Control method of weighing type liquid adding device

Abstract

The invention provides a control method of a weighing type liquid adding device, which relates to the technical field of liquid adding devices, and comprises the following steps: acquiring liquid formula parameters issued by an external production system, and simultaneously acquiring real-time temperature and pipeline pressure of liquid in each raw material hopper and actual measurement data of weighing of the raw material hopper and a batch hopper; the actual mass of raw material liquid is corrected based on a dynamic weighing correction algorithm, the flow of materials instantaneously fed to a batch hopper from each raw material hopper is controlled through a collaborative conveying flow algorithm, the spraying angle of a nozzle is adjusted through a spraying angle self-adaption algorithm, and the accuracy of data interaction with an external production system is ensured in combination with a production data verification algorithm. Temperature and pipeline residual errors are eliminated through a dynamic weighing correction algorithm, the single liquid metering precision deviation is smaller, accurate synchronization of multiple liquids according to the formula proportion is achieved in cooperation with a conveying flow algorithm, the matching error is small, insufficient product efficacy or component standard exceeding caused by matching deviation is avoided, and the product percent of pass is increased.

Description

Technical Field

[0001] This invention relates to the field of liquid addition device technology, and in particular to a control method for a weighing liquid addition device. Background Technology

[0002] In the process of adding liquid additives in industries such as feed and food, metering accuracy and process coordination are the core factors determining product quality. Currently, the mainstream liquid metering methods in the industry are divided into volumetric metering and gravimetric metering: Volumetric metering measures the volume using a flow meter and then converts it to weight using a preset density. However, liquid density is easily affected by fluctuations in temperature, pressure, and concentration, and the final addition accuracy can only be maintained within ±2%, which is difficult to meet the stringent requirements of high-end products for additive content. Gravimetric metering, although a direct metering method, can avoid density conversion errors. However, traditional gravimetric metering relies on a single weighing sensor to collect data, does not consider the influence of liquid temperature on container deformation, and lacks coordinated flow control when multiple liquids are added simultaneously, which can easily lead to proportioning deviations and unstable product quality.

[0003] Existing liquid addition control systems suffer from shortcomings in intelligence and integration. Traditional control methods employ time relays and mini-button counters, controlling liquid addition in fixed-time cycles. This approach cannot dynamically adjust parameters based on liquid characteristics, and the user interface offers limited information, displaying only basic start / stop status and failing to provide real-time feedback on critical data such as metering deviations and pipeline pressures. Operators must manually sample and inspect to assess the addition effect, resulting in significant delays. Furthermore, most control methods rely on manual input of formula parameters for integration with external production systems, leading to low efficiency and susceptibility to formula deviations due to numerical errors and unit confusion. They also lack automatic synchronization and traceability of production data, making them ill-suited for the large-scale, intelligent production demands of modern industry. Therefore, this invention proposes a control method for a weighing liquid addition device to address the problems existing in the prior art. Summary of the Invention

[0004] To address the aforementioned issues, this invention proposes a control method for a weighing liquid addition device. This method eliminates temperature and pipeline residual errors through a dynamic weighing correction algorithm, resulting in smaller deviations in the metering accuracy of a single liquid. A collaborative conveying flow algorithm enables precise synchronization of multiple liquids according to the formula ratio, minimizing mixing errors and preventing insufficient product efficacy or excessive components due to mixing deviations, thereby improving product qualification rates.

[0005] To achieve the objectives of this invention, the invention is implemented through the following technical solution: a control method for a weighing liquid adding device, comprising the following steps:

[0006] S1: Obtain liquid formula parameters issued by the external production system, and simultaneously collect real-time temperature, pipeline pressure, and measured data of the weight of the liquid in each raw material hopper and batching hopper.

[0007] S2: The actual mass of the raw material liquid is corrected based on the dynamic weighing correction algorithm. The instantaneous feed flow rate of each raw material hopper to the batching hopper is controlled by the collaborative conveying flow algorithm. The nozzle spraying angle is adjusted by the spraying angle adaptive algorithm. The data interaction accuracy with the external production system is ensured by the production data verification algorithm. The mixing uniformity of multiple liquids in the batching hopper is adjusted by the stirring speed adaptive algorithm.

[0008] S3: Based on the algorithm output, control the opening of the fast / slow valve, the nozzle angle, the operation of the stirring rod, and the temperature control; monitor the equipment condition in real time and generate operation reports.

[0009] S4: When a parameter is detected to exceed a preset threshold, an audible and visual alarm is triggered on the touchscreen, and troubleshooting suggestions are pushed out. At the same time, the fault information is synchronized to the external production system.

[0010] A further improvement is made in S1, where a temperature control threshold is preset according to the liquid type, and the heating element of the raw material hopper is activated for temperature control to prevent high-viscosity liquid from solidifying and clogging the pipes.

[0011] A further improvement is made in S2, where a dynamic weighing correction algorithm is used to eliminate the influence of temperature on weighing accuracy. The algorithm formula is as follows:

[0012] Mcalibrated = Mmeasured × [1 + α × (Tactual - Tstandard)] - ΔMresidual

[0013] Where: Mcalibrated is the actual mass of the liquid after calibration, in g, which is the reference value for measurement control; Mmeasured is the actual measured mass of the liquid, in g; α is the temperature influence coefficient of the raw material hopper and the batching hopper, in % / ℃, which is determined experimentally based on the material of the raw material hopper and the batching hopper; Tactual is the real-time temperature of the liquid, in ℃; Tstandard is the standard temperature for weighing calibration, in ℃, which is set to 25℃ by default; ΔMresidual is the residual mass of the liquid in the pipe between the raw material hopper and the batching hopper, in g, which is calculated based on the pipe length L and the liquid viscosity μ, ΔMresidual = 0.1 × L × μ; this algorithm controls the weighing error within ±0.5g.

[0014] A further improvement is made in S2, where the collaborative transport flow algorithm is used to achieve synchronous transport of multiple liquids according to the formula ratio. The algorithm formula is as follows:

[0015] Qn=Qtotal×(ωn×ρbase) / Σ(ωi×ρi),

[0016] Where: Qn is the instantaneous delivery flow rate of the nth liquid, in mL / s; Qtotal is the total instantaneous delivery flow rate of all liquids, in mL / s, calculated based on the target total addition and preset delivery time, Qtotal = Vtotal / Tdeliver, where Vtotal is the total liquid volume and Tdeliver is the preset delivery time; ωn is the formula mass fraction of the nth liquid, in %, issued by the external production system; ρbase is the density of the reference liquid, in g / mL, with the lowest viscosity liquid selected by default, ρbase = 1.05 g / mL; ωi is the formula mass fraction of the ith liquid, in %; ρi is the real-time density of the ith liquid, in g / mL, corrected according to the real-time temperature Tact, ρi = ρstandard × [1 - β × (Tact - Tstandard)], where β is the liquid density temperature coefficient and ρstandard is the standard density of the liquid.

[0017] A further improvement is made in S2, where the spraying angle adaptive algorithm is used to adjust the nozzle angle according to the liquid viscosity, preventing the liquid from sticking to the inner wall of the equipment. The algorithm formula is as follows:

[0018] θ_tuning = θ_standard × (1 + k × lgμ),

[0019] Where: θ_adjustment is the adjusted spray angle of the nozzle, in °, with a value range of 30°-90°; θ_standard is the standard spray angle of the nozzle, in °, with a default setting of 60°; k is the viscosity influence coefficient, in ° / lg(mPa·s), calibrated experimentally, with a value of 0.8-1.2; μ is the real-time viscosity of the liquid, in mPa·s; when μ > 200 mPa·s, the upper limit of θ_adjustment is set to 90° to ensure that the liquid covers the target area in a wide fan shape with a viscosity rate of <5%.

[0020] A further improvement is made in S2, where the production data verification algorithm is used to verify the accuracy of data issued by the external production system. The algorithm formula is as follows:

[0021] Cschool = Csend × (1 + δ × ttransmit) + ΔCsupplement

[0022] Where: Ccorrected is the corrected formula data, in g or %; Cgenerated is the original formula data sent by the external production system, in g or %; δ is the data transmission attenuation coefficient, in % / s, set according to the communication distance: <100m δ = 0.001% / s, 100-500m δ = 0.002% / s; ttransmit is the data transmission time, in s, automatically timed by the communication module; ΔCcompensation is the data compensation value, in g or %, derived from historical transmission error statistics, with a value range of -0.1 to 0.1; when |Ccorrected - Cgenerated| > 0.2%, data retransmission is triggered.

[0023] A further improvement is made in S2, where the adaptive stirring speed algorithm is used to optimize the stirring speed based on the liquid viscosity and ratio, thereby improving the mixing uniformity. The algorithm formula is as follows:

[0024] n_stirring = n_standard × (1 + 0.05 × lgμ_balance) × (1 + 0.03 × N_liquid),

[0025] Where: n_stirring is the adjusted rotation speed of the stirring rod, in r / min, with a range of 300-1500 r / min; n_standard is the standard rotation speed of the stirring rod, in r / min, with a default setting of 600 r / min; μ_average is the average viscosity of the various liquids, in mPa·s, calculated by weighting by mass fraction, μ_average = Σ(ωi × μi) / 100, where μi is the viscosity of the i-th liquid; N_liquid is the number of liquid types, in terms of types; when μ_average > 500 mPa·s or N_liquid = 3, the upper limit of n_stirring is set to 1500 r / min to ensure a mixing uniformity > 95%.

[0026] Further improvements are made in S3, which specifically includes real-time monitoring of equipment operating conditions: monitoring the stability of the weighing sensor readings: if the reading fluctuation is greater than 3g within 10s, it is determined to be an abnormal weighing, the conveying is suspended and an alarm is triggered; monitoring the pump's operating current: if the current exceeds the rated value by 10%, it is determined to be an overload of the pump, and the system is automatically switched to the standby pump; monitoring the pipeline pressure: if the pipeline pressure is greater than 50kPa, it is determined to be a pipeline blockage, and if the pressure is less than 5kPa, it is determined to be a pipeline leak, and the system is immediately shut down and troubleshooting suggestions are sent.

[0027] Further improvements are made in the following aspects: In S3, the generation of operation reports specifically includes: automatically recording the actual added mass, delivery time, spraying angle, and equipment operating data of each batch of liquid. The reports are stored in Excel or PDF format and can be exported locally and uploaded remotely to external production systems.

[0028] Further improvements are made in S4, where fault diagnosis and feedback also include: establishing a fault code library, synchronously pushing corresponding troubleshooting steps when a fault is triggered, and sending fault information to the mobile APP of maintenance personnel through the Internet of Things module.

[0029] The beneficial effects of this invention are as follows:

[0030] 1. This invention eliminates temperature and pipeline residual errors through a dynamic weighing correction algorithm, making the metering accuracy of a single liquid smaller. The collaborative delivery flow algorithm enables multiple liquids to be accurately synchronized according to the formula ratio, with small mixing errors, avoiding insufficient product efficacy or excessive components due to mixing deviations, and improving the product qualification rate.

[0031] 2. This invention achieves error-free data interaction with external production systems through a production data verification algorithm, eliminating human input errors; the entire process is automatically controlled by the algorithm for conveying, spraying, temperature control, and fault handling, requiring no manual intervention, reducing the workload of operators, and significantly enhancing production continuity.

[0032] 3. The spraying angle adaptive algorithm of this invention is suitable for liquids ranging from low viscosity to high viscosity. The temperature control and coordination function solves the problem of solidification of high viscosity liquids in low temperature environments, reduces the pipe blockage rate, supports the simultaneous addition of multiple liquids, is suitable for various large-scale production, and the fault code library and remote operation and maintenance function reduce the equipment failure rate and improve the stability and compatibility of operation.

[0033] 4. This invention dynamically adjusts the stirring speed according to the viscosity and type of liquid, improves the uniformity of mixing multiple liquids, avoids excessively high local additive concentrations due to uneven mixing, and further ensures product quality stability. The stirring speed is adjusted as needed, and the speed is increased only when necessary for high-viscosity liquids, taking into account both mixing effect and energy saving requirements. Attached Figure Description

[0034] Figure 1 This is a flowchart of the present invention;

[0035] Figure 2 This is a schematic diagram of the device of the present invention. Detailed Implementation

[0036] To enhance understanding of the present invention, the present invention will be further described in detail below with reference to embodiments. These embodiments are only used to explain the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0037] Example 1

[0038] according to Figure 1 , 2 As shown, this embodiment proposes a control method for a weighing liquid addition device, applied to the addition control of two liquids in a feed mill: methionine and a mold inhibitor.

[0039] Application scenarios: Small and medium-sized feed mills producing compound feed for laying hens, requiring the addition of methionine, with a target weight of 500g / batch and a mass fraction of 0.1%, and a mold inhibitor, with a target weight of 250g / batch and a mass fraction of 0.05%, in a normal temperature environment of 20-25℃.

[0040] Control parameter settings: In the dynamic weighing correction algorithm, α = 0.002% / ℃, stainless steel batching hopper, T_standard = 25℃; In the collaborative conveying flow algorithm, Q_total = 15mL / s, ρ_base = 1.05g / mL, methionine; In the spraying angle adaptive algorithm, θ_standard = 60°, k = 1.0; In the production data verification algorithm, δ = 0.001% / s, communication distance 80m.

[0041] Test method: 50 batches were produced continuously, and the metering accuracy, production efficiency and failure rate of this control method were compared with those of the traditional time control method.

[0042] Validation data:

[0043] Metering accuracy: The actual addition deviation of methionine is 0.3%-0.7%, and the deviation of the antifungal agent is 0.2%-0.6%, compared with the deviation of 1.8%-2.2% for the traditional method, resulting in an accuracy improvement of over 65%; Production efficiency: The production time per batch is reduced from 12 minutes for the traditional method to 8 minutes, resulting in an efficiency improvement of 33%; Equipment failure rate: Only one minor pipeline pressure fluctuation occurred in 50 batches, which was resolved by the system automatically adjusting the pump speed, resulting in a failure rate of 2%, compared with the failure rate of 8% for the traditional method.

[0044] Example 2

[0045] according to Figure 1 , 2 As shown, this embodiment proposes a control method for a weighing liquid addition device, applied to the control of adding molasses and emulsifier to high-viscosity liquids in food factories.

[0046] Application scenarios: Large food factories producing pastry premixes, requiring the addition of molasses, with a target weight of 3000g / batch, and temperature control of 45±1℃, μ=600mPa·s, and emulsifiers, with a target weight of 500g / batch, μ=10mPa·s.

[0047] Control parameter settings: In the dynamic weighing correction algorithm, α = 0.002% / ℃, ΔM_residual = 0.1 × 5 × 600 = 30g, and the pipe length is 5m; In the coordinated transport flow rate algorithm, Q_total = 30mL / s, ρ_base = 1.12g / mL, and the emulsifier is used; In the spraying angle adaptive algorithm, θ_adjust = 60° × (1 + 1.0 × lg600) = 88°, and 85° is selected, which does not exceed the upper limit of 90°; The temperature control threshold is 45℃.

[0048] Test method: 30 batches were produced continuously at an ambient temperature of 10-15℃, and the metering accuracy, pipeline blockage rate and temperature control stability were recorded.

[0049] Validation data:

[0050] Metering accuracy: Molasses addition deviation 0.4%-0.9%, emulsifier deviation 0.3%-0.6%, both <1%; Temperature control stability: Real-time temperature fluctuation of molasses 44.2-45.8℃, temperature control accuracy ±0.8℃, meeting preset requirements; Pipe blockage rate: No pipe blockage in 30 batches, compared to 20% for traditional methods without temperature control and angle self-adaptation, the blockage rate was reduced by 100%; Mixing uniformity: Samples were taken after mixing molasses and emulsifier, and the mixing uniformity reached 96.3%, compared to 82.5% for traditional fixed speed mixing at -600r / min, an improvement of 13.8%.

[0051] Example 3

[0052] according to Figure 1 , 2 As shown, this embodiment proposes a control method for a weighing liquid addition device, applied to the control of adding three liquids (methionine + choline + water) in a feed mill:

[0053] Application scenario: Large feed mills producing fattening pig feed need to add methionine (1000g / batch, μ=8mPa·s), choline (800g / batch, μ=15mPa·s), and water (500g / batch, μ=1mPa·s) at the same time. The three liquids need to be mixed in a ratio of 5:4:2.5.

[0054] Control parameter settings: In the dynamic weighing correction algorithm, α = 0.002% / ℃, T_standard = 25℃; In the coordinated conveying flow rate algorithm, Q_total = 25mL / s, Q_methionine = 25×(5×1.0) = 25×5 / (5+4×1.05+2.5×1.0) = 9.8mL / s, choline ρ = 1.05g / mL, water ρ = 1.0g / mL; In the spraying angle adaptive algorithm, θ_adjustment is 60°×(1+1.0×lg8) = 66° for methionine; 60°×(1+1.0×lg15) = 69° for choline; 60°×(1+1.0×lg1) = 60° for water, taking an average of 65°; In the production data verification algorithm, ΔC_complement = 0.05%.

[0055] Test method: 40 batches were produced continuously, and the mixing ratio error of the three liquids, production efficiency, and data transmission accuracy were recorded.

[0056] Validation data:

[0057] Proportioning error: The actual ratio deviation of methionine-choline-water is 0.2%-0.6%, while the deviation of traditional manual proportioning is 2.5%-3.0%, improving proportioning accuracy by 80%; Production efficiency: The production time per batch is reduced from 15 minutes in the traditional method to 10 minutes, improving efficiency by 33%; Data transmission accuracy: There are no transmission errors in 40 batches of formula data, with an accuracy rate of 100%, while the error rate of traditional manual input is 6%; Stirring effect: The average viscosity deviation after mixing the three liquids is <2%, while the deviation of traditional methods is 8%-10%, significantly improving mixing stability.

[0058] Example 4

[0059] according to Figure 1 , 2 As shown, this embodiment proposes a control method for a weighing liquid addition device, applied to the control of adding two liquids (oil and flavoring agent) in aquatic feed plants:

[0060] Application scenario: Medium-sized aquatic feed mills producing grass carp compound feed, which requires the addition of oil, with a target weight of 1200g / batch, and requires temperature control of 35±1℃, μ=300mPa·s, and flavoring agent, with a target weight of 300g / batch, μ=5mPa·s. The oil and flavoring agent need to be mixed in a 4:1 ratio to enhance the feed's palatability.

[0061] Control parameter settings: In the dynamic weighing correction algorithm, α = 0.002% / ℃ (stainless steel mixing hopper), ΔM_residual = 0.1 × 4 × 300 = 12g, pipe length 4m; In the coordinated conveying flow rate algorithm, Q_total = 20mL / s, ρ_base = 1.03g / mL, fragrance; Q_oil = 20 × (4 × 1.03) / (4 × 1.03 + 1 × 1.0) ≈ 16.2mL / s, oil ρ = 0.92g / mL, corrected ρ = 0.91g / mL; adaptive spraying angle. In the algorithm, θ_adjust = 60° × (1 + 1.0 × lg300) = 85° (μ = 300 mPa·s); in the production data verification algorithm, δ = 0.001% / s, and the communication distance is 60 m; in the stirring speed adaptive algorithm, μ_flat = (1200 × 300 + 300 × 5) / 1500 = 241 mPa·s, N_liquid = 2, n_stirring = 600 × (1 + 0.05 × lg241) × (1 + 0.03 × 2) ≈ 950 r / min; the temperature control threshold is 35℃.

[0062] Test method: 35 batches were produced continuously at an ambient temperature of 18-22℃, and the metering accuracy, temperature control stability, mixing uniformity and equipment failure rate were recorded.

[0063] Validation data:

[0064] Metering accuracy: Actual oil addition deviation 0.3%-0.8%, flavoring agent deviation 0.2%-0.6%, compared to 1.9%-2.3% for traditional methods, representing a 68% improvement in accuracy; Temperature control stability: Real-time oil temperature fluctuation 34.1-35.9℃, temperature control accuracy ±0.9℃, with no solidification; Mixing uniformity: 96.5%, compared to 83.1% for traditional fixed speed (600r / min), representing a 13.4% improvement; Equipment failure rate: 1 minor temperature control failure occurred in 35 batches, a failure rate of 2.9%, compared to 9% for traditional methods; Production efficiency: Single batch production time reduced from 10min for traditional methods to 7.2min, representing a 28% improvement in efficiency.

[0065] This weighing liquid addition device control method eliminates temperature and pipeline residual errors through a dynamic weighing correction algorithm, resulting in smaller deviations in the metering accuracy of single liquids. A collaborative conveying flow algorithm ensures precise synchronization of multiple liquids according to the formula ratio, minimizing mixing errors and preventing insufficient product efficacy or excessive component levels due to mixing deviations, thus improving product qualification rates. Furthermore, a production data verification algorithm enables error-free data interaction with external production systems, eliminating human input errors. The entire process is automatically controlled by algorithms for conveying, spraying, temperature control, and fault handling, requiring no manual intervention, reducing operator workload, and significantly enhancing production continuity. Simultaneously, an adaptive spraying angle algorithm adapts to liquids ranging from low to high viscosity, and the temperature control coordination function solves the problem of high-viscosity liquid solidification in low-temperature environments, reducing pipeline blockage rates. It supports the simultaneous addition of multiple liquids, adapting to various production scales. A fault code library and remote maintenance functions further reduce equipment failure rates, resulting in superior operational stability and compatibility. In addition, the stirring speed is dynamically adjusted according to the viscosity and type of liquid to improve the uniformity of mixing multiple liquids, avoid excessively high local additive concentrations due to uneven mixing, and further ensure product quality stability. The stirring speed is adjusted as needed, and the speed is increased only when necessary for high-viscosity liquids, balancing mixing effect and energy saving requirements.

[0066] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A control method for a weighing liquid addition device, characterized in that, Includes the following steps: S1: Obtain liquid formula parameters issued by the external production system, and simultaneously collect real-time temperature, pipeline pressure, and measured data of the weight of the liquid in each raw material hopper and batching hopper. S2: The actual mass of the raw material liquid is corrected based on the dynamic weighing correction algorithm. The instantaneous feed flow rate of each raw material hopper to the batching hopper is controlled by the collaborative conveying flow algorithm. The nozzle spraying angle is adjusted by the spraying angle adaptive algorithm. The data interaction accuracy with the external production system is ensured by the production data verification algorithm. The mixing uniformity of multiple liquids in the batching hopper is adjusted by the stirring speed adaptive algorithm. S3: Based on the algorithm output, control the opening of the fast / slow valve, the nozzle angle, the operation of the stirring rod, and the temperature control; monitor the equipment condition in real time and generate operation reports. S4: When a parameter is detected to exceed a preset threshold, an audible and visual alarm is triggered on the touchscreen, and troubleshooting suggestions are pushed out. At the same time, the fault information is synchronized to the external production system.

2. The control method for a weighing liquid adding device according to claim 1, characterized in that: In step S1, a temperature control threshold is preset according to the liquid type, and the heating element of the raw material hopper is activated for temperature control to prevent high-viscosity liquid from solidifying and clogging the pipeline.

3. The control method for a weighing liquid adding device according to claim 1, characterized in that: In step S2, the dynamic weighing correction algorithm is used to eliminate the influence of temperature on weighing accuracy. The algorithm formula is as follows: Mcalibrated = Mmeasured × [1 + α × (Tactual - Tstandard)] - ΔMresidual Where: Mcalibrated is the actual mass of the liquid after calibration, in g, which is the reference value for measurement control; Mmeasured is the actual measured mass of the liquid, in g; α is the temperature influence coefficient of the raw material hopper and the batching hopper, in % / ℃, which is determined experimentally based on the material of the raw material hopper and the batching hopper; Tactual is the real-time temperature of the liquid, in ℃; Tstandard is the standard temperature for weighing calibration, in ℃, which is set to 25℃ by default; ΔMresidual is the residual mass of the liquid in the pipe between the raw material hopper and the batching hopper, in g, which is calculated based on the pipe length L and the liquid viscosity μ, ΔMresidual = 0.1 × L × μ; this algorithm controls the weighing error within ±0.5g.

4. The control method for a weighing liquid adding device according to claim 3, characterized in that: In step S2, the collaborative transport flow algorithm is used to achieve synchronous transport of multiple liquids according to the formula ratio. The algorithm formula is as follows: Qn=Qtotal×(ωn×ρbase) / Σ(ωi×ρi), Where: Qn is the instantaneous delivery flow rate of the nth liquid, in mL / s; Qtotal is the total instantaneous delivery flow rate of all liquids, in mL / s, calculated based on the target total addition and preset delivery time, Qtotal = Vtotal / Tdeliver, where Vtotal is the total liquid volume and Tdeliver is the preset delivery time; ωn is the formula mass fraction of the nth liquid, in %, issued by the external production system; ρbase is the density of the reference liquid, in g / mL, with the lowest viscosity liquid selected by default, ρbase = 1.05 g / mL; ωi is the formula mass fraction of the ith liquid, in %; ρi is the real-time density of the ith liquid, in g / mL, corrected according to the real-time temperature Tact, ρi = ρstandard × [1 - β × (Tact - Tstandard)], where β is the liquid density temperature coefficient and ρstandard is the standard density of the liquid.

5. The control method for a weighing liquid adding device according to claim 4, characterized in that: In step S2, the spraying angle adaptive algorithm is used to adjust the nozzle angle according to the liquid viscosity to prevent the liquid from sticking to the inner wall of the equipment. The algorithm formula is as follows: θ_tuning = θ_standard × (1 + k × lgμ), Where: θ_adjustment is the adjusted spray angle of the nozzle, in °, with a value range of 30°-90°; θ_standard is the standard spray angle of the nozzle, in °, with a default setting of 60°; k is the viscosity influence coefficient, in ° / lg(mPa·s), calibrated experimentally, with a value of 0.8-1.2; μ is the real-time viscosity of the liquid, in mPa·s; when μ > 200 mPa·s, the upper limit of θ_adjustment is set to 90° to ensure that the liquid covers the target area in a wide fan shape with a viscosity rate of <5%.

6. The control method for a weighing liquid adding device according to claim 5, characterized in that: In step S2, the production data verification algorithm is used to verify the accuracy of data issued by the external production system. The algorithm formula is as follows: Cschool = Csend × (1 + δ × ttransmit) + ΔCsupplement Where: Ccorrected is the corrected formula data, in g or %; Cgenerated is the original formula data sent by the external production system, in g or %; δ is the data transmission attenuation coefficient, in % / s, set according to the communication distance: <100m δ = 0.001% / s, 100-500m δ = 0.002% / s; ttransmit is the data transmission time, in s, automatically timed by the communication module; ΔCcompensation is the data compensation value, in g or %, derived from historical transmission error statistics, with a value range of -0.1 to 0.1; when |Ccorrected - Cgenerated| > 0.2%, data retransmission is triggered.

7. The control method for a weighing liquid adding device according to claim 6, characterized in that: In step S2, the adaptive stirring speed algorithm is used to optimize the stirring speed based on the liquid viscosity and ratio, thereby improving the mixing uniformity. The algorithm formula is as follows: n_stirring = n_standard × (1 + 0.05 × lgμ_balance) × (1 + 0.03 × N_liquid), Where: n_stirring is the adjusted rotation speed of the stirring rod, in r / min, with a range of 300-1500 r / min; n_standard is the standard rotation speed of the stirring rod, in r / min, with a default setting of 600 r / min; μ_average is the average viscosity of the various liquids, in mPa·s, calculated by weighting by mass fraction, μ_average = Σ(ωi × μi) / 100, where μi is the viscosity of the i-th liquid; N_liquid is the number of liquid types, in terms of types; when μ_average > 500 mPa·s or N_liquid = 3, the upper limit of n_stirring is set to 1500 r / min to ensure a mixing uniformity > 95%.

8. The control method for a weighing liquid adding device according to claim 1, characterized in that: In S3, the real-time monitoring of equipment operating conditions specifically includes: monitoring the stability of the weighing sensor readings: if the reading fluctuation is greater than 3g within 10s, it is determined to be an abnormal weighing, the conveying is suspended and an alarm is triggered; monitoring the pump's operating current: if the current exceeds the rated value by 10%, it is determined to be an overload of the pump, and the system is automatically switched to the standby pump; monitoring the pipeline pressure: if the pipeline pressure is greater than 50kPa, it is determined to be a pipeline blockage, and if the pressure is less than 5kPa, it is determined to be a pipeline leak, and the system is immediately shut down and troubleshooting suggestions are sent.

9. A control method for a weighing liquid adding device according to claim 8, characterized in that: In S3, generating operation reports specifically includes: automatically recording the actual added mass, delivery time, spraying angle, and equipment operating data for each batch of liquid. The reports are stored in Excel or PDF format and can be exported locally or uploaded remotely to external production systems.

10. A control method for a weighing liquid adding device according to claim 1, characterized in that: In S4, fault diagnosis and feedback also include: establishing a fault code library, synchronously pushing corresponding troubleshooting steps when a fault is triggered, and sending fault information to the mobile APP of maintenance personnel through the Internet of Things module.

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