Tomato sauce quality detection method and device, terminal, storage medium and program product

Abstract

The invention discloses a tomato sauce quality detection method and device, a terminal, a storage medium and a computer program product, and the method comprises the steps: controlling the water flow of a water cooling system through employing a BP neural network and a fuzzy PID control algorithm according to the tomato sauce flow, the cooling water temperature, the main pipe temperature and the environment temperature, therefore, the temperature of the tomato sauce in the detection area can be controlled; under the condition that the temperature of the tomato sauce in the detection area reaches the target temperature, whether the quality of the tomato sauce is qualified or not is determined according to the quality parameters of the tomato sauce, and if yes, the main pipeline is communicated with a qualified product pipeline; and if not, the main pipeline is communicated with the unqualified product pipeline. According to the scheme, the branch pipeline and the water cooling system are arranged on the main pipeline of the tomato sauce production process, the electric three-way valve is arranged at the output end of the main pipeline, the water flow of the water cooling system and the opening and closing mode of the electric three-way valve are controlled, online detection and control over the tomato sauce quality are achieved, and the production efficiency can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of ketchup quality detection, and specifically relates to a ketchup quality detection method, device, terminal, storage medium and computer program product, and more particularly to a process improvement and optimization control method, device, terminal, storage medium and computer program product of a ketchup quality online detection system. Background Art

[0002] With the development of ketchup technology, the improvement of control technology and the increase of labor costs, the demand for automation, intelligence and unmanned production in enterprises is becoming more and more urgent. At present, in the control system of ketchup production line, the process transformation and upgrading of ketchup output is emphasized, but the process improvement of ketchup quality detection is ignored.

[0003] At present, the quality inspection of tomato sauce is done by manually taking samples of tomato sauce, waiting for it to cool naturally to the inspection temperature, and then manually inspecting the quality parameters of the tomato sauce through testing instruments. The qualified batches of products enter the sales link, and the unqualified batches are manually returned to the processing flow. At least there is a problem of low production efficiency.

[0004] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The object of the present invention is to provide a tomato sauce quality detection method, device, terminal, storage medium and computer program product to solve the problem of low production efficiency in manual tomato sauce quality detection. By arranging a branch pipeline and a water cooling system on the main pipeline of the tomato sauce production process, arranging an electric three-way valve at the output end of the main pipeline, and controlling the water flow of the water cooling system and the opening and closing mode of the electric three-way valve, the tomato sauce quality can be detected and controlled online, thereby improving the production efficiency.

[0006] The present invention provides a tomato sauce quality detection method, which is applied to the online detection of tomato sauce quality and the control of tomato sauce output channel in the tomato sauce production process; the tomato sauce inspection process has a main pipeline, a qualified product pipeline and a non-qualified product pipeline; a bypass pipeline is arranged in the main pipeline as a branch pipeline; a water cooling system is arranged between the inlet and the outlet of the branch pipeline, and the outlet of the branch pipeline is used as a detection area; a three-way switch is arranged at the outlet of the main pipeline, and the three-way switch is connected to the qualified product pipeline and the non-qualified product pipeline respectively; the tomato sauce quality detection method comprises: obtaining the flow rate of tomato sauce in the branch pipeline, which is recorded as the tomato sauce flow rate; obtaining the temperature of the water cooling system, which is recorded as the cooling water temperature; obtaining the temperature of the main pipeline, which is recorded as the main pipe temperature; obtaining the The temperature of the detection area is recorded as the ambient temperature; and, from the detection area, the quality parameters of the ketchup in the branch pipe are obtained; according to the ketchup flow rate, the cooling water temperature, the main pipe temperature, and the ambient temperature, the BP neural network and the fuzzy PID control algorithm are used to control the water flow rate of the water cooling system to achieve the control of the ketchup temperature in the detection area; when the ketchup temperature in the detection area reaches the target temperature, whether the quality of the ketchup is qualified is determined according to the quality parameters of the ketchup; if it is determined that the quality of the ketchup is qualified, the three-way switch is controlled to connect the main pipe with the qualified product pipe; if it is determined that the quality of the ketchup is unqualified, the three-way switch is controlled to connect the main pipe with the unqualified product pipe.

[0007] In some embodiments, the water flow rate of the water cooling system is controlled according to the ketchup flow rate, the cooling water temperature, the main pipe temperature, and the ambient temperature using a BP neural network and a fuzzy PID control algorithm, including: calculating a target water flow rate of the water cooling system using a BP neural network according to the ketchup flow rate, the cooling water temperature, the main pipe temperature, and the ambient temperature; and controlling the water flow rate of the water cooling system using a fuzzy PID control algorithm according to the target water flow rate of the water cooling system and the cooling water flow rate of the water cooling system.

[0008] In some embodiments, the target water flow of the water cooling system is calculated using a BP neural network based on the ketchup flow, the cooling water temperature, the main pipe temperature, and the ambient temperature, including: using the BP neural network to pre-train a water demand prediction model; and using the water demand prediction model to calculate the water demand as the target water flow of the water cooling system based on the ketchup flow, the cooling water temperature, the main pipe temperature, and the ambient temperature.

[0009] In some embodiments, the water cooling system has a condenser, a cooling pool, a frequency converter and a water pump; the water pump includes: a circulating pump; the cooling pool is connected to the water inlet of the condenser via a water outlet pipe, and the circulating pump is arranged on the water outlet pipe of the cooling pool; the frequency converter is used to control the speed of the water pump; the condenser is arranged on the branch pipe; according to the target water flow of the water cooling system and the cooling water flow of the water cooling system, the water flow of the water cooling system is controlled by a fuzzy PID control algorithm, including: according to the target water flow of the water cooling system and the difference between the cooling water flow of the water cooling system, a fuzzy PID control algorithm is used to calculate a PID control amount; using the PID control amount, the speed of the circulating pump is controlled via the frequency converter to control the water flow of the water cooling system.

[0010] In some embodiments, determining whether the quality of the tomato sauce is qualified based on the quality parameters of the tomato sauce includes: determining whether the quality parameters of the tomato sauce are within a preset quality parameter range; if it is determined that the quality parameters of the tomato sauce are within the preset quality parameter range, determining that the quality of the tomato sauce is qualified; if it is determined that the quality parameters of the tomato sauce are not within the preset quality parameter range, determining that the quality of the tomato sauce is unqualified.

[0011] In some embodiments, the quality of the tomato paste includes at least one of the pH value, viscosity and color of the tomato paste in the branch pipe.

[0012] Matching the above method, the present invention provides a tomato sauce quality detection device on the other hand, which is used for online detection of tomato sauce quality and control of tomato sauce output channel in tomato sauce production process; the tomato sauce review process has a main pipeline, a qualified product pipeline and a failed product pipeline; a bypass pipeline is arranged in the main pipeline as a branch pipeline; a water cooling system is arranged between the inlet and outlet of the branch pipeline, and the outlet of the branch pipeline is used as a detection area; a three-way switch is arranged at the outlet of the main pipeline, and the three-way switch is connected to the qualified product pipeline and the failed product pipeline respectively; the tomato sauce quality detection device includes: an acquisition unit, which is configured to acquire the flow rate of tomato sauce in the branch pipeline, recorded as the tomato sauce flow rate; acquire the temperature of the water cooling system, recorded as the cooling water temperature; acquire the temperature of the main pipeline, recorded as the main pipe temperature; acquire the temperature of the detection area, recorded as the ambient temperature ambient temperature; and, obtaining the quality parameters of the ketchup in the branch pipe from the detection area; the control unit is configured to control the water flow of the water cooling system by using a BP neural network and a fuzzy PID control algorithm according to the ketchup flow, the cooling water temperature, the main pipe temperature, and the ambient temperature, so as to control the ketchup temperature of the detection area; the control unit is further configured to determine whether the quality of the ketchup is qualified according to the quality parameters of the ketchup when the ketchup temperature in the detection area reaches the target temperature; the control unit is further configured to control the three-way switch to connect the main pipe with the qualified product pipe if it is determined that the quality of the ketchup is qualified; the control unit is further configured to control the three-way switch to connect the main pipe with the unqualified product pipe if it is determined that the quality of the ketchup is unqualified.

[0013] Matching the above device, the present invention provides a terminal on another aspect, including: the ketchup quality detection device mentioned above.

[0014] In accordance with the above method, the present invention provides a storage medium on another aspect, wherein the storage medium includes a stored program, wherein when the program is executed, the device where the storage medium is located is controlled to execute the above-mentioned ketchup quality detection method.

[0015] Matching the above method, the present invention provides a computer program product on another aspect, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned tomato sauce quality detection method are implemented.

[0016] Therefore, the scheme of the present invention is to set a bypass pipe as a branch pipe on the main pipe of the ketchup production process, set a water cooling system on the branch pipe for cooling, the water cooling system is set between the inlet and outlet of the branch pipe, the outlet of the branch pipe is an online detection area, an electric three-way valve is set at the output port of the main pipe, and a qualified product pipe and a non-qualified product pipe are set at the rear end of the electric three-way valve; the main pipe temperature is obtained from the main pipe, the cooling water temperature is obtained from the cooling water system, the ambient temperature and the ketchup flow are obtained from the online detection area, and the target flow of the water cooling system is calculated according to the ketchup flow, the main pipe temperature, the detection area temperature, and the cold water temperature based on the target flow calculation model obtained by pre-training using the BP neural network, and the target flow of the water cooling system is calculated. According to the difference between the target flow and the actual flow of the water cooling system, the rotation speed of the water pump in the water cooling system is controlled by using PID fuzzy control to control the ketchup temperature in the detection area; the pH value, viscosity and chromaticity of the ketchup are detected in the detection area to determine whether the quality of the ketchup is qualified, if qualified, the electric three-way valve is controlled to connect the main pipeline with the qualified product pipeline, if unqualified, the electric three-way valve is controlled to connect the main pipeline with the unqualified product pipeline; thus, by arranging a branch pipeline and a water cooling system on the main pipeline of the ketchup production process, arranging an electric three-way valve at the output end of the main pipeline, and controlling the water flow of the water cooling system and the opening and closing mode of the electric three-way valve, online detection and control of the ketchup quality can be achieved, which can improve production efficiency.

[0017] Other features and advantages of the present invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the present invention.

[0018] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of a process of an embodiment of a method for detecting quality of ketchup of the present invention; Figure 2 A flow chart of an embodiment of controlling the water flow of the water cooling system by using a BP neural network and a fuzzy PID control algorithm in the method of the present invention; Figure 3 A flow chart of an embodiment of the method of the present invention for calculating the target water flow rate of the water cooling system by using a BP neural network; Figure 4 A flow chart of an embodiment of controlling the water flow of the water cooling system by using a fuzzy PID control algorithm in the method of the present invention; Figure 5 It is a flow chart of an embodiment of the method of the present invention for determining whether the quality of the tomato sauce is qualified; Figure 6It is a structural schematic diagram of an embodiment of a ketchup quality detection device of the present invention; Figure 7 It is a structural schematic diagram of a ketchup process pipeline of an embodiment of the invention; Figure 8 A schematic diagram of the structure of a water cooling system according to an embodiment of the invention; Fig. 9 It is a schematic diagram of the structure of the overall control system of an embodiment of the invention; Fig.10 A schematic diagram of the structure of a BP neural network prediction model according to an embodiment of the invention; Fig.11 It is a control block diagram of the fuzzy PID algorithm of an embodiment of the invention; Fig.12 The fuzzy rule table is Table 1.

[0020] In conjunction with the accompanying drawings, the reference numerals in the embodiments of the present invention are as follows: 102 - acquisition unit; 104 - control unit. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0022] Considering that the manual method of tomato sauce quality detection has at least the problem of low production efficiency, it is impossible to find unqualified products in real time, and the production cannot be adjusted in time, resulting in low production efficiency and the inability to realize the automation, intelligence and unmanned online detection and control of tomato sauce quality.

[0023] Therefore, the solution of the present invention proposes a tomato sauce quality online detection and control solution, specifically a tomato sauce quality online detection system and control method thereof, which aims to solve the problem of low production efficiency in manual tomato sauce quality detection to a certain extent. Among them, the tomato sauce quality online detection and control system is composed of multiple systems or devices, and is an automatic control system that can complete the online detection of tomato quality.

[0024] According to an embodiment of the present invention, a method for detecting the quality of ketchup is provided. Figure 1The flowchart of an embodiment of the method of the present invention is shown. The tomato sauce quality detection method is applied to the online detection of tomato sauce quality and the control of the tomato sauce output channel in the tomato sauce production process; the tomato sauce inspection process has a main pipeline, a qualified product pipeline and a non-qualified product pipeline; a bypass pipeline is arranged in the main pipeline as a branch pipeline; a water cooling system is arranged between the inlet and outlet of the branch pipeline, and the outlet of the branch pipeline is used as the detection area; a three-way switch (such as an electric three-way valve) is arranged at the outlet of the main pipeline, and the three-way switch is connected to the qualified product pipeline and the non-qualified product pipeline respectively; Figure 7 The structure diagram of the ketchup process pipeline of the embodiment of the invention is as follows: Figure 7 As shown, the branch pipe is used as a bypass pipe of the main pipe, and the bypass pipe passes through the condenser. The condenser has a water inlet and a water outlet. An online detection area is set at the output side of the branch pipe; an electric three-way valve is set at the output end of the main pipe, and the rear part of the electric three-way valve has two paths: one is a qualified product pipeline, and the other is a failed product pipeline. In the scheme of the present invention, Figure 1 As shown, the ketchup quality detection method includes: step S110 to step S150.

[0025] In step S110, the flow rate of ketchup in the branch pipe is obtained, recorded as the ketchup flow rate; the temperature of the water cooling system is obtained, recorded as the cooling water temperature; the temperature of the main pipe is obtained, recorded as the main pipe temperature; the temperature of the detection area is obtained, recorded as the ambient temperature; and the quality parameters of the ketchup in the branch pipe are obtained from the detection area.

[0026] At step S120, the water flow of the water cooling system is controlled according to the ketchup flow, the cooling water temperature, the main pipe temperature, and the ambient temperature using a BP neural network and a fuzzy PID control algorithm to achieve control of the ketchup temperature in the detection area.

[0027] At step S130, when the ketchup temperature in the detection area reaches the target temperature, it is determined whether the quality of the ketchup is qualified according to the quality parameters of the ketchup.

[0028] At step S140, if it is determined that the quality of the ketchup is qualified, the three-way switch is controlled to connect the main pipeline with the qualified product pipeline.

[0029] At step S150, if it is determined that the quality of the ketchup is unqualified, the three-way switch is controlled to connect the main pipeline with the unqualified product pipeline.

[0030] The scheme of the present invention provides an online detection and scheme of tomato sauce quality, including the design of the online detection process of tomato sauce quality. The tomato sauce quality detection is a method based on back propagation neural network (BP) neural network prediction and fuzzy PID control (Proportional-Integral-Derivative Control) to predict the water demand when the branch pipe temperature is controlled. When the temperature of the main pipe of the tomato sauce is not reduced, the unqualified products detected online are directly returned to the previous process and re-made, thereby achieving the purpose of energy saving and emission reduction, and solving the problem of low efficiency of the existing tomato sauce production in the offline detection process. Among them, the purpose of cooling by using a cooling system is to meet the needs of branch pipe detection. There is no need to cool the main pipe. The qualified products can be naturally cooled after being barreled, and the unqualified products are returned to the original process to reduce energy consumption.

[0031] In some embodiments, the specific process of controlling the water flow of the water cooling system in step S120 according to the ketchup flow, the cooling water temperature, the main pipe temperature, and the ambient temperature using a BP neural network and a fuzzy PID control algorithm is described in the following exemplary embodiment.

[0032] Combine the following Figure 2 The method of the present invention is shown as a flow chart of an embodiment of using BP neural network and fuzzy PID control algorithm to control the water flow of the water cooling system, further illustrating the specific process of using BP neural network and fuzzy PID control algorithm to control the water flow of the water cooling system in step S120, including: step S210 to step S220.

[0033] Step S210, calculating the target water flow of the water cooling system using a BP neural network according to the ketchup flow, the cooling water temperature, the main pipe temperature, and the ambient temperature.

[0034] Step S220, controlling the water flow of the water cooling system by using a fuzzy PID control algorithm according to the target water flow of the water cooling system and the cooling water flow of the water cooling system.

[0035] Based on BP neural network and fuzzy PID control: After defuzzification, the PID adopts incremental PID. Through practice, the overall effect of BP neural network and fuzzy PID control is better than the traditional PID control algorithm. The scheme of the present invention is suitable for BP neural network and fuzzy algorithm. The specific overall control block diagram is shown in Fig. 9 .like Fig. 9 As shown, the water storage capacity is obtained based on the ketchup flow rate, the ketchup main pipe temperature, the detection area temperature, and the cooling water temperature.

[0036] In some embodiments, the specific process of calculating the target water flow of the water cooling system in step S210 using a BP neural network according to the ketchup flow, the cooling water temperature, the main pipe temperature, and the ambient temperature is described in the following exemplary embodiment.

[0037] Combine the following Figure 3 The method of the present invention is shown as a flow chart of an embodiment of using the BP neural network to calculate the target water flow of the water cooling system, further illustrating the specific process of using the BP neural network to calculate the target water flow of the water cooling system in step S210, including: steps S310 to S320.

[0038] Step S310: using BP neural network to pre-train and obtain a water demand prediction model.

[0039] Step S320, according to the ketchup flow, the cooling water temperature, the main pipe temperature, and the ambient temperature, the water demand prediction model is used to calculate the water demand as the target water flow of the water cooling system.

[0040] The process of constructing the mathematical model of the water pump includes: constructing the mathematical model of the water pump (centrifugal pump) according to the actual production capacity on site. The rated speed of the water pump is 3000r / min, the rated voltage is 380V, the rated power is 3kW, the pump head is 10m, and the power frequency is 50Hz. Since the water pump control speed regulation system uses an asynchronous motor for speed regulation, the motor output speed transfer function is: ,in, .

[0041] Where: n is the rotation speed; Ka is the fan output frequency; τ is the time constant; f is the inverter frequency; T is the inverter delay time.

[0042] At rated voltage and frequency, the flow rate of the pump is 5m 3 / min, thus the relationship between water demand R and motor speed n can be obtained: .

[0043] Water demand prediction: The water demand described in the scheme of the present invention refers to the water demand flow rate, not the total water demand. The water demand is mainly affected by the flow rate of ketchup, the temperature of the main pipe, the temperature of the detection area, and the temperature of the cooling water. These parameters are highly coupled and the temperature control is a system with a large lag link, which is difficult to control, so it is necessary to predict the water demand in advance. Based on the characteristics of the BP neural network, which is simple, easy to implement and highly accurate, the network is used to predict the water demand for cooling the ketchup branch pipe. The BP neural network model adopts a typical three-layer structure (input layer, hidden layer, output layer). The flow rate of ketchup, the temperature of the ketchup main pipe, the ambient temperature (i.e., the temperature of the detection area), and the temperature of the cooling water have a greater impact on the water demand, so the data of the above four parameters are passed into the input model. The number of input layer nodes is 4; the number of output layer nodes is 1; by observing the learning curve method, the number of hidden layer nodes is 6; because the ReLu function has strong nonlinear fitting ability, there is no gradient vanishing problem, and maximizes the screening ability of neurons, the ReLu function is selected as the activation function to construct a BP neural network prediction model, as shown in the schematic diagram. Fig.10 . Fig.10 The schematic diagram of the structure of the BP neural network prediction model of the embodiment of the invention is shown in FIG. The water demand is obtained by learning and training the BP neural network prediction model (the training set and the test set are derived from the historical data of the field).

[0044] In some embodiments, the water cooling system comprises a condenser, a cooling pool, a frequency converter and a water pump; the water pump comprises: a circulation pump; the cooling pool is connected to the water inlet of the condenser via a water outlet pipe, and the circulation pump is arranged on the water outlet pipe of the cooling pool; the frequency converter is used to control the speed of the water pump; the condenser is arranged on the branch pipe; Figure 8 Schematic diagram of the structure of the water cooling system of the embodiment of the invention. Figure 8 As shown, the water cooling system includes a condenser, a cooling pool, a water supply pump and a circulation pump. The water supply pump is arranged on the water inlet pipe of the cooling pool, the circulation pump is arranged on the water outlet pipe of the cooling pool, the water outlet pipe of the cooling pool is connected to the water inlet of the condenser, and the water outlet of the condenser is connected to the water return port of the cooling pool.

[0045] For the specific process of controlling the water flow rate of the water cooling system in step S220 according to the target water flow rate of the water cooling system and the cooling water flow rate of the water cooling system by using a fuzzy PID control algorithm, please refer to the following exemplary description.

[0046] Combine the following Figure 4 The flowchart of an embodiment of the method of the present invention using a fuzzy PID control algorithm to control the water flow of the water cooling system further illustrates the specific process of using the fuzzy PID control algorithm to control the water flow of the water cooling system in step S220, including: steps S410 to S420.

[0047] Step S410: Calculate a PID control amount using a fuzzy PID control algorithm according to a target water flow rate of the water cooling system and a difference between the cooling water flow rates of the water cooling system.

[0048] Step S420, using the PID control amount to control the rotation speed of the circulation pump via the frequency converter to control the water flow of the water cooling system.

[0049] Fig. 9 Schematic diagram of the overall control system of the embodiment of the invention. Fig. 9 As shown, the mathematical model of the water pump predicts the water demand through the BP neural network to obtain the water demand; the difference between the water demand and the cooling water flow Q at the output end of the water pump is processed by fuzzy PID to control the inverter to adjust the speed of the water pump.

[0050] Fuzzy control: Fuzzy control is based on traditional control and combines fuzzy control theory. It uses the experience of experts and the operating habits of operators to convert control rules into control rules and store them in the fuzzy rule library. According to the difference (deviation) and deviation change rate generated by the comparison between the water demand of the BP neural network prediction model and the actual water flow, fuzzification, fuzzy reasoning, and defuzzification are performed successively to dynamically optimize PID parameters, thereby obtaining a fuzzy PID algorithm. Its control block diagram is shown below: Fig.11 shown. Fig.11 It is a control block diagram of the fuzzy PID algorithm of an embodiment of the invention.

[0051] The inputs of the two fuzzy controls are the deviation e(t) and the deviation change rate obtained by the difference between the predicted value of water demand and the actual detected water flow. de(t) / dt , where 2 input parameters and 3 output parameters are described by 7 fuzzy subsets, namely NL (negative large), NM (negative medium), NS (negative small), CO (appropriate), PS (positive small), PM (positive medium), and PL (positive large). Fuzzy rules are formulated according to the experience of experts and operators and system requirements, as shown in Table 1. Fig.12 The fuzzy rule table is Table 1. Its quantization domain is {-3,-2,-1,0,1,2,3}. The basic domain of water demand deviation is [-1,1], the proportional factor K1 is 2, the basic domain of water demand deviation change rate is [-0.2,0.2], the proportional factor K2 is 8; the proportional coefficient change value ΔKp, the basic domain is [-1,1], the proportional factor K3 is 1 / 6; the integral coefficient change value ΔKi: the basic domain is [-0.3,0.3], the proportional factor K4 is 1 / 12; the differential coefficient change value ΔKd. According to the water demand, the parameters ΔKp, ΔKi and ΔKd can be dynamically calculated.

[0052] In some embodiments, the specific process of determining whether the quality of the tomato sauce is qualified according to the quality parameters of the tomato sauce in step S130 is described in the following exemplary embodiment.

[0053] Combine the following Figure 5 The flowchart of an embodiment of determining whether the quality of the tomato sauce is qualified in the method of the present invention is shown, which further illustrates the specific process of determining whether the quality of the tomato sauce is qualified in step S130, including: step S510 to step S530.

[0054] Step S510, determining whether the quality parameter of the ketchup is within a preset quality parameter range.

[0055] Step S520: If it is determined that the quality parameter of the tomato sauce is within the preset quality parameter range, then it is determined that the quality of the tomato sauce is qualified.

[0056] Step S530: If it is determined that the quality parameter of the tomato sauce is not within the preset quality parameter range, then the quality of the tomato sauce is determined to be unqualified.

[0057] In the scheme of the present invention, prediction is made based on four parameters that have a greater impact on the quality of tomato sauce, namely, sauce flow, main pipe temperature, detection area temperature, and cooling water temperature. Fuzzy PID control is combined with the experience of experts and operators to set fuzzy reasoning. The quality of tomato sauce is determined by real-time detection parameter data of pH value, viscosity, and color difference value, and is classified under the premise of complying with national norms and standards: unqualified products are returned to the sauce making process through the process pipeline to save energy; qualified products are further divided into three grades of excellent, medium and qualified by a deformation method of clear set membership, thereby reducing the energy consumption of the tomato sauce quality online detection system.

[0058] The solution of the present invention optimizes the original off-line detection process, reduces system energy consumption, saves labor costs, reduces labor intensity, flexibly adjusts control strategies according to changes in on-site production environment and load, collects data in real time, quickly distinguishes qualified products from unqualified products, and reasonably distinguishes different grades of qualified products.

[0059] In some embodiments, the quality of the tomato paste includes at least one of the pH value, viscosity and color of the tomato paste in the branch pipe.

[0060] The solution of the present invention provides a tomato sauce quality online detection system and a control method thereof, including process optimization, data acquisition, online detection and temperature control methods. The tomato sauce quality online detection system and the control method thereof include: Step 1: Process optimization: Use process optimization to provide basic conditions for data collection and control, and optimize the process pipelines for production, testing and control. For the schematic diagram of the process pipeline, see Figure 7 Example shown.

[0061] 1) Sauce sampling system: A branch pipe is separated from the main pipe to synchronize the ketchup detection of the branch pipe with the ketchup of the main pipe during the production process, thereby ensuring the real-time nature of the tested ketchup.

[0062] 2) Water cooling system: The ketchup branch pipe enters the condenser for heat exchange, which reduces the temperature of the ketchup to the measurement temperature.

[0063] In the scheme of the present invention, the process improvement refers to a branch pipeline separated from the tomato main pipeline. The electric three-way valve at the outlet of the main pipeline is controlled by online detection of the pH value, viscosity and color data of the branch pipeline, so as to separate qualified products into the barrel filling process and unqualified products into the processing process for reprocessing, thereby realizing the separation of qualified and unqualified tomato sauces, and the qualified products are graded.

[0064] Specifically, the ketchup branch pipe enters the condenser for heat exchange, and the circulating water is used to take away the heat, so that the temperature of the ketchup is reduced to the measurement temperature. See the schematic diagram of the water cooling system. Figure 8 .

[0065] 3) Online detection system: After the water cooling system is completed, the detection instrument detects the pH value, viscosity and color difference of the ketchup.

[0066] 4) Sauce separation system: If the product is determined to be qualified or above through data analysis of the system's online detection, the electric three-way valve (ball valve) is opened to connect the main pipeline with the qualified product pipeline; if the product is determined to be unqualified, the electric three-way valve is opened to connect the main pipeline with the unqualified product pipeline, and the ketchup returns to the processing process along the process pipeline for reprocessing.

[0067] Step 2, data acquisition, specifically, data acquisition system: the signal from the field sensor is converted into a standard 4-20mA signal through a transmitter and transmitted to the PLC. The PLC processes the data and transmits it to the monitoring layer via Ethernet.

[0068] Step 3, temperature control, specifically, control system: by reasonably adjusting the cooling water flow rate of the water cooling system, the tested ketchup reaches the measuring temperature, and the pH value, viscosity, color difference value, etc. of the ketchup are detected. The schematic diagram of the overall control block diagram is shown in Fig. 9 .

[0069] 1) Water cooling: Under the condition of ensuring the maximum capacity, the speed of the water pump is reasonably adjusted to adjust the flow rate of the cooling water, so that the cooling temperature of the ketchup can reach the detection temperature as much as possible. Specifically, under the condition of ensuring the maximum capacity, the speed of the circulating water pump is adjusted based on BP neural network and fuzzy PID control to adjust the flow rate of the cooling water, so that the cooling temperature of the ketchup can reach the detection temperature as much as possible.

[0070] 2) Sauce separation: According to the parameter requirements of canned tomato sauce (GB / T14215-2021), through the analysis of the system's online detection data (PH value, viscosity, chromaticity), it is judged to be qualified or above, and the electric three-way valve (ball valve) is opened to connect the main pipeline and the qualified product pipeline; when it is judged to be an unqualified product, the electric three-way valve is opened to connect the main pipeline and the unqualified product pipeline, and the tomato sauce returns to the processing process along the process pipeline for reprocessing.

[0071] Based on the classification of qualified products in the GB / T14215-2021 standard, the parameters measured by pH value a domain [4.6, 8], viscosity b domain [15, 30], and color difference value c domain [21, 22.5] can be online detected by pH sensor, online viscometer sensor, and color online detector, and substituted into the empirical formula Y=10a+b-lnc. When the quality parameter Y of tomato sauce is ≥85, the quality of tomato sauce is first-class; when 85>Y≥70, the quality of tomato sauce is second-class; when 70>Y and all parameters are qualified, the quality of tomato sauce is third-class.

[0072] 3) Control ideas and methods: For temperature control of large lag links and coupling control of multiple parameters, BP neural network and fuzzy PID control are adopted; multiple parameters are used to judge the qualification and unqualifiedness of products, and the national standard for canned ketchup (GB / T14215-2021) is adopted to ensure the bottom-line thinking of product quality and the use of distinct set deformation to subdivide first-class, second-class and third-class products based on qualified products.

[0073] In the scheme of the present invention, the control method refers to controlling the water cooling adjustment system according to the flow rate of the ketchup, the temperature of the main pipe, the temperature of the detection area, and the temperature of the cooling water. The main pipe is used to separate qualified products from unqualified products, and the branch pipe is used to control the temperature to reach the online detection temperature; the temperature control is to use the BP neural network to predict the water volume and fuzzy PID control, dynamically and in real time adjust the PID setting parameters, so that the control is continuously optimized, adjust the inverter output, thereby adjusting the speed of the water pump, and adjusting the cooling water flow to control the ketchup detection temperature; the BP neural network method is used to predict the water demand (water flow) of the ketchup water pump, and dynamically respond to the change of the set value water demand caused by the change of the external environment of the ketchup and the change of the ketchup load. The fuzzy control algorithm is used to dynamically adjust the setting of the PID parameters according to the change of the ketchup water pump to achieve the effect of self-tuning. The ketchup separation is based on the parameter requirements in the ketchup can (GB / T14215-2021) to determine whether it is qualified or unqualified. The ketchup subdivision is that the ketchup is subdivided into qualified product grades by using a distinct set of instruments that can detect parameters online. The solution is dynamic and real-time, and is automated, intelligent, and unmanned, realizing the automation, intelligence, and unmanned online detection system and control of tomato sauce quality.

[0074] The technical solution of this embodiment is adopted, by setting a bypass pipe as a branch pipe on the main pipe of the ketchup production process, setting a water cooling system on the branch pipe for cooling, the water cooling system is set between the inlet and outlet of the branch pipe, the outlet of the branch pipe is an online detection area, an electric three-way valve is set at the output port of the main pipe, and a qualified product pipe and a non-qualified product pipe are set at the rear end of the electric three-way valve; the main pipe temperature is obtained from the main pipe, the cooling water temperature is obtained from the cooling water system, the ambient temperature and the ketchup flow are obtained from the online detection area, and the target flow of the water cooling system is calculated according to the ketchup flow, the main pipe temperature, the detection area temperature, and the cold water temperature based on the target flow calculation model obtained by pre-training using the BP neural network. According to the difference between the target flow and the actual flow of the water cooling system, the rotation speed of the water pump in the water cooling system is controlled by using PID fuzzy control to control the ketchup temperature in the detection area; the pH value, viscosity and chromaticity of the ketchup are detected in the detection area to determine whether the quality of the ketchup is qualified. If it is qualified, the electric three-way valve is controlled to connect the main pipeline with the qualified product pipeline; if it is unqualified, the electric three-way valve is controlled to connect the main pipeline with the unqualified product pipeline; thus, by arranging a branch pipeline and a water cooling system on the main pipeline of the ketchup production process, arranging an electric three-way valve at the output end of the main pipeline, and controlling the water flow of the water cooling system and the opening and closing mode of the electric three-way valve, online detection and control of the ketchup quality can be achieved, which can improve production efficiency.

[0075] According to an embodiment of the present invention, a tomato sauce quality detection device corresponding to the tomato sauce quality detection method is also provided. Figure 6 The structure diagram of an embodiment of the device of the present invention is shown. The tomato sauce quality detection device is used for online detection of tomato sauce quality and control of tomato sauce output channel in tomato sauce production process; the tomato sauce inspection process has a main pipeline, a qualified product pipeline and a non-qualified product pipeline; a bypass pipeline is arranged in the main pipeline as a branch pipeline; a water cooling system is arranged between the inlet and outlet of the branch pipeline, and the outlet of the branch pipeline is used as a detection area; a three-way switch (such as an electric three-way valve) is arranged at the outlet of the main pipeline, and the three-way switch is connected to the qualified product pipeline and the non-qualified product pipeline respectively; Figure 7 The structure diagram of the ketchup process pipeline of the embodiment of the invention is as follows: Figure 7 As shown, the branch pipe is used as a bypass pipe of the main pipe, and the bypass pipe passes through the condenser. The condenser has a water inlet and a water outlet. An online detection area is set at the output side of the branch pipe; an electric three-way valve is set at the output end of the main pipe, and the rear part of the electric three-way valve has two paths: one is a qualified product pipeline, and the other is a failed product pipeline. In the scheme of the present invention, Figure 8 As shown, the ketchup quality detection device includes: an acquisition unit 102 and a control unit 104.

[0076] The acquisition unit 102 is configured to acquire the flow rate of ketchup in the branch pipe, recorded as the ketchup flow rate; acquire the temperature of the water cooling system, recorded as the cooling water temperature; acquire the temperature of the main pipe, recorded as the main pipe temperature; acquire the temperature of the detection area, recorded as the ambient temperature; and acquire the quality parameter of the ketchup in the branch pipe from the detection area. The specific functions and processing of the acquisition unit 102 refer to step S110.

[0077] The control unit 104 is configured to control the water flow of the water cooling system according to the ketchup flow, the cooling water temperature, the main pipe temperature, and the ambient temperature using a BP neural network and a fuzzy PID control algorithm to achieve control of the ketchup temperature in the detection area. The specific functions and processing of the control unit 104 refer to step S120.

[0078] The control unit 104 is further configured to determine whether the quality of the ketchup is qualified according to the quality parameters of the ketchup when the ketchup temperature in the detection area reaches the target temperature. The specific functions and processing of the control unit 104 are also shown in step S130.

[0079] The control unit 104 is further configured to control the three-way switch to connect the main pipeline with the qualified product pipeline if it is determined that the quality of the ketchup is qualified. The specific functions and processing of the control unit 104 are also shown in step S140.

[0080] The control unit 104 is further configured to control the three-way switch to connect the main pipeline with the unqualified product pipeline if it is determined that the quality of the ketchup is unqualified. The specific functions and processing of the control unit 104 are also shown in step S150.

[0081] The scheme of the present invention includes the design of an online detection process for tomato sauce quality. The tomato sauce quality detection is a method based on BP neural network prediction and fuzzy PID control (Proportional-Integral-Derivative Control) for predicting the water demand when the branch pipe temperature is controlled. Under the condition that the temperature of the main pipe of the tomato sauce is not reduced, the unqualified products detected online are directly returned to the previous process and the sauce is re-made, thereby achieving the purpose of energy saving and emission reduction, and solving the problem of low production efficiency of the existing tomato sauce in the offline detection process.

[0082] Since the processing and functions implemented by the device of this embodiment basically correspond to the embodiments, principles and examples of the aforementioned method, for the details not fully described in this embodiment, please refer to the relevant descriptions in the aforementioned embodiments, and no further elaboration will be made here.

[0083] According to an embodiment of the present invention, a terminal corresponding to the ketchup quality detection device is also provided. The terminal may include: the ketchup quality detection device described above.

[0084] Since the processing and functions implemented by the terminal of this embodiment basically correspond to the embodiments, principles and examples of the aforementioned devices, for the details not fully described in this embodiment, please refer to the relevant descriptions in the aforementioned embodiments, and no further elaboration will be made here.

[0085] According to an embodiment of the present invention, a storage medium corresponding to the ketchup quality detection method is also provided, wherein the storage medium includes a stored program, wherein when the program is run, the device where the storage medium is located is controlled to execute the ketchup quality detection method described above.

[0086] Since the processing and functions implemented by the storage medium of this embodiment basically correspond to the embodiments, principles and examples of the aforementioned method, for the details not fully described in this embodiment, please refer to the relevant descriptions in the aforementioned embodiments, and no further elaboration will be made here.

[0087] According to an embodiment of the present invention, a computer program product corresponding to the tomato sauce quality detection method is also provided, including a computer program, which implements the steps of the tomato sauce quality detection method described above when executed by a processor.

[0088] Since the processing implemented by the product of this embodiment basically corresponds to the embodiments, principles and examples of the aforementioned method, for the details not fully described in this embodiment, please refer to the relevant descriptions in the aforementioned embodiments, and no further elaboration will be made here.

[0089] In summary, it is easy for those skilled in the art to understand that, under the premise of no conflict, the above-mentioned advantageous methods can be freely combined and superimposed.

[0090] The above description is only an embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included in the scope of the claims of the present invention.

Claims

1. A method for detecting the quality of ketchup, characterized in that: Applicable to online detection of ketchup quality and control of ketchup output channel in ketchup production process; the ketchup inspection process has a main pipeline, a qualified product pipeline and a non-qualified product pipeline; a bypass pipeline is arranged in the main pipeline as a branch pipeline; a water cooling system is arranged between the inlet and outlet of the branch pipeline, and the outlet of the branch pipeline is used as a detection area; a three-way switch is arranged at the outlet of the main pipeline, and the three-way switch is connected to the qualified product pipeline and the non-qualified product pipeline respectively; the ketchup quality detection method includes: Obtain the flow rate of ketchup in the branch pipe, recorded as the ketchup flow rate; obtain the temperature of the water cooling system, recorded as the cooling water temperature; obtain the temperature of the main pipe, recorded as the main pipe temperature; obtain the temperature of the detection area, recorded as the ambient temperature; and obtain the quality parameter of the ketchup in the branch pipe from the detection area; According to the ketchup flow rate, the cooling water temperature, the main pipe temperature, and the ambient temperature, the water flow rate of the water cooling system is controlled by using a BP neural network and a fuzzy PID control algorithm to achieve control of the ketchup temperature in the detection area; When the temperature of the ketchup in the detection area reaches the target temperature, determining whether the quality of the ketchup is qualified according to the quality parameters of the ketchup; If it is determined that the quality of the ketchup is qualified, controlling the three-way switch to connect the main pipeline with the qualified product pipeline; If it is determined that the quality of the ketchup is unqualified, the three-way switch is controlled to connect the main pipeline with the unqualified product pipeline.

2. The ketchup quality detection method according to claim 1, characterized in that: According to the ketchup flow rate, the cooling water temperature, the main pipe temperature, and the ambient temperature, the water flow rate of the water cooling system is controlled by using a BP neural network and a fuzzy PID control algorithm, including: Calculate the target water flow of the water cooling system using a BP neural network according to the ketchup flow, the cooling water temperature, the main pipe temperature, and the ambient temperature; The water flow rate of the water cooling system is controlled by using a fuzzy PID control algorithm according to the target water flow rate of the water cooling system and the cooling water flow rate of the water cooling system.

3. The ketchup quality detection method according to claim 2, characterized in that: According to the ketchup flow rate, the cooling water temperature, the main pipe temperature, and the ambient temperature, the target water flow rate of the water cooling system is calculated using a BP neural network, including: Using BP neural network, a water demand prediction model is pre-trained; The water demand prediction model is used to calculate the water demand according to the ketchup flow rate, the cooling water temperature, the main pipe temperature, and the ambient temperature, and the water demand is used as the target water flow rate of the water cooling system.

4. The ketchup quality detection method according to claim 2, characterized in that: The water cooling system comprises a condenser, a cooling pool, a frequency converter and a water pump; the water pump comprises: a circulation pump; the cooling pool is connected to the water inlet of the condenser via a water outlet pipe, and the circulation pump is arranged on the water outlet pipe of the cooling pool; the frequency converter is used to control the speed of the water pump; the condenser is arranged on the branch pipe; According to the target water flow of the water cooling system and the cooling water flow of the water cooling system, the water flow of the water cooling system is controlled by using a fuzzy PID control algorithm, including: According to the target water flow of the water cooling system and the difference of the cooling water flow of the water cooling system, a PID control amount is calculated by using a fuzzy PID control algorithm; The PID control amount is used to control the rotation speed of the circulation pump via the frequency converter to control the water flow of the water cooling system.

5. The tomato paste quality detection method according to any one of claims 1 to 4, characterized in that: Determining whether the quality of the tomato sauce is qualified according to the quality parameters of the tomato sauce comprises: Determining whether the quality parameter of the ketchup is within a preset quality parameter range; If it is determined that the quality parameter of the tomato sauce is within the preset quality parameter range, then the quality of the tomato sauce is determined to be qualified; If it is determined that the quality parameter of the tomato sauce is not within the preset quality parameter range, it is determined that the quality of the tomato sauce is unqualified.

6. The ketchup quality detection method according to claim 5, characterized in that: The quality of the tomato paste includes at least one of the pH value, viscosity and chromaticity of the tomato paste in the branch pipe.

7. A ketchup quality detection device, characterized in that: Applicable to online detection of ketchup quality and control of ketchup output channel in ketchup production process; the ketchup inspection process has a main pipeline, a qualified product pipeline and a non-qualified product pipeline; a bypass pipeline is arranged in the main pipeline as a branch pipeline; a water cooling system is arranged between the inlet and outlet of the branch pipeline, and the outlet of the branch pipeline is used as a detection area; a three-way switch is arranged at the outlet of the main pipeline, and the three-way switch is connected to the qualified product pipeline and the non-qualified product pipeline respectively; the ketchup quality detection device includes: The acquisition unit is configured to acquire the flow rate of the ketchup in the branch pipe, recorded as the ketchup flow rate; acquire the temperature of the water cooling system, recorded as the cooling water temperature; acquire the temperature of the main pipe, recorded as the main pipe temperature; acquire the temperature of the detection area, recorded as the ambient temperature; and acquire the quality parameter of the ketchup in the branch pipe from the detection area; A control unit is configured to control the water flow of the water cooling system according to the ketchup flow, the cooling water temperature, the main pipe temperature, and the ambient temperature by using a BP neural network and a fuzzy PID control algorithm to achieve control of the ketchup temperature in the detection area; The control unit is further configured to determine whether the quality of the tomato sauce is qualified according to the quality parameters of the tomato sauce when the temperature of the tomato sauce in the detection area reaches the target temperature; The control unit is further configured to control the three-way switch to connect the main pipeline with the qualified product pipeline if it is determined that the quality of the ketchup is qualified; The control unit is further configured to control the three-way switch to connect the main pipeline with the unqualified product pipeline if it is determined that the quality of the ketchup is unqualified.

8. A terminal, characterized in that: include: The ketchup quality detection device as claimed in claim 7.

9. A storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the ketchup quality detection method according to any one of claims 1 to 6.

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 tomato sauce quality detection method according to any one of claims 1 to 6 are implemented.

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