Beverage mixing and filling control method and system

Abstract

The invention relates to a beverage mixing and filling control method and system, and belongs to the technical field of intelligent equipment manufacturing. The beverage mixing and filling control method comprises the steps that attribute data of a target beverage is acquired; current environment parameters are obtained, and filling parameters are generated based on the attribute data and the environment parameters; based on the filling parameters, the filling duration of the beverage filling machine for filling the target beverage every time is calculated; parameters of a bottle body used for filling the target beverage and attribute parameters of a conveying belt used for conveying the target beverage in the beverage filling machine are obtained; the filling duration, the gas content index, the bottle body parameters and the conveyor belt attribute parameters are input into a preset optimization model, so that the optimization model outputs conveying parameters of the conveyor belt; and adjusting operation parameters of the beverage filling machine based on the filling parameters and the conveying parameters. Therefore, by setting proper filling parameters and conveying parameters, the filling speed of the filling machine is matched with the speed of the conveying belt, so that the production efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent equipment manufacturing, and in particular to a beverage mixing and filling control method and system. BACKGROUND

[0002] In the beverage production industry, a beverage filling machine is the core equipment for filling beverages, which includes a conveying part and a filling part. The conveying part is usually driven by a motor and is responsible for transporting empty bottles to the filling part. The filling part includes a liquid storage tank, a pump body, a filling valve, and a control system, which accurately controls the filling amount and speed through the control system. Through the coordinated work of the conveying part and the filling part, the whole automatic production process of the beverage from the empty bottle to the finished product output is realized. Therefore, the setting of the conveying belt parameters and the filling parameters is crucial, which directly affects the overall efficiency of the production line.

[0003] However, in the actual production process, the filling speed set for different beverage types is affected by many factors, and the conveying parameters of the conveying belt are related to the filling speed of each bottle of beverage. For example, if the filling speed is increased and the conveying belt speed remains unchanged, the filling machine will not have enough empty bottles for filling, resulting in a decrease in efficiency; if the filling speed is reduced and the conveying belt speed remains unchanged, a large number of empty bottles may accumulate in front of the filling machine, causing the production line to be blocked.

[0004] Therefore, how to set appropriate filling parameters and conveying parameters to match the filling speed of the filling machine and the conveying belt speed, thereby improving production efficiency, is a problem to be solved at present. SUMMARY

[0005] In order to set appropriate filling parameters and conveying parameters to match the filling speed of the filling machine and the conveying belt speed, thereby improving production efficiency, the present application provides a beverage mixing and filling control method and system.

[0006] In a first aspect, the present application provides a beverage mixing and filling control method, which adopts the following technical solution: A beverage mixing and filling control method, comprising: obtaining attribute data of a target beverage; wherein the attribute data includes a temperature-viscosity mapping table and / or a gas content index; obtaining current environmental parameters and generating filling parameters based on the attribute data and the environmental parameters; calculating the filling time of the beverage filling machine for filling the target beverage each time based on the filling parameters; obtaining bottle parameters for filling the target beverage and conveying belt attribute parameters for transporting the target beverage in the beverage filling machine; wherein the conveying belt attribute parameters comprise conveying parameter intervals and a conveying belt friction coefficient; the bottle parameters comprise a bottle body friction coefficient, a bottle body three-dimensional model, and a bottle body weight; inputting the filling duration, the gas content index, the bottle parameters, and the conveying belt attribute parameters into a preset optimization model, so that the optimization model outputs conveying parameters of the conveying belt; adjusting operation parameters of the beverage filling machine based on the filling parameters and the conveying parameters.

[0007] Optionally, before the obtaining of the attribute data of the target beverage, the method further comprises: obtaining viscosity values of the target beverage at preset temperature steps; performing interpolation processing on the viscosity values at each temperature step to obtain a temperature-viscosity mapping table; obtaining a target gas content of the target beverage, and generating the gas content index according to the target gas content.

[0008] Optionally, the environmental parameters comprise air pressure, temperature, and humidity. The generating of the filling parameters based on the attribute data and the environmental parameters comprises: obtaining a value interval of the filling parameters, and generating a plurality of first parameter groups based on a first preset gradient within the value interval; inputting the first parameter groups, the attribute data, and the environmental parameters into a filling quantity prediction model to obtain a first predicted filling quantity per unit time; selecting a second parameter group from all the first parameter groups according to the first predicted filling quantity; expanding the second parameter group based on a second preset gradient to obtain a plurality of third parameter groups; wherein the second preset gradient is smaller than the first preset gradient; inputting the third parameter groups, the attribute data, and the environmental parameters into the filling quantity prediction model to obtain a second predicted filling quantity; determining the filling parameters from the second parameter group and the third parameter groups according to the second predicted filling quantity.

[0009] Optionally, the method further comprises a training step of the filling quantity prediction model, the training step comprising: using the first parameter groups as filling parameter features; randomly combining the filling parameter sample data, the attribute data, and the environmental parameters to obtain sample data in a training data set; acquire an actual filling amount of the beverage filling machine in a unit of time under a condition of sample data, and take the actual filling amount as a label in the training data set; input the sample data into a preset support vector machine model to output a predicted filling amount in a unit of time; generate a loss value according to the label and the predicted filling amount, and iteratively train the preset support vector machine model according to the loss value to obtain the filling amount prediction model.

[0010] Optionally, the second parameter group is expanded based on a second preset gradient to obtain a plurality of third parameter groups, including: adjusting one of the parameters in the second parameter group based on the second preset gradient, and keeping the remaining parameters in the second parameter group unchanged to generate at least one of the third parameter groups; iteratively adjusting each parameter in the second parameter group to obtain a plurality of third parameter groups.

[0011] Optionally, the filling time of the beverage filling machine for filling the target beverage each time is calculated based on the filling parameters, including: acquiring a second filling amount in a unit of time corresponding to the filling parameters; based on the second filling amount and a preset standard filling amount of the target beverage, generating a filling time of the beverage filling machine for filling the target beverage each time.

[0012] Optionally, the preset optimization model is a particle swarm optimization model; and the filling time, the gas content index, the bottle body parameter, and the conveyor belt attribute parameter are input into the preset optimization model to make the optimization model output a conveying parameter of the conveyor belt, including: based on the filling time, the gas content index, the bottle body parameter, and the conveyor belt attribute parameter, creating a fitness function; initializing a plurality of particles based on the conveying parameter interval to obtain an initial particle swarm; generating a fitness value of each initial particle in the initial particle swarm based on the fitness function; iteratively updating each initial particle based on the current fitness value and the historical fitness value of each initial particle until an iteration termination condition is reached to generate an individual optimized particle; comparing the fitness values of the individual optimized particles to obtain a comparison result, and determining a global optimized particle from the individual optimized particles according to the comparison result to obtain the conveying parameter.

[0013] Optionally, the fitness function is created based on the filling duration, the gas content index, the bottle parameter, and the conveyor belt attribute parameter, including: generating an efficiency factor according to the filling duration; generating a dynamic friction factor according to the conveyor belt friction coefficient and the bottle body friction coefficient; determining the center of gravity height of the bottle body according to the three-dimensional model of the bottle body; generating a stability factor of the bottle body according to the center of gravity height and the bottle body weight; creating a fitness function according to the efficiency factor, the dynamic friction factor, and the stability factor.

[0014] In a second aspect, the present application provides a beverage mixing and filling control system, which adopts the following technical solution: A beverage mixing and filling control system, comprising: a first attribute acquisition module configured to acquire attribute data of a target beverage; wherein the attribute data includes a temperature-viscosity mapping table and / or a gas content index; a filling parameter generation module configured to acquire current environmental parameters and generate filling parameters based on the attribute data and the environmental parameters; a filling duration generation module configured to calculate a filling duration of the target beverage for each filling of a beverage filling machine based on the filling parameters; a second attribute acquisition module configured to acquire bottle parameters for filling the target beverage and conveyor belt attribute parameters for transporting the target beverage in the beverage filling machine; wherein the conveyor belt attribute parameters include a conveying parameter interval and a conveyor belt friction coefficient; and the bottle parameters include a bottle body friction coefficient, a three-dimensional model of the bottle body, and a bottle body weight; a conveying parameter generation module configured to input the filling duration, the gas content index, the bottle parameters, and the conveyor belt attribute parameters into a preset optimization model, so that the optimization model outputs conveying parameters of the conveyor belt; a parameter setting module configured to adjust operating parameters of the beverage filling machine based on the filling parameters and the conveying parameters.

[0015] In a third aspect, the present application provides a computer device, which adopts the following technical solution: A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program of any of the above methods.

[0016] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium comprising a computer program stored therein, the computer program being loadable into a processor and capable of executing the method of any one of the preceding methods.

[0017] In summary, the present application includes the following beneficial technical effects: Firstly, by means of the temperature-viscosity mapping table and / or the gas content index, the flow characteristics and the gas content state of the target beverage at different temperatures can be obtained, so that the filling process can adapt to the characteristics of the target beverage, and at the same time, the current environmental parameters are obtained to ensure that the filling process can adapt to the variable external environment. Then, the filling parameters are generated based on the attribute data and the environmental parameters, and the filling time required for each filling is calculated. By obtaining the bottle body parameters and the conveyor belt attribute parameters, the stability of the bottle body during transportation can be improved. Finally, the filling time, the gas content index, the bottle body parameters and the conveyor belt attribute parameters are input into the preset optimization model to solve the optimal conveyor belt conveying parameters, so as to obtain the conveyor belt parameters matched with the filling parameters, and by using the filling parameters and the conveying parameters, the accumulation or excessive spacing of the bottle body during conveying can be avoided, the production efficiency of beverage filling is maximized while ensuring the stable operation of the production line. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 is a flow chart of a beverage mixing and filling control method according to an embodiment of the present application; Figure 2 is a flow chart of a method for generating filling parameters according to an embodiment of the present application; Figure 3 is a flow chart of a method for generating a filling amount prediction model according to an embodiment of the present application; Figure 4 is a flow chart of a method for generating conveying parameters according to an embodiment of the present application; Figure 5 is a flow chart of a method for generating a fitness function according to an embodiment of the present application; Figure 6 is a block diagram of a beverage mixing and filling control system according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to make the purposes, technical solutions and advantages of the present application clearer, further detailed description will be made to the present application in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0021] The embodiments of the present application disclose a beverage mixing and filling control method. Referring to Figure 1 A beverage mixing and filling control method, comprising: Step S101: obtaining attribute data of a target beverage; The attribute data includes a temperature-viscosity mapping table and / or a gas content index. As an example, the temperature-viscosity mapping table reveals the viscosity variation of the target beverage at different temperatures, and the viscosity is a physical quantity for measuring the flow resistance of the target beverage. The viscosity of the target beverage decreases with the increase of the temperature, and vice versa. The gas content index reflects the gas content in the target beverage. If the filling parameters are not suitable for the target beverage with high gas content, the target beverage may generate a large amount of foam due to gas release during filling, resulting in inaccurate filling. Therefore, the temperature-viscosity mapping table and the gas content index are important parameters affecting the filling effect.

[0022] Step S102: Obtain the current environmental parameters, and generate filling parameters based on the attribute data and the environmental parameters. The environmental parameters include air pressure, temperature, and humidity.

[0023] Step S103: Calculate the filling time length of the beverage filling machine for filling the target beverage each time based on the filling parameters.

[0024] Step S104: Obtain the bottle parameter for filling the target beverage and the conveyor belt attribute parameter for transporting the target beverage in the beverage filling machine. The conveyor belt attribute parameter includes a conveying parameter interval and a conveyor belt friction coefficient. The bottle parameter includes a bottle body friction coefficient, a bottle body three-dimensional model, and a bottle body weight. Step S105: Input the filling time length, the gas content index, the bottle parameter, and the conveyor belt attribute parameter into a preset optimization model, so that the optimization model outputs a conveying parameter of the conveyor belt. The preset optimization model is a particle swarm optimization model. Step S106: Adjust the operation parameters of the beverage filling machine based on the filling parameters and the conveying parameter.

[0025] In the above embodiment, firstly, the flow characteristics and the gas content of the target beverage at different temperatures can be obtained through the temperature-viscosity mapping table and / or the gas content index, so that the filling process can adapt to the characteristics of the target beverage, and meanwhile, the current environmental parameters are obtained to ensure that the filling process can adapt to the variable external environment. Then, the filling parameters are generated based on the attribute data and the environmental parameters, and the filling time required for each filling is calculated. By obtaining the bottle body parameters and the conveyor belt attribute parameters, the stability of the bottle body during transportation can be improved. Finally, the filling time, the gas content index, the bottle body parameters and the conveyor belt attribute parameters are input into the preset optimization model to solve the optimal conveyor belt conveying parameters, so as to obtain the conveyor belt parameters matched with the filling parameters, and the filling parameters and the conveying parameters are used to avoid the accumulation or excessive spacing of the bottle body during conveying, thereby ensuring the stable operation of the production line and maximizing the production efficiency of the beverage filling.

[0026] As a further embodiment of the beverage mixing and filling control method, step S101 further comprises: Step S201: obtaining the viscosity value of the target beverage at each preset temperature step; As an example, the temperature steps can be set to multiple temperature points from a refrigeration temperature (such as 4°C) to room temperature (such as 25°C) and to a higher temperature (such as 40°C). The viscosity value can be measured using a viscometer or a rheometer.

[0027] Step S202: interpolating the viscosity value at each temperature step to obtain the temperature-viscosity mapping table; The interpolation processing is used to infer the value of the unknown data point from the known data point. Through the interpolation processing, the corresponding points of temperature and viscosity can be obtained to form a continuous curve, thereby obtaining the temperature-viscosity mapping table.

[0028] Step S203: obtaining the target gas content of the target beverage, and generating the gas content index according to the target gas content.

[0029] As an example, the target gas content is determined according to the formula of the target beverage. The gas content index can be a numerical value obtained by converting the target gas content, so as to quantify the gas content.

[0030] Referring to Figure 2 As an embodiment of step S102, step S102 comprises: Step S1021: obtaining the value range of the filling parameters, and generating a plurality of first parameter groups based on a first preset gradient in the value range.

[0031] As an example, the filling parameters can include filling pressure, caliber size of a filling head, and rotation speed of a metering pump, and the value range of the filling parameters can be determined based on historical data, equipment capacity, process requirements, etc., to ensure that the range of possible values of each filling parameter is as comprehensive as possible during the actual filling process.

[0032] As an example, the first preset gradient can be a preset value, and the size of the first preset gradient is used to represent the interval between the filling parameters.

[0033] Step S1022: input the first parameter group, the attribute data, and the environmental parameter into a filling amount prediction model to obtain a first predicted filling amount per unit time; Step S1023: select a second parameter group from all the first parameter groups according to the first predicted filling amount. As an example, to improve the filling efficiency, the first parameter group corresponding to the maximum first predicted filling amount can be selected as the second parameter group.

[0034] Step S1024: expand the second parameter group based on a second preset gradient to obtain a plurality of third parameter groups; The second preset gradient is smaller than the first preset gradient. A larger first preset gradient can cover the entire value range more quickly, but may miss the optimal solution. A smaller second preset gradient allows more detailed search based on the second parameter group.

[0035] As an embodiment of step S1024, step S1024 includes: step S10241: adjusting one of the parameters of the second parameter group based on the second preset gradient, and keeping the remaining parameters of the second parameter group unchanged to generate at least one third parameter group; and step S10242: adjusting each parameter of the second parameter group to obtain a plurality of third parameter groups.

[0036] As an example, each adjustment of one parameter of the second parameter group keeps the other parameters unchanged, thereby generating a new third parameter group. The second preset gradient can be adjusted multiple times until all parameters of the second parameter group have been adjusted at least once, so that the third parameter groups are slightly adjusted based on the second parameter group to explore whether there is a more optimal parameter combination than the second parameter group.

[0037] Step S1025: input the third parameter group, the attribute data, and the environmental parameter into the filling amount prediction model to obtain a second predicted filling amount; Step S1026: determine the filling parameters from the second parameter group and the third parameter groups according to the second predicted filling amount.

[0038] As an example, based on the second predicted filling volume, the parameter combination with the highest predicted filling volume can be selected from the second and third parameter groups as the final filling parameters, thus finding the optimal filling parameter combination within the value range.

[0039] In the above implementation, firstly, by obtaining the value range of the filling parameters and generating several first parameter sets within this range based on a first preset gradient, preliminary screening and combination of the filling parameters are achieved. Next, using a filling volume prediction model, the first parameter sets, attribute data, and environmental parameters are used as input to obtain the first predicted filling volume per unit time, enabling rapid evaluation of the filling effect under different parameter combinations. Then, a second parameter set is selected based on the first predicted filling volume, further optimizing the preliminary screening results and retaining parameter combinations that meet the requirements. Subsequently, the second parameter set is expanded based on the second preset gradient to obtain several third parameter sets. By narrowing the search range and increasing the granularity of the search, the optimal solution can be located more accurately. Then, the filling volume prediction model is used again, with the third parameter sets as input, to obtain the second predicted filling volume. By comparing the second predicted filling volume, the final filling parameters can be determined from the second and third parameter sets, ensuring the accuracy and reliability of the final filling parameters, thereby improving the production efficiency and filling accuracy of the beverage filling machine.

[0040] Reference Figure 3 As a further embodiment of the beverage mixing and filling control method, the beverage mixing and filling control method further includes a training step for the filling volume prediction model, the training step including: Step S301: Use the first parameter group as the filling parameter feature; Step S302: Randomly combine the filling parameter sample data, the attribute data, and the environmental parameters to obtain sample data in the training dataset; In this way, diverse sample data is generated through random combination to improve the model's generalization ability.

[0041] Step S303: Obtain the actual filling volume generated by the beverage filling machine within a unit time under the sample data conditions, and use the actual filling volume as the label in the training dataset; It should be understood that actual filling data is obtained through experiments or actual production, that is, the amount of beverage actually filled by the beverage filling machine per unit time under given sample data conditions. It should be noted that for different types of target beverages, only attribute data needs to be collected again; it is not necessary to repeatedly train the model.

[0042] Step S304: inputting the sample data into a preset support vector machine model to output a predicted filling amount per unit time; As an example, a support vector machine (SVM) is a machine learning algorithm commonly used for classification and regression problems. In this embodiment, the support vector machine is used as a regression model to predict the filling amount per unit time.

[0043] Step S305: generating a loss value according to the label and the predicted filling amount, and iteratively training the preset support vector machine model according to the loss value to obtain the filling amount prediction model.

[0044] In the above embodiment, the sample data obtained by random combination is used to collect the actual filling amount of each sample data to realize the construction of the training data set. Then the preset support vector machine model is trained by using the training data set, that is, the filling amount prediction model can be obtained.

[0045] As an embodiment of step S103, step S103 comprises: Step S1031: obtaining a second filling amount per unit time corresponding to the filling parameter; Step S1032: generating a filling duration of the beverage filling machine for each filling of the target beverage based on the second filling amount and a preset standard filling amount of the target beverage.

[0046] Referring to Figure 4 As an embodiment of step S105, step S105 comprises: Step S1051: creating a fitness function based on the filling duration, the gas content index, the bottle parameter, and the conveyor belt attribute parameter; Step S1052: initializing a plurality of particles according to the conveyor parameter interval to obtain an initial particle swarm.

[0047] As an example, in the particle swarm optimization algorithm, a particle represents a potential solution. The particles are initialized according to the known conveyor parameter interval to ensure that the generated initial particle swarm is within the effective range, i.e. does not exceed the conveyor parameter interval, so as to use the initial particle swarm composed of particles as the starting point of the optimization process.

[0048] Step S1053: generating a fitness value of each initial particle in the initial particle swarm based on the fitness function; Step S1054: iteratively updating each initial particle based on the current fitness value and the historical fitness value of each initial particle until the iteration termination condition is reached to generate an individual optimized particle; As an example, the position and velocity of a particle are adjusted according to its current fitness value and historical fitness value, aiming to guide the particle to move towards a better solution. The iteration process ends until a termination condition is met (e.g., reaching a maximum number of iterations, the fitness value converging to a certain threshold, or the difference between particles being less than a certain predetermined value), resulting in an individual optimized particle.

[0049] Step S1055: Compare the fitness values of each individual optimized particle to obtain a comparison result, and determine a global optimized particle from each individual optimized particle according to the comparison result to obtain the conveying parameters.

[0050] As an example, the parameter combination represented by the global optimized particle is the optimal solution found in the current optimization process. By selecting the global optimized particle, the final conveying parameter combination can be obtained, which will be used to guide the actual filling line operation to achieve higher production efficiency and product quality.

[0051] In the above embodiment, first, by comprehensively considering multiple key factors such as filling time, gas content index, bottle body parameters, and conveying belt attribute parameters, an fitness function is created to evaluate the performance under different conveying parameter combinations. Second, the particle swarm optimization algorithm is used to initialize the particles according to the preset conveying parameter range, form an initial particle swarm, and constantly approach the optimal solution by iteratively updating the position and velocity of the particles until the iteration termination condition is reached. Finally, the fitness values of each individual optimized particle are compared to determine the global optimized particle, thereby obtaining the optimal conveying parameter combination.

[0052] Reference Figure 5 As an embodiment of step S1051, step S1051 includes: Step S10511: generating an efficiency factor according to the filling time.

[0053] As an example, the shorter the filling time, the more filling operations can be completed per unit time, thereby improving production efficiency. The filling time is converted into an efficiency factor to evaluate the production efficiency of the filling line under different conveying parameter combinations.

[0054] Step S10512: generating a kinetic friction factor according to the conveying belt friction coefficient and the bottle body friction coefficient; As an example, the kinetic friction factor reflects the resistance an object experiences when sliding on the surface of another object. If the friction coefficient is too small, the bottle may slide on the conveying belt. Therefore, a kinetic friction factor is calculated according to the conveying belt friction coefficient and the bottle body friction coefficient to evaluate the stability of the bottle on the conveying belt under the conveying parameter combination.

[0055] Step S10513: determining the center of gravity height of the bottle body according to the three-dimensional model of the bottle body; As an example, the higher the center of gravity, the more likely the bottle will tip over during transport. The center of gravity of the bottle is precisely calculated through a three-dimensional model of the bottle to assess the stability performance of the bottle under different transport parameters.

[0056] Step S10514: generating a stability factor of the bottle according to the center of gravity and the weight of the bottle; Step S10515: creating a fitness function according to the efficiency factor, the dynamic friction factor, and the stability factor.

[0057] In the above embodiment, the fitness function combines the efficiency factor, the dynamic friction factor, and the stability factor to generate the transport parameters that improve efficiency while maintaining stability.

[0058] In addition, the embodiments of the present disclosure disclose a beverage mixing and filling control system. The beverage mixing and filling control system can be applied to a computer device, which is an architectural schematic diagram of a computer device for implementing the above method provided by the embodiments of the present disclosure. In the embodiments, the computer device can include a beverage mixing and filling control system, a machine-readable storage medium, and a processor.

[0059] In the embodiments, the machine-readable storage medium and the processor can be located in the computer device and separately arranged. The machine-readable storage medium can also be independent of the computer device and accessed by the processor. The beverage mixing and filling control system can include a plurality of functional modules stored in the machine-readable storage medium, for example, each software functional module included in the beverage mixing and filling control system. When the processor executes the computer program corresponding to the software functional module in the beverage mixing and filling control system, the beverage mixing and filling control system provided by the foregoing method embodiments is implemented.

[0060] In the embodiments, the computer device can include one or more processors. The processor can process information and / or data related to a service request to perform one or more functions described in the present disclosure. In some embodiments, the processor can include one or more processing engines (e.g., a single-core processor or a multi-core processor). For example only, the processor can include one or more hardware processors, such as one of a central processing unit (CPU), an application-specific integrated circuit (ASIC), an application-specific instruction-set processor (ASIP), a graphics processing unit (GPU), a physics processing unit (PPU), a digital signal processor (DSP), a field-programmable gate array (FPGA), a programmable logic device (PLD), a controller, a microcontroller unit, a reduced instruction set computer (RISC), a microprocessor, and the like, or a combination thereof.

[0061] The machine-readable storage medium can store data and / or instructions. In some embodiments, the machine-readable storage medium can store obtained data or information. In some embodiments, the machine-readable storage medium can store data and / or instructions for execution or use by the computer device, which can implement the exemplary methods described in the present application by executing or using the data and / or instructions. In some embodiments, the machine-readable storage medium can include a mass storage, a removable storage, a volatile read / write memory, a read-only memory (ROM), or the like, or any combination of the above examples. The exemplary mass storage can include a magnetic disk, an optical disk, a solid-state disk, or the like. The exemplary removable storage can include a flash drive, a floppy disk, an optical disk, a memory card, a compact disk, a magnetic tape, or the like. The exemplary volatile read / write memory can include a random access memory (RAM). The exemplary random access memory can include a dynamic RAM, a double data rate synchronous dynamic RAM, a static RAM, a thyristor RAM, and a zero-capacitor RAM, or the like. The exemplary ROM can include a mask ROM, a programmable ROM, an erasable programmable ROM, an electronically erasable programmable ROM, a compact disk ROM, and a digital versatile disk ROM, or the like.

[0062] The computer device can include one or more software function modules. The software function modules can be stored in the machine-readable storage medium as programs or instructions, and when executed by the corresponding processor, the software function modules can be used to implement the above-mentioned methods, such as when executed by the processor of the unmanned aerial vehicle, the software function modules can be used to implement the method steps executed by the unmanned aerial vehicle, or when executed by the computer device, the software function modules can be used to implement the method steps executed by the computer device.

[0063] Referring to the drawings in detail Figure 6 The embodiments of the present application disclose a beverage mixing and filling control system. The beverage mixing and filling control system comprises: a first attribute acquisition module configured to acquire attribute data of a target beverage; wherein the attribute data comprises a temperature-viscosity mapping table and / or a gas content index; a filling parameter generation module configured to acquire current environmental parameters and generate filling parameters based on the attribute data and the environmental parameters; a filling duration generation module configured to calculate a filling duration of the target beverage for each filling of a beverage filling machine based on the filling parameters; a second attribute acquisition module configured to acquire bottle body parameters for filling the target beverage and conveyor belt attribute parameters for transporting the target beverage in the beverage filling machine; wherein the conveyor belt attribute parameters comprise a conveying parameter interval and a conveyor belt friction coefficient; and the bottle body parameters comprise a bottle body friction coefficient, a bottle body three-dimensional model, and a bottle body weight; The transmission parameter generation module is configured to input the filling duration, the gas content index, the bottle parameter, and a conveyor belt attribute parameter into a preset optimization model, so that the optimization model outputs a transmission parameter of the conveyor belt. The parameter setting module is configured to adjust an operation parameter of the beverage filler based on the filling parameter and the transmission parameter.

[0064] The beverage mixing and filling control system provided in the present application can implement the beverage mixing and filling control method described above, and the specific working process of the beverage mixing and filling control system can refer to the corresponding process in the method embodiments described above.

[0065] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0066] The present application also discloses a computer readable storage medium, a computer readable storage medium comprising a computer program stored therein, the computer program being capable of being loaded and executed by a processor to perform any of the methods described above.

[0067] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division, and there can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some communication interface, device or unit, and can be electrical, mechanical or other forms.

[0068] In addition, each functional module in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0069] The above are the preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Any feature disclosed in the specification (including the abstract and the drawings) can be replaced by other equivalent or similar features, unless specifically stated. That is, each feature is only an example of a series of equivalent or similar features, unless specifically stated.

Claims

1. A beverage mixing and filling control method, characterized by, The method comprises the following steps: obtaining attribute data of a target beverage; wherein the attribute data comprises a temperature-viscosity mapping table and / or a gas content index; obtaining current environmental parameters and generating filling parameters based on the attribute data and the environmental parameters; calculating a filling time length of each filling of the target beverage by the beverage filling machine based on the filling parameters; obtaining bottle parameters for filling the target beverage and conveying belt attribute parameters for transporting the target beverage in the beverage filling machine; wherein the conveying belt attribute parameters comprise a conveying parameter interval and a conveying belt friction coefficient; and the bottle parameters comprise a bottle body friction coefficient, a three-dimensional model of the bottle body, and a bottle body weight; inputting the filling time length, the gas content index, the bottle parameters, and the conveying belt attribute parameters into a preset optimization model to make the optimization model output conveying parameters of the conveying belt; adjusting operation parameters of the beverage filling machine based on the filling parameters and the conveying parameters.

2. The method of claim 1, wherein, Before the step of obtaining the attribute data of the target beverage, the method further comprises the following steps: obtaining viscosity values of the target beverage at each preset temperature step; performing interpolation processing on the viscosity values at each temperature step to obtain the temperature-viscosity mapping table; obtaining a target gas content of the target beverage and generating the gas content index according to the target gas content.

3. The method of claim 1, wherein, The environmental parameters comprise air pressure, temperature, and humidity. The step of generating the filling parameters based on the attribute data and the environmental parameters comprises the following steps: obtaining a value interval of the filling parameters and generating a plurality of first parameter groups based on a first preset gradient within the value interval; inputting the first parameter groups, the attribute data, and the environmental parameters into a filling amount prediction model to obtain a first predicted filling amount per unit time; selecting a second parameter group from all the first parameter groups according to the first predicted filling amount; expanding the second parameter group based on a second preset gradient to obtain a plurality of third parameter groups; wherein the second preset gradient is smaller than the first preset gradient; inputting the third parameter groups, the attribute data, and the environmental parameters into the filling amount prediction model to obtain a second predicted filling amount; determining the filling parameters from the second parameter group and the third parameter groups according to the second predicted filling amount.

4. The method of claim 3, wherein, The method further comprises a training step of the filling amount prediction model, which comprises the following steps: using the first parameter groups as filling parameter features; randomly combining the filling parameter sample data, the attribute data, and the environmental parameters to obtain sample data in a training data set; obtaining an actual filling amount per unit time generated by the beverage filling machine under the condition of the sample data and using the actual filling amount as a label in the training data set; inputting the sample data into a preset support vector machine model to output a predicted filling amount per unit time; generating a loss value according to the label and the predicted filling amount, and iteratively training the preset support vector machine model according to the loss value to obtain the filling amount prediction model.

5. The method of claim 4, wherein, The step of expanding the second parameter group based on the second preset gradient to obtain a plurality of third parameter groups comprises the following steps: adjusting one of the parameters of the second parameter set based on a second preset gradient, and keeping the remaining parameters of the second parameter set unchanged, to generate at least one third parameter set; iteratively adjusting each parameter of the second parameter set to obtain a plurality of third parameter sets.

6. The method of claim 3, wherein, The filling time length of the beverage filling machine for filling the target beverage each time is calculated based on the filling parameter, including: obtaining a second filling amount per unit time corresponding to the filling parameter; generating a filling time length of the beverage filling machine for filling the target beverage each time based on the second filling amount and a preset standard filling amount of the target beverage.

7. The method of claim 1, wherein, The preset optimization model is a particle swarm optimization model; and the filling time length, the gas content index, the bottle body parameter, and the conveyor belt attribute parameter are input into the preset optimization model, so that the optimization model outputs a conveying parameter of the conveyor belt, including: creating a fitness function based on the filling time length, the gas content index, the bottle body parameter, and the conveyor belt attribute parameter; initializing a plurality of particles according to the conveying parameter interval to obtain an initial particle swarm; generating a fitness value of each initial particle in the initial particle swarm based on the fitness function; iteratively updating each initial particle based on the current fitness value and the historical fitness value of each initial particle until an iteration termination condition is reached to generate individual optimization particles; comparing the fitness values of the individual optimization particles to obtain a comparison result, and determining a global optimization particle from the individual optimization particles according to the comparison result to obtain the conveying parameter.

8. The method of claim 7, wherein, The fitness function is created based on the filling time length, the gas content index, the bottle body parameter, and the conveyor belt attribute parameter, including: generating an efficiency factor according to the filling time length; generating a dynamic friction factor according to the friction coefficient of the conveyor belt and the friction coefficient of the bottle body; determining the height of the center of gravity of the bottle body according to the three-dimensional model of the bottle body; generating a stability factor of the bottle body according to the height of the center of gravity and the weight of the bottle body; creating a fitness function according to the efficiency factor, the dynamic friction factor, and the stability factor.

9. A beverage mixing and filling control system, characterized by, including: a first attribute acquisition module configured to acquire attribute data of a target beverage; wherein the attribute data includes a temperature-viscosity mapping table and / or a gas content index; a filling parameter generation module configured to acquire a current environmental parameter and generate a filling parameter based on the attribute data and the environmental parameter; a filling time length generation module configured to calculate a filling time length of a beverage filling machine for filling the target beverage each time based on the filling parameter; a second attribute acquisition module configured to acquire a bottle body parameter for filling the target beverage and a conveyor belt attribute parameter for transporting the target beverage in the beverage filling machine; wherein the conveyor belt attribute parameter includes a conveying parameter interval and a friction coefficient of the conveyor belt; and the bottle body parameter includes a friction coefficient of a bottle body, a three-dimensional model of the bottle body, and a weight of the bottle body. The transmission parameter generation module is configured to input the filling duration, the gas content index, the bottle parameter and a conveyor belt attribute parameter into a preset optimization model, so that the optimization model outputs a transmission parameter of the conveyor belt; The parameter setting module is configured to adjust an operation parameter of the beverage filling machine based on the filling parameter and the transmission parameter.

10. A computer device, comprising: A computer program product comprising a memory, a processor and a computer program stored on the memory and loadable on the processor, the processor being configured to execute the computer program of any one of claims 1-8.

Images