A cutting fluid full-automatic centralized liquid supply control method and system

Abstract

The application discloses a kind of cutting fluid full-automatic centralized liquid supply control method and system, cutting fluid concentration data and liquid supply system pressure data are acquired by sensor array, real-time monitoring data is generated using high-frequency sampling technique, if cutting fluid concentration data deviates from pre-set threshold range or liquid supply system pressure data exceeds stable range, then generate abnormal signal;According to abnormal signal, the operating frequency of liquid supply pump is adjusted using proportional-integral-derivative algorithm, a closed-loop control system is formed to maintain target pressure value, while adjusting the opening value of liquid distribution valve according to concentration deviation signal;From the adjusted liquid supply system, cutting fluid return liquid is extracted, and the cutting fluid return liquid is processed through multi-stage filtering device and centrifugal separation device, if the impurity content of cutting fluid return liquid is detected to be lower than pre-set threshold, then determine that purification treatment is completed.The application can keep cutting fluid performance stable, prolong the service life of equipment, improve machining quality and production efficiency, and has important practical value.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent manufacturing, and particularly discloses a full-automatic centralized cutting fluid supply control method and system. BACKGROUND

[0002] Cutting fluid management is a crucial link in modern manufacturing, directly affecting machining precision, equipment life and production efficiency. Under the background of intelligent manufacturing and green production, automatic cutting fluid supply and recycling purification have become key technologies to enhance the competitiveness of factories. However, traditional cutting fluid management methods have significant limitations, relying on manual operation, outdated monitoring methods, and low purification efficiency, leading to resource waste and high operating costs. In particular, frequent manual intervention makes it difficult to meet the needs of large-scale and complex production scenarios, and the lack of real-time data support makes it difficult to achieve precise control and fault prediction.

[0003] The core challenge lies in achieving full-process automation management of cutting fluid supply, monitoring and purification. Cutting fluid supply requires stable pressure output to ensure machining quality, but traditional systems have large pressure fluctuations, making it difficult to meet the needs of high-precision machining. Unstable pressure control further makes it difficult to accurately maintain the concentration of the liquid, and concentration deviation can affect the lubrication and cooling performance of the cutting fluid, increasing equipment wear. The lack of concentration management directly affects the recycling purification effect of the liquid, and traditional filtration technology is difficult to efficiently remove fine impurities and oil, resulting in a shortened cutting fluid life. These factors are interconnected, constituting the core technical problem of automation management.

[0004] Therefore, how to integrate intelligent sensing, closed-loop control and multi-stage purification technology to achieve stable cutting fluid supply pressure, precise concentration control and efficient recycling purification has become a key problem in promoting the upgrading of intelligent manufacturing. SUMMARY

[0005] The present application provides a full-automatic centralized cutting fluid supply control method and system, aiming to solve at least one of the defects in the prior art.

[0006] One aspect of the present application relates to a full-automatic centralized cutting fluid supply control method, comprising the following steps:

[0007] The cutting fluid concentration data and the supply system pressure data are obtained by a sensor array, real-time monitoring data is generated using high-frequency sampling technology, and if the cutting fluid concentration data deviates from the preset threshold range or the supply system pressure data exceeds the stable range, an abnormal signal is generated;

[0008] According to the abnormal signal, a proportional-integral-derivative algorithm is used to adjust the operating frequency of the supply pump to form a closed-loop control system to maintain the target pressure value, and the opening value of the liquid preparation valve is adjusted according to the concentration deviation signal;

[0009] Extracting the cutting fluid return liquid from the adjusted liquid supply system, processing the cutting fluid return liquid through a multi-stage filtering device and a centrifugal separation device, and if the impurity content of the cutting fluid return liquid is detected to be lower than a preset threshold value, determining that the purification treatment is completed;

[0010] Using a machine learning model to perform feature analysis on the performance parameters of the purified cutting fluid and the equipment operation data, and generating equipment wear trend prediction results through a pre-established fault prediction model;

[0011] According to the equipment wear trend prediction results, adjusting the cycle purification frequency and liquid supply parameters of the cutting fluid, and if the equipment wear is predicted to be intensified, updating the purification frequency parameters and optimizing the liquid supply pressure set value.

[0012] Further, through a sensor array, cutting fluid concentration data and liquid supply system pressure data are obtained, and high-frequency sampling technology is used to generate real-time monitoring data, and if the cutting fluid concentration data deviates from the preset threshold range or the liquid supply system pressure data exceeds the stable range, the step of generating an abnormal signal includes:

[0013] Through a sensor array, high-frequency sampling technology is used to obtain cutting fluid concentration data and liquid supply system pressure data from the machining equipment, generate real-time monitoring data streams, and store continuous time series data in a pre-established database;

[0014] According to the continuous time series data, the cutting fluid concentration data is compared with the preset threshold range, and if the cutting fluid concentration data exceeds the preset threshold range, it is marked as a concentration abnormal state to obtain a concentration abnormal identifier;

[0015] According to the continuous time series data, the liquid supply system pressure data is compared with the stable range, and if the liquid supply system pressure data exceeds the stable range, it is marked as a pressure abnormal state to obtain a pressure abnormal identifier;

[0016] Through signal generation logic, the concentration abnormal identifier and the pressure abnormal identifier are obtained, and if at least one of the concentration abnormal identifier and the pressure abnormal identifier is in an abnormal state, an abnormal signal is generated.

[0017] Further, according to the abnormal signal, a proportional-integral-derivative algorithm is used to adjust the operating frequency of the liquid supply pump, forming a closed-loop control system to maintain the target pressure value, and the step of adjusting the opening value of the liquid distribution valve according to the concentration deviation signal includes:

[0018] According to the abnormal signal, the operating frequency adjustment amount of the liquid supply pump and the opening adjustment amount of the liquid distribution valve are calculated through a control algorithm to generate frequency control instructions and opening control instructions;

[0019] The frequency control instruction is used to adjust the operating frequency of the liquid supply pump, and the opening control instruction is used to adjust the opening value of the liquid distribution valve, to generate a closed-loop control data stream, which is stored in a database to obtain real-time adjustment records.

[0020] Further, the cutting fluid return liquid is extracted from the adjusted liquid supply system, and the cutting fluid return liquid is processed through the multi-stage filtering device and the centrifugal separation device, and if the impurity content of the cutting fluid return liquid is detected to be lower than the preset threshold, the step of determining that the purification treatment is completed comprises:

[0021] The cutting fluid return liquid is extracted from the liquid supply system through the return liquid collection device, and the flow data of the cutting fluid return liquid is obtained by using a flow sensor to obtain a return liquid collection data stream;

[0022] The cutting fluid return liquid corresponding to the return liquid collection data stream is processed through the multi-stage filtering device, and the first-stage filtering screen is used to remove large-particle impurities, and the second-stage filtering screen is used to remove small particles, to obtain primary filtered liquid;

[0023] The primary filtered liquid is processed through the centrifugal separation device, and the centrifuge is used to separate residual particles from the liquid at a constant speed to obtain secondary purified liquid;

[0024] The impurity content data of the secondary purified liquid is obtained by using a detection sensor, and if the impurity content is lower than the preset threshold, it is determined that the purification treatment is completed, and a purification completion data stream is obtained.

[0025] Further, a machine learning model is used to analyze the performance parameters of the purified cutting fluid and the equipment operation data, and a fault prediction model is used to generate equipment wear trend prediction results, and the step comprises:

[0026] The cutting fluid performance parameters and equipment operation data are obtained from the equipment operating environment through a sensor data acquisition device, the cutting fluid flow value is collected by using a flow sensor, the cutting fluid temperature value is collected by using a temperature sensor, and the equipment vibration frequency data is collected by using a vibration sensor, to obtain an original collection data stream;

[0027] The original collection data stream is processed by using a data preprocessing module, the cutting fluid flow value, the cutting fluid temperature value, and the equipment vibration frequency data are denoised by using a mean filtering method, and the denoised data is dimensionally unified by using a standardization method, to obtain a preprocessed data stream;

[0028] The preprocessed data stream is analyzed by using a random forest algorithm, the flowability feature is extracted for the cutting fluid flow value, the thermal stability feature is extracted for the cutting fluid temperature value, and the equipment wear feature is extracted for the equipment vibration frequency data, to obtain a feature vector set;

[0029] The feature vector set is processed through a pre-established failure prediction model, a logistic regression method is used for classification and prediction of the liquidity features, thermal stability features and equipment wear features, if the prediction probability is higher than a preset threshold, it is judged that the equipment has a wear trend, and wear trend prediction data flow is obtained.

[0030] Further, according to the equipment wear trend prediction result, the circulation purification frequency and the liquid supply parameters of the cutting fluid are adjusted, if it is predicted that the equipment wear is aggravated, the purification frequency parameter is updated and the supply pressure set value is optimized.

[0031] According to the wear trend prediction data flow, the cutting fluid circulation period parameter is adjusted.

[0032] If it is predicted that the equipment wear is aggravated, the linear regression method is used to optimize the supply pressure set value, and the adjusted purification frequency parameter and the supply pressure value are obtained.

[0033] Another aspect of the application relates to a cutting fluid full-automatic centralized liquid supply control system for realizing the cutting fluid full-automatic centralized liquid supply control method, the cutting fluid full-automatic centralized liquid supply control system comprising:

[0034] The first generation module is used for acquiring cutting fluid concentration data and supply system pressure data through a sensor array, generating real-time monitoring data by using high-frequency sampling technology, and generating an abnormal signal if the cutting fluid concentration data deviates from a preset threshold range or the supply system pressure data exceeds a stable range.

[0035] The adjustment module is used for adjusting the operation frequency of the liquid supply pump according to the abnormal signal by using a proportional-integral-derivative algorithm, forming a closed-loop control system to maintain a target pressure value, and adjusting the opening value of the liquid supply valve according to the concentration deviation signal.

[0036] The determination module is used for extracting cutting fluid return liquid from the adjusted supply system, processing the cutting fluid return liquid through a multi-stage filtering device and a centrifugal separation device, and determining that the purification treatment is completed if the impurity content of the cutting fluid return liquid is lower than a preset threshold.

[0037] The second generation module is used for performing feature analysis on the purified cutting fluid performance parameters and equipment operation data by using a machine learning model, and generating an equipment wear trend prediction result through a pre-established failure prediction model.

[0038] The update module is used for adjusting the circulation purification frequency and the liquid supply parameters of the cutting fluid according to the equipment wear trend prediction result, and updating the purification frequency parameter and optimizing the supply pressure set value if it is predicted that the equipment wear is aggravated.

[0039] Further, the first generation module comprises:

[0040] The first acquisition unit is configured to acquire cutting fluid concentration data and liquid supply system pressure data from the machining equipment by using a high-frequency sampling technique of a sensor array, generate a real-time monitoring data stream, and store continuous time sequence data in a pre-established database;

[0041] The second acquisition unit is configured to compare the cutting fluid concentration data with a preset threshold range based on the continuous time sequence data, mark the cutting fluid concentration data as an abnormal concentration state if the cutting fluid concentration data exceeds the preset threshold range, and obtain concentration abnormality identification;

[0042] The third acquisition unit is configured to compare the liquid supply system pressure data with a stable range based on the continuous time sequence data, mark the liquid supply system pressure data as an abnormal pressure state if the liquid supply system pressure data exceeds the stable range, and obtain pressure abnormality identification;

[0043] The first generation unit is configured to acquire the concentration abnormality identification and the pressure abnormality identification by using a signal generation logic, generate an abnormal signal if at least one of the concentration abnormality identification and the pressure abnormality identification is in an abnormal state.

[0044] Further, the adjustment module comprises:

[0045] The second generation unit is configured to calculate an operating frequency adjustment amount of the liquid supply pump and an opening adjustment amount of the liquid distribution valve based on the abnormal signal by using a control algorithm, generate a frequency control instruction and an opening control instruction, and store the frequency control instruction and the opening control instruction in the database.

[0046] The fourth acquisition unit is configured to adjust the operating frequency of the liquid supply pump by using the frequency control instruction, adjust the opening value of the liquid distribution valve by using the opening control instruction, generate a closed-loop control data stream, and store the closed-loop control data stream in the database to obtain real-time adjustment records.

[0047] Further, the determination module comprises:

[0048] The fifth acquisition unit is configured to extract cutting fluid return liquid from the liquid supply system by using a return liquid collection device, acquire flow data of the cutting fluid return liquid by using a flow sensor, and obtain a return liquid collection data stream.

[0049] The sixth acquisition unit is configured to process the cutting fluid return liquid corresponding to the return liquid collection data stream by using a multi-stage filtering device, remove large-particle impurities by using a first-stage filtering screen, and remove small-particle impurities by using a second-stage filtering screen to obtain primary filtered liquid.

[0050] The seventh acquisition unit is configured to process the primary filtered liquid by using a centrifugal separation device, separate residual particles from liquid by using a centrifuge at a constant rotating speed, and obtain secondary purified liquid.

[0051] The eighth acquisition unit is configured to acquire impurity content data of the secondary purified liquid by using a detection sensor, determine that the purification process is completed if the impurity content is lower than a preset threshold value, and obtain a purification completion data stream.

[0052] The present application has the following beneficial effects:

[0053] The present application provides a kind of cutting fluid full-automatic centralized liquid supply control method and system, cutting fluid concentration and supply pressure are monitored in real time by sensor array, when detecting abnormality, automatically adjust supply pump frequency and liquid distribution valve opening degree, form closed loop control system.Meanwhile, the cutting fluid is filtered and centrifugally separated and purified in multiple stages, and the performance parameters of the cutting fluid and the equipment operation data are analyzed using a machine learning model to predict the equipment wear trend.According to the prediction result, the cutting fluid circulation purification frequency and supply parameters are dynamically adjusted to realize the intelligent management of cutting fluid and the preventive maintenance of equipment wear.The present application can keep the performance of cutting fluid stable, prolong the service life of equipment, improve the processing quality and production efficiency, and has important practical value. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 It is the flow chart of an embodiment of the present application for a kind of cutting fluid full-automatic centralized liquid supply control method. DETAILED DESCRIPTION

[0055] In order to better understand the above technical solutions, the above technical solutions will be described in detail in conjunction with the drawings and specific embodiments of the specification.

[0056] As Figure 1 The first embodiment of the present application proposes a kind of cutting fluid full-automatic centralized liquid supply control method, including the following steps:

[0057] Step S100, cutting fluid concentration data and supply system pressure data are obtained by sensor array, real-time monitoring data is generated by using high-frequency sampling technology, if cutting fluid concentration data deviates from preset threshold range or supply system pressure data exceeds stable range, then generate abnormal signal.

[0058] Cutting fluid concentration data refers to the volume or mass ratio of effective components (such as emulsified oil, additives, etc.) and diluent (usually water) in cutting fluid, which is used to quantify the mixing ratio state of cutting fluid and directly affects the performance of cooling, lubrication, rust prevention, etc.

[0059] Supply system pressure data refers to the sustained pressure value generated by fluid in pipeline per unit area when cutting fluid is transported in centralized liquid supply system, usually measured in megapascal (MPa), which is used to quantify the liquid delivery capacity and stability of the system to machining equipment.

[0060] High-frequency sampling technology refers to the process of continuously and rapidly collecting physical quantities at a rate much higher than the target signal change frequency (usually ≥1kHz). By obtaining dense data points in a short time, it accurately captures the instantaneous change characteristics of dynamic systems. It is a key technology in industrial automation, precision monitoring, and other fields.

[0061] Real-time monitoring data refers to the continuous collection, processing, and feedback of key system parameters (such as pressure, concentration, temperature, etc.) by sensors, instruments, and other hardware devices during industrial production or equipment operation, with millisecond to second response speed. It forms a dynamic closed-loop control chain to ensure that process parameters are within the preset threshold range in real time.

[0062] Abnormal signal refers to the parameter fluctuation, mutation or abnormal characteristics that deviate from the preset threshold or normal state detected by sensors or monitoring devices during the operation of industrial systems (such as cutting fluid supply systems, hydraulic systems). It usually manifests as pressure abnormalities, concentration deviations, contamination mixing, and equipment component failures, which need to trigger early warning or control intervention to ensure system stability and safety.

[0063] Step S200, according to the abnormal signal, the proportional-integral-derivative algorithm is used to adjust the running frequency of the liquid supply pump, and a closed-loop control system is formed to maintain the target pressure value, and the opening value of the liquid distribution valve is adjusted according to the concentration deviation signal.

[0064] Proportional-integral-derivative algorithm (PID algorithm) is a closed-loop control algorithm based on real-time feedback of error signal. By calculating the linear combination of proportional term (P), integral term (I), and derivative term (D), the system output is dynamically adjusted to eliminate the deviation between actual value and set target. The core goal of proportional-integral-derivative algorithm is to achieve accurate and stable control of controlled parameters (such as temperature, pressure, flow, etc.) through negative feedback mechanism.

[0065] Closed-loop control system (Closed-loop Control System) is an automatic adjustment system based on real-time feedback mechanism. It continuously collects the output signal of the controlled object, dynamically compares it with the preset target value (set value), calculates the error, generates correction instructions using control algorithm (such as PID), drives the actuator to adjust the input, and finally makes the output stable within the target range. The core feature of closed-loop control system is the closed-loop logic of "detection → feedback → correction", which has anti-interference and self-adaptive ability, and is widely used in industrial automation, robots, process control and other fields.

[0066] The target pressure value is a preset desired pressure parameter in an industrial control system, serving as the input set value of a closed-loop control process (such as the set pressure of 0.3 MPa in a constant-pressure water supply system). The actual pressure is fed back in real time by a sensor and compared with the target value, driving the actuator (such as a variable frequency pump or valve) to dynamically adjust the output, ultimately stabilizing the system pressure within the target range.

[0067] The concentration deviation signal is a key parameter in a closed-loop control system that characterizes the difference between the actual concentration and the target concentration, defined as the difference between the set concentration value and the actual concentration value detected by the sensor in real time.

[0068] The opening value of the liquid preparation valve is a quantitative parameter that represents the size of the valve's flow passage area, defined as the percentage of the maximum stroke of the valve's displacement or rotation angle from fully closed to fully open, used to accurately control the flow, pressure, and mixing ratio of fluids such as liquid medicine and solvents.

[0069] Step S300: Extracting cutting fluid return from the adjusted liquid supply system, processing the cutting fluid return through a multi-stage filtering device and a centrifugal separation device, and determining that the purification process is complete if the impurity content of the cutting fluid return is detected to be below the preset threshold.

[0070] Cutting fluid return refers to a closed-loop management system that recycles, filters, purifies, and reuses used cutting fluid during metal processing.

[0071] The multi-stage filtering device is a system device that removes impurities (such as metal debris, oil stains, and microorganisms) from cutting fluid or industrial fluid by connecting different precision and functional filtering units in series, aiming to achieve efficient purification and recycling. The core design of the multi-stage filtering device balances filtering efficiency and operating cost through a hierarchical filtering mechanism, ensuring that the fluid meets the cleanliness requirements of the processing technology during the recycling process.

[0072] The centrifugal separation device is a mechanical device that uses centrifugal force to efficiently separate mixed fluids (such as liquid-solid, liquid-liquid, or liquid-gas) by generating inertial centrifugal force through high-speed rotation, causing different density or particle size components to move differently, thereby achieving layering, sedimentation, or filtration.

[0073] Purification treatment refers to a systematic process that removes pollutants (such as suspended particles, oil stains, microorganisms, or toxic substances) from fluids (such as water, cutting fluid, industrial wastewater, etc.) through physical, chemical, or biological methods, making them meet specific use standards or environmental discharge requirements. The core goal of purification treatment is to separate and trap impurities, restore the functional characteristics of the fluid, or reduce its environmental hazards.

[0074] Step S400, using a machine learning model to analyze the performance parameters of the purified cutting fluid and the equipment operation data, and generating an equipment wear trend prediction result through a pre-established fault prediction model.

[0075] A machine learning model is a mathematical function or structure constructed based on data and algorithms. The core function of a machine learning model is to learn the rules and features in the training data set, establish the mapping relationship between input variables and output results, and thus realize the prediction, classification or decision-making of unknown data. The essence of a machine learning model is to optimize the prediction error by adjusting internal parameters (such as weights, biases, etc.), and finally form a generalizable rule system.

[0076] Cutting fluid performance parameters are a set of indicators used to quantify the functional performance of cutting fluid in the machining process, covering core characteristics such as lubricity, cooling, stability, and rust prevention, which directly affect machining efficiency, tool life, and workpiece surface quality.

[0077] Equipment operation data refers to a set of quantitative information reflecting the real-time state and performance parameters of mechanical equipment during operation, obtained through sensors, control systems or manual recording, covering key indicators such as equipment energy consumption, workload, environmental parameters and operating efficiency. The core function of equipment operation data is to realize equipment health monitoring, fault warning and process optimization through data collection and analysis.

[0078] A fault prediction model is a quantitative tool constructed based on real-time or historical operation data of equipment (such as temperature, vibration, current, etc. sensor parameters) through machine learning, statistical analysis methods or physical modeling techniques. Its core goal is to analyze the performance degradation rules and fault characteristics of equipment, predict the probability of future failure and remaining useful life (RUL), and provide decision support for preventive maintenance.

[0079] Equipment wear trend refers to the regular degradation process of the physical properties of equipment over time during use or idle, characterized by significant changes in wear rate at different stages. This trend is usually quantified by wear amount-time curve, reflecting the dynamic evolution law of equipment from initial use to functional failure, and is the core basis for developing preventive maintenance strategies.

[0080] Step S500, adjust the recycling purification frequency and supply parameters of the cutting fluid according to the equipment wear trend prediction result. If the equipment wear is predicted to intensify, update the purification frequency parameter and optimize the supply pressure set value.

[0081] Cyclic purification frequency refers to the number of times a specific system or device completes a full process of pollutant filtration, water quality renewal, or air quality improvement per unit of time. Its core objective is to maintain the cleanliness, safety, and functionality of environmental media through periodic purification, and its specific manifestations vary depending on the application scenario.

[0082] Fluid supply parameters refer to the key control indicators set in industrial processing to ensure the effective delivery of cutting fluid or grinding fluid and maintain its performance. They cover the physical and chemical properties of the liquid, the operating parameters of the circulation system, and the requirements for contaminant control, and directly affect processing quality, equipment life and cost-effectiveness.

[0083] The fluid supply pressure setpoint refers to the pre-set pressure control target value in industrial fluid supply systems (such as cutting fluid and coolant circulation systems) to ensure effective fluid coverage of the machining area, stable chip removal, and matching equipment performance. The fluid supply pressure setpoint directly determines the fluid delivery efficiency, machining surface quality, and energy consumption level, and needs to be dynamically optimized based on pipeline resistance, flow requirements, and process characteristics.

[0084] Furthermore, in the fully automatic centralized cutting fluid supply control method provided in this embodiment, step S100 includes:

[0085] Step S110: Using a sensor array and high-frequency sampling technology, acquire cutting fluid concentration data and fluid supply system pressure data from the processing equipment, generate a real-time monitoring data stream, and store it in a pre-established database to obtain continuous time series data.

[0086] In one possible implementation, the sensor array employs high-frequency sampling technology to acquire cutting fluid concentration and supply system pressure data from the machining equipment, forming a real-time monitoring data stream. For example, the sensors collect concentration and pressure data at a frequency of 100 times per second, ensuring the data stream has high temporal resolution.

[0087] The cutting fluid concentration is measured using an optical refractive sensor, based on the linear relationship between refractive index and concentration, with a typical concentration range of 5% to 15%.

[0088] The pressure of the liquid supply system is obtained through a piezoelectric sensor, and the normal pressure range is 2 to 5 bar.

[0089] The collected data is transmitted to a database via an Industrial Internet of Things (IIoT) protocol, generating a continuous time series. This high-frequency sampling can capture transient changes, significantly improving the timeliness of anomaly detection.

[0090] Specifically, when storing time-series data, a time-series database is used to optimize storage efficiency. For example, each record includes a timestamp, concentration value, and pressure value, such as 2025-05-03 10:00:00, concentration 7.8%, pressure 3.2 bar. The database supports second-level queries, facilitating subsequent analysis. The advantage of storing continuous time series data is that it allows for tracing historical trends, providing a data foundation for analyzing the causes of anomalies.

[0091] Step S120: Based on the continuous time series data, compare the cutting fluid concentration data with the preset threshold range. If the cutting fluid concentration data exceeds the preset threshold range, mark it as an abnormal concentration state and obtain an abnormal concentration identifier.

[0092] In one embodiment, the cutting fluid concentration is compared to a preset threshold range of 5% to 15%. If the concentration is below 5% or above 15%, it is marked as an abnormal concentration. For example, if a sample concentration is 4.2%, which is below the threshold, the system generates an abnormal concentration flag. Low concentrations may lead to insufficient lubrication and increased tool wear; high concentrations may cause foaming, affecting machining accuracy. Generating abnormal concentration flags helps to quickly locate problems and reduce the risk of equipment damage.

[0093] Step S130: Based on the continuous time series data, compare the pressure data of the liquid supply system with the stable range. If the pressure data of the liquid supply system exceeds the stable range, it is marked as a pressure abnormality state, and a pressure abnormality identifier is obtained.

[0094] Preferably, the pressure data of the liquid supply system is compared with a stable range of 2 to 5 bar. If the pressure exceeds the range, such as a detected pressure of 5.8 bar, it is marked as an abnormal pressure condition. Excessive pressure may be caused by pipeline blockage, while insufficient pressure may be due to pump failure. The generation of an abnormal pressure indicator provides timely warnings and avoids processing interruptions.

[0095] It should be noted that pressure anomaly detection needs to be combined with historical data trends to avoid false alarms due to short-term fluctuations. For example, an indicator should only be triggered if the pressure exceeds the range for 5 consecutive seconds.

[0096] Step S140: Obtain concentration abnormality identifier and pressure abnormality identifier through signal generation logic. If at least one of the concentration abnormality identifier and pressure abnormality identifier is in an abnormal state, an abnormal signal is generated.

[0097] For example, the signal generation logic receives concentration anomaly flags and pressure anomaly flags, and determines whether to generate an abnormal signal through a logical OR operation. In one embodiment, if the concentration is 4.2% and the pressure is 3.5 bar, only the concentration is abnormal, and the system generates an abnormal signal; in another example, if the concentration is 7.8% and the pressure is 5.8 bar, only the pressure is abnormal, and an abnormal signal is still generated; if both are abnormal, such as a concentration of 4.2% and a pressure of 5.8 bar, a signal is still generated. This logic ensures that any single abnormality triggers a response, improving system robustness. The abnormal signal can be detected via audible and visual alarms or pushed to the operator interface, prompting timely adjustment of the concentration or inspection of the liquid supply system.

[0098] Understandably, the advantage of the above method lies in achieving precise monitoring of cutting fluid concentration and hydraulic pressure through high-frequency sampling, real-time comparison, and logical judgment.

[0099] For example, a machining workshop prevented a 30% reduction in tool life by promptly detecting abnormal fluid concentration and adjusting the cutting fluid ratio; similarly, a pressure anomaly warning system detected pump malfunctions in advance, reducing downtime by 2 hours. These beneficial effects significantly improved machining efficiency and equipment reliability while reducing maintenance costs.

[0100] Furthermore, in the fully automatic centralized cutting fluid supply control method provided in this embodiment, step S200 includes:

[0101] Step S210: Based on the abnormal signal, calculate the operating frequency adjustment of the liquid supply pump and the opening adjustment of the liquid distribution valve through the control algorithm, and generate frequency control command and opening control command.

[0102] Preferably, the abnormal signal triggers the control algorithm to calculate the adjustment amount of the operating frequency of the liquid supply pump and the adjustment amount of the opening of the liquid dispensing valve. For example, when the concentration is too low, the control algorithm increases the opening of the liquid dispensing valve to replenish the concentrate; when the pressure is too high, it reduces the frequency of the liquid supply pump to reduce the pipeline load.

[0103] In one embodiment, at a concentration of 5.8%, the algorithm generates a command to increase the opening by 10%; at a pressure of 4.8 bar, it generates a command to decrease the frequency by 5 Hz. These commands are transmitted to the actuator via industrial Ethernet to ensure precise adjustment.

[0104] Step S220: Adjust the operating frequency of the liquid supply pump using frequency control commands, adjust the opening value of the liquid distribution valve using opening control commands, generate a closed-loop control data stream, store it in the database, and obtain real-time adjustment records.

[0105] Understandably, frequency control commands and opening control commands drive the supply pump and dispensing valve, forming a closed-loop control data flow. For example, if the supply pump frequency is adjusted from 60Hz to 55Hz, and the dispensing valve opening increases from 50% to 60%, the relevant data is stored in the database, with a record such as 2025-05-03 10:00:01, frequency 55Hz, opening 60%. This closed-loop control stabilizes system operation through real-time feedback.

[0106] It should be noted that the closed-loop control data stream is stored in a database, forming real-time adjustment records that support subsequent traceability. For example, if the concentration is adjusted from 5.8% to 6.5% and the pressure is restored from 4.8 bar to 3.5 bar, the record shows that the adjustment process took 10 seconds. This recording facilitates the analysis of system response speed and stability, improving maintenance efficiency.

[0107] In one embodiment, the control algorithm incorporates historical data to optimize the adjustment amount. For example, analyzing the concentration trend over the past hour can predict changes in the dispensing valve opening and prevent over-adjustment. This data-driven approach enhances control accuracy and reduces manual intervention.

[0108] Furthermore, in the fully automatic centralized cutting fluid supply control method provided in this embodiment, step S300 includes:

[0109] Step S310: Extract the cutting fluid return from the fluid supply system through the return fluid acquisition device, and use a flow sensor to obtain the flow rate data of the cutting fluid return, thus obtaining the return fluid acquisition data stream.

[0110] For example, a fluid return acquisition device is used in a fluid supply system to extract cutting fluid return. Its core function is to provide data support for subsequent processing through precise flow monitoring. Flow sensors collect the flow rate data of the return fluid in real time, forming a return fluid acquisition data stream.

[0111] For example, a factory's liquid supply system returns 200 liters of liquid per minute. A flow sensor records data every second, generating a data stream such as "2025-05-03 10:00:01 flow rate 198 liters / minute". This data stream provides the basis for subsequent filtration and purification, ensuring the system can adjust its treatment strategy based on the actual return volume.

[0112] Step S320: The cutter fluid return corresponding to the data stream of the return fluid is processed by a multi-stage filtration device. Large particles are removed by the first-stage filter screen, and small particles are removed by the second-stage filter screen to obtain the primary filtrate.

[0113] In one possible implementation, a multi-stage filtration device grades the cutter fluid return. The first-stage filter typically uses a metal mesh with larger pores to target large particles such as metal shavings. Preferably, in one embodiment, the first-stage filter has a pore size of 0.5 mm, effectively intercepting particles larger than 0.5 mm in diameter. The second-stage filter uses a finer filter media with a pore size reduced to 0.05 mm, targeting tiny particles such as grinding debris. Specifically, the turbidity of the primary filtrate decreases from an initial 500 NTU to 100 NTU, significantly improving liquid cleanliness. This graded filtration method ensures efficient impurity removal through a progressively decreasing pore size design.

[0114] Step S330: The primary filtrate is treated by a centrifugal separation device. The residual particles and liquid are separated by a centrifuge at a constant speed to obtain the secondary purified liquid.

[0115] Understandably, centrifugal separators further process the primary filtrate by separating residual particles through high-speed rotation.

[0116] The centrifuge operates at a constant speed, such as 3000 rpm, and uses centrifugal force to throw denser particles to the outside, separating a clear secondary purified liquid.

[0117] In one embodiment, the impurity content of the secondary purified liquid after centrifugation decreased from 100 mg / L to 20 mg / L. This constant rotation speed design avoids uneven separation effect caused by rotation speed fluctuations and improves purification stability.

[0118] Step S340: Obtain impurity content data of the secondary purification liquid through the detection sensor. If the impurity content is lower than the preset threshold, it is determined that the purification process is completed and a purification completion data stream is obtained.

[0119] It should be noted that the detection sensor monitors the impurity content of the secondary purification solution in real time to determine whether the purification standard has been met. For example, if the preset impurity content threshold is 10 mg / L, and the detected value is 8 mg / L, a purification completion data stream is generated, recorded as 2025-05-03 10:00:05, impurity content 8 mg / L. Conversely, if the detected value is 15 mg / L, the system will trigger additional cycle processing until the standard is met.

[0120] In one embodiment, the detection sensor employs laser turbidity analysis technology, accurately measuring impurity content through the principle of light scattering to ensure reliable judgment results. Specifically, the generation of return liquid acquisition data streams and purification completion data streams provides traceable evidence for system operation. For example, in a certain process, the flow sensor records a return liquid flow rate of 195 liters / minute, the detection sensor confirms that the impurity content has decreased to 9 milligrams / liter, the entire process takes 30 seconds, and the data stream is stored in the database. This data recording facilitates subsequent analysis of system efficiency and optimization of the processing flow.

[0121] Preferably, the combination of multi-stage filtration and centrifugal separation, along with sensor monitoring, forms a complete purification chain. For example, a factory used this system to increase the recycling rate of cutting fluid from 60% to 85%, significantly reducing fluid waste. This chain design, through multi-level processing and precise detection, ensures efficient purification of the cutting fluid, providing a guarantee for the stable operation of the fluid supply system.

[0122] Furthermore, in the fully automatic centralized cutting fluid supply control method provided in this embodiment, step S400 includes:

[0123] Step S410: Obtain cutting fluid performance parameters and equipment operation data from the equipment operating environment through the sensor data acquisition device. Use a flow sensor to collect the cutting fluid flow rate value, a temperature sensor to collect the cutting fluid temperature value, and a vibration sensor to collect the equipment vibration frequency data to obtain the raw data stream.

[0124] For example, in cutting fluid management, the core of the sensor data acquisition device lies in acquiring key performance parameters in real time.

[0125] Flow sensors monitor the flow rate of cutting fluid, temperature sensors record changes in fluid temperature, and vibration sensors capture frequency signals during equipment operation. These sensors work together to form a multi-dimensional data stream. For example, in a factory's machining equipment, a flow sensor records the cutting fluid flow rate per second, generating data such as "2025-05-03 10:00:01 flow rate 180 liters / minute"; a temperature sensor simultaneously records the temperature as 45 degrees Celsius; and a vibration sensor detects the spindle vibration frequency as 50 Hz. This multi-parameter acquisition provides a comprehensive foundation for subsequent analysis.

[0126] Step S420: The raw data stream is processed by the data preprocessing module. The cutting fluid flow rate, cutting fluid temperature and equipment vibration frequency data are denoised by the mean filtering method. Then, the denoised data is standardized by the standardization method to obtain the preprocessed data stream.

[0127] In one possible implementation, the data preprocessing module optimizes the raw data stream. A mean filtering method smooths noise in flow, temperature, and vibration signals by averaging the continuous data. For example, flow data fluctuating between 178-182 liters / minute over 5 seconds is stabilized at 180 liters / minute after filtering.

[0128] Standardization methods unify data with different dimensions into dimensionless values, facilitating algorithmic processing. For example, temperature data is converted from degrees Celsius to standardized values ​​in the 0-1 range, ensuring it is on the same scale as flow and vibration data. This preprocessing improves data quality and lays the foundation for feature analysis.

[0129] Step S430: Perform feature analysis on the preprocessed data stream using the random forest algorithm. Extract flow characteristics from the cutting fluid flow rate value, extract thermal stability characteristics from the cutting fluid temperature value, and extract equipment wear characteristics from the equipment vibration frequency data to obtain a feature vector set.

[0130] Specifically, the Random Forest algorithm analyzes preprocessed data streams using multiple decision trees to extract key features. For flow rate, the algorithm identifies fluidity characteristics reflecting the circulation efficiency of the cutting fluid; for temperature, it extracts thermal stability features indicating the fluid's temperature control capability during machining; and for vibration frequency, it extracts equipment wear characteristics, suggesting potential damage to the spindle or cutting tool. For example, in one analysis, the algorithm found a flow rate as low as 170 liters / minute, resulting in a decrease in fluidity characteristics. Combined with a high temperature of 50 degrees Celsius and a vibration frequency rising to 60 Hz, this indicated potential equipment wear due to insufficient lubrication. This feature extraction provides accurate input for fault prediction.

[0131] Step S440: Process the feature vector set through the pre-established fault prediction model, and use the logistic regression method to classify and predict the flow characteristics, thermal stability characteristics and equipment wear characteristics. If the prediction probability is higher than the preset threshold, it is determined that the equipment has a wear trend, and the wear trend prediction data stream is obtained.

[0132] Preferably, the fault prediction model is based on logistic regression and classifies the faults by combining fluidity, thermal stability and equipment wear characteristics.

[0133] The model has a preset probability threshold of 0.8. If the predicted result is 0.85, the equipment is judged to have a wear trend. For example, after continuous operation, the feature vector of a certain piece of equipment shows a fluidity feature value of 0.6, a thermal stability feature value of 0.7, and a wear feature value of 0.9. The model outputs a wear probability of 0.87, triggering an early warning. The predicted data stream record is as follows: 2025-05-03 10:00:05, wear probability 0.87. This prediction mechanism supports timely maintenance decisions by quantifying risk.

[0134] Understandably, the combination of the above methods forms a complete link for monitoring the condition of cutting fluid and equipment.

[0135] Sensor acquisition ensures comprehensive data, preprocessing improves data quality, feature analysis uncovers potential problems, and fault prediction provides actionable insights. For example, a factory used this system to proactively identify spindle wear risks, adjust cutting fluid formulations and maintenance plans, and avoid downtime losses. This interconnected design, through multi-level analysis and accurate prediction, ensures stable equipment operation.

[0136] Furthermore, in the fully automatic centralized cutting fluid supply control method provided in this embodiment, step S500 includes:

[0137] Step S510: Based on the wear trend prediction data stream, adjust the cutting fluid circulation cycle parameters.

[0138] Adjusting cutting fluid circulation parameters based on wear trend prediction data streams involves a deep integration of equipment condition monitoring and cutting fluid management. The core of this approach lies in dynamically optimizing fluid supply strategies to address equipment wear risks. For example, wear trend prediction data streams typically include key indicators of equipment operation, such as vibration frequency, cutting fluid flow rate, and temperature. These data are analyzed to adjust circulation parameters. For instance, a machining equipment's prediction data stream shows a wear probability of 0.87 at 10:00:05 on May 3, 2025, indicating accelerated spindle wear. In this case, the circulation cycle needs to be shortened from the usual 4 hours to once every 2 hours to enhance lubrication and cooling.

[0139] It should be noted that the cycle frequency adjustment is based on the wear probability threshold in the data stream. The higher the threshold, the greater the wear risk, and the higher the cycle frequency needs to be. This dynamic adjustment mechanism ensures processing stability by responding to equipment status in real time.

[0140] Step S520: Determine whether the equipment wear is aggravated. If the aggravation of equipment wear is predicted, use the linear regression method to optimize the liquid supply pressure setting value to obtain the adjusted purification frequency parameter and liquid supply pressure value.

[0141] In one possible implementation, if increased equipment wear is predicted, a linear regression method is used to optimize the liquid supply pressure setpoint.

[0142] Linear regression establishes a model of the relationship between wear characteristics and fluid supply pressure using historical data. It takes the current wear characteristic value as input and outputs the optimal pressure setting.

[0143] Specifically, during the operation of equipment in a certain factory, the vibration frequency rose to 60 Hz, the cutting fluid flow rate dropped to 170 liters / minute, and the wear probability was 0.87.

[0144] Linear regression analysis of historical data revealed that when the wear characteristic value is 0.9, the fluid supply pressure needs to be increased from 2.5 bar to 3.0 bar. The adjusted pressure enhances the cutting fluid injection force, improves tool lubrication, and slows down the wear process.

[0145] Understandably, the optimization of hydraulic pressure needs to be combined with the equipment load and the material being processed. For example, when processing high-hardness alloys, the pressure adjustment range may be larger to ensure adequate coverage of the cutting fluid.

[0146] Preferably, the adjusted purification frequency parameters and the fluid supply pressure value need to be optimized in tandem to improve the performance of the cutting fluid.

[0147] Purification frequency refers to the filtration and regeneration cycle of cutting fluid. As wear intensifies, metal shavings and impurities accumulate more rapidly, necessitating a higher purification frequency. For example, the standard purification frequency is once daily; when the wear probability exceeds 0.8, it is adjusted to once every 12 hours. In one embodiment, after predicting wear trends, the equipment increases the purification frequency to twice daily, while the supply pressure is set to 3.0 bar and the flow rate is stabilized at 180 liters / minute. After this adjustment, the cleanliness of the cutting fluid improves, reducing secondary wear on the cutting tools caused by impurities.

[0148] It should be noted that increasing the purification frequency must take into account the capacity of the filter device to avoid overloading. For example, a factory optimized its cutting fluid management using the above methods. To address the risk of spindle wear, the circulation cycle was shortened to 2 hours, the supply pressure was increased to 3.0 bar, and the purification frequency was adjusted to twice daily. Data streams showed that the wear probability decreased from 0.87 to 0.65, indicating the effectiveness of the optimization measures. This multi-faceted, coordinated adjustment, through comprehensive optimization of circulation, pressure, and purification, forms a robust equipment protection chain.

[0149] Each parameter adjustment is based on the accurate input of the predicted data stream, and they support each other to ensure the stability of the equipment operation.

[0150] This invention relates to a fully automatic centralized cutting fluid supply control system, used to implement the aforementioned fully automatic centralized cutting fluid supply control method. The fully automatic centralized cutting fluid supply control system includes a first generation module, an adjustment module, a judgment module, a second generation module, and an update module. The first generation module acquires cutting fluid concentration data and supply system pressure data through a sensor array, and generates real-time monitoring data using high-frequency sampling technology. If the cutting fluid concentration data deviates from a preset threshold range or the supply system pressure data exceeds a stable range, an abnormal signal is generated. The adjustment module adjusts the operating frequency of the supply pump according to the abnormal signal using a proportional-integral-derivative algorithm, forming a closed-loop control system to maintain the target pressure value. Simultaneously, the opening value of the dispensing valve is adjusted according to the concentration deviation signal; the judgment module is used to extract the cutting fluid return from the adjusted fluid supply system, and process the cutting fluid return through a multi-stage filtration device and a centrifugal separation device. If the impurity content of the cutting fluid return is detected to be lower than the preset threshold, the purification process is determined to be complete; the second generation module is used to perform feature analysis on the performance parameters of the purified cutting fluid and the equipment operation data using a machine learning model, and generate equipment wear trend prediction results through a pre-established fault prediction model; the update module is used to adjust the circulation purification frequency and supply parameters of the cutting fluid according to the equipment wear trend prediction results. If the equipment wear is predicted to increase, the purification frequency parameters are updated and the supply pressure setting value is optimized.

[0151] Furthermore, the fully automatic centralized cutting fluid supply control system provided in this embodiment includes a first generation module comprising a first acquisition unit, a second acquisition unit, a third acquisition unit, and a first generation unit. The first acquisition unit is used to acquire cutting fluid concentration data and supply system pressure data from the processing equipment using a sensor array and high-frequency sampling technology, generating a real-time monitoring data stream and storing it in a pre-established database to obtain continuous time-series data. The second acquisition unit is used to compare the cutting fluid concentration data with a preset threshold range based on the continuous time-series data. If the cutting fluid concentration data exceeds the preset threshold range, it is marked as a concentration abnormality state, obtaining a concentration abnormality identifier. The third acquisition unit is used to compare the supply system pressure data with a stable range based on the continuous time-series data. If the supply system pressure data exceeds the stable range, it is marked as a pressure abnormality state, obtaining a pressure abnormality identifier. The first generation unit is used to acquire the concentration abnormality identifier and the pressure abnormality identifier through signal generation logic. If at least one of the concentration abnormality identifier and the pressure abnormality identifier is in an abnormal state, an abnormal signal is generated.

[0152] Preferably, the fully automatic centralized cutting fluid supply control system provided in this embodiment includes an adjustment module comprising a second generation unit and a fourth acquisition unit. The second generation unit is used to calculate the operating frequency adjustment amount of the fluid supply pump and the opening degree adjustment amount of the fluid distribution valve based on abnormal signals using a control algorithm, and generate frequency control commands and opening degree control commands. The fourth acquisition unit is used to adjust the operating frequency of the fluid supply pump using the frequency control commands and adjust the opening degree value of the fluid distribution valve using the opening degree control commands, generate a closed-loop control data stream, store it in a database, and obtain real-time adjustment records.

[0153] Furthermore, the fully automatic centralized cutting fluid supply control system provided in this embodiment includes a determination module comprising a fifth acquisition unit, a sixth acquisition unit, a seventh acquisition unit, and an eighth acquisition unit. The fifth acquisition unit is used to extract cutting fluid return from the supply system via a return fluid collection device, and uses a flow sensor to acquire the flow rate data of the cutting fluid return, obtaining a return fluid collection data stream. The sixth acquisition unit is used to process the cutting fluid return corresponding to the return fluid collection data stream through a multi-stage filtration device, using a first-stage filter to remove large particulate impurities, and then using a second-stage filter to remove small particles, obtaining primary filtrate. The seventh acquisition unit is used to process the primary filtrate through a centrifugal separation device, using a centrifuge at a constant speed to separate residual particles from the liquid, obtaining secondary purified liquid. The eighth acquisition unit is used to acquire impurity content data of the secondary purified liquid through a detection sensor; if the impurity content is lower than a preset threshold, the purification process is determined to be complete, obtaining a purification completion data stream.

[0154] The fully automated centralized cutting fluid supply control method and system provided in this embodiment, compared with existing technologies, uses a sensor array to monitor the cutting fluid concentration and supply pressure in real time. When an anomaly is detected, it automatically adjusts the frequency of the supply pump and the opening of the dispensing valve, forming a closed-loop control system. Simultaneously, the recovered cutting fluid undergoes multi-stage filtration and centrifugal separation purification treatment. Machine learning models are used to analyze cutting fluid performance parameters and equipment operating data to predict equipment wear trends. Based on the prediction results, the cutting fluid circulation and purification frequency and supply parameters are dynamically adjusted, achieving intelligent management of the cutting fluid and preventative maintenance of equipment wear. This embodiment can maintain stable cutting fluid performance, extend equipment service life, improve machining quality and production efficiency, and has significant practical value.

[0155] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if these modifications and modifications of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include these modifications and modifications.

Claims

1. A fully automatic centralized cutting fluid supply control method, characterized in that, Includes the following steps: The cutting fluid concentration data and the fluid supply system pressure data are acquired by a sensor array. Real-time monitoring data are generated by high-frequency sampling technology. If the cutting fluid concentration data deviates from the preset threshold range or the fluid supply system pressure data exceeds the stable range, an abnormal signal is generated. Based on the abnormal signal, the operating frequency of the liquid supply pump is adjusted using a proportional-integral-derivative algorithm to form a closed-loop control system to maintain the target pressure value. At the same time, the opening value of the liquid dispensing valve is adjusted according to the concentration deviation signal. The cutter fluid return is extracted from the adjusted fluid supply system and processed by a multi-stage filtration device and a centrifugal separation device. If the impurity content of the cutter fluid return is found to be lower than a preset threshold, the purification process is considered complete. Machine learning models are used to perform feature analysis on the performance parameters of the purified cutting fluid and equipment operating data. A pre-established fault prediction model is used to generate equipment wear trend prediction results. The cutting fluid performance parameters are a set of indicators used to quantify the functional performance of the cutting fluid during machining, specifically including: The cutting fluid performance parameters and equipment operation data are obtained from the equipment operating environment through a sensor data acquisition device. The flow sensor is used to collect the cutting fluid flow rate value, the temperature sensor is used to collect the cutting fluid temperature value, and the vibration sensor is used to collect the equipment vibration frequency data to obtain the raw data stream. The raw data stream is processed by a data preprocessing module. The cutting fluid flow rate, cutting fluid temperature and equipment vibration frequency data are denoised by mean filtering. Then, the denoised data is standardized by a standardization method to obtain a preprocessed data stream. The preprocessed data stream is subjected to feature analysis using the random forest algorithm. Flowability features are extracted from the cutting fluid flow rate, thermal stability features are extracted from the cutting fluid temperature, and equipment wear features are extracted from the equipment vibration frequency data, resulting in a feature vector set. The feature vector set is processed by a pre-established fault prediction model. Logistic regression is used to classify and predict the flow characteristics, thermal stability characteristics and equipment wear characteristics. If the prediction probability is higher than a preset threshold, it is determined that the equipment has a wear trend, and a wear trend prediction data stream is obtained. Adjust the circulation and purification frequency and supply parameters of the cutting fluid based on the predicted equipment wear trend. If the predicted equipment wear is to be aggravated, update the purification frequency parameters and optimize the supply pressure setting.

2. The fully automatic centralized cutting fluid supply control method as described in claim 1, characterized in that, The step of acquiring cutting fluid concentration data and supply system pressure data through a sensor array, generating real-time monitoring data using high-frequency sampling technology, and generating an abnormal signal if the cutting fluid concentration data deviates from a preset threshold range or the supply system pressure data exceeds a stable range includes: By using a sensor array and high-frequency sampling technology, cutting fluid concentration data and fluid supply system pressure data are obtained from the processing equipment, generating a real-time monitoring data stream, which is stored in a pre-established database to obtain continuous time series data. Based on the continuous time series data, the cutting fluid concentration data is compared with a preset threshold range. If the cutting fluid concentration data exceeds the preset threshold range, it is marked as an abnormal concentration state, and an abnormal concentration identifier is obtained. Based on the continuous time series data, the pressure data of the liquid supply system is compared with the stable range. If the pressure data of the liquid supply system exceeds the stable range, it is marked as a pressure abnormality state, and a pressure abnormality identifier is obtained. The concentration anomaly identifier and the pressure anomaly identifier are obtained through signal generation logic. If at least one of the concentration anomaly identifier and the pressure anomaly identifier is in an abnormal state, an abnormal signal is generated.

3. The fully automatic centralized cutting fluid supply control method as described in claim 1, characterized in that, Based on the abnormal signal, the operating frequency of the liquid supply pump is adjusted using a proportional-integral-derivative algorithm to form a closed-loop control system to maintain the target pressure value. Simultaneously, the opening value of the liquid dispensing valve is adjusted based on the concentration deviation signal. The steps include: Based on the abnormal signal, the operating frequency adjustment of the liquid supply pump and the opening adjustment of the liquid distribution valve are calculated by the control algorithm, and frequency control command and opening control command are generated. The operating frequency of the liquid supply pump is adjusted using the frequency control command, and the opening value of the liquid distribution valve is adjusted using the opening control command. A closed-loop control data stream is generated and stored in the database to obtain real-time adjustment records.

4. The fully automatic centralized cutting fluid supply control method as described in claim 1, characterized in that, The steps of extracting cutting fluid return from the regulated fluid supply system, processing the cutting fluid return through a multi-stage filtration device and a centrifugal separation device, and determining that the purification process is complete if the impurity content of the cutting fluid return is detected to be lower than a preset threshold include: The cutting fluid return is extracted from the fluid supply system through the return fluid collection device, and the flow rate data of the cutting fluid return is obtained by the flow sensor to obtain the return fluid collection data stream; The cutting fluid return fluid corresponding to the data stream of the returned fluid is processed by a multi-stage filtration device. The first-stage filter screen removes large particulate impurities, and the second-stage filter screen removes small particles to obtain primary filtrate. The primary filtrate is processed by a centrifugal separation device, and the residual particles and liquid are separated by a centrifuge at a constant speed to obtain a secondary purified liquid. The impurity content data of the secondary purification liquid is obtained by the detection sensor. If the impurity content is lower than the preset threshold, the purification process is determined to be complete, and a purification completion data stream is obtained.

5. The fully automatic centralized cutting fluid supply control method as described in claim 1, characterized in that, The steps of adjusting the cutting fluid circulation and purification frequency and supply parameters based on the equipment wear trend prediction results, and updating the purification frequency parameters and optimizing the supply pressure setting value if the predicted equipment wear is to be accelerated, include: Adjust the cutting fluid circulation cycle parameters based on the wear trend prediction data stream; To determine whether equipment wear is accelerating, if accelerated wear is predicted, a linear regression method is used to optimize the liquid supply pressure setting, resulting in adjusted purification frequency parameters and liquid supply pressure values.

6. A fully automatic centralized cutting fluid supply control system, used to implement the fully automatic centralized cutting fluid supply control method as described in any one of claims 1 to 5, characterized in that, The fully automatic centralized cutting fluid supply control system includes: The first generation module is used to acquire cutting fluid concentration data and supply system pressure data through a sensor array, and generate real-time monitoring data using high-frequency sampling technology. If the cutting fluid concentration data deviates from the preset threshold range or the supply system pressure data exceeds the stable range, an abnormal signal is generated. The adjustment module is used to adjust the operating frequency of the liquid supply pump according to the abnormal signal using a proportional-integral-derivative algorithm, forming a closed-loop control system to maintain the target pressure value, and at the same time adjust the opening value of the liquid dispensing valve according to the concentration deviation signal. The determination module is used to extract the cutter fluid return liquid from the adjusted fluid supply system, process the cutter fluid return liquid through a multi-stage filtration device and a centrifugal separation device, and determine that the purification process is completed if the impurity content of the cutter fluid return liquid is detected to be lower than a preset threshold. The second generation module is used to perform feature analysis on the performance parameters of the purified cutting fluid and the equipment operation data using a machine learning model, and generate equipment wear trend prediction results through a pre-established fault prediction model. The update module is used to adjust the circulation and purification frequency and supply parameters of the cutting fluid based on the predicted equipment wear trend. If the predicted equipment wear is to be aggravated, the purification frequency parameters are updated and the supply pressure setting is optimized.

7. The fully automatic centralized cutting fluid supply control system as described in claim 6, characterized in that, The first generation module includes: The first acquisition unit is used to acquire cutting fluid concentration data and fluid supply system pressure data from the processing equipment through a sensor array using high-frequency sampling technology, generate a real-time monitoring data stream, and store it in a pre-established database to obtain continuous time series data. The second acquisition unit is used to compare the cutting fluid concentration data with a preset threshold range based on the continuous time series data. If the cutting fluid concentration data exceeds the preset threshold range, it is marked as an abnormal concentration state, and an abnormal concentration identifier is obtained. The third acquisition unit is used to compare the pressure data of the liquid supply system with the stable range based on the continuous time series data. If the pressure data of the liquid supply system exceeds the stable range, it is marked as a pressure abnormality state, and a pressure abnormality identifier is obtained. The first generation unit is used to obtain the concentration anomaly identifier and the pressure anomaly identifier through signal generation logic, and generate an anomaly signal if at least one of the concentration anomaly identifier and the pressure anomaly identifier is in an abnormal state.

8. The fully automatic centralized cutting fluid supply control system as described in claim 6, characterized in that, The adjustment module includes: The second generation unit is used to calculate the operating frequency adjustment amount of the liquid supply pump and the opening adjustment amount of the liquid dispensing valve according to the abnormal signal through a control algorithm, and generate frequency control command and opening control command. The fourth acquisition unit is used to adjust the operating frequency of the liquid supply pump using the frequency control command, adjust the opening value of the liquid distribution valve using the opening control command, generate a closed-loop control data stream, store it in the database, and obtain real-time adjustment records.

9. The fully automatic centralized cutting fluid supply control system as described in claim 6, characterized in that, The determination module includes: The fifth acquisition unit is used to extract cutting fluid return from the fluid supply system through a return fluid acquisition device, and to acquire the flow rate data of the cutting fluid return using a flow sensor to obtain a return fluid acquisition data stream; The sixth acquisition unit is used to process the cutting fluid return corresponding to the return fluid acquisition data stream through a multi-stage filtration device. It uses a first-stage filter to remove large particulate impurities and a second-stage filter to remove small particles, thus obtaining primary filtrate. The seventh acquisition unit is used to process the primary filtrate through a centrifugal separation device, using a centrifuge to separate residual particles and liquid at a constant speed to obtain a secondary purified liquid; The eighth acquisition unit is used to acquire the impurity content data of the secondary purification liquid through a detection sensor. If the impurity content is lower than a preset threshold, the purification process is determined to be complete, and a purification completion data stream is obtained.

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