Intelligent filling machine control system

Abstract

The invention belongs to the technical field of filling equipment control, and particularly discloses and provides an intelligent filling machine control system which comprises a filling data acquisition module, an assembly health recognition module, a filling configuration module, a filling configuration updating module and a fault tolerance module. The filling data acquisition module acquires the voltage and pressure of a filling pump and the liquid level height of a to-be-filled object in real time; the component health recognition module dynamically updates a deviation threshold value based on a historical health record and predicts the pump health state, if it is judged that the pump health state is abnormal, the fault tolerance module is triggered to switch a redundant pump set and generate a block chain encryption alarm log, and otherwise, the filling configuration module is driven to configure the current filling rate according to the data set and issue an execution instruction; and the filling configuration updating module executes establishment of a dynamic adjustment mechanism, updates the rate in real time and triggers a change instruction. Real-time monitoring, health prediction, dynamic speed regulation and fault tolerance are integrated, and the filling efficiency and the equipment stability are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of filling equipment control, and in particular, relates to an intelligent filling machine control system. Background Art

[0002] The filling machine is an automated equipment used for precise delivery of liquid or gaseous media in industrial production and people's livelihood. It is widely used in the energy, chemical and other industries. With the upgrading of industrial automation and intelligent needs, traditional mechanical filling machines are gradually evolving towards high precision, intelligence and remote control.

[0003] Prior art, such as the Chinese invention patent application with application number 202110564943.7, discloses an intelligent control device and method for the state of a fuel filling oil pump, which monitors the current value, sets a threshold, and controls the operating state of the oil pump according to whether the current exceeds the threshold. The effect is to prevent the oil pump from idling, save energy, and extend the life of the equipment.

[0004] With regard to the above technical solutions, it is obvious that the filling equipment control still has the following deficiencies: 1. Currently, the equipment status is judged by a single current, which is a fixed value and a single factor judgment method. It does not take into account health issues such as equipment aging, which makes the validity of the judgment result have certain deviations.

[0005] 2. The existing fault setting is to shut down when idling, which affects the connectivity of the filling process. No fault-tolerant mechanism is set up for timely processing, and thus the filling efficiency cannot be guaranteed. It is also not suitable for filling operations in large production workshops.

[0006] 3. The impact of medium characteristics such as viscosity, temperature or bubbles on the filling process was not considered, and thus no treatment was made or changes were made to the filling operation, making it difficult to achieve targeted filling operations under different risk situations. Summary of the invention

[0007] In view of this, in order to solve the problems raised in the above background technology, an intelligent filling machine control system is now proposed.

[0008] The objective of the present invention can be achieved through the following technical scheme: The present invention provides an intelligent filling machine control system, which includes: a filling data acquisition module, which collects the voltage value and pressure value of the filling pump and the target filling liquid level height and current liquid level height of the object to be filled in real time, and generates a filling pump-filling pipe-filling object data set.

[0009] The component health identification module automatically updates the deviation threshold in combination with the historical health record table of the filling pump and predicts the health status of the filling pump. When the prediction is unhealthy, the fault tolerance mechanism is triggered, otherwise the filling configuration module is triggered.

[0010] The filling configuration module configures the current filling rate based on the filling pump-filling pipe-filling object data set, and sends the filling execution instruction to carry out filling.

[0011] The filling configuration update module, when executing the filling execution instruction, establishes a dynamic filling rate adjustment mechanism, outputs the current updated filling rate, and triggers the filling rate change instruction.

[0012] The fault tolerance module switches to the redundant pump group when the fault tolerance mechanism is triggered, and generates a blockchain encrypted alarm log.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention automatically updates the deviation threshold and predicts the health status of the filling pump by combining historical health records, dynamically configures and adjusts the filling rate, and switches to the redundant pump group and generates a blockchain encrypted alarm log when an unhealthy state is detected, thereby realizing real-time monitoring of the filling process, health status prediction, dynamic rate adjustment and fault tolerance, and improving filling efficiency, equipment reliability and operational safety.

[0014] (2) The present invention avoids shutdown caused by misjudgment due to fixed thresholds by dynamically updating the deviation threshold and using multi-parameter health prediction of voltage and pressure, solves the limitation of current fixed value and single factor judgment, realizes dynamic and accurate prediction of equipment health status, and avoids unnecessary shutdown.

[0015] (3) The present invention solves the production interruption problem caused by idling shutdown by setting up a fault-tolerant mechanism and automatically switching redundant pump groups, ensures the connectivity and efficiency of the filling process, and upgrades equipment maintenance from passive response to active prevention. It can meet the requirements of continuous operation of large workshops and realize timely handling of production problems.

[0016] (4) The present invention sets a compensating filling rate by combining the liquid level height and the medium viscosity, thereby configuring the filling rate, which can effectively improve the filling accuracy and avoid overflow or shortage caused by liquid level changes.

[0017] (5) The present invention monitors the bubble size and density in real time, corrects the liquid level measurement value, divides the risk interval, and updates the filling rate in a targeted manner, thereby solving the current problems of low filling accuracy and poor adaptability to working conditions caused by ignoring medium characteristics and bubble interference. It significantly reduces the filling error and achieves stable filling operations under different risk conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0019] Figure 1 This is a schematic diagram of the connection of the system modules of the present invention.

[0020] Figure 2 The present invention is a schematic flow chart of the steps for implementing the method.

[0021] Figure 3 This is a schematic diagram of the blockchain encryption alarm log generation process of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0023] See also Figure 1 to Figure 2 As shown, the present invention provides an intelligent filling machine control system, which includes: a filling data acquisition module, a component health identification module, a filling configuration module, a filling configuration update module and a fault tolerance module.

[0024] In the above, the filling configuration module is connected to the filling data acquisition module, the component health identification module and the filling configuration update module respectively, and the component health identification module is also connected to the fault tolerance module.

[0025] The filling data acquisition module collects the voltage value and pressure value of the filling pump and the target filling liquid level height and current liquid level height of the object to be filled in real time, and generates a filling pump-filling pipe-filling object data set.

[0026] Specifically, the voltage value of the filling pump can be monitored by a voltage sensor carried by the filling pump, and the pressure value can be monitored by a pressure sensor installed on the outlet or pipeline of the filling pump. The target filling liquid level height of the object to be filled can be automatically input manually or automatically set by the production system, that is, the target liquid level height is set through the human-machine interface (HMI), upper computer software or preset parameter input module, and the current liquid level height can be collected accordingly through an ultrasonic level meter.

[0027] The component health identification module automatically updates the deviation threshold in combination with the historical health record table of the filling pump, and predicts the health status of the filling pump. When the prediction is unhealthy, the fault tolerance mechanism is triggered, otherwise the filling configuration module is triggered.

[0028] Understandably, the historical health record table of the filling pump includes but is not limited to maintenance and inspection records, etc. The maintenance personnel record the degree of pump body wear, seal replacement cycle, lubricating oil status and other information during regular inspections to form a health score, and input the health score through a handheld terminal or HMI, and mark the input date.

[0029] Specifically, the deviation threshold is automatically updated, including: A1, extracting the cumulative running time from the historical health record table, setting the running time decay weight, and extracting the health score and inspection date of each historical inspection.

[0030] It is understandable that the running time decay weight can be set in the form of an exponential function. For example, the accumulated running time is recorded as , the specific expression formula of the running time decay weight is: , To set the reference cumulative running time, the specific value can be the healthy running time of the pump set at the factory, such as the cumulative running time of 100h. To set the constant, is a natural constant.

[0031] For example, if the difference between the maximum and minimum health maintenance days is less than or equal to the set number of days, The value is the initial default value, such as 0.01. If the difference is greater than the set number of days, the difference is recorded as ,but After the filling pump has been running for a long time, mechanical wear, material fatigue and other factors will cause the performance to gradually deteriorate. Combined with the increase in cumulative running time, the attenuation coefficient is automatically increased, which can ensure the adaptability and scientificity of the weight setting under the premise.

[0032] A2. Record the historical inspections with health scores greater than the set benchmark health score as target inspections, compare the inspection dates of each target inspection, output the number of days between each target inspection, and mark them as health maintenance days.

[0033] A3. Calculate the decay rate of the health score of the health maintenance days by curve fitting, and calculate the standard deviation of the health score corresponding to each historical reduction, which is recorded as volatility.

[0034] It can be understood that the target nucleic acid inspection order is used as the horizontal axis and the number of healthy maintenance days is used as the vertical axis. Curve fitting is performed by, for example, a linear regression curve, and the equation of the fitting curve is calculated by the least squares method and other methods. Among them, the decay rate can be expressed by the ratio of the opposite of the slope to the number of healthy maintenance days of the first nucleic acid inspection. Among them, linear regression curve fitting and the least squares method are used to calculate the equation of the fitting curve. Both are relatively common existing technologies and will not be elaborated here.

[0035] A4. Calculate the comprehensive adjustment coefficient based on the health maintenance days and the decay rate of the health score as well as the volatility and time decay weight of the health score, and correct the preset benchmark voltage deviation threshold, pressure deviation threshold, voltage fluctuation threshold and pressure fluctuation threshold, and combine and output the updated deviation threshold.

[0036] In a specific embodiment, the specific calculation process for calculating the comprehensive adjustment coefficient is: standardize the health maintenance days, the decay rate of the health score, and the volatility of the health score respectively to obtain each standardized processing item, and perform weighted average calculation on the time decay weight and each standardized processing item to obtain the comprehensive adjustment coefficient. Among them, the time decay weight is directly related to the timeliness of the data, which is the basis for dynamically correcting the health threshold, avoiding historical data from covering up the current aging trend, setting the maximum weight, and specifically taking a value of 0.35. The decay rate of the health score reflects the speed of equipment performance degradation, and rapid decay requires priority warning, and the weight is second, and specifically can take a value of 0.3. The volatility of the health score can reflect potential anomalies, and the value set can be 0.25. The decay rate of the health maintenance days is a long-term stability indicator, but it is less sensitive to sudden failures, so the weight is set to the lowest, which can take a value of 0.1.

[0037] In another specific embodiment, the standardization processing can be carried out through the minimum-maximum standardization processing formula, and when the number of healthy maintenance days, the decay rate of the health score, and the volatility of the health score exceed the corresponding reference interval set according to historical data, truncation processing is performed. For example, a value below the lower limit of the interval is directly assigned to 0, and a value exceeding the upper limit of the interval is directly assigned to 1.

[0038] Exemplarily, taking voltage deviation as an example, the specific correction formula for correcting the voltage deviation threshold is: , is the voltage deviation threshold of the preset reference, is the comprehensive adjustment coefficient, This is the updated voltage deviation threshold, which makes the deviation permission more stringent, thereby increasing the sensitivity to abnormal conditions of the pump group.

[0039] The embodiment of the present invention avoids shutdown caused by false positives due to fixed thresholds by dynamically updating deviation thresholds and using multi-parameter health predictions of voltage and pressure, solves the limitations of current fixed values ​​and single-factor judgments, and achieves dynamic and accurate prediction of equipment health status to avoid unnecessary shutdowns.

[0040] In yet another specific embodiment, predicting the health status of the filling pump includes: extracting a voltage data set and a pressure data set of the filling pump in a time series, and obtaining a maximum voltage deviation value and a maximum pressure deviation value after calculation with corresponding preset values.

[0041] The standard deviation of the voltage data set and the pressure data set of the filling pump in the time series is calculated and recorded as voltage fluctuation and pressure fluctuation respectively.

[0042] If the filling pump meets any of the following conditions, the filling pump is predicted to be unhealthy, otherwise the filling pump is predicted to be healthy.

[0043] 1) The maximum voltage deviation value or the maximum pressure deviation value exceeds the corresponding updated deviation threshold value.

[0044] 2) The voltage fluctuation or pressure fluctuation exceeds the corresponding updated fluctuation threshold.

[0045] The filling configuration module configures the current filling rate based on the filling pump-filling pipe-filling object data set, and sends a filling execution instruction to perform filling.

[0046] Specifically, the current filling rate is configured, including: B1, recording the difference between the target filling liquid level height and the current liquid level height as the remaining filling liquid level, and comparing it with the target filling liquid level height, and outputting the remaining liquid level filling ratio.

[0047] B2. Match the remaining liquid level filling ratio with the preset filling stage matching rule, and output the current matching filling stage.

[0048] Understandably, each filling stage includes a rapid filling stage, a precise adjustment stage, and a fine-tuning stage. Exemplarily, the filling stage matching rules are as follows: in the rapid filling stage, the remaining liquid level filling ratio is >10%, in the precise adjustment stage, the remaining liquid level filling ratio is 5%≤remaining liquid level filling ratio≤10%, and in the fine-tuning stage, the remaining liquid level filling ratio is <5%.

[0049] B3. Locate the baseline filling rate and theoretical upper limit filling rate set for the current matching filling stage from the filling operation plan table.

[0050] B4. Set the compensation filling rate, and obtain the adjusted filling rate by combining the compensation filling rate and the baseline filling rate, and use it as the current filling rate.

[0051] Furthermore, a compensation filling rate is set, including: B41, calculating the difference between the remaining liquid level filling ratio and the triggering remaining liquid level filling ratio corresponding to the current matching filling stage, and obtaining the liquid level deviation level adjustment factor through relative error calculation.

[0052] Understandably, the specific calculation formula of the liquid level deviation adjustment factor is as follows: ,in, Indicates the level deviation adjustment factor, Indicates the difference between the remaining liquid level filling ratio and the setting triggering the remaining liquid level filling ratio of the current matching filling stage. Indicates that the setting of the current matching filling stage triggers the remaining liquid level filling ratio. For example, when in the fast filling filling stage, the preset remaining liquid level filling ratio of this stage is 10%. If the actual remaining liquid level filling ratio is 15%, the excess remaining liquid level filling ratio is 15%-10%=5%, and the liquid level deviation level adjustment factor = 5%÷(1-10%)≈5.56%.

[0053] B42. Introduce the liquid level deviation adjustment factor to dynamically adjust the theoretical upper limit of the filling rate and output the actual permitted upper limit of the filling rate.

[0054] It can be understood that the theoretical upper limit filling rate is dynamically adjusted to the product of the liquid level deviation adjustment factor and the theoretical upper limit filling rate.

[0055] B43. Collect the current viscosity of the filling medium and the current temperature of the filling port, compare them with the corresponding reference values, and set the medium level adjustment factor.

[0056] B44. If the medium level adjustment factor is less than or equal to the set reference value, the difference between the actual permitted filling rate upper limit and the baseline filling rate will be used as the compensating filling rate. Otherwise, based on the difference between the medium level adjustment factor and the corresponding set reference value, the reduced filling rate value of the current filling stage will be matched from the filling operation plan table as the compensating filling rate.

[0057] For example, for further understanding, a specific value example in the filling operation plan table is performed with the current filling stage as the precise adjustment stage, which can be specifically shown in Table 1.

[0058] Table 1 Filling operation schedule

[0059]

[0060] Understandably, according to the relationship between the adjustment factor and the set value, the corresponding reduced filling rate value is obtained from the filling operation reference table, or by adjusting the upper limit of the actual permitted filling rate, the system can maintain stable filling performance under various complex situations, thereby improving the reliability and adaptability of the entire filling system. This method of setting the compensation filling rate comprehensively considers multiple factors such as liquid level deviation, medium characteristics and temperature, and can comprehensively improve the quality and efficiency of the filling process.

[0061] The embodiment of the present invention sets a compensation filling rate in combination with the liquid level height and the medium viscosity, thereby configuring the filling rate, which can effectively improve the filling accuracy and avoid overflow or shortage caused by liquid level changes.

[0062] Furthermore, in step B43, a medium level adjustment factor is set, including: B43-1, if the viscosity of the filling medium or if the current temperature of the filling port exceeds the current temperature of the filling port, a speed reduction mark is performed.

[0063] Understandably, fluids with high viscosity have high internal friction between molecules and poor fluidity. If filled at a normal rate, the fluid will have great resistance to flow in the pipeline, which may cause poor flow and blockage of the pipeline. Reducing the rate can make the fluid flow more smoothly.

[0064] The monitoring and corresponding marking of the viscosity and temperature of the filling medium can flexibly adjust the filling rate according to the actual situation of the medium, ensuring stable and efficient filling under different medium conditions. For example, when the medium viscosity exceeds the preset value, the filling rate is reduced through the speed reduction mark to prevent poor flow or equipment damage caused by high viscosity.

[0065] B43-2. ​​If no speed reduction mark is triggered, the medium level adjustment factor is assigned a value of 0.

[0066] B43-3. If a speed reduction mark is triggered once, when it is triggered by the viscosity of the filling medium, the viscosity of the filling medium is standardized, and the sigmoid function is input to generate the medium level adjustment factor. When it is triggered by the current temperature, the current temperature is standardized, and the sigmoid function is input to generate the medium level adjustment factor.

[0067] B43-4. If the speed reduction mark is triggered twice, the medium level adjustment factor corresponding to the viscosity of the filling medium and the current temperature is weighted and fused, and the medium level adjustment factor is output.

[0068] It can be understood that viscosity directly affects pumping power and pipeline resistance. The fusion weight is set larger to ensure the dominance of viscosity influence. For example, the value can be 0.6. Temperature fluctuations in the short term may accelerate viscosity changes. For example, the value can be 0.4 to balance the indirect effects.

[0069] The filling configuration update module, when executing the filling execution instruction, establishes a dynamic filling rate adjustment mechanism, outputs the current updated filling rate, and triggers the filling rate change instruction.

[0070] Specifically, a dynamic filling rate adjustment mechanism is established, including: D1, real-time collection of the density and size of bubbles in the filling liquid surface corresponding to the current object to be filled, and real-time detection of the liquid level height.

[0071] Understandably, an optical interference sensor array (wavelength range 400-1000nm) is used to scan the liquid surface in the filling container in real time, detect the density and size of the surface bubbles, and a capacitive liquid level sensor (resolution ±0.05mm) is used to synchronously monitor the real-time liquid level height in the container, and redundant verification is used to eliminate liquid level fluctuation errors.

[0072] D2. Based on the bubble size in the real-time filling liquid surface, the real-time detected liquid level height is corrected, and the real-time remaining liquid level filling ratio is calculated based on the corrected real-time liquid level height.

[0073] The real-time detected liquid level height is corrected, including: extracting each bubble height from the real-time liquid surface bubble size, obtaining the average bubble height by mean calculation, and extracting the maximum bubble height.

[0074] Based on the real-time detected liquid level height, average bubble height and maximum bubble height, the corrected real-time liquid level height is calculated through comprehensive analysis.

[0075] Specifically, the specific calculation formula for calculating the corrected real-time liquid level height is: , Indicates the time point number. , Indicates The detected liquid level height at each time point is Indicates the corrected The liquid level height at a time point, represents the average bubble height, Indicates the maximum bubble height.

[0076] Understandably, the average bubble height reflects the overall foam coverage level of the liquid surface and is used for routine corrections. The maximum bubble height is used to capture local anomalies such as cavitation or violent stirring to prevent misjudgment caused by single-point interference. If only the average value is relied upon, local high-level foam will be missed when the bubble distribution is uneven. If only the maximum value is relied upon, overshoot will occur during sensor noise or short-term bubble bursts. Therefore, the comprehensive average and maximum bubble heights can effectively compensate for liquid level deviations in local large bubble scenarios and maintain correction stability when the foam is evenly distributed.

[0077] D3. Extract the maximum diameter from the bubble size in the current filling liquid surface, and divide the control intervals into three levels: low risk, medium risk and high risk based on the bubble density, maximum diameter and remaining liquid level filling ratio.

[0078] For ease of understanding, the control interval division example is shown in table form, as shown in Table 2.

[0079] Table 2 Schematic diagram of control area division rules

[0080]

[0081] D4. In the low-risk area, the proportional-integral algorithm is triggered based on the remaining liquid level filling ratio to generate a linear correction coefficient, and the current filling rate is corrected to output the current updated filling rate.

[0082] For example, the calculation formula for the current update filling rate adopted for the low risk interval is as follows: ,in, is the current filling rate, is the current update injection rate, is the remaining liquid level filling ratio, The calculated result corresponds to the linear correction coefficient, is the proportional coefficient, which is multiplied by the remaining liquid level filling ratio. size, and respond quickly and proportionally, so that the system can make preliminary adjustments to the deviation in time. is the integral coefficient, which is related to the integral term Multiply, is the remaining liquid level filling ratio The integral from the initial time 0 to the current time t reflects the accumulation effect of the liquid level deviation rate over time, which can eliminate the steady-state error of the system. By comprehensively considering the current liquid level deviation and the historical accumulation of deviations, a correction coefficient is obtained to adjust the control output of the system so that the liquid level can be stabilized near the target value.

[0083] D5. Trigger activation of the fuzzy adaptive control output in the medium risk interval to update the current refueling rate.

[0084] The specific control process of the fuzzy adaptive control includes: normalizing the bubble density, the maximum diameter and the remaining liquid level filling ratio, and taking the processing results as input variables.

[0085] The input variables are calculated through the fuzzy hierarchical algorithm to output the number of filling rate adjustments, and the current filling rate is corrected based on the filling rate adjustment coefficient to obtain the current updated filling rate.

[0086] Understandably, a specific example process of calculating the output filling rate adjustment times by using the fuzzy hierarchy algorithm includes: Step 1, defining the fuzzy set and membership function of the input variables.

[0087] For example, the bubble density (pieces / cm²) is divided into three fuzzy sets of "low", "medium" and "high", and its membership function is described by a triangular function, where "low" corresponds to 0-10 pieces / cm², "medium" corresponds to 5-25 pieces / cm², and "high" corresponds to 20-50 pieces / cm². This means that the degree to which the bubble density belongs to each fuzzy set in different intervals is different, and the triangular function can flexibly describe this fuzzy membership relationship.

[0088] For the maximum bubble diameter (mm), it is divided into three fuzzy sets of "small", "medium" and "large", and the trapezoidal function is used as the membership function. For example, "small" is 0-1mm, "medium" is 0.5-3mm, and "large" is 2-5mm. The trapezoidal function can better handle boundary conditions and is more suitable for parameters with clear boundaries such as the maximum bubble diameter.

[0089] For the remaining liquid level filling ratio, it is divided into three fuzzy sets of "less", "medium" and "more", and the Gaussian function is used as the membership function. For example, "less" corresponds to μ=0, σ=0.5. "Medium" corresponds to μ=1, σ=0.7. "More" corresponds to μ=2, σ=1. The Gaussian function has good smoothness and symmetry, and can better describe the fuzzy characteristics of the parameter such as the liquid level deviation change rate, which changes continuously and has a certain degree of uncertainty.

[0090] Step 2: match the fuzzy set combination of the current input variable in the preset fuzzy rule library, and calculate the membership value of the input variable based on the membership function, and filter out the minimum membership value under each matching rule as the activation strength of each matching rule.

[0091] Understandably, for each triggered rule, the activation strength of the rule is calculated based on the degree of matching of its conditional part with the input parameters. This process usually involves the operation of membership functions to determine the membership of the input parameters in various fuzzy sets, and then obtain the activation degree of the rule. For example, if the membership of bubble density in the "low" fuzzy set is 0.8, the membership of maximum bubble diameter in the "small" fuzzy set is 0.7, and the membership of liquid level change rate in the "slow" fuzzy set is 0.6, then the activation strength of rule 1 can be calculated comprehensively based on these three memberships.

[0092] For each triggered rule, the activation strength of the rule is calculated based on the degree of match between its conditional part and the input parameter. This process usually involves the operation of membership functions to determine the membership of the input parameters in various fuzzy sets, and then obtain the activation degree of the rule. For example, if the membership of bubble density in the "low" fuzzy set is 0.8, the membership of maximum bubble diameter in the "small" fuzzy set is 0.7, and the membership of liquid level change rate in the "slow" fuzzy set is 0.6, then the activation strength of rule 1 can be calculated based on these three memberships.

[0093] Step 3: Input the adjustment amount and activation strength corresponding to each triggered rule into the calculation formula through the center of gravity method, output the adjustment coefficient, adjust the current filling rate based on the adjustment coefficient, and output the current updated filling rate.

[0094] It can be understood that the calculation formula of the center of gravity method is a relatively common formula currently available, and its specific calculation formula is no longer given as an example.

[0095] D6. Trigger the step adjustment mode in the high-risk range and output the current updated refueling rate based on the step adjustment mode.

[0096] The embodiments of the present invention monitor the bubble size and density in real time, correct the liquid level measurement value, and divide the risk interval to perform targeted filling rate updates, thereby solving the current problems of low filling accuracy and poor adaptability to working conditions caused by ignoring medium characteristics and bubble interference, significantly reducing the filling error, and achieving stable filling operations under different risk situations.

[0097] Furthermore, the specific adjustment process of the step adjustment mode is as follows: based on the settings of each filling stage, trigger the remaining liquid level filling ratio, and output the filling level interval of each filling stage.

[0098] The ratio of the corrected current liquid level height to the target filling liquid level height is recorded as the critical filling trend ratio.

[0099] The upper limit value of the filling level interval in the current filling stage is recorded as the focus filling level height, and based on the corrected current liquid level height and the rich liquid level height, the remaining filling level height and the filling completion ratio are calculated.

[0100] If the critical trend ratio of filling is higher than the set reference value, 0 is used as the current updated filling rate and the bubble processing instruction is triggered. Otherwise, based on the critical trend ratio of filling and the filling completion ratio, the number of division echelons and the setting adjustment coefficients of each division echelon are matched from the filling echelon division data table.

[0101] The current filling rate is adjusted based on the set adjustment coefficients of each divided echelon, and the adjustment results are combined into the current updated filling rate.

[0102] It can be understood that adjusting the current filling rate is to set the product of the adjustment coefficient and the current filling rate.

[0103] In a specific embodiment, the filling echelon division data table takes three division echelons as an example, which can be specifically referred to as shown in Table 3, wherein the data shown in the table are jointly formulated by combining experimental data, historical data, expert experience, etc., and can be manually imported in advance by production management personnel.

[0104] Table 3 Refueling echelon division data table

[0105]

[0106] The fault tolerance module switches to the redundant pump group when the fault tolerance mechanism is triggered, and generates a blockchain encrypted alarm log.

[0107] The embodiment of the present invention solves the production interruption problem caused by idling shutdown by setting up a fault-tolerant mechanism and automatically switching redundant pump groups, ensures the connectivity and efficiency of the filling process, and upgrades equipment maintenance from passive response to active prevention. It can meet the requirements of continuous operation of large workshops and realize timely processing of production problems.

[0108] For further information, see Figure 3 As shown in the figure, the specific process of generating blockchain encrypted alarm logs includes:

[0109] (1) Detection fault

[0110] For example, if the maximum voltage deviation value of the filling pump exceeds the corresponding updated deviation threshold or the voltage fluctuation exceeds the corresponding updated fluctuation threshold, the fault will be recorded as an internal pressure abnormality; if the pressure fluctuation value of the filling pump exceeds the corresponding updated fluctuation threshold or the maximum pressure deviation value exceeds the corresponding updated deviation threshold, the fault will be recorded as a power supply abnormality.

[0111] (2) Generate alarm information

[0112] Exemplarily, the alarm log is formatted and generated to include the fields of timestamp, fault type, filling pump ID, and filling pump-filling pipe-filling object data set.

[0113] (3) Encrypted alarm information

[0114] Exemplarily, SHA3-256 is used to generate a unique hash value for the alarm information, and RSA-2048 is used to encrypt sensitive fields, such as voltage data sets and pressure data sets in time series, to ensure transmission security.

[0115] (4) Write encrypted information into the blockchain

[0116] Understandably, the steps of writing into the blockchain are: Packing encrypted data, hashes, and signatures into a new block. The validity of the data is confirmed by three verification nodes, such as the factory server, cloud platform, and auditor. The block is added to the chain through point-to-point broadcasting and synchronized to all nodes.

[0117] (5) Generate blockchain encrypted alarm log

[0118] The embodiments of the present invention automatically update the deviation threshold and predict the health status of the filling pump in combination with historical health records, dynamically configure and adjust the filling rate, and switch to the redundant pump group and generate a blockchain encrypted alarm log when an unhealthy state is detected, thereby realizing real-time monitoring of the filling process, health status prediction, dynamic rate adjustment and fault tolerance, and improving filling efficiency, equipment reliability and operational safety.

[0119] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. An intelligent filling machine control system, characterized in that: Includes the following modules: The filling data acquisition module collects the voltage and pressure values ​​of the filling pump and the target filling liquid level and current liquid level of the object to be filled in real time, and generates a filling pump-filling pipe-filling object data set; The component fault identification module automatically updates the deviation threshold based on the historical health record table of the filling pump and predicts the health status of the filling pump. If the prediction is unhealthy, the fault tolerance mechanism is triggered, otherwise the filling configuration module is triggered; The filling configuration module configures the current filling rate based on the filling pump-filling pipe-filling object data set, and sends a filling execution instruction to perform filling; The filling configuration update module, when executing the filling execution instruction, establishes a dynamic filling rate adjustment mechanism, outputs the current updated filling rate, and triggers the filling rate change instruction; The fault tolerance module switches to the redundant pump group when the fault tolerance mechanism is triggered, and generates a blockchain encrypted alarm log.

2. The intelligent filling machine control system according to claim 1, characterized in that: The automatic updating of the deviation threshold comprises: Extract the accumulated running time from the historical health record table, set the running time decay weight, and extract the health score and inspection date of each historical inspection; Record the historical examinations with health scores greater than the set benchmark health score as target examinations, compare the examination dates of each target examination, output the number of days between each target examination, and mark it as health maintenance days; The decay rate of the health score of the health maintenance days was calculated by curve fitting, and the standard deviation of the health score corresponding to each historical reduction was calculated as the volatility of the health score; The comprehensive adjustment coefficient is calculated based on the health maintenance days, the decay rate of the health score, the volatility of the health score and the time decay weight, and the preset benchmark voltage deviation threshold, pressure deviation threshold, voltage fluctuation threshold and pressure fluctuation threshold are corrected, and the updated deviation threshold is combined and output.

3. An intelligent filling machine control system as claimed in claim 2, characterized in that: The predicted filling pump health status includes: Extract the voltage data set and pressure data set of the filling pump in the time series, and calculate with the corresponding preset values ​​to obtain the maximum voltage deviation value and the maximum pressure deviation value; Calculate the standard deviation of the voltage data set and the pressure data set of the filling pump in the time series, which are recorded as voltage fluctuation and pressure fluctuation respectively; If the filling pump meets any of the following conditions, the filling pump is predicted to be unhealthy, otherwise it is predicted to be healthy; 1) The maximum voltage deviation value or the maximum pressure deviation value exceeds the corresponding updated deviation threshold value; 2) The voltage fluctuation or pressure fluctuation exceeds the corresponding updated fluctuation threshold.

4. The intelligent filling machine control system according to claim 1, characterized in that: The configuration of the current filling rate includes: The difference between the target filling level height and the current level height is recorded as the remaining filling level, and compared with the target filling level height, and the remaining level filling ratio is output; Match the remaining liquid level filling ratio with the preset filling stage matching rule, and output the current matching filling stage; Locate the baseline filling rate and theoretical upper limit filling rate of the current matching filling stage from the filling operation plan table; The compensation filling rate is set, and the adjusted filling rate is obtained by combining the compensation filling rate and the baseline filling rate, and is used as the current filling rate.

5. An intelligent filling machine control system as claimed in claim 4, characterized in that: The setting of the compensation filling rate includes: Calculate the difference between the remaining liquid level filling ratio and the corresponding setting triggering the remaining liquid level filling ratio in the current matching filling stage, and obtain the liquid level deviation level adjustment factor through relative error calculation; Introduce the liquid level deviation adjustment factor to dynamically adjust the theoretical upper limit filling rate and output the actual permitted filling rate upper limit; Collect the current viscosity of the filling medium and the current temperature of the filling port, compare them with the corresponding reference values, and set the medium level adjustment factor; If the medium level adjustment factor is less than or equal to the set reference value, the difference between the actual permitted filling rate upper limit and the baseline filling rate is used as the compensating filling rate; otherwise, based on the difference between the medium level adjustment factor and the corresponding set reference value, the reduced filling rate value of the current filling stage is matched from the filling operation plan table as the compensating filling rate.

6. An intelligent filling machine control system as claimed in claim 5, characterized in that: The step of setting the medium level adjustment factor includes: If the viscosity of the filling medium or the current temperature of the filling port exceeds the current temperature of the filling port, a speed reduction mark is performed; If no speed reduction mark is triggered, the medium level adjustment factor is set to 0; If a speed reduction mark is triggered once, when it is triggered by the viscosity of the filling medium, the viscosity of the filling medium is standardized, and the sigmoid function is input to generate the medium level adjustment factor; when it is triggered by the current temperature, the current temperature is standardized, and the sigmoid function is input to generate the medium level adjustment factor; If the speed reduction mark is triggered twice, the medium level adjustment factors corresponding to the viscosity of the filling medium and the current temperature are weighted and fused to output the medium level adjustment factors.

7. An intelligent filling machine control system as claimed in claim 4, characterized in that: The establishment of a dynamic filling rate adjustment mechanism includes: Real-time collection of the density and size of bubbles in the liquid surface corresponding to the object to be filled, and real-time detection of the liquid level height; Based on the bubble size in the real-time filling liquid surface, the real-time detected liquid level height is corrected, and the real-time remaining liquid level filling ratio is calculated based on the corrected real-time liquid level height; Extract the maximum diameter from the bubble size in the current filling liquid surface, and divide the control interval into three levels: low risk, medium risk, and high risk based on the bubble density, maximum diameter, and remaining liquid level filling ratio; In the low-risk area, the proportional-integral algorithm is triggered based on the remaining liquid level filling ratio to generate a linear correction coefficient, and the current filling rate is corrected to output the current updated filling rate; In the medium risk interval, the activation of fuzzy adaptive control is triggered to output the current updated filling rate; The step adjustment mode is triggered in the high-risk interval, and the current updated filling rate is output based on the step adjustment mode.

8. An intelligent filling machine control system as claimed in claim 7, characterized in that: The correcting of the real-time detected liquid level height comprises: Extract the height of each bubble from the real-time liquid surface bubble size, calculate the average bubble height by averaging, and extract the maximum bubble height at the same time; Based on the real-time detected liquid level height, average bubble height and maximum bubble height, the corrected real-time liquid level height is calculated through comprehensive analysis.

9. The intelligent filling machine control system according to claim 7, characterized in that: The specific control process of the fuzzy adaptive control includes: The bubble density, maximum diameter and remaining liquid level filling ratio are normalized and the processing results are used as input variables; The input variables are calculated through the fuzzy hierarchical algorithm to output the number of filling rate adjustments, and the current filling rate is corrected based on the filling rate adjustment coefficient to obtain the current updated filling rate.

10. The intelligent filling machine control system according to claim 7, characterized in that: The specific adjustment process of the step adjustment mode is as follows: Based on the settings of each filling stage, the remaining liquid level filling ratio is triggered and the filling level range of each filling stage is output; The ratio of the corrected current liquid level height to the target filling liquid level height is recorded as the critical filling trend ratio; The upper limit of the filling level interval in the current filling stage is recorded as the focus filling level height, and based on the corrected current liquid level height and the rich liquid level height, the remaining filling level height and the filling completion ratio are calculated; If the critical trend ratio of filling is higher than the set reference value, 0 is used as the current updated filling rate and the bubble processing instruction is triggered. Otherwise, the number of division echelons, the setting adjustment coefficient of each division echelon and the division liquid level ratio are matched from the filling echelon division data table based on the critical trend ratio of filling and the filling completion ratio. The current filling rate is adjusted based on the set adjustment coefficients of each divided echelon, and the adjustment results are combined into the current updated filling rate.

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