A digital multi-specification pH electrode titrator and a control method thereof

Abstract

The application relates to the technical field of analytical instrument control, and discloses a digital multi-specification pH electrode titrator and a control method thereof. The method collects an identification parameter set of a multi-specification pH electrode and an input requirement set of a titration task, evaluates an electrode distribution difference value, generates an electrode selection scheme, determines a starting time point of a key titration task and a task interval isolation parameter, performs a priority sorting operation on titration resources, calculates a task resource occupation rate and an interval influence weight, generates a task scheduling sequence, analyzes titrant supply cycles and electrode use time length data, detects electrode deployment time conflicts and resource shortage tasks, adjusts a task execution interval parameter, and outputs a task constraint parameter set; the task constraint parameter set is used to aggregate titration time nodes and task interval distribution difference values, map the relationship between resource occupation rates and electrode available time, calculate a task time adjustment amplitude, reconfigure a task time line and electrode resource allocation, and form a titration progress schedule.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of analytical instrument control, in particular to a digital multi-specification pH electrode titrator and a control method thereof. BACKGROUND

[0002] In the fields of chemical analysis, environmental monitoring, pharmaceutical industry and food inspection, titration analysis is a basic and important quantitative analysis method. As the core sensor in the titration process, the performance stability and measurement accuracy of the pH electrode directly determine the reliability of the analysis results. With the increasing demand for laboratory automation and the increasing analysis throughput, digital titrators capable of supporting multiple specifications of pH electrodes and automatically completing a series of titration tasks have been widely used. However, the control method of the existing titrator has many shortcomings when facing multiple different specifications of pH electrodes and complex continuous titration task sequences. The traditional control logic often focuses on the accurate control of the internal process of a single titration task, such as the addition rate of titrant, the judgment of equivalence point, etc., but lacks effective strategies for overall planning between multiple tasks, rational allocation of electrode resources, and overall optimization of long-time sequence tasks.

[0003] In actual operation, different specifications of pH electrodes have different measurement ranges, response characteristics, service life and maintenance requirements. When the titrator is equipped with multiple electrodes and can perform multiple types of titration tasks, how to dynamically select the most suitable electrode and arrange its execution sequence according to the properties of the sample to be measured, the priority of the task and the current state of the electrode is a complex problem. The existing systems mostly use pre-manual specification or simple polling strategies, which cannot realize intelligent and refined matching between electrode resources and task requirements. Such improper matching may cause key electrodes to be consumed on non-key tasks too early, or unsuitable electrodes to be used for specific samples causing measurement deviation, or even affect the execution of subsequent important tasks due to untimely electrode maintenance.

[0004] The execution of a series of titration tasks is not an isolated event, and there is a time sequence correlation and resource competition between tasks. For example, different tasks may share limited titrant resources, and electrodes need maintenance operations such as cleaning, calibration or regeneration after continuous use, which all require time. The task scheduling of the current titrator is mostly based on simple first-come-first-served or fixed order, and the mutual influence between tasks is not fully considered, such as a long time-consuming task may block the start of subsequent urgent tasks, or multiple tasks may compete for the same electrode or titrant, resulting in conflicts. The lack of prediction and optimization ability of the overall resource occupation, time conflict of the task sequence leads to low overall operation efficiency of the instrument and prolongs the task completion period. At the same time, in the process of long-term automated operation, the environmental conditions may fluctuate, the electrode performance will decay over time, and the titrant will be consumed. The fixed task schedule and electrode use strategy are difficult to adapt to these dynamic changes, which may introduce additional measurement errors or cause task interruption. Therefore, there is an urgent need for a titrator control method that can intelligently coordinate multi-specification electrode resources, optimize task scheduling sequence, and adapt to dynamic changes in the running environment, to improve analysis efficiency, resource utilization and result reliability. SUMMARY

[0005] The purpose of the present application is to provide a digital multi-specification pH electrode titrator and its control method to solve the problems raised in the background art.

[0006] To achieve the above-mentioned purpose, the present application provides a control method for a digital multi-specification pH electrode titrator, the method comprising:

[0007] Collecting the identification parameter set of the multi-specification pH electrode and the input requirement set of the titration task, calculating the matching degree of the electrode available time and the task requirement sequence, evaluating the electrode allocation difference value, and integrating the available state data of the titrant to generate an electrode selection scheme;

[0008] Based on the electrode selection scheme, determining the starting time point of the key titration task and the task interval isolation parameter, performing priority sorting operation on the titration resource, calculating the task resource occupation rate and interval influence weight, and generating a task scheduling sequence;

[0009] According to the task scheduling sequence, analyzing the titrant supply period and electrode use time data, detecting electrode deployment time conflict and resource shortage task, adjusting the task execution interval parameter, and outputting a task constraint parameter set;

[0010] Using the task constraint parameter set, aggregating the titration time node and the task interval distribution difference value, mapping the relationship between the resource occupation rate and the electrode available time, calculating the task time adjustment amplitude, reconfiguring the task timeline and electrode resource allocation, and forming the titration progress arrangement;

[0011] According to the titration schedule, monitor the titration environment change data and electrode operation state indicators, calculate the deviation value of the task time and the environment change, count the electrode cumulative working hours and the titration agent remaining amount, dynamically adjust the electrode deployment strategy and the time node, and generate the titration deviation correction result.

[0012] Preferably, the identification parameter set of the multi-specification pH electrode and the input requirement set of the titration task are collected, the matching degree of the electrode available time and the task requirement sequence is calculated, the electrode allocation difference value is evaluated, and the available state data of the titration agent is integrated to generate an electrode selection scheme, including:

[0013] The requirement description information of the titration task and the available time data set of the multi-specification pH electrode are collected, the matching degree of the available time of the multi-electrode and the task requirement is comprehensively calculated, the difference value between the total available time of the electrode and the task requirement is determined, and an electrode time difference value set is established;

[0014] The electrode time difference value set is analyzed, the allocation relationship of the electrode and the titration agent is adjusted in combination with the availability state of the titration agent, and an electrode-titration agent adjustment mapping table is constructed;

[0015] The electrode allocation deviation value is calculated through the electrode-titration agent adjustment mapping table, and the calculation process of the electrode allocation deviation value includes: summing the absolute values of the differences between the requirement values of each titration task and the available time of the electrode, dividing the summing result by a dynamic adjustment coefficient, multiplying by a weight factor parameter, and then dividing by a linear combination of an adjustment parameter and a task quantity value;

[0016] According to the electrode allocation deviation value, an electrode selection scheme is generated.

[0017] Preferably, based on the electrode selection scheme, the starting time point and the task interval isolation parameter of the key titration task are determined, the priority sorting operation is performed on the titration resources, the task resource occupation rate and the interval influence weight are calculated, and a task scheduling sequence is generated, including:

[0018] The resource allocation description information of each key titration task is extracted from the electrode selection scheme, the task starting working time is calculated, and the optimal task order is predicted by applying a resource optimization algorithm to generate a time scheduling table of the key titration task;

[0019] Through the time scheduling table of the key titration task, a priority evaluation model is used to perform hierarchical sorting on the titration resources, the task emergency degree index is combined to adjust the resource working hour allocation, and a task priority queue is established;

[0020] The task priority queue is used to calculate the task resource occupancy rate and the task interval influence weight of the key titration task considering the interval requirement in task execution, wherein the task resource occupancy rate is obtained by multiplying the priority value of each task by the sum of interval influence weights, dividing by the sum of required man-hours of the task multiplied by the environmental adjustment coefficient;

[0021] Based on the task resource occupancy rate and the task interval influence weight, a task scheduling sequence is generated.

[0022] Preferably, according to the task scheduling sequence, the titration agent supply cycle and the electrode use time length data are parsed, including:

[0023] The starting time point and material cycle information of each task are obtained from the task scheduling sequence, the electrode usage is analyzed, the resource demand value of each task is calculated by using the resource allocation algorithm, and a resource demand analysis report is generated;

[0024] According to the resource demand analysis report, the time conflict points of resource supply and demand, the conflicts and resource shortage tasks in electrode allocation are marked, and a conflict and shortage index list is created;

[0025] Using the conflict and shortage index list, the execution interval parameters of each task are recalculated, the resource demand of each task is divided by the average value of the resource supply cycle plus the allocation conflict factor to optimize the task execution plan;

[0026] Based on the optimized task execution plan, a set of task constraint parameters is output.

[0027] Preferably, using the set of task constraint parameters, the titration time node and the task interval distribution difference value are aggregated, the relationship between resource occupancy rate and electrode available time is mapped, the task time adjustment amplitude is calculated, the task timeline and electrode resource allocation are reconfigured, and the titration progress schedule is formed, including:

[0028] The titration time node and the task interval distribution difference information are extracted from the set of task constraint parameters, the task timeline and the resource distribution are compared, the task execution order and the resource utilization efficiency are analyzed, and a time node and task difference distribution diagram is generated;

[0029] Using the time node and task difference distribution diagram, the relationship between resource occupancy rate and electrode available time is mapped, based on the matching analysis of resource allocation and task requirements, the time conflict and resource insufficient tasks are identified, and a resource conflict and insufficient task list is created;

[0030] According to the resource conflict and insufficient task list, a task execution adjustment range and a time node correction value are calculated, wherein the time node correction value is obtained by multiplying the resource occupation rate of each task by the task priority weight divided by the sum of the original planned time node plus the square root of the task adjustment factor;

[0031] Based on the time node correction value, a titration schedule is formed.

[0032] Preferably, according to the titration schedule, titration environment change data and electrode operating state indicators are monitored, a task time and environment change deviation value is calculated, electrode cumulative working hours and titrant remaining amount are counted, electrode deployment strategies and time nodes are dynamically adjusted, and a titration deviation correction result is generated, including:

[0033] Real-time monitoring data is obtained from the titration schedule, including titration environment changes and electrode operating state information, combined with task time nodes, using data analysis techniques, the influence of the environment and electrode state on the titration schedule is determined, and an environment and electrode state evaluation report is generated;

[0034] Using the environment and electrode state evaluation report, the deviation value between task time and environment change is calculated, and the cumulative working hours and titrant remaining amount of each electrode are counted, and through quantitative analysis method, the electrode deployment strategy and time node that need to be adjusted are identified, and a task and environment deviation table is created;

[0035] Based on the task and environment deviation table, the electrode deployment strategy and time node are adjusted to match the actual titration environment changes, the overall adjustment efficiency is calculated, and the overall adjustment efficiency is obtained by summing the time deviation value of each task multiplied by the electrode operating efficiency associated with the task divided by the original planned working hours plus the global adjustment coefficient;

[0036] According to the overall adjustment efficiency, a titration deviation correction result is generated.

[0037] Preferably, after the titration deviation correction result is generated, it further includes:

[0038] Verify the consistency of the titration deviation correction result and the actual titration data, and calibrate the electrode deployment parameters by comparing the planned value and the actual measured value;

[0039] According to the calibrated electrode deployment parameters, update the titration schedule and feedback to the task scheduling sequence for iterative optimization.

[0040] Preferably, the update of the titration schedule includes:

[0041] Re-evaluate the titrant remaining amount and the electrode wear state, and adjust the task time node allocation parameters;

[0042] According to the adjusted task time node allocation parameter, the resource occupation rate is recalculated, and the electrode selection scheme is optimized.

[0043] Preferably, the recalculating the resource occupation rate comprises:

[0044] The pH value change curve in the titration process is dynamically tracked, and the titration speed parameter and the electrode switching frequency are adjusted.

[0045] Based on the adjusted titration speed parameter, the task interval influence weight is corrected, and the task constraint parameter set is updated.

[0046] The dynamically tracking the pH value change curve in the titration process comprises:

[0047] The measurement data stream of the multi-specification pH electrode is collected in real time, and the pH value deviation and change trend line are calculated.

[0048] According to the pH value deviation and change trend line, the titrant addition rate and electrode calibration cycle parameter are adjusted.

[0049] Based on the adjusted titrant addition rate and electrode calibration cycle parameter, the titration deviation correction result is optimized.

[0050] Preferably, the present application also includes a digital multi-specification pH electrode titrator, comprising a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the control method of the digital multi-specification pH electrode titrator when executing the computer program.

[0051] Compared with the prior art, the beneficial effects of the present application are:

[0052] The present application collects electrode parameters and task requirements, and calculates the matching degree and allocation difference value, so that the selection of the electrode is no longer random or fixed, but the optimal or suboptimal matching based on the current task characteristics and electrode state. This intelligent matching method can use suitable electrode resources for suitable analysis tasks, which helps to maintain the electrode performance, prolong the effective service life, and reduce the potential measurement risks caused by improper selection of electrodes from the source. The integrated titrant available state is used for comprehensive decision-making, which avoids the task interruption caused by insufficient resources.

[0053] The process of generating the task scheduling sequence introduces the consideration of task priority, resource occupation rate and interval influence weight. This makes the execution order of the task optimized and sorted, and important or urgent tasks are arranged in priority, while considering the necessary isolation between tasks and reducing the mutual interference between tasks. This pre-planning helps to improve the overall execution efficiency of a series of titration tasks. Further detect electrode deployment time conflicts and resource shortage tasks, and adjust the execution interval parameters, which is equivalent to a conflict checking and feasibility analysis of the preliminary scheduling scheme. It can detect potential resource contention or time overlap problems in advance and make adjustments before the actual execution of the task, thereby enhancing the robustness and executability of the scheduling scheme.

[0054] The process of forming the titration schedule is a process of fine configuration of task timeline and resource allocation. By mapping the relationship between resource occupation and electrode available time, and calculating the time adjustment amplitude, the final schedule formed is not only logically feasible, but also efficient in the use of time resources, reducing equipment idle waiting time and optimizing analysis flux. Most importantly, the method does not regard the schedule as a fixed program. It introduces a dynamic monitoring and correction mechanism to continuously track the changes in the titration environment and the indicators of the electrode running state. By calculating the deviation of task time from the actual environmental changes and counting the resource consumption, the system can sense the difference between the actual running and the plan. Based on these real-time feedback information, the electrode deployment strategy and task time node are dynamically adjusted to generate the deviation correction result. This closed-loop control mechanism gives the system strong adaptability to cope with various uncertainties that may occur during operation, such as environmental temperature fluctuations, electrode response speed changes, titrant consumption speed not meeting expectations, etc., thereby ensuring that the titration process can proceed steadily towards the predetermined goal even under dynamic conditions, improving the anti-interference ability of the entire analysis process and the consistency of the results. The whole method embodies a whole-process optimization idea from resource intelligent matching, task forward scheduling, schedule fine planning to running adaptive adjustment. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 The working principle diagram of the digital multi-specification pH electrode titrator and the control method thereof;

[0056] Figure 2 The flowchart for electrode selection scheme generation;

[0057] Figure 3 The flowchart for generating the task scheduling sequence;

[0058] Figure 4 The pH electrode titration process deviation trend analysis chart;

[0059] Figure 5Task initial vs. optimized duration comparison chart. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the present application will be apparently and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work belong to the protection scope of the present application.

[0061] Please refer to Figure 1 The present application provides a digital multi-specification pH electrode titrator and a control method thereof. After system initialization, the method includes collecting identification parameter sets of multi-specification pH electrodes, such as electrode model, calibration period, historical use time length, current state, etc., while receiving a user input titration task requirement set, including sample quantity to be measured, target pH value, accuracy requirement, time constraint, etc. Based on the matching degree calculation of electrode available time length and task requirement sequence, the difference value under different electrode allocation schemes is evaluated, and the available state data such as the inventory, concentration and expiration date of the current titrant are integrated, and a preliminary electrode selection scheme is generated through an optimization algorithm. Subsequently, according to the electrode selection scheme, the planned starting time point of the key titration task and the necessary interval parameters between tasks are determined, the priority sorting operation based on the task urgency and resource dependency relationship is performed on the involved titration resources, the occupation rate of each task on the resources and the influence weight of the task interval on the overall efficiency are calculated, so as to generate an ordered task scheduling sequence. Next, the system analyzes the supply period of the titrant and the predicted use time length data of each electrode according to the generated task scheduling sequence, detects potential electrode deployment time conflicts and resource shortage task nodes, and outputs a set of task constraint parameter sets containing time constraints and resource constraints by dynamically adjusting task execution interval parameters. On this basis, the system uses the task constraint parameter set to aggregate the time node information of all titration tasks and calculate the time distribution difference value between tasks, establishes the mapping relationship between resource occupation rate and actual available time period of the electrode, calculates the task time adjustment amplitude required due to resource conflicts or optimization requirements, and then reconfigures the time line of the entire task and the allocation scheme of the electrode resources, and finally forms an executable titration schedule. In the titration execution phase, the system monitors the change data of the titration environment and the electrode operation state indicators according to the schedule, calculates the deviation value of the actual task execution time from the planned time due to environmental changes, and calculates the cumulative working time of each electrode and the real-time remaining amount of the titrant, adjusts the deployment strategy of the electrode and the time node of the subsequent task based on these dynamic information, generates a titration deviation correction result for correcting the current execution plan, so as to realize closed-loop control.

[0062] Example 1:Figure 2 In specific implementation, the system connects with the database through the hardware interface module, collects the identification parameter set of multi-specification pH electrode in real time, the identification parameter set includes electrode serial number, model specification, calibration history, maximum available duration, current state flag and maintenance record, receives the input requirement set of titration task from the user input interface or the upper computer system, the input requirement set contains task number, sample quantity, target pH value range, allowed error limit, time constraint condition and priority identification. The data processing unit built-in the system starts the matching degree calculation process, the matching degree calculation process is based on the comparison of electrode available duration and task demand sequence, the electrode available duration is extracted from the electrode historical use data, the task demand sequence is parsed from the input requirement set to obtain the expected time consumption demand of each task, the matching degree calculation adopts the weighted Euclidean distance algorithm to quantify the fitting degree of each electrode and each task in time dimension. In specific implementation, after calculating the matching degree of electrode available duration and task demand sequence, the system evaluates the electrode allocation difference value, the evaluation process first establishes the electrode time difference value set, the electrode time difference value set is obtained by subtracting the available duration of the allocated electrode from the demand duration of each task, and the difference values of all tasks are sorted, the evaluation of the electrode allocation difference value integrates the available state data of titrant, the available state data of titrant includes titrant type, current inventory, concentration value, expiration date and replenishment plan. In specific implementation, when generating the electrode selection scheme, the system calls the optimization algorithm module, the optimization algorithm module takes minimizing the electrode allocation difference value as the objective function, the constraint conditions include electrode state, titrant inventory and task time window, the electrode selection scheme output is a mapping table, the mapping table lists in detail the pH electrode number recommended for each titration task, backup electrode options and associated titrant information.

[0063] In specific implementation, when collecting the requirement description information of titration task and the available time dataset of multi-specification pH electrode, the system acquires raw data from networked sensors and manual input channels, the requirement description information is parsed into structured format including task start time, duration, resource dependency, the available time dataset of multi-specification pH electrode is derived from electrode scheduling log, and the working time period and unavailability reason of each electrode are labeled. The matching degree of the available time of multiple electrodes and task requirement is calculated by using multi-objective optimization method, the matching degree calculation introduces fuzzy logic controller to handle the uncertainty of time window, and the difference between the total available time of electrode and task requirement is calculated by subtracting the total available time of all electrodes from the total requirement time of all tasks. The established electrode time difference value set is stored in the system memory for subsequent analysis. In specific implementation, when analyzing the electrode time difference value set combined with the availability state of titrant, the system performs real-time data fusion, the availability state of titrant is verified by liquid level sensor and inventory database, and the allocation relationship between electrode and titrant is adjusted by using heuristic rule engine. The rule engine considers titrant consumption rate and electrode use efficiency, and constructs an electrode-titrant adjustment mapping table in matrix form, where the rows correspond to electrodes, the columns correspond to titrant types, and the cell values represent allocation weights. In specific implementation, the electrode allocation deviation value is calculated through the electrode-titrant adjustment mapping table, and the calculation process is coded as a software function. The function input includes difference set, adjustment mapping table, dynamic adjustment coefficient, weight factor parameter, adjustment parameter and task quantity value. The calculation steps are executed in sequence, including absolute value summation, division operation, multiplication operation and linear combination division. The electrode allocation deviation value output is a scalar value, which is used to guide scheme generation. In specific implementation, the electrode selection scheme is generated according to the electrode allocation deviation value, and the generation logic is based on deviation value threshold comparison. If the deviation value is lower than the set threshold, the current allocation is adopted, otherwise the iterative adjustment loop is started. The electrode selection scheme is finally output in XML or JSON format for calling by downstream modules.

[0064] In some embodiments, when collecting requirement description information, the system supports batch import function, which allows users to upload standardized template files containing task parameters and electrode configurations. The available time dataset updating mechanism of multi-specification pH electrode adopts event-driven mode, which triggers data refresh when the electrode state changes. In some embodiments, when calculating the matching degree, the system can enable a machine learning model trained using historical data to predict the optimal pairing of electrodes and tasks. The analysis of electrode time difference value set introduces time series prediction algorithm to estimate future resource conflict probability.

[0065] Optionally, the system integrates Internet of Things tag data when collecting the set of identification parameters, the Internet of Things tag data provides real-time position and health status of the electrode, the input requirement set of the titration task supports dynamic modification, the dynamic modification is realized through a graphical interface, and the user is allowed to adjust the task priority. Optionally, an environmental factor compensation is added when calculating the matching degree, the environmental factor compensation corrects the influence of temperature and humidity on the performance of the electrode, a risk weight is introduced in the electrode allocation difference value evaluation, and the risk weight is dynamically adjusted based on the criticality of the task.

[0066] It can be understood that the matching degree calculation of the available duration of the electrode and the task demand sequence is the basis of resource scheduling, and the matching degree calculation ensures the maximization of electrode utilization rate and reduces idle time. It can be understood that the integration of the available state data of the titrant avoids resource bottlenecks, and the available state data and the electrode data cooperatively improve the overall scheduling reliability.

[0067] In a specific implementation, the system initialization stage loads the set of identification parameters of all pH electrodes, the set of identification parameters is read from the non-volatile memory and verified for integrity by a data verification module, the input requirement set of the titration task is input through a human-computer interaction interface, the input requirement set is parsed into an internal data structure, and the data structure includes a task list and a resource requirement field. When calculating the matching degree of the available duration of the electrode and the task demand sequence, the system creates a timeline model, the timeline model simulates the overlapping part of the available interval of the electrode and the task time window, the matching degree value is calculated as the ratio of the overlapping duration to the task demand duration, and a comprehensive matching degree score is obtained after weighted averaging. The differential evolution algorithm is used to evaluate the electrode allocation difference value, the differential evolution algorithm compares the difference values under different allocation schemes, and the difference values are normalized for comparison. When integrating the available state data of the titrant, the system queries the inventory management system, the inventory management system returns the real-time inventory of the titrant and the planned replenishment time, and the available state data and the electrode data are merged into a resource pool. The last step of generating an electrode selection scheme calls a decision tree algorithm, the decision tree algorithm generates a scheme based on the principle of minimizing the difference value, and the scheme output includes an electrode allocation list and a confidence score.

[0068] In specific implementation, the set of identification parameters of multi-specification pH electrodes is collected through a distributed sensor network, the distributed sensor network periodically reports electrode state data, a data aggregation center processes the original signals, and extracts identification parameters such as electrode serial number, model specification, calibration history, maximum available duration, current state flag, and maintenance record. The input requirement set of the titration task is synchronized from the laboratory information management system, the input requirement set is parsed into a machine-readable format, and the format includes a task attribute dictionary. The matching degree of the electrode available duration and the task requirement sequence is calculated by matrix operation, the matrix operation is the dot product of the electrode available time vector and the task requirement vector, and the result vector is standardized as the matching degree index. The difference value of electrode allocation is evaluated by introducing a multivariate regression model, the multivariate regression model fits historical allocation data, and predicts the trend of the difference value. When the available state data of the titration agent is integrated, the system checks the compatibility of the titration agent expiration date and the task timeline, and records the incompatible case as an alarm event. The logic of generating the electrode selection scheme is embedded in a rule base, and the rule base includes business rules such as preferentially using high-precision electrodes and avoiding cross-contamination.

[0069] In specific implementation, the electrode available duration is calculated from electrode metadata, the electrode metadata is stored in a relational database, and a query statement filters invalid records. The time constraint is extracted from the input requirement set, and the time constraint is converted into a Unix timestamp for easy calculation. The matching degree calculation uses a similarity algorithm, such as cosine similarity, to compare the feature vectors of the electrodes and the tasks. In the electrode allocation difference value evaluation stage, the system constructs a conflict graph, the nodes of the conflict graph represent tasks, and the edges represent resource conflicts. The difference value is derived from the graph coloring algorithm. When the available state data of the titration agent is integrated, the system simulates the titration agent consumption curve, and the consumption curve is aligned with the task scheduling. After the electrode selection scheme is generated, the scheme verification module performs a feasibility check, and the feasibility check verifies the sufficiency of resources by simulating execution.

[0070] In specific implementation, the update frequency of the set of identification parameters of multi-specification pH electrodes is configurable, the default update frequency is once per minute, the input requirement set of the titration task supports version control, and version control allows rolling back incorrect inputs. The matching degree calculation uses a sliding window technique, the sliding window technique processes continuous task streams, and the electrode allocation difference value calculation introduces a penalty term that increases the weight for over-limit allocation. The available state data of the titration agent is obtained from a cloud API, and the cloud API provides real-time inventory information. The output format of the electrode selection scheme is compatible with industry standards, such as OPCUA, which facilitates system integration.

[0071] In a specific implementation, the system records audit logs during the collection process, the audit logs track parameter changes, parallel processing is used when calculating the matching degree, and parallel processing accelerates large data set processing. Electrode allocation difference value evaluation is combined with user feedback, which is collected through a scoring system. When integrating titrant status, the system handles abnormal situations such as titrant expiration and automatically marks it as unavailable. After generating an electrode selection scheme, the scheme is pushed to the executor module, which controls the physical device to allocate electrodes.

[0072] Example 2: refer to Figure 3 In a specific implementation, the system extracts resource allocation description information of key titration tasks from the electrode selection scheme generated and stored in the shared memory area, which includes task unique identifier, bound pH electrode serial number, allocated titrant type and estimated amount, task basic operation duration estimate value, and task dependency relationship constraint. The starting time point of the key titration task is determined by analyzing the system global clock and resource readiness state, and the task isolation parameters are set according to the electrode cleaning procedure, system stability time requirement, and minimum time interval standard to prevent cross contamination. These isolation parameters are stored in the configuration file in the form of key-value pairs for the scheduling algorithm to call. The priority sorting operation on titration resources is completed by a special resource scheduler module, which loads a pre-defined priority evaluation model that considers task urgency index, task type inherent priority, resource scarcity coefficient, and task submission timestamp to generate a dynamic task priority queue. Calculating task resource occupancy requires statistics on the occupancy proportion of each key titration task on specific pH electrodes, titrant channels, and data processing units within the planned execution period. The process of calculating task resource occupancy will also calculate the task interval impact weight, which reflects the influence of the isolation time set between tasks on the overall scheduling efficiency. The value is positively correlated with the isolation time length but negatively correlated with the overall device utilization. Generating a task scheduling sequence is the final output, which is an ordered task execution instruction list. Each item in the list clearly specifies the task start time, occupied resource list, expected duration, and interval length from the previous task.

[0073] In a specific implementation, after extracting the resource allocation description information of each key titration task from the electrode selection scheme, the system calculates the task start working time, which needs to be topologically sorted according to the resource dependency graph to ensure that the subsequent task can start after the completion of the preceding task, and applies a resource optimization algorithm to predict the optimal task order. The resource optimization algorithm uses a genetic algorithm or a simulated annealing algorithm with constraints, which takes minimizing the total completion time or maximizing resource utilization as the optimization goal, and is strictly limited by resource availability constraints and task isolation parameters. The generated time schedule of the key titration task is stored in the memory in the form of a Gantt chart data structure for visualization and adjustment. Through the time schedule of the key titration task, a priority evaluation model is used to rank the titration resources in order of priority. The priority evaluation model calculates a priority score for each resource instance, which is based on the historical use efficiency of the resource, the current load state, and the priority weighted sum of the tasks it supports. Combining the task urgency index means that high-urgency tasks will have priority to obtain high-quality resources or longer continuous use periods. The established task priority queue is a max-heap data structure that ensures that the scheduler can always quickly access the highest-priority task to be processed. When calculating the task resource occupancy rate and task interval influence weight of the key titration task using the task priority queue, the system traverses each task node in the priority queue. The calculation formula of the task resource occupancy rate involves combining the priority value of the task, the task interval influence weight, the task required working hours, and the environmental adjustment coefficient. The environmental adjustment coefficient is used to compensate for the impact of environmental fluctuations on the actual time consumption of the task, ensuring the accuracy of resource occupancy rate estimation. Based on the calculated task resource occupancy rate and task interval influence weight, the final step of generating the task scheduling sequence is performed by the scheduling sequence generator. The scheduling sequence generator integrates all the calculated parameters, performs conflict detection and resolution, and outputs a linear task sequence that is conflict-free and meets all constraint conditions. This sequence is serialized and saved as a scheduling file.

[0074] In some embodiments, when determining the starting time point of the key titration task, the system supports a manual intervention mode that allows experienced operators to fine-tune the automatically calculated starting time point based on professional knowledge to deal with special samples or complex titration processes. In some embodiments, when performing priority sorting operations on titration resources, the system can integrate an external business rule engine that allows users to customize complex priority rules, such as dynamically adjusting task priorities based on customer levels or contract terms.

[0075] Optionally, the system can introduce a machine learning prediction module to predict the resource occupation pattern of new tasks by analyzing historical scheduling data, thereby improving the accuracy of resource reservation. Optionally, after generating the task scheduling sequence, the system provides a simulation running function to verify the feasibility of the scheduling sequence without actually driving the hardware, and reports potential resource conflicts or time overflow problems.

[0076] It can be understood that determining the starting time point of the key titration task is the timing reference of the entire scheduling process, and accurate setting of the starting time point can effectively avoid resource idling and task congestion. It can be understood that calculating the task interval influence weight is an important link to balance device utilization and operation quality, and reasonable weight setting helps to improve the overall throughput while ensuring titration accuracy.

[0077] In specific implementation, determining the starting time point of the key titration task based on the electrode selection scheme needs to consider the device calibration period and preventive maintenance plan synchronously, which are taken as hard time constraints in scheduling calculation to ensure that the key task will not be delayed due to device unavailability. The setting of the task interval isolation parameter not only contains time interval, but also may contain physical space isolation requirements, for example, titration tasks of different properties may need to use different titration tables or cleaning stations, and these space constraints also need to be modeled and included in the calculation of the isolation parameter. The priority sorting operation on titration resources is a dynamic process, and the priority sorting operation will be updated in real time with the addition of new tasks, the completion or cancellation of existing tasks, and the change of device state, and the system maintains a priority score cache to speed up the sorting process. When calculating the task resource occupation rate, the occupation rate calculation will distinguish between exclusive resources and shared resources. For pH electrodes, which are exclusive resources, the occupation rate is 100% during task execution, while for computing units, which are shared resources, the occupation rate is calculated by time slice. In the final stage of generating the task scheduling sequence, the system will generate a set of atomic operation instructions for each task in the sequence, including electrode activation, titrant loading, start titration, data recording, etc. These instructions are packaged into task execution objects.

[0078] In specific implementation, after extracting resource allocation description information, the system verifies the integrity and consistency of the resource allocation description information, the verification process includes checking whether the electrode exists, whether the titrant is sufficient, whether the time parameter is legal, etc. The task that fails to verify will be marked and returned to reselect the electrode. When applying the resource optimization algorithm to predict the optimal task order, the algorithm will evaluate thousands of possible permutations and combinations, and the evaluation criteria include total process time, resource switching times, peak load and other indicators. The final selected order is the scheme with the highest weighted comprehensive score of these indicators. After establishing the task priority queue, the system will monitor the waiting time of tasks in the queue. If the waiting time of high-priority tasks is too long, the system may trigger the resource preemption mechanism to temporarily interrupt low-priority tasks to ensure the execution of high-priority tasks. When calculating the interval impact weight, the system will establish an interval impact weight table. The table determines the influence coefficient of different isolation time on electrode stability and measurement accuracy based on experimental data, so that the weight assignment has scientific basis. When generating the task scheduling sequence, the sequence generator will insert necessary system tasks, such as periodic calibration, electrode cleaning, etc. These system tasks have fixed priority and resource requirements, which ensure the long-term stable operation of the equipment.

[0079] In specific implementation, the identification of key titration tasks can be completed through preset rules or machine learning classifiers. The preset rules, for example, the task target pH value accuracy is higher than a certain threshold or the sample value is higher than a certain threshold, and the machine learning classifier automatically identifies key tasks by analyzing task feature vectors. The calculation of task interval isolation parameters may use dynamic programming method, which finds the optimal interval setting sequence to minimize the total interval time loss of all tasks. The parameters of the priority evaluation model can be optimized online through reinforcement learning, which adjusts the model parameters according to the good or bad of historical scheduling results, so that the priority evaluation model continuously adapts to the actual operating environment. The calculation results of task resource occupancy rate will be presented to the operator in the form of visual charts, which help the operator intuitively understand the load status and resource bottleneck of the system. The final generated task scheduling sequence will be assigned a version number, which is used to track the change history of the scheduling plan and supports version rollback operation to deal with unexpected situations.

[0080] In a specific implementation, the system maintains a task context block for each critical titration task, which stores all scheduling-related parameters and intermediate calculation results of the task, and the task context block is frequently accessed and updated during the scheduling process. The iteration number and population size of the resource optimization algorithm, and other hyperparameters, can be adaptively adjusted according to the problem size, with fast convergence settings for small-scale problems and more thorough search settings for large-scale problems to ensure the quality of the solution. The implementation of the priority queue uses a thread-safe data structure that ensures no data race or state inconsistency when accessing the queue concurrently in a multi-threaded environment. The calculation of the interval impact weight takes into account the task chain effect, which refers to the cumulative error that may occur due to device fatigue when continuously executing multiple similar tasks, thereby adjusting the weight accordingly. The sequence file output by the scheduling sequence generator uses a standardized format, which facilitates data exchange and integration with other production management systems or manufacturing execution systems.

[0081] Referring to Figure 4 In the bias correction process of the digital multi-specification pH electrode titrator, the multi-curve analysis of the figure relies on dynamic monitoring techniques for environmental changes, electrode state, and post-correction bias. In specific operations, the environmental change bias (red curve) reflects the influence of environmental fluctuations on measurement results during titration, the electrode state bias (blue curve) represents the change in pH electrode performance over time, and the post-correction bias (green curve) is the bias after dynamically adjusting the electrode deployment strategy and time node by the system. The quantitative analysis of the three biases is achieved through numerical fluctuations over time: the original data of the environmental change bias and the electrode state bias are collected in real time, and the bias values and trends at different time points are recorded. The post-correction bias is the result of the system's dynamic adjustment based on the cumulative electrode hours, titrant remaining amount, and environmental and electrode state evaluation reports. The bias control effect between the three is measured by the concentration of numerical distribution (the fluctuation range around the 0% bias line), which is used as a key indicator to evaluate the effectiveness of titration bias correction and optimize electrode selection schemes and task scheduling sequences. In parameter interpretation, the time point (hour) is the horizontal axis, covering the entire period of titration; the vertical axis deviation (%) is set to 0% as the baseline, with positive and negative deviations reflecting the deviation direction of the measured value from the true value. The sharp fluctuations of the environmental change bias (such as multiple peaks exceeding ±4%) indicate significant environmental interference on titration; the periodic changes in the electrode state bias reflect the dynamic decay of electrode performance; and the post-correction bias mostly fluctuates around 0%, indicating that the system's bias correction mechanism effectively suppresses the adverse effects of the environment and electrode state, ensuring the accuracy of titration.

[0082] In a specific implementation, the system reads a task scheduling sequence from the persistent storage, the task scheduling sequence contains a sorted task list and resource allocation information and scheduled timestamps associated with each task, parsing the titration supply cycle requires querying the application programming interface of the external inventory management system to obtain the standard replenishment interval time and the expected arrival date of each titration, and parsing the electrode usage duration data is achieved by analyzing the electrode log database, which records the start time, stop time and working mode of each electrode. Detecting electrode deployment time conflicts is done by constructing a resource timeline snapshot, which clearly shows the occupancy status of each electrode on the continuous time axis. The system uses a sliding window algorithm to scan the entire timeline to identify any overlapping time segments allocated to an electrode. Detecting resource shortages uses a forward-looking simulation method that virtually executes tasks in the order of the task scheduling sequence from the current time and dynamically reduces the global virtual inventory of titrations during execution. When the simulation finds that the resource demand of a task exceeds the current virtual inventory, the task is immediately marked. Adjusting the task execution interval parameters is the core means to solve conflicts and shortages, which is achieved by re-planning the buffer time between tasks. The extension or shortening of the buffer time can change the peak time point of resource demand. The output of the task constraint parameter set marks the completion of the conflict resolution phase. The task constraint parameter set is an enhanced data structure that not only contains basic time constraints but also adds resource sufficiency flags and conflict resolution history records.

[0083] In a specific implementation, after obtaining the starting time point and material cycle information of each task from the task scheduling sequence, the system starts a multi-dimensional resource analysis process, which considers time resources, equipment resources, and consumable resources simultaneously. The analysis of electrode usage not only focuses on the total usage time but also deeply analyzes the degree of compliance of the continuous working time of the electrode with the recommended rest period. The resource allocation algorithm is used to calculate the resource demand value of each task. Based on the principles of chemical metrology and equipment performance curves, the abstract titration target is converted into specific electrode occupation time and titrant consumption. The generated resource demand analysis report adopts a hierarchical structure, which includes an abstract layer, a detailed data layer, and a suggestion measure layer. When marking the time conflict points of resource supply and demand according to the resource demand analysis report, the system uses a highlight marking algorithm to visually display the conflict position on the Gantt chart. The conflict that occurs in electrode deployment needs to compare the physical location of the electrode and the location requirement of the task. A conflict and shortage index list is created using a double-linked list structure, which allows traversal of conflict events from front to back or back to front, facilitating the use of different optimization algorithms. The execution interval parameters of each task are recalculated using the conflict and shortage index list. The recalculating process uses the iterative relaxation method, which gradually adjusts the interval parameters until all conflict and shortage problems are solved. The optimization of interval parameters can be represented by the following mathematical relationship:

[0084] ;

[0085] wherein: represents the time adjustment amplitude of the i-th task, represents the resource demand intensity of the i-th task, represents the system reference time unit, represents the resource supply capacity coefficient, represents the system safety factor.

[0086] ​​In a specific implementation, when aggregating the task constraint parameter set, the system calls a time series analysis toolkit, which provides a variety of statistical functions for calculating the central tendency and dispersion of the time nodes. Mapping the relationship between resource occupancy and electrode available time requires the establishment of a three-dimensional relationship model, with the three dimensions being time point, electrode identifier, and occupancy value. The gradient descent method is used to find the local optimal solution for calculating the task time adjustment amplitude. The gradient descent method determines the adjustment direction by calculating the derivative of the objective function with respect to the time variable. Reconfiguring the task timeline and electrode resource allocation involves a large-scale combinatorial optimization problem, which is solved using a meta-heuristic algorithm that can find an approximate optimal solution within a reasonable time. The formed titration schedule is the final output of the scheduling plan, which includes machine-readable instruction sequences and human-readable documentation to ensure that operating personnel can understand and monitor the execution process.

[0087] In some embodiments, when analyzing electrode usage duration data, the system can integrate a machine learning prediction model that predicts the remaining service life of the electrode based on its historical performance data, thereby more accurately assessing the available duration of the electrode. In some embodiments, when detecting resource shortage tasks, the system can use a Monte Carlo simulation method that considers the uncertainty of the supply cycle by random sampling to provide a probabilistic shortage warning. In some embodiments, when calculating the task time adjustment amplitude, an elasticity coefficient concept can be introduced, which assigns different adjustment elasticity ranges to tasks of different priorities, with high-priority tasks being more strictly limited in adjustment amplitude.

[0088] Optionally, after mapping the relationship between resource occupancy and electrode available time, the system can generate a resource heat map that visually displays the degree of resource tension in different time periods to assist artificial scheduling decisions. Adjusting the task execution interval parameter is a key operation for balancing resource utilization and task delay, and fine interval parameter settings can improve system throughput without increasing resource investment. It can be understood that reconfiguring the task timeline and electrode resource allocation is a complex decision-making process that requires overall consideration of multiple optimization objectives and constraint conditions, and any local optimization may have a chain effect on the global.

[0089] In specific implementations, the system establishes a data quality check mechanism when parsing titrant supply cycle and electrode usage duration data according to the task scheduling sequence, which verifies the integrity and reasonableness of the input data and rejects obviously abnormal data records. After generating the resource demand analysis report, the report is automatically compared with historical same-period data, which helps to find abnormal demand patterns. When creating the conflict and shortage index list, the system calculates an urgency score for each index entry, which is based on the impact size of the conflict and the estimated time of shortage occurrence. The iterative process of recalculating the execution interval parameters is provided with a convergence judgment condition, which is usually that the improvement amount of the objective function in consecutive iterations is less than a certain threshold. Before outputting the task constraint parameter set, the parameter set is subjected to consistency verification to ensure that there is no logical contradiction between the parameters.

[0090] In specific implementations, when aggregating titration time node and inter-task distribution difference information, the system calculates the Gini coefficient of time distribution, which is used to quantify the uniformity of task distribution on the timeline and guide the optimization direction of scheduling. Comparing the task timeline with the resource distribution requires time alignment operation, which unifies data of different time resolutions to the same sampling frequency. Analyzing the task execution order and resource utilization efficiency is achieved by calculating indicators such as resource idle rate and resource peak load, which are important basis for evaluating the pros and cons of scheduling schemes. The generated time node and task difference distribution chart supports interactive exploration, and the operator can view detailed data sources and calculation processes by clicking chart elements. The resource conflict and shortage task list created after identifying time conflicts and resource insufficient tasks supports batch operations, which allows the same solution strategy to be applied to multiple similar problem tasks.

[0091] In specific implementations, when mapping the relationship between resource occupancy rate and electrode available time, the system detects the periodic pattern of resource usage, such as daily or weekly patterns, which can be used to predict future resource demand fluctuations. Based on the matching analysis of resource allocation and task requirements, the system generates a matching degree matrix, with rows representing tasks and columns representing resources, and cell values representing matching scores. Calculating the adjustment range and time node correction value of task execution requires solving a linear programming problem with constraints, and the objective function of the linear programming problem is to minimize the total adjustment cost. The calculation of time node correction values takes into account the dependency relationship network between tasks, which determines how adjusting one node will affect subsequent nodes. The final step in forming the titration schedule includes generating a checksum, which is used to verify whether the schedule has been accidentally modified during transmission and storage.

[0092] In a specific implementation, the system adopts a hierarchical processing strategy when dealing with the conflict and insufficient task list, which prioritizes the processing of conflicts with high urgency and wide impact, ensuring that tasks on the critical path are not affected. When calculating the time node correction value, the system establishes an adjustment impact propagation model to predict how time adjustments spread through the task dependency network. Reconfiguring the task timeline may involve task splitting or merging, task splitting breaks down large tasks into multiple parallel-executable subtasks, and task merging combines multiple small tasks into a composite task to reduce setup time. The reconfiguration of electrode resource allocation needs to comply with the electrode compatibility matrix, which defines the types of titration tasks that different models of electrodes can handle.

[0093] In a specific implementation, the system loads an established titration schedule, which includes task start times accurate to the second, expected duration, designated electrode resources, and associated titrant information. Monitoring titration environment change data is achieved through a sensor network deployed around the titrator, which continuously collects parameters such as environmental temperature, humidity, air pressure, and experimental table vibration amplitude. Monitoring electrode operating state indicators relies on built-in diagnostic functions or external impedance analysis modules. Electrode operating state indicators include electrode response slope, electrode internal resistance, electrode zero potential drift, and electrode signal-to-noise ratio. Calculating the deviation value of task time and environmental changes requires the establishment of a real-time data stream processing pipeline, which compares sensor readings with planned environmental conditions in the schedule. The deviation value is calculated as the standardized difference between actual measurements and planned values. Statistical electrode cumulative hours aggregate the total activation time of each electrode from the electrode usage record database. Statistical titrant remaining quantity is calculated by real-time measurement of titrant consumption using high-precision flow and weight sensors and deduction from initial inventory. Dynamic adjustment of electrode deployment strategy and time nodes is a closed-loop control process based on deviation analysis results and resource statistics. Adjustment operations may include switching to backup electrodes, reallocating task time windows, modifying titration rate parameters, or triggering emergency calibration procedures. Generating titration deviation correction results is the final output of this stage, which is a set of executable correction instructions detailing how to correct tasks currently being executed and how to update plans for subsequent unexecuted tasks.

[0094] In practice, referring to Table 1, after obtaining real-time monitoring data from the titration schedule, the system activates an anomaly detection engine. Based on preset threshold rules and machine learning models, the anomaly detection engine identifies abnormal patterns in environmental data and electrode status. Combined with task time nodes, it calculates environmental impact factors. The environmental impact factors quantify the intensity of environmental fluctuations on task execution progress. Data analysis techniques are used to determine the degree of influence of the environment and electrode status on the titration schedule. Data analysis techniques include time series correlation analysis, regression analysis, and principal component analysis. The generated environmental and electrode status assessment report adopts a structured format, which includes a summary, detailed analysis data, trend prediction, and risk level assessment. When calculating the deviation between task time and environmental changes using the environmental and electrode status assessment report, the system uses a sliding window method to calculate the moving average and standard deviation of recent deviations. The moving average and standard deviation are used to distinguish between random fluctuations and systematic deviations. When calculating the cumulative working hours and titrant remaining amount for each electrode, the cumulative working hours data is extracted from the electrode lifecycle management system, and the titrant remaining amount is queried through the real-time inventory management system. Quantitative analysis methods are used to identify the electrode preparation strategies and time nodes that need to be adjusted. The quantitative analysis methods include sensitivity analysis and hypothesis testing. The created task and environment deviation table records the difference between the actual execution time and the planned time for each task, the contribution of environmental deviation, and the suggested adjustment measures.

[0095] Table 1: Task and Environment Deviation Table

[0096] ;

[0097] The electrode preparation strategy and time nodes are adjusted based on the task and environment deviation table. The adjustment of the electrode preparation strategy and time nodes is achieved by reallocating resources and modifying task timestamps through a scheduling algorithm. The environmental parameter model needs to be dynamically updated to match the actual changes in the titration environment. The environmental parameter model predicts the impact of future environmental conditions on titration accuracy. The overall adjustment efficiency is calculated using a multi-index weighted evaluation method. The multi-index weighted evaluation method comprehensively considers the degree of time deviation correction, changes in resource utilization, and the cost of correction operations. The final step in generating titration deviation correction results includes the encoding and verification of correction instructions. The encoding of correction instructions uses a standardized command language, and the verification process ensures that the correction instructions do not introduce new conflicts or errors through simulation execution.

[0098] In specific implementation, when monitoring titration environment change data and electrode operation state indicators, the system adopts a hierarchical alarm mechanism, which sets different alarm thresholds and response processes for different levels of deviation. The deviation value of task time and environmental change introduces adaptive filtering technology to filter high-frequency noise in sensor data to improve the accuracy of deviation detection. After counting the cumulative working hours of the electrode and the remaining amount of titrant, the system generates a resource health report, which predicts the remaining life of the electrode and the titrant depletion time, dynamically adjusts the electrode deployment strategy and time node, and follows the principle of gradual adjustment to avoid system oscillation caused by a single large adjustment. When generating the titration deviation correction result, the system will retain the logical trajectory of the adjustment decision, which is used for post-analysis and algorithm optimization.

[0099] In some embodiments, when monitoring electrode operation state indicators, the system can integrate digital twin data output by the electrode self-diagnosis function, which provides a virtual model of the internal state of the electrode, enhancing the depth and accuracy of state monitoring. When calculating the deviation value of task time and environmental change, the system can use a distributed stream processing framework that can handle multiple sensor data streams in parallel, meeting the real-time requirements of high-throughput titration scenarios. Optionally, when dynamically adjusting the electrode deployment strategy and time node, a reinforcement learning agent can be introduced, which learns the optimal adjustment strategy through interaction with the environment and adapts to complex nonlinear system dynamics.

[0100] In specific implementation, according to the titration schedule, high-precision time synchronization mechanism is required to monitor titration environment change data and electrode operation state indicators, which ensures that the timestamps of monitoring data are strictly aligned with the time reference of schedule arrangement. Window function analysis is used to calculate the statistical characteristics of data within a specific time window. When counting the cumulative working hours of the electrode and the remaining amount of titrant, the system will check the consistency of the data to prevent statistical errors caused by sensor failure or data transmission errors. Dynamic adjustment of electrode deployment strategy and time node involves multi-objective optimization, which simultaneously weighs time accuracy, resource consumption, and operational risk. After generating the titration deviation correction result, the correction result is immediately issued to the actuator unit, which drives the physical device to complete the correction action.

[0101] In specific implementations, after acquiring real-time monitoring data from titration scheduling, the system performs data validity verification, which checks the range, rate of change, and consistency with other sensor readings of the data. When calculating environmental impact factors in combination with task time nodes, the environmental impact factor model is periodically recalibrated using historical data. When determining the degree of influence of the environment and electrode state on titration progress using data analysis techniques, the system creates an influence propagation graph, which describes how environmental changes indirectly affect titration progress through electrode states. The generated environmental and electrode state assessment report is distributed to monitoring terminals, which provide a visual interface for operators to review. When calculating deviation values using the environmental and electrode state assessment report, the system distinguishes between compensable and non-compensable deviations. Compensable deviations can be corrected by adjusting operations, while non-compensable deviations require task termination or emergency plan activation. After calculating the cumulative working hours of the electrode and the remaining amount of titrant, the system updates the resource prediction model, which estimates the balance point between future resource demand and supply.

[0102] In specific implementations, when creating the task and environmental deviation table, the system assigns a unique event identifier to each deviation event, which is used to track the entire process of handling deviations. The adjustment of electrode deployment strategies and time nodes based on the task and environmental deviation table uses a combination of rule engines and optimization algorithms. Matching actual titration environmental changes requires real-time updating of environmental baseline values, which serve as reference points for calculating deviations. When calculating overall adjustment efficiency, the system generates an adjustment efficiency report that details the effectiveness evaluation of each adjustment measure. In the final stage of generating titration deviation correction results, the system performs dependency relationship checks on correction instructions to ensure that the execution order of the correction instructions does not cause logical errors.

[0103] In specific implementations, the system records the history of original sensor data and derived indicators during the monitoring process, which is used for trend analysis and fault diagnosis. The algorithm used to calculate deviation values has configurable parameters that allow the sensitivity of the algorithm to be adjusted according to different titration application scenarios. When statistical resource data is collected, the system detects abnormal jumps in resource usage, which may indicate resource leaks or sensor failures. Dynamic adjustment strategies take into account the physical inertia of devices, which requires a certain response time for some adjustment operations to fully take effect. The generation process of correction results includes a simulation verification link that tests the effectiveness of the correction scheme in a virtual environment before actual execution.

[0104] In a specific implementation, the sampling frequency of the environmental monitoring data is dynamically adjusted according to the criticality of the task, with higher criticality tasks corresponding to higher sampling frequencies. The bias value calculation introduces the concept of confidence interval, which helps to distinguish between true bias and measurement uncertainty. Resource statistics information is presented in the form of a dashboard, which provides an overview of resource status and drilling functions. The adjustment decision takes into account the preference settings of the operator, which allows experienced operators to fine-tune the automatic adjustment strategy. The titration bias correction result has a rollback function, which allows the system to quickly recover to the state before adjustment when the correction effect is not ideal. The entire implementation process emphasizes the traceability of data, and all monitoring data, intermediate results of calculation, and adjustment instructions are time-stamped and versioned and stored in an audit log.

[0105] In a specific implementation, the system initiates a verification process immediately after generating the titration bias correction result to verify the consistency of the titration bias correction result with the actual titration data. The consistency verification is achieved through parallel data stream comparison, one data stream coming from the predicted or planned values in the titration bias correction result, and the other data stream coming from the actual measurement values collected in real-time by high-precision sensors installed on the titration instrument. The actual measurement values include key parameters such as titration endpoint pH value, actual consumption of titrant volume, actual time required to reach the titration endpoint, etc. The comparison between planned values and actual measurement values uses statistical hypothesis testing methods to calculate the t-statistic between planned values and actual measurement values and compare it with the critical value to determine whether the bias is statistically significant. Calibration electrode deployment parameters are adjusted based on the comparison results, which specifically involve adjusting the slope and offset parameters in the electrode response time model, updating the electrode efficiency coefficient, and adjusting the electrode switching delay time constant. Based on the calibrated electrode deployment parameters, the titration schedule is updated, which is a dynamic re-planning process. The updated titration schedule recalculates the start time, end time, and resource allocation scheme for all unexecuted tasks and feeds back to the task scheduling sequence for iterative optimization. The iterative optimization mechanism triggers the recalculation of the task scheduling sequence module, which uses the calibrated new parameters to replace the old parameters, thereby generating a scheduling scheme that is more close to the actual system behavior.

[0106] In a specific implementation, when verifying the consistency of titration bias correction results with actual titration data, the system will generate a consistency evaluation report that lists the planned value, actual value, absolute deviation, relative deviation, and consistency score for each comparison item. By comparing the planned value with the actual measurement value, the system can identify systematic measurement errors or model biases, and the calibration electrode deployment parameters can be adjusted to compensate for these systematic biases. The calibration operation can be based on the least squares method to fit the relationship between the actual data and the model predicted value. When updating the titration schedule, the system will retain a version history of the original schedule, which supports schedule rollback and change tracking, and feedback to the task scheduling sequence for iterative optimization to form a closed-loop control loop that continuously narrows the gap between planning and reality. Re-evaluating the remaining amount of titrant and the electrode wear state is an important part of the update process. Re-evaluating the remaining amount of titrant is achieved by obtaining the latest data from the real-time inventory management system, and re-evaluating the electrode wear state is achieved by analyzing the historical trends of electrode performance indicators. Adjusting the task time node allocation parameters needs to consider the dependency relationship between tasks and resource constraints, and the constraint propagation algorithm is used to adjust the task time node allocation parameters, which ensures that the time adjustment of one task will automatically propagate to all associated tasks. According to the adjusted task time node allocation parameters, the resource occupancy rate needs to be recalculated by reconstructing the resource timeline, and the resource occupancy rate will update the resource utilization chart and bottleneck analysis report. Optimizing the electrode selection scheme is based on the latest resource occupancy rate and device performance data, and optimizing the electrode selection scheme may change the electrode allocation strategy for subsequent tasks, such as switching tasks from electrodes with declining performance to electrodes with better performance.

[0107] In a specific implementation, recalculating resource occupancy includes dynamically tracking a pH value change curve in the titration process, the pH value change curve in the titration process is achieved by high-frequency sampling of the signal of the pH electrode, and the sampling data is plotted into a real-time pH-time curve after digital filtering processing. Adjusting the titration speed parameter and the electrode switching frequency is an optimization operation based on the characteristics of the pH value change curve, and adjusting the titration speed parameter involves modifying the pulse frequency or single addition volume of titrant addition, and adjusting the electrode switching frequency determines whether the electrodes need to be rotated more frequently in a long sequence task according to the electrode stability index. Based on the adjusted titration speed parameter, the task interval influence weight needs to be re-evaluated to re-evaluate the degree of mutual influence between tasks, and the task interval influence weight will change the buffer time setting in the task scheduling. Updating the task constraint parameter set is the final step of dynamic adjustment, and updating the task constraint parameter set will solidify the new speed parameter, interval weight, etc. into the system constraint database. Dynamically tracking the pH value change curve in the titration process includes real-time acquisition of measurement data streams of multiple specifications of pH electrodes, and real-time acquisition of measurement data streams of multiple specifications of pH electrodes needs to handle multi-channel synchronization problems to ensure that the data collected by different electrodes are comparable. The linear regression analysis method is used to calculate the pH value deviation and the trend line, and the linear regression analysis method fits the slope of the pH-time curve to judge the reaction rate. According to the pH value deviation and the trend line, the titrant addition rate and the electrode calibration cycle parameter are adjusted to realize adaptive control, the titrant addition rate is reduced to improve the accuracy when approaching the stoichiometric point, and the electrode calibration cycle parameter determines whether to perform online calibration in advance according to the stability of the electrode signal. Based on the adjusted titrant addition rate and electrode calibration cycle parameter, the titration deviation correction result is optimized to make the correction strategy more consistent with the actual chemical reaction kinetics.

[0108] In some embodiments, a sliding window verification mechanism can be used when verifying consistency, which only verifies the validity of the correction result generated in the recent period of time, improving verification efficiency. In some embodiments, a simulated annealing algorithm can be introduced into the iterative optimization process, which helps to jump out of the local optimal solution and find a globally better scheduling scheme.

[0109] Optionally, an automatic parameter tuning routine can be enabled when calibrating the electrode deployment parameters, which automatically adjusts the hyperparameters of the calibration algorithm according to historical error data. Optionally, dynamic tracking of the pH value change curve can be combined with image processing technology, which analyzes the video stream of color changes in the titration process to provide redundant information for pH judgment. Optionally, an optimization termination condition can be set when feedback is made to the task scheduling sequence for iterative optimization, such as continuous iterations improving less than a threshold value or reaching a maximum number of iterations.

[0110] In specific implementation, after generating the titration bias correction result, the system enters a monitoring-verification-calibration loop, which runs continuously until all titration tasks are completed. By comparing the planned values with the actual measured values, the system can learn the device characteristics and reaction characteristics, and the learning results are used to improve the planning accuracy of future tasks. Calibrating the electrode deployment parameters is a delicate operation, and over-calibration may cause the model to overfit, so a reasonable calibration amplitude limit needs to be set. When updating the titration schedule, the system will evaluate the impact of the update on the overall completion time, and if the update significantly delays the completion time, the system may suggest manual intervention by the operator. When feedback is given to the task scheduling sequence for iterative optimization, the optimization process records the objective function value of each iteration, and the objective function value curve is used to evaluate the convergence of the optimization.

[0111] In specific implementation, when re-evaluating the remaining amount of titrant and the electrode wear state, the system will consider the resource replenishment plan, such as the arrival of titrant or the completion of electrode maintenance, which will affect the evaluation results. Adjusting the task time node allocation parameters may have a chain reaction, and simulating the adjustment impact of the entire task chain is required. After recalculating the resource occupancy rate, the system will generate a resource warning report predicting possible future resource shortages. When optimizing the electrode selection scheme, the scheme optimization will consider historical performance data of the electrodes, such as the repeatability accuracy of a certain type of titration. When dynamically tracking the pH value change curve during titration, the system will detect abnormal inflection points, which may indicate abnormal reactions or electrode failure. Adjusting the titration speed parameter and electrode switching frequency is a balancing operation that needs to find the optimal balance point between titration speed, accuracy, and electrode life.

[0112] In specific implementation, the correction task interval impact weight is based on actual task execution data, such as the deviation statistics of actual time consumption and planned time consumption. Updating the task constraint parameter set will trigger cache updates of related modules, ensuring that the system uses the latest constraint conditions. Real-time acquisition of measurement data streams of multi-specification pH electrodes involves data synchronization issues, which are ensured by time alignment of multi-channel data through hardware clock signals. When calculating the pH value deviation and trend line, the system will eliminate obvious abnormal noise points based on statistical outlier detection algorithms. Adjusting the titrant addition rate and electrode calibration period parameters requires permission verification to ensure that the modification of key parameters is authorized. After optimizing the titration bias correction result, the optimized correction result will be immediately applied to subsequent titration processes, forming a continuous improvement cycle.

[0113] In specific implementations, when the verification process finds significant inconsistencies, the system triggers an exception handling procedure, which can include pausing the titration, notifying the operator, or enabling a backup analysis method. The calibration operation can involve a coordinated adjustment of multiple parameters, which uses a multivariate optimization algorithm to avoid interference between parameters. When updating the schedule, the system checks for conflicts with the equipment maintenance plan, such as the time window for periodic calibration must be reserved. The recalculation of resource occupancy takes into account the time characteristics of resource usage, such as some resources are unavailable during certain time periods. The optimization of the electrode selection scheme can be subject to physical connection constraints, such as the physical compatibility of some electrodes with certain titration cups. The dynamic tracking module records characteristic values of the pH curve, such as the inflection point pH value, reaction endpoint time, etc. for establishing a reaction fingerprint library.

[0114] Referring to Figure 5 , which shows the comparison of initial duration and optimized duration of digital multi-specification pH electrode titrator in 8 titration tasks. In the figure, the blue column represents the initial duration, and the orange column represents the optimized duration. Above each task, the time optimization amplitude is labeled (e.g., task 3 optimization amplitude is +8.8 min). From the data, the optimized duration of all tasks is lower than the initial duration, reflecting the optimization effect of the titrator control method in task scheduling, resource allocation, etc. Through precise electrode selection, task scheduling sequence generation, constraint parameter adjustment, etc. processes, the execution time of each titration task is effectively shortened, among which task 3 optimization amplitude is the largest, reaching 8.8 minutes, and task 6 optimization amplitude is the smallest, being 0.2 minutes. Overall, it presents a significant time optimization capability, reflecting the professional improvement of the control method in resource utilization efficiency and task execution efficiency.

[0115] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method of controlling a digital multiscale pH electrode titrator, characterized by, The method comprises: Collecting the identification parameter set of multi-specification pH electrodes and the input requirement set of titration tasks, calculating the matching degree of electrode available time and task demand sequence, evaluating electrode allocation difference value, and integrating titrant available state data to generate electrode selection scheme; Based on the electrode selection scheme, determining the starting time point and task interval isolation parameter of key titration tasks, performing priority sorting operation on titration resources, calculating task resource occupation rate and interval influence weight, and generating task scheduling sequence; According to the task scheduling sequence, analyzing titrant supply cycle and electrode use time data, detecting electrode deployment time conflict and resource shortage task, adjusting task execution interval parameter, and outputting task constraint parameter set; Using the task constraint parameter set, aggregating titration time node and task interval distribution difference value, mapping the relationship between resource occupation rate and electrode available time, calculating task time adjustment amplitude, reconfiguring task timeline and electrode resource allocation, and forming titration progress arrangement; According to the titration progress arrangement, monitoring titration environment change data and electrode running state index, calculating the deviation value of task time and environment change, counting electrode cumulative working hours and titrant remaining amount, dynamically adjusting electrode deployment strategy and time node, and generating titration deviation correction result.

2. The method of claim 1, wherein the method further comprises: The method comprises: Collecting the identification parameter set of multi-specification pH electrodes and the input requirement set of titration tasks, calculating the matching degree of electrode available time and task demand sequence, evaluating electrode allocation difference value, and integrating titrant available state data to generate electrode selection scheme, comprising: Collecting the requirement description information of titration tasks and the available time data set of multi-specification pH electrodes, comprehensively calculating the matching degree of multi-electrode available time and task demand, determining the difference value between electrode total available time and task demand, and establishing electrode time difference value set; Analyzing the electrode time difference value set, adjusting the allocation relationship of electrodes and titrants in combination with the availability state of titrants, and constructing electrode-titrant adjustment mapping table; Through the electrode-titrant adjustment mapping table, calculating electrode allocation deviation value, the calculation process of the electrode allocation deviation value comprising: summing the absolute value of the difference between the requirement value of each titration task and the electrode available time, dividing the summing result by a dynamic adjustment coefficient, multiplying by a weight factor parameter, and dividing by the linear combination of adjustment parameter and task quantity value; 3. A method of controlling a digital multi-specification pH electrode titrator as claimed in claim 2, characterized in that, Generating electrode selection scheme according to the electrode allocation deviation value. The method comprises: From the electrode selection scheme, extracting the resource allocation description information of each key titration task, calculating the starting working time of the task, and applying a resource optimization algorithm to predict the optimal task order, generating a time arrangement table of key titration tasks; Through the time arrangement table of key titration tasks, using a priority evaluation model to perform hierarchical sorting on titration resources, adjusting resource working hour allocation in combination with task urgency index, and establishing task priority queue; The task priority queue is used to calculate the task resource occupancy rate and the task interval influence weight of the key titration task considering the interval requirement in task execution, wherein the task resource occupancy rate is obtained by multiplying the priority value of each task by the sum of interval influence weights, dividing by the sum of required man-hours of the task multiplied by the environmental adjustment coefficient; Based on the task resource occupancy rate and the task interval influence weight, a task scheduling sequence is generated.

4. The method of claim 3, wherein the method further comprises: According to the task scheduling sequence, the titrant supply period and the electrode use time length data are analyzed, including: From the task scheduling sequence, the starting time point and material cycle information of each task are obtained, the electrode usage is analyzed, the resource demand value of each task is calculated using the resource allocation algorithm, and a resource demand analysis report is generated; According to the resource demand analysis report, the time conflict points of resource supply and demand, as well as the conflicts and resource shortage tasks in electrode allocation, are marked, and a conflict and shortage index list is created; Using the conflict and shortage index list, the execution interval parameters of each task are recalculated, and the task execution plan is optimized by dividing the resource demand of each task by the sum of the resource supply period and the average value of the allocation conflict factor; Based on the optimized task execution plan, a set of task constraint parameters is output.

5. A method of controlling a digital multi-specification pH electrode titrator as claimed in claim 4, characterized in that, Using the set of task constraint parameters, the titration time node and the task interval distribution difference value are aggregated, the relationship between resource occupancy rate and electrode available time is mapped, the task time adjustment range is calculated, the task timeline and electrode resource allocation are reconfigured, and a titration schedule is formed, including: From the set of task constraint parameters, the titration time node and the task interval distribution difference information are extracted, the task timeline and resource distribution are compared, the task execution order and resource utilization efficiency are analyzed, and a time node and task difference distribution diagram is generated; Using the time node and task difference distribution diagram, the relationship between resource occupancy rate and electrode available time is mapped, based on the matching analysis of resource allocation and task requirements, the time conflict and resource insufficient tasks are identified, and a resource conflict and insufficient task list is created; According to the resource conflict and insufficient task list, the adjustment range and time node correction value of task execution are calculated, wherein the time node correction value is obtained by multiplying the resource occupancy rate of each task by the task priority weight, dividing by the sum of the original planned time node and the square root of the task adjustment factor; Based on the time node correction value, a titration schedule is formed.

6. A method of controlling a digital multi-specification pH electrode titrator as claimed in claim 5, characterized in that, According to the titration schedule, the titration environment change data and the electrode running state indicators are monitored, the deviation value of task time and environmental change is calculated, the electrode cumulative working hours and titrant remaining amount are counted, the electrode allocation strategy and time node are dynamically adjusted, and a titration deviation correction result is generated, including: From the titration schedule, real-time monitoring data including titration environment change and electrode running state information are obtained, combined with the task time node, data analysis technology is used to determine the influence degree of environment and electrode state on titration schedule, and an environment and electrode state evaluation report is generated; Using the environment and electrode state evaluation report, the deviation value between task time and environment change is calculated, and the cumulative working hours and titrant remaining amount of each electrode are counted. Through quantitative analysis method, the electrode deployment strategy and time node that need to be adjusted are identified, and the task and environment deviation table is created; Based on the task and environment deviation table, the electrode deployment strategy and time node are adjusted to match the actual titration environment change, the overall adjustment efficiency is calculated, and the overall adjustment efficiency is calculated by summing up the time deviation value of each task multiplied by the electrode operation efficiency associated with the task divided by the planned working hours plus the global adjustment coefficient; According to the overall adjustment efficiency, the titration deviation correction result is generated.

7. A method of controlling a digital multi-specification pH electrode titrator as claimed in claim 6, characterized in that, After the titration deviation correction result is generated, it further includes: Verify the consistency of the titration deviation correction result and the actual titration data, and calibrate the electrode deployment parameters by comparing the planned value and the actual measured value; According to the calibrated electrode deployment parameters, update the titration schedule and feedback to the task scheduling sequence for iterative optimization.

8. A method of controlling a digital multi-specification pH electrode titrator as claimed in claim 7, characterized in that, The update of the titration schedule includes: Re-evaluate the titrant remaining amount and electrode wear state, and adjust the task time node allocation parameters; According to the adjusted task time node allocation parameters, recalculate the resource occupancy rate, and optimize the electrode selection scheme.

9. A method of controlling a digital multi-specification pH electrode titrator as defined in claim 8, characterized by, The recalculation of the resource occupancy rate includes: Dynamically track the pH value change curve in the titration process, adjust the titration speed parameters and electrode switching frequency; Based on the adjusted titration speed parameters, correct the task interval influence weight and update the task constraint parameter set; The dynamic tracking of the pH value change curve in the titration process includes: Real-time acquisition of measurement data stream of multi-specification pH electrode, calculation of pH value deviation and change trend line; According to the pH value deviation and change trend line, adjust the titrant addition rate and electrode calibration cycle parameters; Based on the adjusted titrant addition rate and electrode calibration cycle parameters, optimize the titration deviation correction result.

10. A digital multiscale pH electrode titrator comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, The processor, when executing the computer program, realizes the steps of the control method of the digital multi-specification pH electrode titrator of any one of claims 1 to 9.

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