used as abnormal discharge information of each abnormal discharge pipe.

[0113] Step S210, when there is no liquid discharge deviation type of excessive liquid discharge, the material replenishment range of each abnormal discharge pipe is generated based on the liquid discharge deviation amount corresponding to each abnormal discharge pipe, and the material replenishment range of all abnormal discharge pipes is used as the abnormal control processing solution of the intelligent liquid discharge machine.

[0114] Step S211, when there is a liquid discharge deviation type of excessive liquid discharge, based on the liquid discharge deviation of each abnormal discharge pipe of excessive liquid discharge type and the product demand ratio value of the corresponding material type of each abnormal discharge pipe of excessive liquid discharge type, select the target abnormal discharge pipe with the largest actual proportion of discharge volume among each abnormal discharge pipe.

[0115] Step S212, based on the target abnormal discharge pipe and the product demand ratio value of the material type corresponding to the target abnormal discharge pipe, calculate the new product capacity of the current product, and when the new product capacity is greater than the product capacity upper limit value corresponding to the product specification requirement information, determine the abnormal control processing plan of the intelligent liquid discharge machine as the product order plan.

[0116] Step S213, when the capacity of the new product is not greater than the upper limit of the product capacity corresponding to the product specification requirement information, the new liquid output of each material type is calculated based on the capacity of the new product and the product requirement ratio value of each material type.

[0117] Step S214, based on the new liquid discharge volume of the material type corresponding to each discharge pipe and the liquid discharge deviation of the material type corresponding to each discharge pipe, calculate the actual material replenishment volume of each discharge pipe, and use the actual material replenishment volume of all discharge pipes as the abnormal control processing plan of the intelligent liquid discharge machine.

[0118] Step S215: When the abnormal control solution selected by the staff is the product order solution, the product progress information of the current product is updated, and a new product generation task for the current product is generated.

[0119] Step S216: Based on the product types corresponding to the various sequence positions in the current product sequence and the product type of the current product, a new sequence position for the current product is generated through a product optimization generation method. At the production time point of the new sequence position of the current product, the current product is regenerated based on the new product generation task of the current product.

[0120] Step S217, return to execute the task of obtaining the weight sensor fluctuation data of each discharge pipe of the intelligent liquid discharging machine, the product demand information of the current product of the intelligent liquid discharging machine, and the material type task corresponding to each discharge pipe, until there are no abnormal discharge pipes, and the current product generated by the last iteration is used as the target product.

[0121] Step S218, when the abnormal control processing scheme selected by the staff is not the product order scheme, the current feeding amount of each discharge pipe is identified, and based on the current feeding amount of each discharge pipe, each discharge pipe is controlled to perform material replenishment processing.

[0122] Step S219, return to execute the task of obtaining the weight sensor fluctuation data of each discharge pipe of the intelligent liquid discharging machine, the product demand information of the current product of the intelligent liquid discharging machine, and the material type corresponding to each discharge pipe, until there are no abnormal discharge pipes, and the current product generated by the last iteration is used as the target product.

[0123] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0124] Based on the same inventive concept, the embodiments of the present application also provide a device for controlling the abnormal discharge volume of an intelligent liquid dispensing machine for implementing the aforementioned method for controlling the abnormal discharge volume of an intelligent liquid dispensing machine. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of the embodiments of one or more devices for controlling the abnormal discharge volume of an intelligent liquid dispensing machine provided below can be found in the above-mentioned limitations of the method for controlling the abnormal discharge volume of an intelligent liquid dispensing machine, and will not be repeated here.

[0125] In an exemplary embodiment, Figure 3 As shown, a device for controlling abnormal discharge volume of an intelligent liquid dispensing machine is provided, comprising: an acquisition module 310, an identification module 320 and a control module 330, wherein:

[0126] An acquisition module 310 is configured to acquire weight sensor fluctuation data of each discharge pipe of the intelligent liquid dispensing machine, product demand information of the current product of the intelligent liquid dispensing machine, and the material type corresponding to each discharge pipe, and identify a target liquid discharge volume for each material type based on the product demand information of the current product;

[0127] an identification module 320 for identifying the current actual liquid discharge of each of the discharge pipes based on the weight sensor fluctuation data and a liquid discharge analysis strategy, and identifying abnormal discharge information of each abnormal discharge pipe based on the material type corresponding to each of the discharge pipes, the target liquid discharge of each material type, and the current actual liquid discharge of each of the discharge pipes;

[0128] The control module 330 is used to generate various abnormal control and processing schemes for the intelligent liquid discharging machine based on the abnormal discharge information of each abnormal discharge pipe, and based on the various abnormal control and processing schemes for the intelligent liquid discharging machine, through the selection operation of the staff, perform abnormal control processing on the current product to obtain the target product.

[0129] Optionally, the acquisition module 310 is specifically configured to:

[0130] Based on the product demand information, identifying the material types required for the current product, the product demand ratio value of each material type, and the product specification requirement information of the current product;

[0131] Based on the product specification requirement information, the target product capacity of the current product is identified, and based on the target product capacity and the product demand ratio value of each material type, the theoretical liquid output of each material type is calculated, and based on the theoretical liquid output of each material type, a preset material deviation adjustment strategy is used to generate a liquid output range for each material type;

[0132] The liquid output range of each material type is used as the target liquid output of each material type.

[0133] Optionally, the identification module 320 is specifically configured to:

[0134] For each discharge pipe, generating weight fluctuation distribution information of the discharge pipe based on weight sensor fluctuation data of the discharge pipe;

[0135] Performing linear fitting processing on the weight fluctuation distribution information through a linear fitting strategy to obtain target weight change distribution information of the discharge pipe;

[0136] Based on the target weight change distribution information, the current liquid discharge range of the discharge pipe is identified, and the current liquid discharge range of the discharge pipe is used as the current actual liquid discharge of the discharge pipe.

[0137] Optionally, the identification module 320 is specifically configured to:

[0138] Based on the target liquid discharge volume of each material type and the current actual liquid discharge volume of each material type, identifying the liquid discharge deviation of each material type and the liquid discharge deviation type of each material type, and screening the material type corresponding to the liquid discharge deviation greater than the liquid discharge deviation range of each material type as the abnormal material type;

[0139] The discharge pipes corresponding to the abnormal material types are used as the abnormal discharge pipes, and the discharge deviation amount and the discharge deviation type of the abnormal material types are used as the abnormal discharge information of the abnormal discharge pipes.

[0140] Optionally, the control module 330 is specifically configured to:

[0141] When there is no liquid discharge deviation type of excessive liquid discharge, based on the liquid discharge deviation corresponding to each abnormal discharge pipe, the material replenishment range of each abnormal discharge pipe is generated, and the material replenishment range of all abnormal discharge pipes is used as the abnormal control processing plan of the intelligent liquid discharge machine;

[0142] When there is a liquid discharge deviation type of excessive liquid discharge, based on the liquid discharge deviation of each abnormal discharge pipe of the excessive liquid discharge type and the product demand ratio value of the material type corresponding to each abnormal discharge pipe of the excessive liquid discharge type, the target abnormal discharge pipe with the largest actual proportion of discharge volume is selected from each abnormal discharge pipe;

[0143] Based on the target abnormal discharge pipe and the product demand ratio value of the material type corresponding to the target abnormal discharge pipe, the new product capacity of the current product is calculated, and when the new product capacity is greater than the product capacity upper limit corresponding to the product specification requirement information, the abnormal control processing scheme of the intelligent liquid dispensing machine is determined to be a product order placement scheme;

[0144] When the capacity of the new product is not greater than the upper limit of the product capacity corresponding to the product specification requirement information, calculating the new liquid output of each material type based on the capacity of the new product and the product demand ratio value of each material type;

[0145] Based on the new liquid discharge volume of the material type corresponding to each discharge pipe and the liquid discharge deviation of the material type corresponding to each discharge pipe, the actual material replenishment volume of each discharge pipe is calculated, and the actual material replenishment volume of all discharge pipes is used as the abnormal control processing plan of the intelligent liquid discharge machine.

[0146] Optionally, the control module 330 is specifically configured to:

[0147] When the abnormal control solution selected by the staff is a product order solution, the product progress information of the current product is updated, and a new product generation task for the current product is generated;

[0148] Based on the product types corresponding to the respective sequence positions in the current product sequence and the product type of the current product, a new sequence position for the current product is generated through a product optimization generation method. When the production time point of the new sequence position of the current product arrives, the current product is regenerated based on the new product generation task for the current product.

[0149] Return to the task of obtaining the weight sensor fluctuation data of each discharging pipe of the intelligent liquid discharging machine, the product demand information of the current product of the intelligent liquid discharging machine, and the material type corresponding to each discharging pipe. This continues until no abnormal discharging pipe exists. The current product generated by the last iteration is used as the target product.

[0150] When the abnormal control processing scheme selected by the staff is not the product order scheme, identifying the current feeding amount of each of the discharge pipes, and controlling each of the discharge pipes to perform material replenishment processing based on the current feeding amount of each of the discharge pipes;

[0151] Return to execute the task of obtaining the weight sensor fluctuation data of each discharge pipe of the intelligent liquid dispensing machine, the product demand information of the current product of the intelligent liquid dispensing machine, and the material type corresponding to each discharge pipe, until there are no abnormal discharge pipes, and the current product generated by the last iteration will be used as the target product.

[0152] Each module in the aforementioned intelligent liquid dispensing machine's abnormal discharge volume control device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device's memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0153] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 4 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface, the display unit and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for controlling the abnormal discharge volume of an intelligent liquid dispensing machine is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0154] Those skilled in the art will understand that Figure 4The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0155] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, steps corresponding to the method for abnormal discharge volume control of an intelligent liquid discharging machine are implemented.

[0156] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps corresponding to the method for abnormal discharge volume control of an intelligent liquid discharging machine are implemented.

[0157] In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements steps corresponding to the method for abnormal discharge volume control of an intelligent liquid dispensing machine.

[0158] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0159] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0160] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0161] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for regulating abnormal discharge volume of an intelligent liquid discharging machine, characterized in that: The method comprises: Obtain weight sensor fluctuation data of each discharge pipe of the intelligent liquid dispensing machine, product demand information of the current product of the intelligent liquid dispensing machine, and the material type corresponding to each discharge pipe, and identify the target liquid discharge volume of each material type based on the product demand information of the current product; Based on the weight sensor fluctuation data, the current actual liquid discharge of each discharge pipe is identified through a liquid discharge analysis strategy, and based on the material type corresponding to each discharge pipe, the target liquid discharge of each material type, and the current actual liquid discharge of each discharge pipe, the abnormal discharge information of each abnormal discharge pipe is identified; Based on the abnormal discharge information of each abnormal discharge pipe, each abnormal control and processing scheme of the intelligent liquid discharge machine is generated, and based on the abnormal control and processing scheme of the intelligent liquid discharge machine, the current product is subjected to abnormal control processing through the selection operation of the staff to obtain the target product.

2. The method according to claim 1, characterized in that The identifying the target liquid output of each material type based on the product demand information of the current product includes: Based on the product demand information, identifying the material types required for the current product, the product demand ratio value of each material type, and the product specification requirement information of the current product; Based on the product specification requirement information, the target product capacity of the current product is identified, and based on the target product capacity and the product demand ratio value of each material type, the theoretical liquid output of each material type is calculated, and based on the theoretical liquid output of each material type, a preset material deviation adjustment strategy is used to generate a liquid output range for each material type; The liquid output range of each material type is used as the target liquid output of each material type.

3. The method according to claim 1, characterized in that The method of identifying the current actual liquid discharge volume of each discharge pipe based on the weight sensor fluctuation data and using a liquid discharge volume analysis strategy includes: For each discharge pipe, generating weight fluctuation distribution information of the discharge pipe based on weight sensor fluctuation data of the discharge pipe; Performing linear fitting processing on the weight fluctuation distribution information through a linear fitting strategy to obtain target weight change distribution information of the discharge pipe; Based on the target weight change distribution information, the current liquid discharge range of the discharge pipe is identified, and the current liquid discharge range of the discharge pipe is used as the current actual liquid discharge of the discharge pipe.

4. The method according to claim 2, characterized in that The identifying of abnormal discharge information of each abnormal discharge pipe based on the material type corresponding to each discharge pipe, the target liquid discharge volume of each material type, and the current actual liquid discharge volume of each discharge pipe includes: Based on the target liquid discharge volume of each material type and the current actual liquid discharge volume of each material type, identifying the liquid discharge deviation of each material type and the liquid discharge deviation type of each material type, and screening the material type corresponding to the liquid discharge deviation greater than the liquid discharge deviation range of each material type as the abnormal material type; The discharge pipes corresponding to the abnormal material types are used as the abnormal discharge pipes, and the discharge deviation amount and the discharge deviation type of the abnormal material types are used as the abnormal discharge information of the abnormal discharge pipes.

5. The method according to claim 4, characterized in that The abnormal discharge information of each abnormal discharge pipe is used to generate each abnormal control processing scheme of the intelligent liquid discharge machine, including: When there is no liquid discharge deviation type of excessive liquid discharge, based on the liquid discharge deviation corresponding to each abnormal discharge pipe, the material replenishment range of each abnormal discharge pipe is generated, and the material replenishment range of all abnormal discharge pipes is used as the abnormal control processing plan of the intelligent liquid discharge machine; When there is a liquid discharge deviation type of excessive liquid discharge, based on the liquid discharge deviation of each abnormal discharge pipe of the excessive liquid discharge type and the product demand ratio value of the material type corresponding to each abnormal discharge pipe of the excessive liquid discharge type, the target abnormal discharge pipe with the largest actual proportion of discharge volume is selected from each abnormal discharge pipe; Based on the target abnormal discharge pipe and the product demand ratio value of the material type corresponding to the target abnormal discharge pipe, the new product capacity of the current product is calculated, and when the new product capacity is greater than the product capacity upper limit corresponding to the product specification requirement information, the abnormal control processing scheme of the intelligent liquid dispensing machine is determined to be a product order placement scheme; When the capacity of the new product is not greater than the upper limit of the product capacity corresponding to the product specification requirement information, calculating the new liquid output of each material type based on the capacity of the new product and the product demand ratio value of each material type; Based on the new liquid discharge volume of the material type corresponding to each discharge pipe and the liquid discharge deviation of the material type corresponding to each discharge pipe, the actual material replenishment volume of each discharge pipe is calculated, and the actual material replenishment volume of all discharge pipes is used as the abnormal control processing plan of the intelligent liquid discharge machine.

6. The method according to claim 1, characterized in that The abnormal control and processing schemes based on the intelligent liquid dispensing machine are used to perform abnormal control and processing on the current product through the selection operation of the staff to obtain the target product, including: When the abnormal control solution selected by the staff is a product order solution, the product progress information of the current product is updated, and a new product generation task for the current product is generated; Based on the product types corresponding to the respective sequence positions in the current product sequence and the product type of the current product, a new sequence position for the current product is generated through a product optimization generation method. When the production time point of the new sequence position of the current product arrives, the current product is regenerated based on the new product generation task for the current product. Return to the task of obtaining the weight sensor fluctuation data of each discharging pipe of the intelligent liquid discharging machine, the product demand information of the current product of the intelligent liquid discharging machine, and the material type corresponding to each discharging pipe. This continues until no abnormal discharging pipe exists. The current product generated by the last iteration is used as the target product. When the abnormal control processing scheme selected by the staff is not the product order scheme, identifying the current feeding amount of each of the discharge pipes, and controlling each of the discharge pipes to perform material replenishment processing based on the current feeding amount of each of the discharge pipes; Return to execute the task of obtaining the weight sensor fluctuation data of each discharge pipe of the intelligent liquid dispensing machine, the product demand information of the current product of the intelligent liquid dispensing machine, and the material type corresponding to each discharge pipe, until there are no abnormal discharge pipes, and the current product generated by the last iteration will be used as the target product.

7. A device for controlling abnormal discharge volume of an intelligent liquid discharging machine, characterized in that: The device comprises: an acquisition module, configured to acquire weight sensor fluctuation data of each discharge pipe of the intelligent liquid dispensing machine, product demand information of the current product of the intelligent liquid dispensing machine, and the material type corresponding to each of the discharge pipes, and identify a target liquid discharge volume for each material type based on the product demand information of the current product; an identification module for identifying the current actual liquid discharge of each of the discharge pipes based on the weight sensor fluctuation data and a liquid discharge analysis strategy, and identifying abnormal discharge information of each abnormal discharge pipe based on the material type corresponding to each of the discharge pipes, the target liquid discharge of each material type, and the current actual liquid discharge of each of the discharge pipes; The control module is used to generate various abnormal control and processing schemes for the intelligent liquid discharging machine based on the abnormal discharge information of each abnormal discharge pipe, and based on the various abnormal control and processing schemes for the intelligent liquid discharging machine, through the selection operation of the staff, perform abnormal control processing on the current product to obtain the target product.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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