Laboratory intelligent liquid preparation control method and system based on Internet of Things

Abstract

The invention discloses a laboratory intelligent liquid preparation control method and system based on the Internet of Things, and relates to the technical field of laboratory automatic control and Internet of Things crossing, and the method comprises the steps: obtaining liquid preparation request data, and detecting the concentration value of a stock solution in a current liquid storage tank; calculating a stock solution feeding amount and a diluent feeding amount according to a preset dilution calculation model, packaging the stock solution feeding amount and the diluent feeding amount into a control instruction data packet, and issuing the control instruction data packet to a solution preparation terminal; the liquid preparation terminal receives the control instruction data packet, controls the first electromagnetic valve to open for the first electromagnetic valve opening time so as to put the stock solution putting amount, and controls the second electromagnetic valve to open for the second electromagnetic valve opening time so as to put the diluent putting amount at the same time; starting the stirring device for mixing, detecting the concentration value of the mixed solution in real time, stopping stirring when the difference between the concentration value of the mixed solution and the concentration value of the target solution is less than or equal to a preset concentration error threshold value, and feeding back the concentration value of the mixed solution and the solution preparation completion state data to the cloud control server. The invention has the beneficial effects.

Description

Technical Field

[0001] The present invention relates to the technical field of laboratory automation control and the Internet of Things, and in particular to an Internet of Things-based laboratory intelligent liquid dispensing control method and system. Background Art

[0002] With the popularization of Internet of Things technology, existing technologies have gradually realized remote monitoring and centralized management of laboratory equipment, such as laboratory environment monitoring systems built by connecting various terminal devices through intelligent gateways. However, current technologies still have significant limitations: first, existing systems focus more on environmental parameter monitoring and basic equipment control, and lack professional support for precision experimental operations (such as solution preparation); second, the ability to coordinate between devices is insufficient, and closed-loop optimization of experimental processes cannot be achieved; third, the safety protection mechanism is single, which makes it difficult to meet the active warning needs for dangerous operations in chemical experiments. Especially in experiments involving toxic and hazardous reagents, how to achieve high-precision and high-safety intelligent control has become a technical bottleneck that needs to be broken through.

[0003] CN113325903A discloses an IoT-based laboratory intelligent control system that uses a three-tier architecture consisting of a client, a remote server, and an intelligent gateway to achieve centralized monitoring of terminal devices such as circuit breakers and environmental sensors. The system uses an independent IoT transmission module to connect various devices, capable of collecting temperature, humidity, security, and other data in real time and feeding it back to the user. However, this technology has the following shortcomings: (1) It only implements basic environmental monitoring functions and does not involve the core aspects of experimental operations; (2) The device control logic is simple and cannot dynamically adjust the control strategy according to experimental requirements; (3) It lacks safety algorithm design for chemical experiments, such as automatic identification and interception mechanisms for dangerous concentrations.

[0004] Although this existing technology has built a basic framework for the laboratory Internet of Things, its functional design has obvious limitations: (1) The system architecture does not integrate dedicated experimental equipment, making it impossible to support standardized experimental processes; (2) Data processing remains at the state monitoring level, and a collaborative optimization model of experimental operation-environmental parameters-safety protection has not been established; (3) Safety protection relies solely on traditional sensors and fails to provide intelligent warnings for chemical risks that may arise during the experiment. These defects seriously restrict the application value of the system in precision experimental scenarios. Summary of the Invention

[0005] The present invention is proposed in view of the problems that existing laboratory intelligent control systems cannot support precise experimental operations such as accurate solution preparation, lack the closed-loop optimization capability of experimental processes, and have insufficient safety protection mechanisms for chemical experiments.

[0006] Therefore, the problem to be solved by the present invention is how to achieve high-precision intelligent control of the laboratory liquid preparation process, and to build a cloud-coordinated closed-loop control system through the Internet of Things technology to solve the problems of insufficient liquid preparation accuracy, lack of safety protection and poor system coordination in the existing technology.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a laboratory intelligent liquid preparation control method based on the Internet of Things, which comprises:

[0009] Obtaining liquid preparation request data, detecting the original liquid concentration value C_source in the current liquid storage tank, and transmitting it to the cloud control server, wherein the liquid preparation request data includes the target solution concentration value C_target, the target solution volume value V_target and the solute type identifier ID_solute;

[0010] Calculate the stock solution dosage V_source and the diluent dosage V_diluent according to a preset dilution calculation model, and encapsulate the stock solution dosage V_source and the diluent dosage V_diluent into a control instruction data packet, and send it to the liquid preparation terminal;

[0011] The liquid dispensing terminal receives the control instruction data packet, controls the first solenoid valve to open for a first solenoid valve opening time T1 to dispense the original liquid dosage V_source, and controls the second solenoid valve to open for a second solenoid valve opening time T2 to dispense the diluent dosage V_diluent;

[0012] The stirring device is started to perform a mixing operation, and the mixed liquid concentration value C_real is detected in real time, and the mixed liquid concentration value C_real and the liquid preparation completion status data are fed back to the cloud control server.

[0013] As a preferred solution of the laboratory intelligent liquid preparation control method based on the Internet of Things of the present invention, wherein: starting the stirring device to perform the mixing operation, and detecting the mixed liquid concentration value C_real in real time, and feeding back the mixed liquid concentration value C_real and the liquid preparation completion status data to the cloud control server, including:

[0014] After the addition of the stock solution amount V_source and the diluent amount V_diluent is completed, a start instruction is sent to the control module of the stirring device;

[0015] A concentration sensor is installed on the side wall of the mixing container to detect and collect the mixed liquid concentration value C_real in real time with a sampling period Δt;

[0016] Establish a concentration convergence judgment model to calculate the absolute deviation ΔC = |C_real-C_target| between the current mixed solution concentration value C_real and the target solution concentration value C_target, and compare the absolute deviation ΔC with the preset concentration error threshold δ to determine whether the current mixed solution has reached the target concentration requirement;

[0017] When the absolute deviation ΔC is less than the preset concentration error threshold δ, the delay verification timer is started, the delay judgment timer T_delay is set, and the absolute deviation ΔC is continuously monitored during the delay judgment time T_delay. If the absolute deviation ΔC of all sampling points remains within the preset concentration error threshold δ, the concentration of the mixed liquid is determined to meet the standard, the stirring device is controlled to stop according to the preset deceleration curve, and the final mixed liquid concentration value C_real_final is recorded;

[0018] When the absolute deviation ΔC is greater than or equal to the preset concentration error threshold δ, the compensation calculation is started according to the deviation direction, and the liquid replenishing device is controlled to perform the multi-step replenishment operation;

[0019] Obtain the final mixed liquid concentration value C_real_final from the concentration sensor, combine it with the current timestamp and the device identifier to form a liquid preparation completion data packet, and upload it to the cloud control server using an encrypted transmission protocol;

[0020] The cloud control server receives the liquid preparation completion data packet, compares and verifies the final mixed liquid concentration value C_real_final in the liquid preparation completion data packet with the original liquid preparation request data, generates a liquid preparation quality report and stores it in the database.

[0021] As a preferred solution of the laboratory intelligent liquid dispensing control method based on the Internet of Things of the present invention, the compensation calculation is started according to the deviation direction, including:

[0022] If the mixed solution concentration value C_real is less than the target solution concentration value C_target, the volume of the original solution to be added V_add_source is calculated;

[0023] If the mixed solution concentration value C_real> the target solution concentration value C_target, the volume of diluent to be added V_add_diluent is calculated.

[0024] As a preferred solution of the laboratory intelligent liquid dispensing control method based on the Internet of Things of the present invention, the liquid dispensing terminal receives the control instruction data packet, controls the first solenoid valve to open for the first solenoid valve opening time T1, and releases the raw liquid delivery amount V_source, including:

[0025] Read the control instruction data packet from the local cache area, parse and obtain the first solenoid valve opening time T1, the second solenoid valve opening time T2, the original liquid dosage V_source and the diluent dosage V_diluent;

[0026] Sending a self-test instruction to the first solenoid valve and the second solenoid valve to obtain a first state feedback signal and a second state feedback signal;

[0027] When the first state feedback signal and the second state feedback signal are both normal, the first flow sensor provided in the raw liquid pipeline and the second flow sensor provided in the diluent pipeline start flow monitoring;

[0028] Sending an opening instruction to the first solenoid valve, simultaneously starting to record the actual opening time t1 of the first solenoid valve, and calculating the cumulative raw liquid dosage V1 through integration operation;

[0029] When the actual opening time t1 ≥ the first solenoid valve opening time T1 or the accumulated raw liquid injection amount V1 ≥ the raw liquid injection amount V_source is satisfied, a closing instruction is sent to the first solenoid valve, and the actual raw liquid injection amount V1_final is recorded.

[0030] As a preferred solution of the laboratory intelligent liquid dispensing control method based on the Internet of Things of the present invention, wherein: simultaneously controlling the second solenoid valve to open the second solenoid valve opening time T2 to dispense the diluent dosage V_diluent, including:

[0031] After closing the first solenoid valve, an opening instruction is immediately sent to the second solenoid valve, and the actual opening time t2 of the second solenoid valve is recorded, and the cumulative amount of diluent added is calculated by integral operation;

[0032] When the actual opening time t2 ≥ the second solenoid valve opening time T2 or the cumulative diluent dosage V2 ≥ the diluent dosage V_diluent is satisfied, a closing instruction is sent to the second solenoid valve, and the actual diluent dosage V2_final is recorded;

[0033] The actual amount of raw liquid V1_final and the actual amount of diluted liquid V2_final are stored in the liquid preparation log and uploaded to the cloud control server.

[0034] As a preferred solution of the laboratory intelligent liquid preparation control method based on the Internet of Things of the present invention, the method for obtaining the original liquid dosage V_source and the diluent dosage V_diluent is as follows:

[0035] The cloud control server extracts the verified liquid preparation request data and the original liquid concentration value C_source from the task queue, and calls the preset dilution calculation model for calculation;

[0036] Based on the solute type identifier ID_solute, the pre-stored fluid property database is queried to obtain the dynamic viscosity coefficient and temperature compensation parameters of the solute, and the original liquid dosage V_source and the diluent dosage V_diluent are corrected to compensate for the viscous loss in the fluid flow process;

[0037] Based on the flow characteristic parameters of the solenoid valve of the liquid dispensing terminal, the first solenoid valve opening time T1 corresponding to the raw liquid dosage V_source and the second solenoid valve opening time T2 corresponding to the diluent dosage V_diluent are calculated;

[0038] Encapsulate the original liquid dosage V_source, the diluent dosage V_diluent, the first solenoid valve opening time T1, and the second solenoid valve opening time T2 into a structured control instruction data packet;

[0039] The control instruction data packet is encoded using a lightweight Internet of Things communication protocol, and a CRC check code is added, and the control instruction data packet is sent to the liquid dispensing terminal using an adaptive retransmission mechanism;

[0040] The communication module of the liquid dispensing terminal receives the control instruction data packet, performs CRC check, and parses out the original liquid dosage V_source, the diluent dosage V_diluent, the first solenoid valve opening time T1 and the second solenoid valve opening time T2, and stores them in the local cache area, waiting for the liquid dispensing operation to be executed.

[0041] As a preferred solution of the laboratory intelligent liquid dispensing control method based on the Internet of Things of the present invention, the specific formulas for the opening time T1 of the first solenoid valve and the opening time T2 of the second solenoid valve are as follows:

[0042] T1=V_source / (K1×ΔP)

[0043] T2 = V_diluent / (K2×ΔP);

[0044] Wherein, K1 is the calibrated flow coefficient of the first solenoid valve, K2 is the calibrated flow coefficient of the second solenoid valve, and ΔP is the pipeline pressure difference.

[0045] In a second aspect, the present invention provides a laboratory intelligent liquid dispensing control system based on the Internet of Things, which includes:

[0046] The data acquisition module is used to obtain the liquid preparation request data, detect the original liquid concentration value C_source in the current liquid storage tank, and transmit it to the cloud control server, wherein the liquid preparation request data includes the target solution concentration value C_target, the target solution volume value V_target and the solute type identifier ID_solute;

[0047] A calculation module is used to calculate the original liquid dosage V_source and the diluent dosage V_diluent according to a preset dilution calculation model, and encapsulate the original liquid dosage V_source and the diluent dosage V_diluent into a control instruction data packet, and send it to the liquid preparation terminal;

[0048] An execution control module is configured to receive the control instruction data packet at the liquid dispensing terminal, control the first solenoid valve to open for a first solenoid valve opening time T1 to dispense the raw liquid dosage V_source, and control the second solenoid valve to open for a second solenoid valve opening time T2 to dispense the diluent dosage V_diluent;

[0049] The concentration detection module starts the stirring device to perform a mixing operation, and detects the mixed liquid concentration value C_real in real time, and feeds back the mixed liquid concentration value C_real and the liquid preparation completion status data to the cloud control server.

[0050] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the laboratory intelligent liquid preparation control method based on the Internet of Things as described in the first aspect of the present invention are implemented.

[0051] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the laboratory intelligent liquid preparation control method based on the Internet of Things as described in the first aspect of the present invention are implemented.

[0052] Compared with the existing technology, the beneficial effects of the present invention are as follows: by dynamically acquiring the liquid preparation request data and detecting the stock solution concentration in real time, the accurate adaptation of the liquid preparation parameters is ensured, effectively solving the problem of ratio deviation caused by the fluctuation of the stock solution concentration in the traditional method; based on the preset dilution calculation model and combined with the dynamic correction of the fluid characteristic parameters, the accurate calculation of the liquid preparation amount is achieved, which significantly improves the ratio accuracy of solutions with different physicochemical characteristics. In the liquid delivery link, a redundant control mechanism is formed through the timing control of the dual solenoid valves and the closed-loop feedback of the flow sensor, which not only ensures the delivery accuracy but also improves the reliability of the system. It not only meets the traceability requirements of the experimental process, but also provides data support for the optimization of the liquid preparation process through cloud-based intelligent analysis. This method significantly reduces the risk of human operation while improving the accuracy and efficiency of liquid preparation, and is suitable for all kinds of laboratory scenarios with strict requirements on liquid preparation quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0054] Figure 1 This is a flow chart of the laboratory intelligent liquid preparation control method based on the Internet of Things. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0056] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0057] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0058] As mentioned in the background technology above, although the basic framework of the laboratory Internet of Things has been built, its functional design has obvious limitations: (1) The system architecture does not integrate special experimental equipment, which makes it impossible to support standardized experimental processes; (2) Data processing remains at the state monitoring level, and a collaborative optimization model of experimental operation-environmental parameters-safety protection has not been established; (3) Safety protection relies solely on traditional sensors and fails to provide intelligent warnings for chemical risks that may arise during the experiment. These defects seriously restrict the application value of the system in precision experimental scenarios.

[0059] Figure 1 FIG. 1 is a flow chart of a laboratory intelligent liquid distribution control method based on the Internet of Things according to an embodiment of the present invention. Figure 1 As shown, in a laboratory intelligent liquid preparation control method based on the Internet of Things, it includes:

[0060] S1: Obtain the liquid preparation request data, detect the original liquid concentration value C_source in the current liquid storage tank, and transmit it to the cloud control server. The liquid preparation request data includes the target solution concentration value C_target, the target solution volume value V_target and the solute type identifier ID_solute.

[0061] S1.1: The liquid preparation terminal receives liquid preparation request data input by the user through the human-computer interaction interface or the external system interface, wherein the liquid preparation request data includes a target solution concentration value C_target, a target solution volume value V_target, and a solute type identifier ID_solute.

[0062] S1.2: The concentration sensor installed in the outlet pipe of the liquid storage tank collects the original solution concentration value C_source corresponding to the solute type identifier ID_solute in the current liquid storage tank in real time.

[0063] It should be noted that the concentration sensor uses an optical refractive index detection module, and its detection accuracy reaches ±0.5% FS.

[0064] S1.3: Combine the solution preparation request data and the original solution concentration value C_source into a structured data packet.

[0065] S1.4: Upload the structured data packet to the cloud control server using the MQTT protocol through the IoT communication module.

[0066] It should be noted that the IoT communication module has a built-in data verification mechanism to ensure the numerical integrity of the original solution concentration value C_source and the target solution concentration value C_target during the transmission process.

[0067] S1.5: The cloud control server receives the structured data packet, calls the data parsing service to extract the solute type identifier ID_solute, queries the pre-stored safe concentration database to verify whether the target solution concentration value C_target is within the safe ratio range of this solute, and executes the safe ratio verification process.

[0068] Specifically, the safety ratio verification process is executed: calling the data analysis service to extract the solute type identifier ID_solute; querying the pre-stored safety concentration database to obtain the maximum allowable concentration value C_max of the solute; comparing the target solution concentration value C_target with the maximum allowable concentration value C_max.

[0069] Preferably, when the target solution concentration value C_target ≤ the maximum allowable concentration value C_max, the safety verification is recorded in the task log, the dilution calculation service is triggered to read the original solution concentration value C_source, and a control instruction containing the safety verification result is generated; when the target solution concentration value C_target > the maximum allowable concentration value C_max, a safety alarm instruction and an over-limit ratio (C_target-C_max) / C_max×100% are generated, and a liquid preparation termination instruction is sent to the liquid preparation terminal, and the following operations are triggered simultaneously: a) a red warning message is displayed on the human-computer interaction interface: the target concentration exceeds the safety limit C_max; b) a third-level alarm is issued through the sound and light alarm device; c) the alarm record is written into the security event database; d) a safety warning notification is sent to the preset supervisor.

[0070] It should be noted that the method for determining the maximum allowable concentration value C_max is: for conventional chemical reagents, the MSDS standard value is used; for special solutes, the safety threshold is determined experimentally and a 5% safety margin is set, that is, the maximum allowable concentration value C_max = 0.85 × theoretical limit value.

[0071] S1.6: Temporarily store the verified liquid preparation request data and the original liquid concentration value C_source in the task queue, waiting for the dilution calculation process to be executed.

[0072] S2: Calculate the original liquid dosage V_source and the diluent dosage V_diluent according to the preset dilution calculation model, and encapsulate the original liquid dosage V_source and the diluent dosage V_diluent into a control instruction data packet, and send it to the liquid preparation terminal.

[0073] S2.1: The cloud control server extracts the verified solution preparation request data and the original solution concentration value C_source from the task queue and calls the preset dilution calculation model for calculation;

[0074] Preferably, the preset dilution calculation model is based on the law of conservation of mass, and the required original liquid dosage V_source is calculated according to the formula V_source=(C_target×V_target) / C_source, and the required diluent dosage V_diluent is calculated according to the formula V_diluent=V_target-V_source.

[0075] S2.2: Based on the flow characteristic parameters of the solenoid valve of the liquid dispensing terminal, the opening time T1 of the first solenoid valve corresponding to the raw liquid dosage V_source and the opening time T2 of the second solenoid valve corresponding to the diluent dosage V_diluent are calculated.

[0076] Preferably, the specific formulas for the first solenoid valve opening time T1 and the second solenoid valve opening time T2 are as follows:

[0077] T1=V_source / (K1×ΔP)

[0078] T2 = V_diluent / (K2×ΔP);

[0079] Wherein, K1 is the calibrated flow coefficient of the first solenoid valve, K2 is the calibrated flow coefficient of the second solenoid valve, and ΔP is the pipeline pressure difference.

[0080] S2.3: Encapsulate the original liquid dosage V_source, the diluent dosage V_diluent, the first solenoid valve opening time T1, and the second solenoid valve opening time T2 into a structured control instruction data packet.

[0081] S2.4: A lightweight IoT communication protocol is used to encode the control instruction data packet and append a CRC checksum. An adaptive retransmission mechanism is then used to send the control instruction data packet to the liquid dispensing terminal.

[0082] S2.5: The communication module of the liquid dispensing terminal receives the control instruction data packet, performs CRC check, and parses out the original liquid dosage V_source, the diluent dosage V_diluent, the first solenoid valve opening time T1, and the second solenoid valve opening time T2, and stores them in the local cache area, waiting for the execution of the liquid dispensing operation.

[0083] S3: The liquid dispensing terminal receives the control instruction data packet, controls the first solenoid valve to open for a duration T1 to dispense the original liquid amount V_source, and controls the second solenoid valve to open for a duration T2 to dispense the diluent amount V_diluent.

[0084] S3.1: Read the control instruction data packet from the local cache, parse it to obtain the first solenoid valve opening time T1, the second solenoid valve opening time T2, the original liquid dosage V_source, and the diluent dosage V_diluent;

[0085] S3.2: Sending a self-test instruction to the first solenoid valve and the second solenoid valve to obtain a first state feedback signal and a second state feedback signal;

[0086] S3.3: When the first state feedback signal and the second state feedback signal are both normal, the first flow sensor provided in the raw liquid pipeline and the second flow sensor provided in the diluent pipeline start flow monitoring;

[0087] Specifically, when the first state feedback signal or the second state feedback signal is abnormal, the following diagnosis is performed:

[0088] (1) Read the fault code register and identify the abnormality type, where the abnormality types include type A: solenoid valve drive circuit failure (code E01), type B: valve core blocking failure (code E02) and type C: position sensor failure (code E03).

[0089] (2) Hierarchical disposal strategy:

[0090] Type A fault occurs: ① Cut off the power supply circuit of the solenoid valve; ② Start the backup drive channel (if redundant design is available); ③ Display on the HMI: drive circuit fault, code E01; ④ Upload the fault data packet to the cloud control server.

[0091] Type B fault occurs: ① Trigger three automatic stroke actions (open-close-open-close-open-close) ② If the error is still reported after the stroke, perform the following: a) Close the emergency shut-off valve of the associated pipeline; b) Activate the vibration motor (amplitude 0.5mm, frequency 30Hz); c) Delay for 5 seconds and then retest; ③ Record the number of times the jam is released in the maintenance counter N_clear.

[0092] If a Type C fault occurs: ① Switch to indirect flow monitoring mode: Use the data from the first flow sensor Q1(t) instead of position feedback; establish a flow-time relationship model to estimate the valve position; ② Generate a degraded operation flag Flag_degrade = 1; ③ Add a fault tolerance compensation coefficient η = 1.15 to the control instruction data packet;

[0093] (3) Safety interlock mechanism: For any fault that is not recovered within three retries: ① Activate the safety interlock sequence: a) Close all solenoid valves; b) Drain the mixing container (open the drain valve V_drain); c) Lock the operation interface; ② Send a STOP command to the cloud control server; ③ Generate a maintenance request work order, which includes: a fault solenoid valve location map, historical fault statistics, and recommended maintenance measures.

[0094] S3.4: Send an opening instruction to the first solenoid valve, and simultaneously start recording the actual opening time t1 of the first solenoid valve, and calculate the cumulative raw liquid dosage V1 through integration operation.

[0095] S3.5: When the actual opening time t1 ≥ the opening time T1 of the first solenoid valve or the cumulative raw liquid injection amount V1 ≥ the raw liquid injection amount V_source is satisfied, a closing instruction is sent to the first solenoid valve, and the actual raw liquid injection amount V1_final is recorded.

[0096] S3.6: After closing the first solenoid valve, an opening instruction is immediately sent to the second solenoid valve. At the same time, the actual opening time t2 of the second solenoid valve is recorded, and the cumulative amount of diluent added is calculated through integration operation.

[0097] S3.7: When the actual opening time t2 ≥ the second solenoid valve opening time T2 or the cumulative diluent dosage V2 ≥ the diluent dosage V_diluent is satisfied, a closing instruction is sent to the second solenoid valve, and the actual diluent dosage V2_final is recorded.

[0098] S3.8: The actual amount of raw liquid V1_final and the actual amount of diluted liquid V2_final are stored in the liquid preparation log and uploaded to the cloud control server.

[0099] S4: Start the stirring device to perform the mixing operation, and detect the mixed liquid concentration value C_real in real time, and feed back the mixed liquid concentration value C_real and the liquid preparation completion status data to the cloud control server.

[0100] S4.1: After the addition of the original liquid amount V_source and the diluent amount V_diluent is completed, a start instruction is sent to the control module of the stirring device.

[0101] It should be noted that the control module of the stirring device drives the stirring motor to operate according to a preset speed curve, wherein the preset speed curve includes an initial low-speed stage, an acceleration stage and a constant-speed mixing stage.

[0102] S4.2: Install a concentration sensor on the side wall of the mixing container to detect and collect the mixed liquid concentration value C_real in real time with a sampling period Δt.

[0103] It should be noted that the sampling period Δt is retrieved from the preset parameter library according to the solute type identifier ID_solute.

[0104] S4.3: Establish a concentration convergence judgment model, calculate the absolute deviation ΔC = |C_real-C_target| between the current mixed solution concentration value C_real and the target solution concentration value C_target, and compare the absolute deviation ΔC with the preset concentration error threshold δ to determine whether the current mixed solution has reached the target concentration requirement.

[0105] It should be noted that the concentration convergence judgment model is a mixing process evaluation model based on dynamic sliding window filtering and exponential decay prediction algorithm. It judges the convergence state of solution mixing by real-time analysis of the dual indicators of concentration change rate and prediction deviation.

[0106] S4.5: When the absolute deviation ΔC is less than the preset concentration error threshold δ, the delay verification timer is started, the delay judgment time T_delay is set, and the absolute deviation ΔC is continuously monitored during the delay judgment time T_delay. If the absolute deviation ΔC of all sampling points remains within the preset concentration error threshold δ, the concentration of the mixed liquid is determined to be up to standard, the stirring device is controlled to stop running according to the preset deceleration curve, and the final mixed liquid concentration value C_real_final is recorded.

[0107] Preferably, the compensation calculation is started according to the deviation direction, including: if the mixed solution concentration value C_real is less than the target solution concentration value C_target, then the volume of the original solution V_add_source to be added is calculated; if the mixed solution concentration value C_real is greater than the target solution concentration value C_target, then the volume of the diluent V_add_diluent to be added is calculated.

[0108] Furthermore, the relevant formulas for the original solution volume V_add_source and the dilution volume V_add_diluent are as follows:

[0109] V_add_source=(C_target-C_real)×V_current / (C_source-C_real)

[0110] V_add_diluent=V_current×((C_real / C_target)-1);

[0111] Wherein, V_current is the real-time volume of the solution in the current mixing container.

[0112] S4.6: When the absolute deviation ΔC is greater than or equal to the preset concentration error threshold δ, the compensation calculation is started according to the deviation direction, and the liquid replenishing device is controlled to perform the multi-step replenishing operation.

[0113] Preferably, the fluid replenishing device is a precision fluid replenishing pump, which is arranged on the diluent supply path and is used to perform multiple micro-fluid replenishments according to a preset replenishing amount when the mixed concentration deviation exceeds the limit, so as to achieve precise adjustment of the mixed concentration.

[0114] It should be noted that the amount of each addition shall not exceed the maximum addition amount ΔV_max. After each addition, the preset time T_wait shall be left to stand, the absolute deviation ΔC shall be re-detected and the compensation calculation shall be iteratively performed until the absolute deviation ΔC is less than the preset concentration error threshold δ, and then the normal verification process shall be entered; if the number of consecutive compensations exceeds the maximum compensation N_max times and still does not meet the absolute deviation ΔC less than the preset concentration error threshold δ, the exception handling procedure will be triggered, the safe discharge operation will be executed and a fault alarm signal will be generated.

[0115] S4.7: When the mixing conditions are met, a stop command is sent to the stirring device control module, and the stirring motor stops smoothly according to the preset deceleration curve.

[0116] S4.8: Obtain the final mixed liquid concentration value C_real_final from the concentration sensor, combine it with the current timestamp and device identifier to form a liquid preparation completion data packet, and upload it to the cloud control server using an encrypted transmission protocol.

[0117] S4.9: The cloud control server receives the liquid preparation completion data packet, compares and verifies the final mixed liquid concentration value C_real_final in the liquid preparation completion data packet with the original liquid preparation request data, generates a liquid preparation quality report and stores it in the database.

[0118] In summary, the present invention ensures accurate adaptation of the liquid preparation parameters by dynamically acquiring the liquid preparation request data and detecting the stock solution concentration in real time, effectively solving the problem of ratio deviation caused by the stock solution concentration fluctuation in the traditional method; based on the preset dilution calculation model and combined with the dynamic correction of the fluid characteristic parameters, the accurate calculation of the liquid preparation amount is achieved, which significantly improves the ratio accuracy of solutions with different physicochemical characteristics. In the liquid delivery link, a redundant control mechanism is formed through the timing control of the dual solenoid valves and the closed-loop feedback of the flow sensor, which not only ensures the delivery accuracy but also improves the system reliability. It not only meets the traceability requirements of the experimental process, but also provides data support for the optimization of the liquid preparation process through cloud-based intelligent analysis. This method significantly reduces the risk of human operation while improving the accuracy and efficiency of liquid preparation, and is suitable for all kinds of laboratory scenarios with strict requirements on liquid preparation quality.

[0119] Furthermore, this embodiment provides a laboratory intelligent liquid preparation control system based on the Internet of Things, including: a data acquisition module, used to obtain liquid preparation request data, detect the original liquid concentration value C_source of the current liquid storage tank, and transmit it to a cloud control server, wherein the liquid preparation request data includes a target solution concentration value C_target, a target solution volume value V_target, and a solute type identifier ID_solute; a calculation module, used to calculate the original liquid dosage V_source and the diluent dosage V_diluent according to a preset dilution calculation model, and encapsulate the original liquid dosage V_source and the diluent dosage V_diluent into a control instruction data packet, and send it to a liquid preparation terminal; an execution control module, used for the liquid preparation terminal to receive the control instruction data packet, control the first solenoid valve to open for the first solenoid valve opening time T1 to dispense the original liquid dosage V_source, and at the same time control the second solenoid valve to open for the second solenoid valve opening time T2 to dispense the diluent dosage V_diluent; a concentration detection module, which starts the stirring device to perform a mixing operation, and detects the mixed liquid concentration value C_real in real time, and feeds back the mixed liquid concentration value C_real and liquid preparation completion status data to the cloud control server.

[0120] This embodiment also provides a computer device suitable for the laboratory intelligent liquid preparation control method based on the Internet of Things, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the laboratory intelligent liquid preparation control method based on the Internet of Things proposed in the above embodiment.

[0121] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0122] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A laboratory intelligent liquid preparation control method based on the Internet of Things, characterized by: include, Obtaining liquid preparation request data, detecting the original liquid concentration value C_source in the current liquid storage tank, and transmitting it to the cloud control server, wherein the liquid preparation request data includes the target solution concentration value C_target, the target solution volume value V_target and the solute type identifier ID_solute; Calculate the stock solution dosage V_source and the diluent dosage V_diluent according to a preset dilution calculation model, and encapsulate the stock solution dosage V_source and the diluent dosage V_diluent into a control instruction data packet, and send it to the liquid preparation terminal; The liquid dispensing terminal receives the control instruction data packet, controls the first solenoid valve to open for a first solenoid valve opening time T1 to dispense the original liquid dosage V_source, and controls the second solenoid valve to open for a second solenoid valve opening time T2 to dispense the diluent dosage V_diluent; The stirring device is started to perform a mixing operation, and the mixed liquid concentration value C_real is detected in real time, and the mixed liquid concentration value C_real and the liquid preparation completion status data are fed back to the cloud control server.

2. The method for controlling laboratory liquid preparation based on the Internet of Things according to claim 3, wherein: The stirring device is started to perform a mixing operation, and the mixed liquid concentration value C_real is detected in real time, and the mixed liquid concentration value C_real and the liquid preparation completion status data are fed back to the cloud control server, including: After the addition of the stock solution amount V_source and the diluent amount V_diluent is completed, a start instruction is sent to the control module of the stirring device; A concentration sensor is installed on the side wall of the mixing container to detect and collect the mixed liquid concentration value C_real in real time with a sampling period Δt; Establish a concentration convergence judgment model to calculate the absolute deviation ΔC = |C_real-C_target| between the current mixed solution concentration value C_real and the target solution concentration value C_target, and compare the absolute deviation ΔC with the preset concentration error threshold δ to determine whether the current mixed solution has reached the target concentration requirement; When the absolute deviation ΔC is less than the preset concentration error threshold δ, the delay verification timer is started, the delay judgment timer T_delay is set, and the absolute deviation ΔC is continuously monitored during the delay judgment time T_delay. If the absolute deviation ΔC of all sampling points remains within the preset concentration error threshold δ, the concentration of the mixed liquid is determined to meet the standard, the stirring device is controlled to stop according to the preset deceleration curve, and the final mixed liquid concentration value C_real_final is recorded; When the absolute deviation ΔC is greater than or equal to the preset concentration error threshold δ, the compensation calculation is started according to the deviation direction, and the liquid replenishing device is controlled to perform the multi-step replenishment operation; Obtain the final mixed liquid concentration value C_real_final from the concentration sensor, combine it with the current timestamp and the device identifier to form a liquid preparation completion data packet, and upload it to the cloud control server using an encrypted transmission protocol; The cloud control server receives the liquid preparation completion data packet, compares and verifies the final mixed liquid concentration value C_real_final in the liquid preparation completion data packet with the original liquid preparation request data, generates a liquid preparation quality report and stores it in the database.

3. The method for controlling laboratory liquid preparation based on the Internet of Things according to claim 2, wherein: Initiate compensation calculations based on the deviation direction, including: If the mixed solution concentration value C_real is less than the target solution concentration value C_target, the volume of the original solution to be added V_add_source is calculated; If the mixed solution concentration value C_real> the target solution concentration value C_target, the volume of diluent to be added V_add_diluent is calculated.

4. The method for controlling laboratory liquid preparation based on the Internet of Things according to claim 3, wherein: The liquid dispensing terminal receives the control instruction data packet and controls the first solenoid valve to open for a first solenoid valve opening time T1 to dispense the raw liquid dosage V_source, including: Read the control instruction data packet from the local cache area, parse and obtain the first solenoid valve opening time T1, the second solenoid valve opening time T2, the original liquid dosage V_source and the diluent dosage V_diluent; Sending a self-test instruction to the first solenoid valve and the second solenoid valve to obtain a first state feedback signal and a second state feedback signal; When the first state feedback signal and the second state feedback signal are both normal, the first flow sensor provided in the raw liquid pipeline and the second flow sensor provided in the diluent pipeline start flow monitoring; Sending an opening instruction to the first solenoid valve, simultaneously starting to record the actual opening time t1 of the first solenoid valve, and calculating the cumulative raw liquid dosage V1 through integration operation; When the actual opening time t1 ≥ the first solenoid valve opening time T1 or the accumulated raw liquid injection amount V1 ≥ the raw liquid injection amount V_source is satisfied, a closing instruction is sent to the first solenoid valve, and the actual raw liquid injection amount V1_final is recorded.

5. The method for controlling laboratory liquid preparation based on the Internet of Things according to claim 4, characterized in that: At the same time, the second solenoid valve is controlled to be open for a second solenoid valve opening time T2 to dispense the diluent in an amount V_diluent, including: After closing the first solenoid valve, an opening instruction is immediately sent to the second solenoid valve, and the actual opening time t2 of the second solenoid valve is recorded, and the cumulative amount of diluent added is calculated by integral operation; When the actual opening time t2 ≥ the second solenoid valve opening time T2 or the cumulative diluent dosage V2 ≥ the diluent dosage V_diluent is satisfied, a closing instruction is sent to the second solenoid valve, and the actual diluent dosage V2_final is recorded; The actual amount of raw liquid V1_final and the actual amount of diluted liquid V2_final are stored in the liquid preparation log and uploaded to the cloud control server.

6. The method for controlling laboratory intelligent liquid preparation based on the Internet of Things according to claim 4, characterized in that: The method for obtaining the original solution dosage V_source and the diluent dosage V_diluent is as follows: The cloud control server extracts the verified liquid preparation request data and the original liquid concentration value C_source from the task queue, and calls the preset dilution calculation model for calculation; Based on the solute type identifier ID_solute, the pre-stored fluid property database is queried to obtain the dynamic viscosity coefficient and temperature compensation parameters of the solute, and the original liquid dosage V_source and the diluent dosage V_diluent are corrected to compensate for the viscous loss in the fluid flow process; Based on the flow characteristic parameters of the solenoid valve of the liquid dispensing terminal, the first solenoid valve opening time T1 corresponding to the raw liquid dosage V_source and the second solenoid valve opening time T2 corresponding to the diluent dosage V_diluent are calculated; Encapsulate the original liquid dosage V_source, the diluent dosage V_diluent, the first solenoid valve opening time T1, and the second solenoid valve opening time T2 into a structured control instruction data packet; The control instruction data packet is encoded using a lightweight Internet of Things communication protocol, and a CRC check code is added, and the control instruction data packet is sent to the liquid dispensing terminal using an adaptive retransmission mechanism; The communication module of the liquid dispensing terminal receives the control instruction data packet, performs CRC check, and parses out the original liquid dosage V_source, the diluent dosage V_diluent, the first solenoid valve opening time T1 and the second solenoid valve opening time T2, and stores them in the local cache area, waiting for the liquid dispensing operation to be executed.

7. The method for controlling laboratory liquid preparation based on the Internet of Things according to claim 6, wherein: The specific formulas for the first solenoid valve opening time T1 and the second solenoid valve opening time T2 are as follows: T1=V_source / (K1×ΔP) T2 = V_diluent / (K2×ΔP); Wherein, K1 is the calibrated flow coefficient of the first solenoid valve, K2 is the calibrated flow coefficient of the second solenoid valve, and ΔP is the pipeline pressure difference.

8. A laboratory intelligent liquid dispensing control system based on the Internet of Things, based on the laboratory intelligent liquid dispensing control method based on the Internet of Things according to any one of claims 1 to 7, characterized in that: include, The data acquisition module is used to obtain the liquid preparation request data, detect the original liquid concentration value C_source in the current liquid storage tank, and transmit it to the cloud control server, wherein the liquid preparation request data includes the target solution concentration value C_target, the target solution volume value V_target and the solute type identifier ID_solute; A calculation module is used to calculate the original liquid dosage V_source and the diluent dosage V_diluent according to a preset dilution calculation model, and encapsulate the original liquid dosage V_source and the diluent dosage V_diluent into a control instruction data packet, and send it to the liquid preparation terminal; An execution control module is configured to receive the control instruction data packet at the liquid dispensing terminal, control the first solenoid valve to open for a first solenoid valve opening time T1 to dispense the raw liquid dosage V_source, and simultaneously control the second solenoid valve to open for a second solenoid valve opening time T2 to dispense the diluent dosage V_diluent; The concentration detection module starts the stirring device to perform a mixing operation, and detects the mixed liquid concentration value C_real in real time, and feeds back the mixed liquid concentration value C_real and the liquid preparation completion status data to the cloud control server.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the laboratory intelligent liquid preparation control method based on the Internet of Things according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the laboratory intelligent liquid preparation control method based on the Internet of Things according to any one of claims 1 to 7 are implemented.

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