Intelligent low-temperature circulating pump equipment control system and method based on Internet of Things

Abstract

The invention relates to the technical field of low-temperature circulating pump control, and discloses an intelligent low-temperature circulating pump equipment control system and method based on the Internet of Things, and the system comprises an Internet of Things intelligent gateway module which is used for achieving the data communication among low-temperature circulating pump equipment, a cloud platform and a user terminal, collecting the operation parameters of the low-temperature circulating pump equipment, and sending the operation parameters to a cloud server; uploading to a cloud platform, and receiving a control instruction issued by the cloud platform; the low-temperature circulating pump equipment module comprises a sensor used for measuring operation parameters and an executing mechanism used for adjusting the equipment state; the cloud platform module is used for storing and processing the operation parameters of the low-temperature circulating pump equipment and generating an optimized control strategy; and a user terminal module. By adopting the technical scheme of data acquisition and transmission based on the intelligent gateway module of the internet of things, the accurate acquisition and real-time uploading of the operating parameters of the low-temperature circulating pump equipment are realized. The technical effects of efficient data transmission and real-time monitoring of the equipment state are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of cryogenic circulation pump control, and in particular to an intelligent cryogenic circulation pump equipment control system and method based on the Internet of Things. Background Art

[0002] Cryogenic circulating pump equipment is widely used in laboratories, industrial cooling, scientific research and other scenarios that require precise control of temperature and fluid flow. Especially in chemical experiments, material processing and other fields that have extremely high requirements for temperature control and fluid stability, the precise control of medium temperature and flow directly determines the accuracy of experimental results or the stability of production processes. However, current cryogenic circulating pump equipment still mainly relies on traditional control technology, which has many deficiencies in operational flexibility, data processing capabilities and automated control, and is difficult to meet the growing control needs in complex scenarios.

[0003] Existing cryogenic circulating pump systems generally adopt a local operation mode, and the operation is adjusted by manually setting the pump speed and temperature. This control method relies too much on manual operation and experience, which is not only inefficient, but also cannot achieve rapid response when the operating state fluctuates. For example, when the temperature, pressure or flow rate exceeds the expected range, the system cannot automatically identify or adjust, and manual intervention is often required to restore the normal operation of the equipment. This operation mode makes the real-time performance and reliability of the equipment significantly insufficient, especially in complex industrial or laboratory application scenarios, which can easily lead to operation errors or fail to achieve precise control goals. In addition, the data acquisition and processing capabilities of existing equipment are relatively limited. Although some equipment is equipped with sensors, the data collected by the sensors are usually only used for basic display or alarm functions, and lack the ability to deeply process and analyze data. For example, the equipment cannot identify the changing trend of temperature and flow rate through data, and cannot use historical data for optimization control or fault prediction. When the equipment parameters are abnormal, the system often cannot provide an effective automated response strategy, which may not only cause the equipment operation state to further deteriorate, but also seriously affect the accuracy of the experimental results or the stability of the production process. To this end, those skilled in the art propose an intelligent cryogenic circulating pump equipment control system and method based on the Internet of Things to solve the above problems. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides an intelligent low-temperature circulating pump equipment control system and method based on the Internet of Things, which solves the problem that the prior art adjusts the operation by manually setting the pump speed and temperature, and cannot identify the changing trends of temperature and flow through data.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent low-temperature circulation pump equipment control system based on the Internet of Things, comprising: The IoT intelligent gateway module is used to realize data communication between the cryogenic circulation pump equipment and the cloud platform and user terminals, collect the operating parameters of the cryogenic circulation pump equipment, upload them to the cloud platform, and receive control instructions issued by the cloud platform; Cryogenic circulating pump equipment module, sensors for measuring operating parameters and actuators for regulating equipment status; The cloud platform module is used to store and process the operating parameters of the low-temperature circulation pump equipment and generate optimized control strategies; the user terminal module is used to access the cloud platform through the network, remotely view the operating status of the low-temperature circulation pump equipment and set the operating parameters.

[0006] Preferably, the IoT smart gateway module includes: A multi-protocol conversion unit, used to convert the communication protocol of the cryogenic circulation pump equipment into a unified Internet of Things communication protocol; A data processing unit, used for filtering, detecting abnormal values ​​and converting the format of the collected equipment operation data; The communication unit is used to upload the processed data to the cloud platform and receive control instructions from the cloud platform.

[0007] Preferably, the low-temperature circulating pump equipment module comprises: Liquid level sensing unit, used for real-time detection of liquid medium level in the circulation system; A temperature sensing unit, used to collect the temperature of the circulating medium; A pressure sensing unit, used to detect the pressure difference between the inlet and outlet of the cryogenic circulation pump; A flow sensing unit, used to measure the flow of the circulating medium; The execution unit includes a pump speed control unit for adjusting the pump speed and a refrigeration control unit for adjusting the refrigeration power.

[0008] Preferably, the cloud platform module includes: Equipment information database unit, used to store model information, historical operation data and maintenance records of low-temperature circulating pump equipment; A data analysis unit, used to analyze the equipment operation status using a multi-objective optimization algorithm based on the historical data and real-time data of the cryogenic circulating pump equipment; A control strategy generating unit, used to generate control instructions according to target parameters set by the user; The user management unit is used to manage user permissions and record operation logs.

[0009] Preferably, the user terminal module includes: Equipment monitoring unit, used to display the real-time operating parameters of the cryogenic circulation pump equipment; Data query unit, used to query and analyze the trend of historical operation data; A remote control unit for setting target parameters and operating modes of the cryogenic circulation pump; The message notification unit is used to receive equipment failure warning information and maintenance suggestions from the cloud platform.

[0010] Preferably, the data analysis unit generates a control strategy through a multi-objective optimization algorithm, and the optimization objectives include: Minimize the deviation between the temperature of the circulating medium and the target temperature; Minimize the deviation between the flow rate of the circulating medium and the target flow rate; The overall energy consumption of equipment operation is minimized.

[0011] Preferably, the objective function of the multi-objective optimization algorithm balances temperature deviation, flow deviation and control energy consumption by setting different weight parameters, and the specific weight parameters can be dynamically adjusted according to the actual application scenario.

[0012] Preferably, the cloud platform module dynamically updates the control strategy in conjunction with a reinforcement learning algorithm, wherein the reinforcement learning algorithm includes a deep deterministic policy gradient algorithm, by: Establish a dynamic equipment status model to obtain equipment operating parameters in real time and predict future status; Generate control actions based on optimization goals and adjust strategies in real time based on equipment operation feedback; Dynamically optimizes the balance between temperature, flow and energy consumption.

[0013] Preferably, the liquid level sensing unit works in coordination with the flow sensing unit and the pressure sensing unit to form a liquid level-flow-pressure coupling control strategy for: When the liquid level is insufficient, the automatic liquid replenishment control is triggered to prevent the circulation pump from running idle; Adjust pump speed according to real-time changes in pressure and flow to maintain system stability.

[0014] An intelligent low-temperature circulation pump equipment control method based on the Internet of Things comprises the following steps: The operating parameters of the cryogenic circulation pump equipment, including temperature, flow, pressure and liquid level, are collected in real time through the IoT intelligent gateway module; Upload the operating parameters to the cloud platform module and process them using the data analysis unit; Generate control strategies based on historical and real-time data of equipment through multi-objective optimization algorithms and reinforcement learning algorithms; The control strategy is sent to the low-temperature circulation pump equipment module through the IoT intelligent gateway module to adjust the pump speed and refrigeration power; the equipment operating status is monitored through the sensor unit, and the control strategy is continuously adjusted in combination with the optimization algorithm to form a closed-loop feedback control.

[0015] The present invention provides an intelligent low-temperature circulation pump equipment control system and method based on the Internet of Things. It has the following beneficial effects: 1. The present invention adopts a data acquisition and transmission technology solution based on the IoT intelligent gateway module to achieve accurate acquisition and real-time upload of the operating parameters of the cryogenic circulation pump equipment. It achieves the technical effect of efficient data transmission and real-time monitoring of equipment status. Compared with the problem of poor equipment compatibility caused by relying on a single communication protocol in the prior art, it solves the defects of difficult access to multi-protocol equipment and insufficient real-time performance.

[0016] 2. The present invention combines multi-objective optimization algorithm and reinforcement learning algorithm to generate dynamic control strategy for the temperature, flow rate and energy consumption optimization of low-temperature circulating pump equipment. It achieves the effect of precise control and rapid response under complex working conditions. Compared with the traditional static control method, the present invention solves the problem of low equipment operation efficiency caused by rigid control strategy due to environmental changes.

[0017] 3. The present invention uses the cloud platform module to jointly analyze the historical data and real-time data of the equipment, and generates equipment operation adjustment suggestions based on trend prediction. It achieves the technical effect of comprehensive understanding of the operating status and preventive maintenance. Compared with the existing technology that only relies on real-time data analysis and ignores the accumulated value of historical data, it solves the problems of difficulty in early identification of equipment failures and insufficient system tuning capabilities.

[0018] 4. The present invention utilizes a closed-loop feedback control mechanism to continuously monitor the device status through the sensor unit and dynamically adjust the device operating parameters in combination with the cloud platform optimization strategy. The technical effects of high device operation stability and energy saving and consumption reduction are achieved. Compared with the traditional control scheme that is difficult to respond to parameter changes in a timely manner, the technical problem that the low-temperature circulation system is easily affected by environmental fluctuations and has high energy consumption is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the architecture of the system of the present invention; Figure 2 This is a schematic diagram of the IoT smart gateway module architecture of the system of the present invention; Figure 3 It is a schematic diagram of the module structure of the low-temperature circulating pump equipment of the system of the present invention; Figure 4 It is a schematic diagram of the cloud platform module architecture of the system of the present invention; Figure 5 A schematic diagram of the user terminal module architecture of the system of the present invention; Figure 6 Schematic diagram of the steps of the method of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the specification 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.

[0021] Please refer to the attached Figure 1-Figure 5 The embodiment of the present invention provides an intelligent low-temperature circulation pump equipment control system based on the Internet of Things, including: The IoT intelligent gateway module is used to realize data communication between the cryogenic circulation pump equipment and the cloud platform and user terminals, collect the operating parameters of the cryogenic circulation pump equipment, upload them to the cloud platform, and receive control instructions issued by the cloud platform; Specifically, the IoT intelligent gateway module serves as a bridge between the cryogenic circulation pump equipment, the cloud platform and the user terminal, and is a key part of data interaction and command forwarding in the entire system.

[0022] In the entire system, the role of the IoT smart gateway module is to realize the networking and intelligence of the cryogenic circulation pump equipment. This module is responsible for collecting real-time operating parameters from the cryogenic circulation pump equipment, and uploading the collected data to the cloud platform through protocol conversion and data processing; at the same time, it is also responsible for receiving the optimization control strategy generated by the cloud platform and forwarding the control instructions to the actuator of the cryogenic circulation pump equipment to adjust the equipment operation status.

[0023] The IoT smart gateway module consists of hardware and software, including a multi-protocol conversion unit, a data processing unit, and a communication unit. The following is a detailed description of the structure and function of each unit.

[0024] Multi-protocol conversion unit The multi-protocol conversion unit is used to solve the compatibility issues of communication protocols of different cryogenic circulation pump equipment, and convert the communication protocols on the equipment side (such as RS485, Modbus, etc.) into IoT communication protocols (such as MQTT, HTTPS) that can be recognized by the cloud platform and user terminals.

[0025] There are differences in the communication interfaces and protocols used by different devices. For example, a cryogenic circulation pump device may use the Modbus protocol to transmit data through the RS485 interface, while the cloud platform uses the lightweight MQTT protocol to receive data. The multi-protocol conversion unit completes the protocol conversion through the following steps: First, automatically detect the communication interface type and parameters of the device, such as baud rate, data bit, check bit, etc.; Then, the operating parameters, such as temperature, flow rate, and liquid level, are extracted according to the frame structure of the device protocol; Finally, the extracted data is encapsulated into standardized messages in MQTT or HTTPS format and uploaded to the cloud platform.

[0026] The converted MQTT message structure includes the following fields: Topic: identifies the purpose of the data, for example, device / temperature represents temperature data; Payload: contains the specific values ​​of the device operating parameters; QoS (Quality of Service): defines the reliability level of message transmission.

[0027] Data processing unit The data processing unit is used to process the operating parameters collected by the equipment in real time, including filtering, anomaly detection, and data formatting conversion, to ensure that the data uploaded to the cloud platform is accurate and reliable.

[0028] In order to deal with the noise and sudden changes in the equipment operation data, the data processing unit uses the Kalman filter algorithm to smooth the collected data. The Kalman filter algorithm optimizes the data through state prediction and update. The specific formula is: Status prediction: Status Update: in: Indicates the predicted value of the state at the current moment; represents the estimated value of the state at the current moment; F is the state transfer matrix, which describes the time change of the equipment operating parameters; B is the control input matrix, which represents the influence of the control input on the state; u t is the controlled variable, such as pump speed or cooling power; t is the measured value at the current moment; H is the observation matrix, which is used to map the real state of the device to the measured value; K t is the Kalman gain, which represents the weight coefficient between the predicted value and the measured value.

[0029] In addition, the data processing unit also supports outlier detection. For example, when a parameter (such as liquid level, L(t)) exceeds a preset threshold value L max or below the threshold L min When the system detects abnormal data, it will mark the data as abnormal and send an alert to the cloud platform.

[0030] In terms of data formatting, the data processing unit converts the collected analog signals into digital signals according to the requirements of the cloud platform and unifies them into a standardized format (such as JSON) to ensure data compatibility. For example, the standardized representation of liquid level data is: LiquidLevel = \{"Timestamp":t,"Value":L(t)}.

[0031] Communication unit The communication unit is used to realize two-way communication between the IoT smart gateway module and the cloud platform and the user terminal. In this embodiment, the communication unit supports wired communication (such as Ethernet) and wireless communication (such as 4G LTE), and selects a suitable transmission mode according to the actual scenario.

[0032] The communication unit uploads data and receives instructions in the following ways: Data upload: The real-time collected device parameters are uploaded to the cloud platform at fixed time intervals (such as every 30 seconds). At the same time, it supports event-triggered upload mechanisms. For example, when the temperature T(t) exceeds the safety threshold T max When an alarm occurs, the communication unit will immediately send an alarm message.

[0033] Instruction reception: The communication unit receives the control instruction from the cloud platform and forwards it to the low-temperature circulation pump equipment module. The instruction content includes the target temperature T target , target flow Q target and operating modes, etc.

[0034] As an option, the communication unit adopts the MQTT protocol for data transmission, and the quality of service level (QoS) of message delivery is set to 1, that is, "at least once" transmission to ensure data reliability.

[0035] In order to improve communication security, the communication unit integrates the TLS encryption protocol to encrypt the transmitted data to prevent the data from being tampered with or stolen during transmission.

[0036] The cryogenic circulation pump equipment module has sensors for measuring operating parameters and actuators for adjusting the equipment status. Specifically, the cryogenic circulation pump equipment module mainly collects the equipment's operating parameters (such as temperature, pressure, flow, etc.) and accurately controls the equipment's operating status through the actuator. In the entire system, the cryogenic circulation pump equipment module works closely with the IoT smart gateway module and the cloud platform module. The real-time collected data is uploaded through the IoT smart gateway module, and the cloud platform generates an optimized control strategy after analysis, and transmits the control instructions back to the actuator to adjust the equipment's operating status, thereby achieving closed-loop control.

[0037] The cryogenic circulating pump equipment module is one of the core parts of the system, responsible for the physical acquisition and real-time response of operating parameters. Its main components include multiple sensor units (such as temperature sensing unit, pressure sensing unit and flow sensing unit) and execution units (including pump speed control unit and refrigeration control unit). The following describes in detail the specific structure and working principle of the cryogenic circulating pump equipment module.

[0038] The low-temperature circulation pump equipment module realizes comprehensive monitoring and real-time control of the equipment operation status through the combination of sensor units and execution units. The sensor unit is mainly used to collect temperature, pressure and flow data during the operation of the equipment, while the execution unit realizes dynamic adjustment of the equipment status by adjusting the pump speed and refrigeration power.

[0039] Sensor unit The operation of the cryogenic circulating pump requires real-time monitoring of the temperature, flow rate, pressure and other parameters of the circulating medium. In order to ensure the accuracy and timeliness of the data, the cryogenic circulating pump equipment module of the present invention is provided with multiple sensor units, each of which corresponds to a monitoring requirement of an operating parameter.

[0040] The liquid level sensing unit is used to detect the real-time liquid level of the circulating liquid and trigger the corresponding alarm or liquid replenishment mechanism. The liquid level sensing unit uses a float liquid level sensor, which reflects the liquid height through the change of the float position. The liquid level signal is output to the IoT smart gateway module in the form of analog or digital quantity. If the liquid level is lower than the threshold L min , the system will start the refill pump and send an alarm signal to the cloud platform. The liquid level sensing unit can also use an ultrasonic liquid level sensor to accurately calculate the liquid level by detecting the distance between the liquid surface and the sensor. For example, the liquid level L(t) can be calculated by the following formula: L(t)=H max -d(t) Where: L(t) is the current liquid level; H max is the maximum height of the container; d(t) is the distance measured by the sensor.

[0041] The signal of the liquid level sensing unit is not only used for monitoring, but also works together with the flow and pressure sensing units to form a liquid level-flow-pressure coupling control logic.

[0042] The temperature sensing unit is used to monitor the temperature of the circulating medium to ensure that the equipment is maintained within the target temperature range. In this embodiment, the temperature sensing unit uses a high-precision thermocouple, and the detection range is set to -50°C to 20°C. The temperature sensing unit converts the collected signal into a standardized temperature value through a temperature compensation circuit and an analog-to-digital conversion module. For example, the real-time temperature T(t) can be expressed as: T(t)=k·V+T offset Where: T(t) is the real-time measured temperature; V is the sensor output voltage; k is the conversion coefficient between temperature and voltage; T offset is the temperature offset of the sensor.

[0043] The flow sensing unit is used to monitor the flow of the circulating medium and ensure that the flow is stable within the target range. In this embodiment, the flow sensing unit uses a turbine flowmeter, which drives the turbine to rotate through the fluid and outputs a pulse signal proportional to the flow. The calculation formula for the flow Q(t) is: Q(t)=f·k flow Where: Q(t) is the real-time flow; f is the frequency of the pulse signal; k flow is the calibration factor of the flow sensor.

[0044] Pressure sensing unit The pressure sensing unit is used to detect the inlet and outlet pressure difference of the low-temperature circulation pump and determine the system resistance and pump operation efficiency. The calculation formula of the pressure signal P(t) is: Where: P(t) is the real-time pressure value; V is the sensor output voltage; V ref is the reference voltage; R is the sensor sensitivity.

[0045] The liquid level sensing unit works in coordination with the flow sensing unit and the pressure sensing unit to enhance the stability and safety of equipment operation through the liquid level-flow-pressure coupling control logic.

[0046] Insufficient liquid level will cause the pump to suck in air, which will cause flow fluctuations and pressure abnormalities. To this end, the system achieves dynamic adjustment through the following logic: Flow fluctuation detection: When the real-time flow deviation ΔQ(t) = |Q(t)-Q target |Exceeds the set threshold ΔQ max , the system will reduce the pump speed N(t): N(t+1)=N(t)-α·ΔQ(t) Where: N(t) is the pump speed at the current moment; N(t+1) is the adjusted pump speed (the pump speed at the next moment); α is the pump speed adjustment coefficient; ΔQ(t) is the real-time flow deviation; Q(t) is the actual flow measured at the current moment.

[0047] Liquid level filling control: When the liquid level drops When the fluid replenishment capacity is exceeded, the system dynamically increases the fluid replenishment flow rate Q in : Where: Q in (t) is the fluid replenishment flow rate at the current moment; Q in (t+1) is the adjusted infusion flow rate at the next moment; γ is the infusion adjustment coefficient; is the liquid level drop speed; L is the liquid level height at the current moment; β is the liquid replenishment rate threshold.

[0048] Pressure balance adjustment: When the outlet pressure P out When the fluctuation is abnormal, the system mitigates the pressure fluctuation by adjusting the liquid level and reduces the cooling power u(t) to maintain the thermal balance of the system.

[0049] Execution Unit The execution unit is an important component in the low-temperature circulation pump equipment module for adjusting the operating status of the equipment. Its main function is to dynamically adjust the pump speed and refrigeration power of the circulation pump according to the control instructions issued by the cloud platform.

[0050] The pump speed control unit controls the flow of the circulating medium by adjusting the motor speed of the circulating pump. The pump speed control unit uses variable frequency control technology to accurately control the motor speed by adjusting the input voltage and frequency. The relationship between pump speed and flow can be approximately expressed as: Q=α·N Where: Q is the flow rate; N is the pump speed (rotation speed); α is the proportional coefficient of flow rate and rotation speed (related to the characteristics of the pump).

[0051] The refrigeration control unit is responsible for adjusting the power of the refrigeration compressor, thereby changing the refrigeration capacity of the refrigeration cycle system and ensuring that the temperature of the circulating medium remains within the target range. The control principle of the refrigeration control unit can be expressed as: Q in =η c ·P cooling Where: Q in is the cooling power; c is the refrigeration efficiency; P cooling is the power input of the compressor.

[0052] The refrigeration control unit adopts a closed-loop control structure, which automatically adjusts the compressor power according to the deviation between the real-time temperature value fed back by the temperature sensing unit and the target temperature value. The control algorithm of the closed-loop control can use proportional-integral-differential (PID) control, and its control formula is: Where: u(t) is the control input (compressor power adjustment value); K p is the proportionality coefficient, which indicates the direct influence of temperature deviation on control input. When e(t) increases, control input u(t) is proportionally enlarged, and compressor power is adjusted to quickly reduce deviation. K i The integral coefficient represents the cumulative effect of past deviations on the current control input and is used to eliminate the steady-state error of the system. The integral term calculates the cumulative sum of deviations over time and adjusts the control input to maintain the target temperature for a long time. K dThe differential coefficient represents the predictive effect of the deviation change rate on the control input, which is used to improve the response speed and stability of the system. The differential term quickly adjusts the compressor power and reduces temperature fluctuations by predicting the temperature change trend. τ is the integral time variable, which is used to calculate the accumulated temperature deviation from the start of the system operation to the current moment. is the rate of change of the deviation over time, that is, the derivative of the deviation, indicating the speed and direction of temperature change; e(t) is the current temperature deviation, and the calculation formula is: e(t)=T target -T(t) Where: T target The target temperature set by the user; T(t) is the circulating medium temperature monitored in real time by the temperature sensing unit.

[0053] The execution unit can combine the linkage control strategy of the pump speed control unit and the refrigeration control unit to meet the comprehensive optimization needs of temperature and flow. For example, when the flow increases and the temperature of the circulating medium rises, the refrigeration control unit will appropriately increase the refrigeration power to compensate for the temperature rise, while the pump speed control unit keeps the flow stable.

[0054] In order to improve the acquisition accuracy of the sensor unit and the response speed of the execution unit, the cryogenic circulation pump equipment module can be integrated with an embedded microcontroller (MCU). The functions of the MCU include: Digitally process the analog signal output by the sensor; Calculate the required execution parameters according to the control instructions; The sensor data and execution results are sent to the IoT smart gateway module in a standardized format.

[0055] In addition, to meet the needs of different application scenarios, the low-temperature circulation pump equipment module can be equipped with different types of sensors and actuators. For example, in industrial cooling applications, turbine flow meters with a larger flow range and high-power compressors can be selected; while in laboratory cooling applications, higher-precision thermocouples and small variable-frequency motors can be selected.

[0056] The cloud platform module is used to store and process the operating parameters of the cryogenic circulation pump equipment and generate optimized control strategies; Specifically, the cloud platform module plays a core role in the entire control system. The cloud platform module receives the operation data uploaded by the cryogenic circulation pump equipment module through real-time communication with the IoT intelligent gateway module, and generates optimized control strategies through storage, analysis and processing of these data, thereby performing efficient remote control of the equipment operation. The cloud platform module also supports users to monitor and control the equipment operation status by interacting with the user terminal module.

[0057] The main functions of the cloud platform module include storage and management of operating data, analysis and prediction of equipment status, generation of optimized control strategies, and user authority management, etc. The following is a detailed description of each functional unit of the cloud platform module in conjunction with a specific technical solution.

[0058] The cloud platform module completes the storage, analysis and optimization control of equipment operation data through the collaborative work of the equipment information database unit, data analysis unit, control strategy generation unit and user management unit. The functions and implementation methods of these units are described in detail below.

[0059] Device Information Database Unit The operation data of the cryogenic circulating pump equipment needs to be stored for a long time in order to analyze the historical data and trace the equipment failure. To this end, the cloud platform module sets up an equipment information database unit to classify, store and manage the equipment-related data.

[0060] Device static information: including device model, communication protocol type, initial operating parameters, etc. This information is uploaded by the IoT smart gateway when the device is connected to the smart gateway and registered in the device information database.

[0061] Equipment dynamic information: including real-time collected operating parameters such as temperature, pressure, flow, and the operating status of the equipment (such as working mode, fault status, etc.). Dynamic information is uploaded and updated in real time through the IoT smart gateway.

[0062] Historical operation data: including long-term storage data of operating parameters such as temperature, pressure, flow, as well as fault records and maintenance records of equipment operation.

[0063] The equipment information database unit adopts a distributed storage structure to support efficient management of massive data. The database can use a relational database (such as MySQL) to store structured data and index dynamic data by timestamp. For example, the historical records of circulating medium temperature T(t), flow rate Q(t) and pressure P(t) are stored as the following structure: Timestamp,T(t),Q(t),P(t) The device information database unit can also compress and store data. For example, for flow data, an incremental storage method is used to record only the flow change between adjacent time points, thereby reducing storage space requirements.

[0064] Data Analysis Unit The data analysis unit is a key unit in the cloud platform module. It is responsible for analyzing and processing the data uploaded by the cryogenic circulation pump equipment and providing support for the subsequent control strategy generation.

[0065] Specifically, the data analysis unit mainly includes the following functions: In actual operation, the uploaded data may contain measurement errors or outliers. The data analysis unit first cleans the data, filters out outliers and corrects measurement errors. For example, the Kalman filter algorithm is used to smooth the real-time uploaded temperature data, and its formula is: in: is the state prediction value at the current time t, that is, the state value predicted by the state estimation value and control input at the previous time; is the state estimate at the current time t, that is, combined with the current observation data y t The state value after the prediction value is corrected; x represents the state variables of the equipment operation, such as the temperature T(t), flow Q(t), and pressure P(t) of the circulating medium; F is the state transfer matrix, which is used to describe the change of the system state from the previous moment t-1 to the current moment t, such as the trend of temperature change over time; B is the control input matrix, which is used to describe the influence of the control variable on the system state, such as the regulating effect of the change of pump speed or refrigeration power on the temperature; u t To control input variables, such as the cooling power of a compressor or the speed of a pump; t is the actual measurement value, such as the real-time temperature of the circulating medium collected by the temperature sensor; H is the observation matrix, which is used to map the system state to the measurement space, for example, to model the relationship between the actual state of the system (such as the real temperature) and the sensor measurement data; K t is the Kalman gain, which is used to weigh the contribution of the predicted value and the measured value in the state update, and its value depends on the system noise and the measurement noise.

[0066] The data analysis unit uses historical data and real-time data combined with the stochastic differential equation (SDE) model to predict the future operating status of the equipment. For example, the change in the temperature of the circulating medium can be described by the following formula: Where: T t is the real-time temperature of the circulating medium, which is one of the state variables of the system; C is the heat capacity of the circulating medium, which indicates the heat required for unit temperature change; Q in Q is the cooling power, which indicates the cooling power provided by the refrigeration system to the circulating medium; out Q is the heat loss, which means the heat lost by the circulating medium to the environment through the pipe wall or other means; leak is the amount of heat leakage inside the system, such as the unavoidable heat transfer caused by incomplete insulation; σ T is the temperature noise intensity, which indicates the random fluctuations in temperature caused by measurement errors or external interference; W t Brownian motion, used to describe the changes in random noise in a system, satisfies the characteristics of standard Brownian motion.

[0067] The data analysis unit can also establish a prediction model for the equipment's operating status through machine learning algorithms (such as support vector regression) to further improve the accuracy of the prediction.

[0068] Control strategy generation unit The control strategy generation unit is the core unit of the cloud platform module to realize intelligent control. It generates optimized control strategies by processing data analysis results.

[0069] Specifically, the control strategy generation unit uses a multi-objective optimization algorithm to adjust the pump speed and refrigeration power based on the optimization objective function.

[0070] The optimization objectives include: making the temperature deviation of the circulating medium T target -T(t) is minimum; make the flow deviation Q target -Q(t) is minimum; minimize the total energy consumption P of the system total (t).

[0071] The optimization objective function can be described as: J = ∫ 0 T [w 1 (T target -T(t) 2 +w 2 (Q target -Q(t) 2 +w 3 P total (t)]dt in: J is the objective function value, which is used to measure the overall performance of the low-temperature circulation pump equipment operating status; T target The target temperature is the temperature value that the circulating medium needs to maintain and is preset by the user or the system. The unit is Celsius (℃); T(t) is the real-time temperature of the circulating medium, in degrees Celsius (℃); Q target is the target flow rate, the target flow rate value of the circulating medium preset by the user or the system, in liters per minute (L / min); Q(t) is the real-time flow rate of the circulating medium, in liters per minute (L / min); P total (t) is the total power consumption of the equipment, including pump speed power consumption and cooling power consumption, in watts (W). The total power consumption can be further defined as: P total (t) = P pump (t)+P cooling (t), where: P pump (t) is the power consumption corresponding to the pump speed, in watts (W), Pcooling (t) is the power consumption corresponding to cooling, in watts (W); w 1 、w 2 、w 3 is the weight coefficient, which is used to control the relative importance of each optimization item in the objective function. 1 The weight coefficient optimized for temperature deviation is used to measure the priority of temperature control, w 2 The weight coefficient optimized for flow deviation is used to measure the priority of flow control, w 3 The weight coefficient for energy consumption optimization is used to measure the priority of energy consumption optimization; T is the total duration of the optimization period, in seconds (s); t is a time variable, which is used to represent any time point within the optimization period.

[0072] The control strategy generation unit uses the Pontryagin extreme value principle (PMP) to solve the optimal control input. According to the optimization target, the generated control strategy can include pump speed adjustment instructions and cooling power adjustment instructions to ensure that the temperature and flow meet the target requirements while reducing the energy consumption of the equipment.

[0073] Reinforcement learning algorithm, the cloud platform module uses the deep deterministic policy gradient algorithm (DDPG) to achieve dynamic adjustment of control strategies. The basic framework of reinforcement learning includes: State space, which defines the state s(t) of the system operation, including temperature T(t), flow Q(t), liquid level L(t) and energy consumption P total (t): s(t)=[T(t),Q(t),L(t),P total (t)] Action space, define the control action a(t), including the pump speed adjustment ΔN(t) and the cooling power adjustment Δu(t): a(t)=[ΔN(t),Δu(t)] Reward function, the system evaluates the effect of the current control strategy through the following reward function: R(t)=-[w 1 ·|T target -T(t)|+w 2 ·|Q target -Q(t)|+w 3 ·P total (t)] Where: R(t) is the reward function value at the current time t, which is used to measure the quality of the control strategy; T target is the target temperature; T(t) is the actual temperature measured at the current moment; Q targetis the target flow rate; Q(t) is the actual flow rate measured at the current moment; P total (t) is the total energy consumption at the current moment; w 1 To control the weight of temperature deviation, the larger the value, the higher the sensitivity of the system to temperature deviation; 2 To control the weight of flow deviation, the larger the value, the higher the sensitivity of the system to flow deviation; 3 To control the weight of energy consumption, the larger the value, the higher the degree of energy consumption optimization of the system.

[0074] The DDPG algorithm is based on a continuous action space optimization strategy, using a policy network to generate control actions, a value network to evaluate the long-term benefits of actions, and continuously updating the strategy through a historical experience playback mechanism.

[0075] User Management Unit The user management unit is used to implement hierarchical permission management and operation log recording of the cloud platform.

[0076] Specifically, the user management unit divides user permissions through the role-based access control (RBAC) model. For example, administrators can view the operating status of all devices and control them, while ordinary users can only view the data of specific devices. The user management unit also records each user's operations and generates operation logs to trace the system operation history.

[0077] The user management unit uses two-factor authentication technology (such as password + SMS verification code) to improve the security of the platform and prevent unauthorized access.

[0078] The user terminal module is used to access the cloud platform through the network, remotely view the operating status of the low-temperature circulation pump equipment and set the operating parameters.

[0079] Specifically, the user terminal module serves as an interactive interface between the user and the cloud platform, realizing remote monitoring and operation of the cryogenic circulation pump equipment. By accessing the cloud platform, the user terminal module can view the operating status of the equipment in real time, set and adjust the operating parameters of the equipment, and receive fault warning information and maintenance suggestions for the equipment. In the entire system, the user terminal module works in conjunction with the cloud platform module, realizing convenient management of intelligent cryogenic circulation pump equipment through a friendly user interface and efficient data interaction.

[0080] The main functions of the user terminal module include equipment operation status monitoring, historical data query and analysis, remote control operation and message notification, etc. The following describes each unit of the user terminal module in detail in combination with the specific technical solution.

[0081] The user terminal module is mainly composed of a device monitoring unit, a data query unit, a remote control unit and a message notification unit. Each unit completes the data interaction and the transmission of control instructions between the user and the device through real-time communication with the cloud platform.

[0082] The user of the equipment monitoring unit needs to know the operating status of the low temperature circulating pump equipment in real time to ensure that the equipment operates under normal working conditions. To this end, the present invention provides an equipment monitoring unit for displaying the key operating parameters of the equipment in real time.

[0083] Specifically, the equipment monitoring unit is used to display the operating parameters and status information of the cryogenic circulation pump equipment in real time. Through the visual interface, users can intuitively understand the current status of the equipment.

[0084] The main parameters monitored include: medium temperature T(t); circulation flow Q(t); liquid level height L(t); pump inlet and outlet pressure P in (t),P out (t); Equipment operating mode (such as automatic or manual mode).

[0085] The equipment monitoring unit uses dynamic charts to display the real-time trend of parameters. For example: The temperature data is displayed in the form of a line graph, and the update frequency is once a second; Flow and pressure data are displayed in bar graphs to help users quickly determine operating status.

[0086] In order to improve the user's perception of device operation, the device monitoring unit also introduces a status color identification mechanism. For example: When the device parameters are within the normal range, the data will be displayed in green; If the parameter is close to the warning threshold (such as the temperature is close to the upper limit), it will be displayed in yellow; When the parameter exceeds the safety threshold, the data is displayed in red and flashes to alert the user.

[0087] In addition, the equipment monitoring unit supports multi-device management mode. Users can switch the monitoring data of different devices through the interface, which facilitates the centralized management of multiple low-temperature circulation pump equipment.

[0088] Data query unit In order to facilitate the user to analyze the operation rules and historical fault conditions of the equipment, the user terminal module of the present invention is provided with a data query unit for querying and analyzing the historical data of the low-temperature circulating pump equipment.

[0089] Specifically, the data query unit can access the device information database unit of the cloud platform to obtain the operating parameter records of the device within a specified time period. For example, users can choose to query the circulating medium temperature, pressure and flow data for the past 24 hours, and display these data in the form of line graphs, bar graphs, etc.

[0090] The data query unit can perform statistical analysis on historical data. For example, it can calculate the average temperature, maximum pressure, and minimum flow rate over the past week to help users evaluate the operating performance of the equipment. The specific calculation formula is as follows: Average temperature: Where: T avg is the average temperature of the past week; T i is the temperature value of the i-th sampling point; n is the total number of sampling points (i.e. the number of sampling points, the total amount of sampling in the past week); The data query unit also supports trend prediction. Based on historical data, users can view the future trend of equipment parameters. For example, the future value of flow Q(t) is predicted by the time series analysis model. The model expression is: Q(t+1)=a 1 Q(t)+a 2 Q(t-1)+∈ Where: Q(t) is the flow value at the current moment t, which represents the actual flow collected by the sensor; Q(t-1) is the flow value at the previous moment t-1, which is used to represent the historical flow data of the time series; Q(t+1) is the predicted flow value at the next moment, that is, the flow at the future moment calculated by the model; a 1 is the weighted impact of the current flow rate Q(t) on the predicted value Q(t+1); a 2 is the weighted influence of the flow Q(t-1) at the previous moment on the predicted value Q(t+1); ∈ is the random error, indicating the fluctuation factors or unpredictable random interference that the model cannot fully capture.

[0091] Maximum pressure formula: P max =max{P 1 ,P 2 ,…,P n} Where: P max The greatest pressure in the past week; P i is the pressure value of the i-th sampling point; n is the total number of sampling points; Minimum flow formula: Q min =min{Q 1 ,Q 2 ,…,Q n} Where: Q minis the minimum flow in the past week; Q i is the flow value of the i-th sampling point; n is the total number of sampling points.

[0092] The data query unit can also support users to export historical data. The exported data format can be CSV or Excel file, so that users can further process it in other analysis tools.

[0093] Remote Control Unit The main function of the remote control unit is to remotely adjust and operate the cryogenic circulation pump equipment through the cloud platform. Users can send control instructions through the terminal interface to adjust the equipment operating parameters or modes.

[0094] The user can set the following parameters via the remote control unit: Target temperature T target : The target temperature of the circulating medium set by the user; Target flow rate Q target : The circulation flow rate that the user wants the device to maintain; Liquid level alarm threshold L min : The lower limit of the liquid level that triggers the liquid replenishment function; Operation mode: automatic mode or manual mode.

[0095] The control command format generated by the user terminal is: Instruction = {T target ,Q target ,L min ,Mode} The remote control unit supports security authentication mechanisms such as: User login password authentication; Two-factor authentication (such as SMS verification code).

[0096] To meet the needs of multi-user concurrent control, the remote control unit also supports hierarchical authority management. For example, the administrator can modify all device parameters, while ordinary users can only adjust the target temperature and flow rate.

[0097] Message notification unit In order to timely transmit the running status and fault information of the equipment to the user, the user terminal module of the present invention is provided with a message notification unit.

[0098] Specifically, the message notification unit is responsible for delivering the equipment's operating status, alarm information, and optimization suggestions to users in a timely manner.

[0099] Notification methods include: In-app push notifications; SMS or email reminders.

[0100] When the equipment is operating abnormally, the message notification unit will generate an alarm message. For example: Liquid level alarm: When the liquid level is lower than the threshold L min When the system is activated, the user will be notified to replenish fluids; Over temperature alarm: When the temperature T(t) exceeds the target range T target ±ΔT, an alarm message is sent.

[0101] The message content structure is: Message = {Timestamp, Alert Type, Details} Among them: Timestamp is the time when the alarm is generated; Alert Type is the alarm type (such as insufficient liquid level, excessive temperature, etc.); Details includes the current parameter value and recommended operations.

[0102] The message notification unit also supports regular maintenance reminders. For example, when the cumulative running time of the equipment exceeds the set threshold, the system will suggest users to arrange equipment maintenance.

[0103] Please refer to the attached Figure 6 The embodiment of the present invention provides an intelligent low-temperature circulation pump equipment control method based on the Internet of Things, comprising the following steps: The operating parameters of the cryogenic circulation pump equipment, including temperature, flow, pressure and liquid level, are collected in real time through the IoT intelligent gateway module; Upload the operating parameters to the cloud platform module and process them using the data analysis unit; Generate control strategies based on historical and real-time data of equipment through multi-objective optimization algorithms and reinforcement learning algorithms; The control strategy is sent to the low-temperature circulation pump equipment module through the IoT intelligent gateway module to adjust the pump speed and refrigeration power; the equipment operating status is monitored through the sensor unit, and the control strategy is continuously adjusted in combination with the optimization algorithm to form a closed-loop feedback control.

[0104] Specifically, Step 1: Collect operating parameters in real time The IoT smart gateway module collects the operating parameters of the cryogenic circulation pump equipment in real time, including medium temperature, flow, pressure and liquid level. The operating parameters are key indicators that reflect the real-time status of the equipment, and the accuracy of the collection directly affects the effect of subsequent optimization control.

[0105] The temperature is detected by a high-precision thermocouple or thermistor sensor, the flow is measured by a turbine or ultrasonic flowmeter, the pressure is captured by a piezoresistive pressure sensor, and the liquid level is monitored in real time by a float or ultrasonic level sensor. All data is pre-processed (including filtering and formatting) by the intelligent gateway module and uploaded to the cloud platform.

[0106] The data collection frequency can be dynamically adjusted according to the equipment operation requirements. For example, when the equipment load is large or the operating parameters fluctuate violently, the sampling frequency can be increased to capture more real-time data, thereby improving the control accuracy and response speed.

[0107] Step 2: Upload to the cloud platform and process the data The collected operating parameters are uploaded to the cloud platform module through the IoT smart gateway module. The cloud platform uses the data analysis unit to process the operating data, including noise filtering, outlier detection and historical data update. The purpose of data processing is to ensure the accuracy and consistency of the uploaded data and provide a basis for subsequent optimization analysis.

[0108] The cloud platform can analyze and predict data trends through time series analysis methods to evaluate the long-term status of equipment operation. For example, by analyzing the flow change trend, it can identify the possible risk of equipment blockage, or predict the need for refilling through historical liquid level data. These analysis results can also provide prior knowledge for multi-objective optimization algorithms.

[0109] Step 3: Generate control strategy The cloud platform module generates control strategies by combining historical and real-time data of the equipment using multi-objective optimization algorithms and reinforcement learning algorithms. The multi-objective optimization algorithm balances the optimal solution between equipment operating parameters (such as temperature, flow, and energy consumption), while the reinforcement learning algorithm dynamically adjusts the control strategy through continuous interaction with the environment to adapt to real-time operating condition changes.

[0110] The generated control strategy includes pump speed adjustment instructions and refrigeration power adjustment instructions to ensure that the circulating medium temperature and flow rate are within the target range while minimizing the equipment operation energy consumption.

[0111] Step 4: Control strategy issuance and execution The generated control strategy is sent to the low-temperature circulation pump equipment module through the IoT smart gateway module. After receiving the instruction, the execution unit of the equipment module dynamically adjusts the pump speed and refrigeration power, directly acting on the circulation system.

[0112] For example, when the flow deviation is large, the system reduces or increases the pump speed through the pump speed control unit to restore the target flow; when the temperature deviation exceeds the target range, the system adjusts the compressor power through the refrigeration control unit to ensure that the medium temperature is maintained near the set value.

[0113] Step 5: Closed-loop feedback control During the operation of the equipment, the operating status is continuously monitored through the sensor unit, and the real-time data is uploaded to the cloud platform module. The control strategy is dynamically adjusted in combination with the optimization algorithm to form a closed-loop feedback control.

[0114] The core of closed-loop control lies in real-time and adaptability. For example, when the external environment temperature fluctuates or the equipment operating load changes, the system can quickly generate new control strategies through feedback data, dynamically adapt to the current working conditions, and ensure the stable operation of the equipment.

[0115] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent low-temperature circulation pump equipment control system based on the Internet of Things, characterized in that: include: The IoT intelligent gateway module is used to realize data communication between the cryogenic circulation pump equipment and the cloud platform and user terminals, collect the operating parameters of the cryogenic circulation pump equipment, upload them to the cloud platform, and receive control instructions issued by the cloud platform; Cryogenic circulating pump equipment module, sensors for measuring operating parameters and actuators for regulating equipment status; The cloud platform module is used to store and process the operating parameters of the cryogenic circulation pump equipment and generate optimized control strategies; The user terminal module is used to access the cloud platform through the network, remotely view the operating status of the low-temperature circulation pump equipment and set the operating parameters.

2. According to the Internet of Things-based intelligent low-temperature circulation pump equipment control system of claim 1, it is characterized in that: The IoT smart gateway module includes: A multi-protocol conversion unit, used to convert the communication protocol of the cryogenic circulation pump equipment into a unified Internet of Things communication protocol; A data processing unit, used for filtering, detecting abnormal values ​​and converting the format of the collected equipment operation data; The communication unit is used to upload the processed data to the cloud platform and receive control instructions from the cloud platform.

3. According to the intelligent low-temperature circulation pump equipment control system based on the Internet of Things in claim 1, it is characterized in that: The low temperature circulation pump equipment module comprises: Liquid level sensing unit, used for real-time detection of liquid medium level in the circulation system; A temperature sensing unit, used to collect the temperature of the circulating medium; A pressure sensing unit, used to detect the pressure difference between the inlet and outlet of the cryogenic circulation pump; A flow sensing unit, used to measure the flow of the circulating medium; The execution unit includes a pump speed control unit for adjusting the pump speed and a refrigeration control unit for adjusting the refrigeration power.

4. According to the intelligent low-temperature circulation pump equipment control system based on the Internet of Things in claim 1, it is characterized in that: The cloud platform module includes: Equipment information database unit, used to store model information, historical operation data and maintenance records of low-temperature circulating pump equipment; A data analysis unit, used to analyze the equipment operation status using a multi-objective optimization algorithm based on the historical data and real-time data of the cryogenic circulating pump equipment; A control strategy generating unit, used to generate control instructions according to target parameters set by the user; The user management unit is used to manage user permissions and record operation logs.

5. The intelligent low-temperature circulating pump equipment control system based on the Internet of Things according to claim 1 is characterized in that: The user terminal module comprises: Equipment monitoring unit, used to display the real-time operating parameters of the cryogenic circulation pump equipment; Data query unit, used to query and analyze the trend of historical operation data; A remote control unit for setting target parameters and operating modes of the cryogenic circulation pump; The message notification unit is used to receive equipment failure warning information and maintenance suggestions from the cloud platform.

6. The intelligent low-temperature circulating pump equipment control system based on the Internet of Things according to claim 4 is characterized in that: The data analysis unit generates a control strategy through a multi-objective optimization algorithm, and the optimization objectives include: Minimize the deviation between the temperature of the circulating medium and the target temperature; Minimize the deviation between the flow rate of the circulating medium and the target flow rate; The overall energy consumption of equipment operation is minimized.

7. The intelligent low-temperature circulating pump equipment control system based on the Internet of Things according to claim 6 is characterized in that: The objective function of the multi-objective optimization algorithm balances temperature deviation, flow deviation and control energy consumption by setting different weight parameters. The specific weight parameters can be dynamically adjusted according to the actual application scenario.

8. The intelligent low-temperature circulating pump equipment control system based on the Internet of Things according to claim 1 is characterized in that: The cloud platform module dynamically updates the control strategy in conjunction with a reinforcement learning algorithm, which includes a deep deterministic policy gradient algorithm, by: Establish a dynamic equipment status model to obtain equipment operating parameters in real time and predict future status; Generate control actions based on optimization goals and adjust strategies in real time based on equipment operation feedback; Dynamically optimizes the balance between temperature, flow and energy consumption.

9. The intelligent low-temperature circulating pump equipment control system based on the Internet of Things according to claim 3 is characterized in that: The liquid level sensing unit works in conjunction with the flow sensing unit and the pressure sensing unit to form a liquid level-flow-pressure coupling control strategy for: When the liquid level is insufficient, the automatic liquid replenishment control is triggered to prevent the circulation pump from running idle; Adjust pump speed according to real-time changes in pressure and flow to maintain system stability.

10. An intelligent low-temperature circulation pump equipment control method based on the Internet of Things, applied to the system according to any one of claims 1 to 9, characterized in that: The following steps are involved: The operating parameters of the cryogenic circulation pump equipment, including temperature, flow, pressure and liquid level, are collected in real time through the IoT intelligent gateway module; Upload the operating parameters to the cloud platform module and process them using the data analysis unit; Generate control strategies based on historical and real-time data of equipment through multi-objective optimization algorithms and reinforcement learning algorithms; The control strategy is sent to the low-temperature circulation pump equipment module through the IoT smart gateway module to adjust the pump speed and refrigeration power; The operating status of the equipment is monitored through the sensor unit, and the control strategy is continuously adjusted in combination with the optimization algorithm to form a closed-loop feedback control.

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