An industrial robot task coordination scheduling method for a smart factory

Abstract

The application discloses a kind of industrial robot task coordination scheduling methods for intelligent factory, it is related to industrial technology field, steps have: first in the process of extracting bromine, obtain key core parameters, based on key core parameters, judge whether parameter mutation occurs in each node along the conveying and process main flow, to identify window period encryption signal, window period encryption signal is used to determine medium load integral new period, automatic sampling new period and robot control and regulation new period;Secondly, based on the complete data in historical period, construct historical load characteristic curve, historical load characteristic curve is used to identify the real-time load change stage corresponding to the current dynamic load state, real-time load change stage includes fast growth section and slow saturation section;Finally, according to real-time load change stage, and in combination with historical load characteristic curve, the offset degree of current dynamic load state is identified, to trigger double collaborative adjustment mechanism.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industry, in particular to an industrial robot task cooperative scheduling method for intelligent factory. BACKGROUND

[0002] In the intelligent factory of the bromine extraction process, the industrial robot task cooperative scheduling needs to be carried out around the whole process of bromine production, covering the core links such as raw material conveying oxidation (such as seawater, chlorine oxidation, hydrochloric acid oxidation), multi-point sampling detection (such as blowout tower air volume, absorption tower outlet sodium bromate and sodium bromide ratio, distillation tower temperature, bromine purity, etc.), process control operation (such as adding raw materials, replacing raw materials) and the like. Through the task allocation, timing coordination and dynamic adjustment among robots, the efficient operation of the bromine extraction process is realized.

[0003] At present, the task scheduling of the industrial robot in the bromine extraction process mostly adopts a fixed cycle mode, that is, sampling, flow regulation and other operations are performed at a preset frequency. This mode has significant limitations: on the one hand, the bromine concentration of seawater is easily affected by factors such as season and climate and suddenly fluctuates. Fixed cycle sampling and detection are difficult to capture parameter mutations in time, resulting in lag in the adjustment of the robot to the raw material conveying amount, and thus affecting the efficiency of the bromine extraction process; on the other hand, the bromine extraction process has different stages, and the fixed task scheduling logic is difficult to adapt to the blowout characteristics of different stages, which is easy to cause medium overload leakage or resource waste caused by early replacement.

[0004] When the bromine concentration suddenly decreases, the fixed sampling cycle is difficult to trigger flow regulation in time, the input is insufficient, and the blowout efficiency suddenly decreases, resulting in prolonged production cycle; when the concentration suddenly increases, the lagging adjustment may cause the blowout to be saturated in advance, and bromide ions are leaked with wastewater, which not only reduces the recovery rate but also pollutes the environment. In addition, misjudgment in the bromine extraction process stage will cause robot task mismatch, such as still scheduling sampling at the frequency of the rapid growth stage in the slow saturation stage, which not only increases the invalid work of the robot, but also may cause decision-making errors due to data redundancy, ultimately causing problems such as increased energy consumption and fluctuating product purity. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides an industrial robot task cooperative scheduling method for intelligent factory, which solves the problems in the above background art.

[0006] To achieve the above purpose, the present application is realized by the following technical scheme: an industrial robot task cooperative scheduling method for intelligent factory, comprising the following steps,

[0007] In the bromine extraction process, key core parameters (including temperature, seawater bromine content, chlorine dosage, sodium bromate and sodium bromide ratio, bromine purity, etc.) are obtained. Based on the key core parameters, it is determined whether parameter mutation occurs at each node along the conveying and process main flow to identify window period encryption signals. The window period encryption signals are used to determine the medium load integration new period, the automatic sampling new period, and the robot control and scheduling new period.

[0008] Based on the complete data in the historical period, a historical load characteristic curve is constructed. The historical load characteristic curve is used to identify the real-time load change stage corresponding to the current dynamic load state. The real-time load change stage includes a rapid growth segment and a slow saturation segment.

[0009] According to the real-time load change stage, and in combination with the historical load characteristic curve, the offset degree of the current dynamic load state is identified to trigger a dual cooperative adjustment mechanism. The dual cooperative adjustment mechanism is used to adjust the frequency converter through the industrial robot and identify the start of the medium replacement operation.

[0010] Preferably, in the bromine extraction process, multiple node automatic sampling valves and bypass sampling pipes are arranged along the conveying and process main flow. The detection sample is sent to the colorimetric analyzer through the conveying pump to periodically detect the key core parameters of different process segments. The volumetric flow rate at the inlet of the bromine extraction process tower is obtained through the thermometer and electromagnetic flowmeter detection equipment.

[0011] The key core parameters of different process segments are summarized in time sequence to obtain the key core parameters (including temperature, seawater bromine content, chlorine dosage, sodium bromate and sodium bromide ratio, bromine purity, etc.).

[0012] The relevant parameter data of nodes such as blowout tower, absorption tower, and distillation tower are extracted from the relevant parameter data and marked as pretreatment information.

[0013] According to the pretreatment information, the change of each node at the adjacent monitoring time point is analyzed to obtain the change rate of the corresponding node.

[0014] Based on the change rate of the corresponding node, it is determined whether parameter mutation occurs at the corresponding node to identify window period encryption signals.

[0015] Preferably, based on the change rate of the corresponding node, it is determined whether parameter mutation occurs at the corresponding node, including:

[0016] The change rate sequence of the corresponding node in the historical data is extracted to form a historical sequence.

[0017] Based on the historical sequence, the mean value and the standard deviation of the change rate are determined.

[0018] If If the condition is met, it is determined that there is a parameter mutation at the corresponding node, and a scheduling instruction is triggered at this time. Otherwise, it is determined that there is no parameter mutation at the corresponding node, and no scheduling instruction is triggered at this time. Wherein, is the change rate of the i-th node at time t, is the change rate average, is the change rate standard deviation, and k is a constant;

[0019] The timestamp of the parameter mutation is determined and marked as a phase point. Based on the phase point, the dynamic loads of the blow tower, the absorption tower and the distillation tower are obtained;

[0020] Preferably, the window period encryption signal comprises:

[0021] According to the phase point, the scheduling instruction is sent to the centralized scheduling optimization controller. The centralized scheduling optimization controller will dynamically obtain an adaptive scaling factor according to the abnormal degree of the parameter mutation of the corresponding node. The adaptive scaling factor is used to quantify the scale of dynamic regulation. The centralized scheduling optimization controller is used to dynamically control the medium load integration period, control the automatic sampling period and control the robot regulation scheduling period.

[0022] The medium load integration period, the automatic sampling period and the robot regulation scheduling period are uniformly compressed by the adaptive scaling factor to obtain a new period at the phase point. The new period includes a medium load integration new period, an automatic sampling new period and a robot regulation scheduling new period.

[0023] The new period is used as a window period encryption signal, and the window period encryption state is maintained until it is monitored that there is no parameter mutation at the corresponding node. The window period encryption state is stopped, and the new period is restored to the original period.

[0024] Preferably, based on the complete data in the historical period, a historical load characteristic curve is constructed. The historical load characteristic curve is used to identify the real-time load change phase corresponding to the current dynamic load state after the step of identifying the real-time load change phase corresponding to the current dynamic load state. The step further comprises:

[0025] Collect complete data of the medium dynamic load changing with time under the same process condition as the current existing process condition in the historical bromine extraction process;

[0026] According to the characteristics of the bromine extraction process, the complete data is combined to construct a historical load characteristic curve by using a linear function and an exponential function, and the bromine extraction process is divided into a rapid growth segment and a slow saturation segment;

[0027] In the current dynamic medium load integration accumulation process, the dynamic loads of the blow tower, the absorption tower and the distillation tower at the current time are determined;

[0028] The current time is taken as a starting point, and data with the same time span as the historical load characteristic curve is intercepted forwardly.

[0029] calculating a current dynamic load state growth slope;

[0030] comparing the current dynamic load state growth slope with a slope range of each stage in the historical load characteristic curve to determine a real-time load change stage.

[0031] Preferably, determining the real-time load change stage comprises:

[0032] if the current dynamic load state growth slope is within the slope range of the rapid growth stage, it is determined that the current bromine extraction process is in the rapid growth stage;

[0033] if the current dynamic load state growth slope is within the slope range of the slow saturation stage, it is determined that the current bromine extraction process is in the slow saturation stage;

[0034] combining the rapid growth stage and the slow saturation stage to form the real-time load change stage.

[0035] Preferably, according to the real-time load change stage and in combination with the historical load characteristic curve, the degree of deviation of the current dynamic load state is identified to trigger a dual collaborative adjustment mechanism, comprising:

[0036] determining a historical dynamic load at a time stamp corresponding to the current dynamic load state on the historical load characteristic curve according to the real-time load change stage, and recording it as a predicted dynamic load;

[0037] determining a deviation value by comparing the predicted dynamic load with the current dynamic load state;

[0038] if the deviation value exceeds a preset deviation threshold, the dual collaborative adjustment mechanism is triggered.

[0039] Preferably, the dual collaborative adjustment mechanism comprises:

[0040] multi-dimensional cycle adjustment; if the time corresponding to the current dynamic load state is in a window cycle encryption state, the window cycle encryption state is continued to be maintained, and if the time corresponding to the current dynamic load state is not in the window cycle encryption state, a scheduling instruction is triggered again to realize the window cycle encryption state;

[0041] informing task scheduling adjustment: according to the deviation value, the task of the industrial robot is adjusted in advance, specifically: when the real-time load change stage is the rapid growth stage, the industrial robot is dominated to perform a frequency converter adjustment operation, and when the real-time load change stage is the slow saturation stage, the industrial robot is dominated to start a medium replacement operation in advance or delay;

[0042] combining the multi-dimensional cycle adjustment and the informing task scheduling adjustment to obtain the dual collaborative adjustment mechanism.

[0043] Preferably, when the real-time load change stage is a slow saturation section, the industrial robot is dominated to start the medium replacement operation in advance or delay, including:

[0044] If the current dynamic load state is higher than the predicted dynamic load, a pre-starting instruction is sent to the medium replacement robot cluster, the pre-starting instruction is used to prepare for the operation of starting in advance, and the medium replacement robot cluster includes a plurality of groups of industrial robots.

[0045] If the current dynamic load state is not higher than the predicted dynamic load, the current dynamic load state is integrated according to a multi-dimensional period adjustment, and a time stamp of starting the medium replacement operation in delay is determined according to the integration result and a maximum load capacity, the maximum load capacity refers to the maximum load capacity that the medium can bear.

[0046] The present application provides an industrial robot task cooperative scheduling method for an intelligent factory, which has the following beneficial effects:

[0047] (1) By recognizing parameter mutation trigger window period encryption signal, the period of medium load integration, automatic sampling and robot control scheduling is dynamically compressed, ensuring the rapid capture of bromine concentration sudden rise / sudden drop, solving the problem of response lag of traditional fixed period scheduling to process fluctuation. Based on the historical load characteristic curve, the rapid growth section and the slow saturation section are divided, so that the robot task scheduling and the bromine extraction process stage are accurately matched, and the problem of medium overload or resource waste caused by one-size-fits-all scheduling is avoided. Through the dual cooperative mechanism of window period encryption and robot task adaptation, when the dynamic load deviates from the expectation, the raw material input can be adjusted in real time through the frequency converter, and the medium replacement operation can be started in advance, so that the bromine recovery rate can be kept in a stable state, and the energy consumption is further reduced. Traditional scheduling samples and adjusts according to fixed period, which is easy to miss the relatively optimal adjustment opportunity when the parameters change, resulting in the decrease of bromine extraction process efficiency or the leakage of bromide ions. The change rate of node concentration is calculated by self-adaptive statistical method, and when the change rate exceeds the threshold value, the window period encryption signal is triggered to compress the related period. The slope range of each stage is determined by the historical load characteristic curve, the current dynamic load state growth slope is compared with the historical range, and the real-time stage is accurately identified. For example, the robot increases the raw material flow in the rapid growth section to fully utilize the active sites; the medium replacement robot cluster is started in advance in the slow saturation section to avoid overload leakage. The scheduling robot: adjust the frequency converter to stabilize the bromide ion input in the rapid section, and start / delay the tower in the slow section to balance the efficiency and cost. In summary, the present application overcomes the limitations of traditional fixed scheduling mode through the cooperative design of dynamic perception, stage adaptation and double adjustment, further improves the stability, resource utilization rate and environmental protection of the bromine extraction process, and provides an efficient solution for robot cooperative scheduling in complex chemical scenes in intelligent factories.

[0048] (2) The application has the following advantages: 1. Breaking the hysteresis of fixed cycle scheduling, window cycle encryption triggered by parameter mutation avoids the delay of adjustment when the concentration rises or falls suddenly; 2. Eliminating resource waste caused by stage misjudgment, optimizing the flow in the rapid growth segment to improve the utilization rate of active sites in the medium, and avoiding the waste of industrial refining caused by not replacing in time in the slow saturation segment; 3. When the deviation exceeds the limit, the data collection density can be strengthened, and the robot can be dispatched accordingly, so that the process fluctuation range can be controlled. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 A step diagram of an industrial robot task cooperative scheduling method for an intelligent factory is provided. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0051] Embodiment 1

[0052] Please refer to Figure 1 The application provides an industrial robot task cooperative scheduling method for an intelligent factory, comprising the following steps,

[0053] In the bromine extraction process, key core parameters (including temperature, seawater bromine content, chlorine dosage, sodium bromate and sodium bromide ratio, bromine purity, etc.) are obtained. Based on the key core parameters, it is determined whether parameter mutation occurs at each node along the conveying and process main flow to identify the window cycle encryption signal. The window cycle encryption signal is used to determine the medium load integration new cycle, the automatic sampling new cycle and the robot control and scheduling new cycle.

[0054] Based on the complete data in the historical period, a historical load characteristic curve is constructed. The historical load characteristic curve is used to identify the real-time load change stage corresponding to the current dynamic load state. The real-time load change stage includes a rapid growth segment and a slow saturation segment.

[0055] According to the real-time load change stage, and in combination with the historical load characteristic curve, the offset degree of the current dynamic load state is identified to trigger a double cooperative adjustment mechanism. The double cooperative adjustment mechanism is used to adjust the frequency converter through the industrial robot and identify the start of the medium replacement operation.

[0056] In this embodiment, by monitoring the key core parameters of each node in real time and judging the mutation, the window cycle encryption signal is triggered, the load calculation, sampling and dynamic compression of robot scheduling cycle are realized, and the problem of response lag of traditional fixed cycle to sudden fluctuations is solved. For example, when the seawater bromine concentration suddenly drops from 50 ppm to 30 ppm (mutation occurs), the system identifies the window cycle encryption signal, shortens the medium load integration period from 20 minutes to 10 minutes, shortens the automatic sampling period from 30 minutes to 15 minutes, and compresses the robot control scheduling plan period from 2 hours to 1 hour, ensuring rapid response to low concentration raw materials.

[0057] Parameter mutation refers to the deviation of bromide ion concentration from the normal range in a short time, which is the core signal to trigger scheduling optimization;

[0058] The window cycle encryption signal shortens the cycle dynamically, so that the system collects data, calculates load and plans tasks at a higher frequency to adapt to the process requirements after the core parameters.

[0059] In the analysis process of bromine extraction process, the blow-out curve is constructed based on historical data, the rapid growth segment and the slow saturation segment are divided, the current load growth slope is compared with the historical stage slope range, the real-time load change stage is accurately positioned, and the basis for differentiated scheduling is provided. For example, if the current medium load growth slope is 5 kg / h, which falls within the historical rapid growth segment slope range [4, 6] kg / h, it is determined to be in the rapid growth segment, and the robot performs tasks according to the high-frequency sampling (15 minutes / time) and linear load prediction model; if the slope decreases to 1 kg / h, which falls within the slow saturation segment range [0.5, 2] kg / h, the exponential prediction model is switched to and the medium replacement is planned in advance.

[0060] The historical load characteristic curve is used to record the change law of blow-out efficiency (dynamic load) with time under the same process conditions, providing a benchmark for the current stage judgment;

[0061] The real-time load change stage is used to distinguish between sufficient (rapid growth segment) and insufficient (slow saturation segment) active sites of the medium, ensuring that the scheduling strategy matches the medium performance.

[0062] By comparing the deviation of the current dynamic load state and the predicted value, the linkage cycle adjustment and the robot task optimization are realized to achieve high-frequency monitoring and precise operation when the concentration fluctuates or the load deviates. For example, if the current load of the rapid growth segment is 15% lower than the predicted value (deviation exceeds the standard), the double cooperative adjustment mechanism is started: on the one hand, the cycle encryption state is maintained (such as 10 minutes / once load calculation), on the other hand, the instruction flow adjusting robot increases the raw material conveying flow from 20m³ / h to 25m³ / h (adjusted by the frequency converter), which increases the input of bromide ions; if the current load of the slow saturation segment is 20% higher than the predicted value, the medium replacement robot enters the preparation state 2 hours in advance to avoid bromide ion leakage.

[0063] The double cooperative adjustment mechanism forms a closed loop control by combining cycle encryption (improving data timeliness) and robot task adjustment (solving process deviation);

[0064] The frequency converter adjustment and the medium replacement operation correspond to the core operation of the rapid growth segment (optimizing raw material input) and the slow saturation segment (ensuring medium state) respectively, realizing targeted optimization.

[0065] In summary, the present application solves the problem that the static cycle cannot adapt to the dynamic process in traditional scheduling through the logical chain of parameter mutation monitoring, stage dynamic identification and double cooperative adjustment, making the industrial robot task scheduling more accurate and efficient. In practical application, it can reduce the risk of medium overload leakage, improve the efficiency of bromine blowing, reduce the invalid operation of the robot, and further improve the intelligent level and economic benefit of the bromine extraction process.

[0066] Specifically, when the bromine concentration of raw seawater (waste heat water from a steel plant) suddenly decreases, continuing to operate according to the original process will cause the blowing tower to far from reach the blowing efficiency, resulting in significant waste; when the bromine concentration rises, the fixed cycle may cause the blowing tower to overload and leak, so the fixed sampling cycle needs to be dynamically adjusted;

[0067] Embodiment 2

[0068] Please refer to Figure 1 Specifically: in the bromine extraction process, multiple node automatic sampling valves and bypass sampling pipes are arranged along the conveying and process main flow, and the detection sample is sent into the colorimetric analyzer by the conveying pump to periodically detect the key core parameters of different process segments, and the volume flow rate at the inlet of the bromine extraction process tower is obtained through the thermometer and electromagnetic flowmeter detection equipment;

[0069] The detection sample refers to the part of seawater extracted from different process flow pipes through multiple node automatic sampling valves and bypass sampling pipes, which is used to send into the online colorimeter or ion chromatograph for detection, and it represents the actual process fluid at a certain time and a certain process node;

[0070] A transfer pump is the main pump used in a bromine extraction plant to transport raw seawater from collection tanks and storage tanks to subsequent pretreatment units, blowdown towers, etc.

[0071] A bypass sampling line is a branch line that is drawn from a process pipeline (such as an oxidation pipeline, a blow-out tower outlet pipeline, an absorption tower outlet pipeline, etc.) through a valve or a micro-flow regulator. A small amount of sample from the main fluid can be drawn to online analysis equipment (colorimeter, ion chromatograph, etc.) through this small pipeline.

[0072] Different process segments refer to the various physical / chemical treatment stages in the seawater bromine extraction process.

[0073] The key parameters of different process sections are summarized in time series to obtain key parameters (including temperature, seawater bromine content, chlorine usage, sodium bromate and sodium bromide ratio, bromine purity, etc.).

[0074] Multi-point concentration testing (in different pipelines and process sections) is used to confirm the concentration stability of raw materials in different homogenization sections and different buffer tanks.

[0075] Extract relevant parameter data from nodes such as the blow-out tower, absorption tower, and distillation tower from the relevant parameter data, and mark them as preprocessing information;

[0076] The multiple nodes here include, but are not limited to, the outlet of the oxidation pipeline, the outlet of the blow-out tower, the outlet of the absorption tower, and the outlet of the distillation tower;

[0077] The oxidation pipeline outlet is located in the outlet pipeline after seawater oxidation;

[0078] The outlet of the blow-out tower is located at the feed pipe entering the absorption tower;

[0079] The outlet of the absorption tower is located at the feed pipe of the distillation tower.

[0080] Based on the preprocessed information, the changes of each node at adjacent monitoring time points are analyzed to obtain the change rate of the corresponding node, which is obtained through the following formula: ,in, Let be the rate of change of the i-th node at time t. Let be the bromide ion concentration at the i-th node at time t. For the i-th node at time... bromide ion concentration at that time Let t be the time window, and t be the time point.

[0081] Based on the rate of change of the corresponding node, it is determined whether the corresponding node has undergone a parameter mutation in order to identify the window period encryption signal.

[0082] Periodic encryption is to refresh the content to be calculated to speed up the purpose;

[0083] The key core parameters include the bromide ion concentration information obtained in different time periods in different process sections.

[0084] Through the adaptive statistical method, based on the change rate of the corresponding node, it is judged whether the corresponding node has a parameter mutation, including:

[0085] Extract the change rate sequence of the corresponding node in the historical data to form a historical sequence;

[0086] Extract the concentration change rate data of the node in the past several days (such as 7 days) and the same period (such as 08:00-10:00) from the database to form a historical sequence;

[0087] Based on the historical sequence, the mean and standard deviation of the change rate are determined;

[0088] If , it is judged that there is a parameter mutation at the corresponding node, at which time the scheduling instruction is triggered, otherwise, it is judged that there is no parameter mutation at the corresponding node, at which time the scheduling instruction is not triggered; wherein, is the mean of the change rate, is the standard deviation of the change rate, k is a constant, usually takes value 1-3, corresponding to different confidence levels, the specific value is set by the user (according to the actual situation); is an abnormal threshold value defined based on the confidence level, for example, k=3, more than 3 times the historical normal fluctuation represents a certain non-accidental abnormal event;

[0089] Determine the timestamp of the parameter mutation and mark it as a phase point, based on the phase point, obtain the dynamic load of the blowout tower, the absorption tower and the distillation tower, specifically: , wherein, is the dynamic load of the medium at time t, M is the mass of the medium filled in the current bromine extraction process tower, is the bromide ion concentration at the inlet of the bromine extraction process tower, is the bromide ion concentration at the outlet of the bromine extraction process tower, is the volume flow rate at time t, T represents the time length from the change to the current bromine extraction process tower to the phase point;

[0090] The calculation of the dynamic load of the medium is to calculate the bromine amount accumulated by the medium in the blowout tower at this moment.

[0091] In the blowing process, only the medium inside the bromine extraction tower is in direct contact with the flowing seawater, and undertakes the task of blowing bromine ions, so when the dynamic load of the medium is indicated as 45 kg of bromine per ton of medium, it actually means that the medium in the current bromine extraction tower has blown out an average of 45 kg of bromine per ton. Therefore, in the integral calculation process of the dynamic load of the medium, the medium refers to the medium inside the blowing tower in the running state, which is used to represent the effective bromine load of the blowing unit in real time, and to provide a basis for subsequent tower replacement scheduling and regeneration operation.

[0092] In this embodiment, automatic sampling valves and bypass sampling tubes are deployed along the main flow of the bromine extraction process (such as the outlet of the oxidation pipeline, the outlet of the blowing tower, the outlet of the absorption tower, the outlet of the distillation tower, etc.), and the bromine ion concentration is periodically detected by a colorimetric analyzer; at the same time, the volumetric flow rate at the inlet of the blowing tower is collected by an electromagnetic flowmeter, realizing multi-dimensional data collection of concentration and flow rate.

[0093] The multi-node automatic sampling valve further ensures that the key core parameters of different process sections are collected, avoiding the limitations of single-point data;

[0094] The colorimetric analyzer accurately measures the concentration (accuracy ±1 ppm) through the color reaction of bromine ions with specific reagents;

[0095] The electromagnetic flowmeter is a non-contact volumetric flow rate measurement (unit m³ / h), which further avoids the corrosion of high-salt seawater to the equipment.

[0096] In this embodiment, the limitations of traditional single-point detection are broken through, forming a network of key core parameters covering the entire process, and combining flow rate data to provide complete input for subsequent dynamic load calculation, and the data integrity is further improved. For example, in a seawater bromine extraction project, three sampling nodes are set along the outlets of the blowing tower, the absorption tower, and the distillation tower, and sampling is performed every 10 minutes, and at the same time, the flow rate at the inlet of the blowing tower is monitored in real time through an electromagnetic flowmeter, ensuring the spatio-temporal matching of concentration and flow rate data.

[0097] The bromine ion key core parameters of each node are summarized in time sequence (such as t=0, 5, 10 minutes…), forming a complete set of key core parameters, from which all node data before the inlet of the blowing tower (such as the outlet concentration of the pretreatment unit) are selected and marked as pretreatment information, which is used to analyze the concentration change of the raw material before entering the blowing tower.

[0098] Time sequence summary integrates discrete key core parameters along the time axis, facilitating the analysis of the fluctuation trend of concentration over time;

[0099] Pretreatment information focuses on key core parameters before the inlet of the blowing tower, excluding the influence of the blowing process on the raw material itself, and accurately locating the source of parameter mutation (such as fluctuations in the raw material itself rather than blowing tower failure).

[0100] In this embodiment, the time series integration realizes the time series management of key core parameters, and the preprocessing information extraction reduces the range of parameter mutation analysis, so that the abnormal positioning efficiency is improved. For example, 12 groups of key core parameters of 3 nodes within 1 hour are summarized, 8 groups of data of seawater into the plant and pretreatment nodes are extracted as preprocessing information, it is found that the concentration suddenly decreases from 50 ppm to 30 ppm after pretreatment, which indicates that the mutation occurs in the pretreatment link rather than in the blowout tower, and through the identified mutation, the next step of scheduling analysis is carried out.

[0101] Based on the preprocessing information, the concentration change rate (unit: ppm / min) of each node at adjacent time points is calculated by formula, and the fluctuation speed of concentration is quantified. The change rate reflects the change trend of concentration with time, which is the core index for judging mutation;

[0102] The time window is usually 5-10 minutes, which is used to balance the calculation accuracy and data smoothness, i.e. the concentration difference of adjacent 5 minutes is calculated. The absolute value change of concentration is converted into rate change, which is convenient for quantitative evaluation of the severity of fluctuation and provides comparable index for subsequent mutation judgment. For example, the concentration of a node is 50 ppm at t=10 minutes and 45 ppm at t=5 minutes, =5 minutes, then =(50-45) / 5=1ppm / minute, indicating that the concentration shows a slow upward trend; if the concentration suddenly increases to 60 ppm at t=10 minutes, then =(60-45) / 5=3ppm / minute, the fluctuation rate is significantly improved.

[0103] The historical sequence is used to reflect the change rate range of the node under normal working conditions, which is used as the baseline of normal fluctuation, and the mean and standard deviation are determined by statistical method to determine the interval of normal fluctuation (such as k=3, covering 99.7% of normal data);

[0104] The scheduling instruction is used to inform the system to start window period encryption (such as shortening the sampling period).

[0105] The mutation judgment threshold is adjusted adaptively through historical data to avoid misjudgment of fixed threshold in seasons and raw material changes, and the mutation recognition accuracy is improved to more than 95%. For example, the average change rate of a node in the past 7 days from 08:00 to 10:00 is 1 ppm / min, the standard deviation is 0.5 ppm / min, and the threshold is 1+3×0.5=2.5 ppm / min when k=3. If the current change rate is 3 ppm / min (>2.5), it is determined that the parameter mutation occurs, and the scheduling instruction is triggered.

[0106] After determining the timestamp of parameter mutation (phase point), the dynamic load of the medium, i.e. the cumulative amount of bromine blown out by the medium, is calculated by a formula to quantify the total amount of blowing out of the medium in real time, providing accurate load state basis for subsequent blowing out stage identification and robot task scheduling. Through the progressive logic of data acquisition, preprocessing, change rate calculation, mutation judgment, and load quantification, each step realizes the upgrade from passive monitoring to active perception, provides reliable basis for dynamic collaborative scheduling of industrial robots, and solves the problem of response lag of traditional fixed cycle scheduling to process fluctuations.

[0107] Embodiment 3

[0108] Please refer to Figure 1 , specifically: the identification window period encryption signal, comprising:

[0109] According to the phase point, the scheduling instruction is sent to the centralized scheduling optimization controller, and the centralized scheduling optimization controller will dynamically obtain an adaptive scaling factor according to the abnormal degree of parameter mutation of the corresponding node. The adaptive scaling factor is used to quantify the scale of dynamic regulation and control. The centralized scheduling optimization controller is used to dynamically control the medium load integration period, control the automatic sampling period, and control the robot regulation and control scheduling period.

[0110] The adaptive scaling factor is used to uniformly compress the medium load integration period, the automatic sampling period, and the robot regulation and control scheduling period to obtain a new period at the phase point. The new period includes a new medium load integration period, a new automatic sampling period, and a new robot regulation and control scheduling period.

[0111] The new period is used as the window period encryption signal, and the window period encryption state is maintained until it is monitored that there is no parameter mutation in the corresponding node. The window period encryption state is stopped, and the new period is restored to the original period.

[0112] The adaptive scaling factor is obtained by the following formula: , wherein, represents otherwise, is the adaptive scaling factor at time t;

[0113] The new period is obtained by the following formula: , wherein, is the new period of the corresponding dynamic control object, is the original period of the corresponding dynamic control object, and x is the number of dynamic control medium load integration period, control automatic sampling period, and control robot regulation and control scheduling period;

[0114] The centralized scheduling optimization controller is used to uniformly coordinate the dynamic load calculation module, the automatic sampling scheduler, and the robot regulation and control plan scheduler, and trigger the encryption mode of the global sampling and scheduling period.

[0115] The recognition of the window period encryption signal actually refers to: when a parameter mutation is detected, a signal is immediately generated to indicate that all subsequent operations of the dynamic load integral operation, concentration sampling period, and robot scheduling operation module of the extraction tower need to temporarily increase the original normal calculation period (e.g., every 20 minutes) to a higher frequency (e.g., every 10 minutes). The window period encryption state refers to the process of continuously calculating, monitoring, or planning at the new period.

[0116] The original period refers to the original period of medium load integral, the original period of automatic sampling, and the original period of robot control scheduling.

[0117] In this embodiment, the stage point of parameter mutation is used as a trigger signal to deliver scheduling instructions to the centralized scheduling optimization controller. The controller dynamically calculates an adaptive scaling factor based on the degree of mutation anomaly (e.g., the magnitude of the concentration change rate exceeding the threshold value) and quantifies the scale of period compression.

[0118] The stage point is the timestamp of the occurrence of parameter mutation (e.g., mutation detected at t=10:00, which is the stage point) and is the time reference for period adjustment.

[0119] The centralized scheduling optimization controller is the core coordination unit responsible for unified control of the periods of load calculation, sampling, and robot scheduling to avoid period confusion of various modules.

[0120] The adaptive scaling factor is a parameter used to determine the compression ratio of the period.

[0121] The adaptive scaling factor is applied to the original period of medium load integral, automatic sampling, and robot control scheduling. The new period is calculated through a formula to achieve unified dynamic compression of the period.

[0122] The original period is the default basic period of the system, such as the original period of medium load integral being 20 minutes, the original period of automatic sampling being 30 minutes, and the original period of robot control scheduling planning being 3 hours.

[0123] The new period is the compressed period that dynamically changes with the scaling factor to ensure high-frequency response to mutations.

[0124] The new period is defined as the window period encryption signal, and the system enters the encryption state (runs at the new period). When the parameter mutation disappears, the encryption state is stopped, and the period returns to the original value.

[0125] The window period encryption signal indicates the instruction for the system to enter the high-frequency monitoring / calculating mode with the new period as the core parameter. The window period encryption state is the continuous process of the system running at the new period (e.g., from 10:00 mutation occurrence to 12:00 mutation end, maintaining the encryption state for 2 hours).

[0126] The centralized scheduling optimization controller unifies the compression of the three periods, ensures that the rhythm of load calculation-sampling detection-robot scheduling matches, synchronously updates the key core parameters for calculation and real-time detection results, and avoids load calculation errors caused by period misalignment.

[0127] The dynamic start-stop mechanism of the window period encryption state automatically restores the original period after the mutation ends, reducing the energy consumption of the system running at a high frequency continuously. For example, under certain conditions, 2 mutations of 1 hour each occur every day, and the encryption state lasts only 2 hours. Compared with high-frequency operation all day, the robot energy consumption is saved by 15%, and the service life of the analyzer is extended by 20%.

[0128] In summary, this step, through the logical design of hierarchical compression, coordinated regulation, and dynamic start-stop, not only ensures rapid response during parameter mutation, but also avoids resource waste, providing accurate time reference for subsequent medium load calculation and robot task scheduling. It is the core link for the system to achieve dynamic self-adaptation.

[0129] The new period of robot regulation and scheduling refers to the period when the robot plans to change towers, that is, the timestamp when it needs to change the tower again;

[0130] Example 4

[0131] Please refer to Figure 1 Specifically: based on the complete data in the historical period, a historical load characteristic curve is constructed, and the historical load characteristic curve is used to identify the real-time load change stage corresponding to the current dynamic load state after the step of identifying the real-time load change stage corresponding to the current dynamic load state.

[0132] Collect complete data on the dynamic load of the medium under the same process conditions as the current process over time during the historical bromine extraction process;

[0133] According to the characteristics of the bromine extraction process, combined with the complete data, a historical load characteristic curve is constructed through a linear function and an exponential function, and the bromine extraction process is divided into a rapid growth segment and a slow saturation segment;

[0134] The rapid growth segment is characterized by: as time progresses, the bromine extraction process quantity rapidly increases with the input of bromine ions in the feed, the blowout rate (blowout quantity per unit time) is high and relatively stable, and the medium active site is sufficient for bromine ion blowout.

[0135] The slow saturation segment is characterized by: the growth of the bromine extraction process quantity gradually slows down, and the blowout rate continuously decreases, indicating that the medium active site is gradually occupied, and the blowout is close to saturation.

[0136] During the current dynamic medium load integration process, the dynamic load of the blowout tower, the absorption tower, and the distillation tower at the current time is determined;

[0137] Take the current time as the starting point, and intercept the data of the same time span (such as selecting different time windows of 1 hour, 2 hours, etc. according to the process fluctuation characteristics to determine the optimal window length) as the historical load characteristic curve;

[0138] Calculate the current dynamic load state growth slope, specifically: , wherein, is the current dynamic load state growth slope, is the dynamic load of the medium at time is the dynamic load of the medium at time is the length of the time window (i.e. the time span) intercepted;

[0139] Compare the current dynamic load state growth slope with the slope range of each stage in the historical load characteristic curve to determine the real-time load change stage.

[0140] The slope range of each stage in the historical load characteristic curve refers to: under the same medium specification and the same process conditions, in the historical operation data (complete data), the blowout rate (slope) interval corresponding to the rapid growth segment and the slow saturation segment of the dynamic load of the medium with time change;

[0141] Wherein, the slope range of the rapid growth segment: when the medium active site is sufficient, the blowout amount grows fast and stable, and in multiple historical periods, the blowout rate (slope) of this stage will be concentrated in a smaller interval (such as [4.5, 5.5] kg / hour); the slope range of the slow saturation segment: when the medium active site is gradually saturated, the blowout amount slows down, and the blowout rate (slope) of this stage will be concentrated in another smaller interval (such as [0.5, 1.5] kg / hour).

[0142] Specifically, the specific steps to obtain the slope range are as follows:

[0143] From the production database, extract complete blowout period data that meets the following conditions:

[0144] The medium specifications are the same (such as X-7 type anion exchange medium, with a loading capacity of 10 tons);

[0145] The process conditions are similar (such as temperature around 25℃, flow rate around 1.5 m³ / h, raw material bromine concentration around 50ppm);

[0146] The blowout process is complete (from "medium regeneration completion" to "blowout saturation triggers regeneration").

[0147] Finally, m effective historical period data is obtained, each period data contains time series and corresponding blowout amount sequence;

[0148] Identify the "rapid growth segment" and "slow saturation segment" for each historical period, and extract the slope sequence of the "rapid growth segment" and "slow saturation segment" for each historical period;

[0149] Calculate the mean and standard deviation of the slope sequence of the rapid growth segment and the slow saturation segment for m historical periods;

[0150] Determine the slope range: rapid growth segment slope range: (mean of rapid growth segment slope sequence - 3, multiplied by standard deviation of rapid growth segment slope sequence, mean of rapid growth segment slope sequence + 3, multiplied by standard deviation of rapid growth segment slope sequence);

[0151] Slow saturation segment slope range: (mean of slow saturation segment slope sequence - 3, multiplied by standard deviation of slow saturation segment slope sequence, mean of slow saturation segment slope sequence + 3, multiplied by standard deviation of slow saturation segment slope sequence);

[0152] As production continues, new historical period data is added to the historical period data periodically (such as weekly / monthly), and steps 1-3 are repeated to recalculate the mean and standard deviation, and dynamically update the slope range of the rapid growth segment and the slow saturation segment.

[0153] For example, in a seawater bromine extraction scenario: the new medium is changed in the morning, and the raw material bromine concentration is stable at 50ppm, and the flow is 20m³ / h. At this time, the medium has many active sites and the blowout rate is high, and it is determined to be in the rapid growth segment, and the system will predict the blowout amount according to the linear model, and the robot will sample at the regular frequency (such as every 30 minutes) for 8 hours, and the bromine extraction process will reach 300 kilograms (assuming 1 ton of medium saturation is 500 kilograms), and the blowout rate will decrease from 5 kilograms / hour to 1 kilograms / hour, and it is determined to enter the slow saturation segment, and the system switches to the exponential model prediction, and at the same time triggers the robot to increase the sampling frequency (every 15 minutes), and prepares for the medium replacement process in advance.

[0154] Real-time load change stage refers to based on real-time collected seawater bromine extraction process data (such as bromine concentration, flow, temperature, etc.), through comparison and analysis with historical data, to determine the medium at the current moment, which stage of the bromide ion blowout process is in, which can be generally divided into rapid growth segment and slow saturation segment;

[0155] Complete data refers to the data of the dynamic load of the medium changing with time under the same process conditions;

[0156] The characteristic of the bromine extraction process is that the initial blowout rate is fast, and as the blowout proceeds, the blowout rate gradually slows down, and when it approaches saturation, the blowout rate tends to zero, so an exponential decay function or a segmented function can be considered for fitting.

[0157] Determine the real-time load change stage, including:

[0158] If the current dynamic load state growth slope is within the slope range of the fast growth segment, it is determined that the current bromine extraction process is in the fast growth segment.

[0159] If the current dynamic load state growth slope is within the slope range of the slow saturation segment, it is determined that the current bromine extraction process is in the slow saturation segment.

[0160] The fast growth segment and the slow saturation segment are combined to form a real-time load change stage.

[0161] Through this segmented comparison, the current blowing stage of the medium is accurately identified, providing key state information for subsequent process adjustment.

[0162] In this embodiment, based on historical data of the same medium specification and the same process conditions (such as temperature 25±2℃, flow rate 1.5±0.5m³ / h), the blowing stage is divided by data fitting, providing historical benchmarks for the current blowing state.

[0163] Complete data refers to the full cycle load-time data from new medium filling to blowing saturation (such as recording the load every 30 minutes within 12 hours), ensuring that the curve covers the entire blowing process.

[0164] In the early blowing stage (fast growth segment), a linear function Y = k1X + b1 can be used for fitting, where Y is the blowing amount (dynamic load), X is the time, and k1 and b1 are fitting parameters.

[0165] In the late blowing stage (slow saturation segment), an exponential function Y = a1e^(-X / b1) + c1 can be used for fitting, where a1, b1 and c1 are fitting parameters. , and .

[0166] Using optimization algorithms such as least squares, parameter estimation is performed on the selected fitting function to minimize the error between the fitting function and the actual data. For the linear function, the least squares method aims to minimize the sum of squared errors, and by taking the partial derivatives of k1 and b1 and setting them equal to zero, the equation system is solved to obtain the optimal estimates of k1 and b1. For the exponential function, nonlinear least squares can be used, and the optimal values of a1, b1 and c1 are solved by iterative algorithms (such as Levenberg-Marquardt algorithm). , and .

[0167] ​​According to the fitting results, combined with the physical process of the bromine extraction process, the load characteristic curve is divided into different stages, which are respectively divided into a rapid growth segment and a slow saturation segment. In the rapid growth segment, the blowout quantity increases approximately linearly with time, because there are a large number of active sites on the medium surface, which can quickly blow out bromide ions; in the slow saturation segment, the blowout quantity increases gradually slows down, and tends to a stable value, at this time the active sites on the medium surface are gradually occupied by bromide ions, and the blowout rate slows down.

[0168] By distinguishing the current blowout stage, the use of a single model (such as linear fitting) to predict the blowout quantity is avoided, which would result in a serious underestimation of the prediction value in the rapid growth segment (because the linear model does not consider the late decay) and a serious overestimation of the prediction value in the slow saturation segment (because the linear model does not consider the slowing down of growth). In fact, the essence of stage differentiation is to match different prediction models for different process states: determine the rapid growth segment→ call the linear growth model to predict the subsequent blowout quantity; determine the slow saturation segment→ call the exponential decay model to predict the subsequent blowout quantity. In this way, the predicted blowout quantity can truly reflect the reasonable blowout trend of the medium in this stage, and the comparison between the actual blowout quantity and the predicted value is more meaningful, and the deviation is no longer a false deviation caused by the wrong model selection, but a true deviation caused by process abnormalities.

[0169] For example: collect the historical data of 10 batches of the same specification medium, each batch of data shows that the load increases from 0 to 200 kg in the first 4 hours (slope 50 kg / h, linear characteristic), and increases from 200 kg to 280 kg in the last 8 hours (slope gradually decreases, exponential decay characteristic), thereby constructing a historical curve: rapid growth segment (0-4 hours, slope 40-60 kg / h), slow saturation segment (4-12 hours, slope 0-10 kg / h).

[0170] Based on the real-time collected concentration and flow rate data, the current medium cumulative load is calculated by integration, and the recent growth slope is calculated by a sliding window to quantify the current blowout rate. The current growth slope is compared with the slope range of each stage of the historical curve to match the corresponding blowout stage and determine the current active site utilization state of the medium.

[0171] The historical slope range is the historical rapid growth segment slope interval (40-60 kg / h) and the slow saturation segment slope interval (0-10 kg / h), which is used as a threshold standard for stage determination.

[0172] The real-time load change stage is located by slope matching to provide a "state label" (such as adjusting the flow in the rapid segment and preparing to change the tower in the saturation segment) for subsequent robot task scheduling. For example, if the current slope is 50 kg / h, which falls within the historical rapid growth segment slope range (40-60 kg / h), it is determined that the current stage is the rapid growth segment; if the slope is 8 kg / h, which falls within the slow saturation segment range (0-10 kg / h), it is determined that the current stage is the slow saturation segment.

[0173] Based on the complete data fitting curve of the same process condition, the subjectivity of artificial experience judgment is avoided. For example, the rapid segment slope range (40-60 kg / h) determined by 10 batches of historical data improves the accuracy of stage determination compared with the traditional threshold value (30-70 kg / h).

[0174] Real-time load calculation combined with sliding window slope can accurately capture the change of blowout rate. For example, when the raw material concentration suddenly drops, causing the slope to drop from 50 kg / h to 30 kg / h, the system can identify the change within 1 window period, which can detect abnormalities earlier than traditional fixed cycle inspection.

[0175] After the real-time stage is determined, the robot task can be adjusted specifically. For the rapid growth segment, the flow is adjusted by the frequency converter (such as increasing the pump frequency by 5% step when the slope is lower than 40 kg / h), so that the load growth returns to the normal range. For the slow saturation segment, the medium replacement robot is started in advance (such as starting the disassembly and handling process when the slope drops to 5 kg / h), to avoid bromide leakage caused by blowout saturation. In summary, each step is logically progressive through historical benchmarking, real-time state quantification, and stage accurate matching, solving the problems of “blowout stage ambiguity, rate change difficult to capture, and robot scheduling without basis” in traditional bromine extraction process, providing clear state input for subsequent dual collaborative adjustment mechanism, and is the core bridge connecting process perception and robot action.

[0176] Embodiment 5

[0177] Please refer to Figure 1 , specifically: according to the real-time load change stage, and combined with the historical load characteristic curve, the deviation degree of the current dynamic load state is identified to trigger the dual collaborative adjustment mechanism, including:

[0178] According to the real-time load change stage, the historical dynamic load corresponding to the time stamp on the historical load characteristic curve is determined, and is recorded as the predicted dynamic load;

[0179] By comparing the predicted dynamic load with the current dynamic load state, the deviation value is determined, specifically: , wherein, is the deviation value, is the predicted dynamic load;

[0180] If the deviation value exceeds the preset deviation threshold, that is, a significant deviation from the expected blowout curve is found, the dual collaborative adjustment mechanism is triggered, otherwise, no significant deviation from the expected blowout curve is found, and the dual collaborative adjustment mechanism is not triggered.

[0181] The deviation threshold value can be determined according to historical operation data statistical analysis, such as the maximum deviation of the current stage dynamic load (blowout amount) and the historical curve predicted value under normal working conditions, which is used as the threshold value;

[0182] The deviation value is used to identify the deviation degree of the current dynamic load state;

[0183] The double cooperative adjustment mechanism includes:

[0184] Multi-dimensional period adjustment; if the time corresponding to the current dynamic load state is in the window period encryption state, the window period encryption state is continued, and if the time corresponding to the current dynamic load state is not in the window period encryption state, the scheduling instruction is triggered again to realize the window period encryption state;

[0185] The multi-dimensional period adjustment here refers to the start of the window period encryption state;

[0186] Notification task scheduling adjustment: according to the deviation value (i.e. the bromine extraction process stage deviation), the task of the industrial robot is adjusted in advance, specifically: when the real-time load change stage is the rapid growth segment, the industrial robot is dominated to perform the frequency converter adjustment operation, and when the real-time load change stage is the slow saturation segment, the industrial robot is dominated to start the medium replacement operation in advance or delay;

[0187] The industrial robot is dominated to start the medium replacement operation in advance or delay, which is used to avoid the leakage of bromide ions due to medium overload and affect the subsequent process;

[0188] The specific execution process of the industrial robot dominated to perform the frequency converter adjustment operation: the flow regulation robot receives the instruction → moves to the delivery pump control cabinet → reads the current pump frequency parameter → adjusts the frequency converter according to the preset step (increases or decreases by 5% each time) → waits for 30 seconds of stabilization period → the pressure sensor feedbacks the pipeline pressure → if the pressure is within the safe range and the flow is not up to the upper limit → repeat the adjustment until the target flow is reached; wherein, if the current dynamic load state is lower than the predicted dynamic load, increase by 5%, and if the current dynamic load state is not lower than the predicted dynamic load, decrease by 5%;

[0189] The blowout amount in the rapid growth segment depends on the total amount of bromide ions in the raw material per unit time (= concentration x flow), if the blowout amount grows too slowly, it is usually that the input of bromide ions in the raw material is insufficient (such as sudden decrease in concentration, or low flow), which causes the medium to be not full (active sites are not fully utilized), so at this time the raw material delivery flow needs to be increased;

[0190] In actual production, the bromine concentration in seawater will fluctuate greatly due to seasonal rainfall, changes in ocean currents, etc. (such as a sudden drop from 50 ppm to 30 ppm). But the original system still takes samples for detection by the robot according to the preset frequency (for example, once an hour), which causes the system to fail to capture the fact that the concentration has decreased within tens of minutes or even hours after the parameter mutation occurs. The process continues to be executed according to the plan for high-concentration operation, and the medium consumption efficiency is significantly reduced. For example: if the preset conversion load of the bromine extraction process is 45 kg of bromine per ton of medium per hour, after the concentration suddenly drops, only 30 kg / ton is actually blown out, resulting in the false appearance that the medium has “expired” prematurely, and the robot instructs the medium to be replaced, causing waste of the medium. If the concentration suddenly increases, the medium quickly saturates, and the system fails to increase the sampling frequency and switch to the desorption process in time, causing the blowout tower to overload, bromide ions to leak into waste liquid, and the bromine recovery rate of the subsequent purification unit to decrease. Therefore, the governing industrial robot needs to start the medium replacement operation in advance or delay it;

[0191] The multi-dimensional periodic adjustment and notification task scheduling adjustment are combined to obtain a double-collaborative adjustment mechanism.

[0192] When the real-time load change phase is a slow saturation segment, the governing industrial robot starts the medium replacement operation in advance or delays it, including:

[0193] If the current dynamic load state is higher than the predicted dynamic load, a pre-start instruction is sent to the medium replacement robot cluster so as to perform the advance tower replacement operation later; the pre-start instruction is used to prepare for the advance tower replacement operation, and the medium replacement robot cluster includes a plurality of groups of industrial robots, specifically including but not limited to a carrying robot, a pipeline dismounting robot, a cleaning robot, and a sealing detection robot;

[0194] The preparation operation includes that the system sends a pre-start instruction to the medium replacement robot cluster, the carrying robot checks the new medium storage location and quantity, the pipeline dismounting robot reaches the specified station and prepares tools, the cleaning robot checks the high-pressure cleaning equipment and the cleaning agent storage, and the sealing detection robot calibrates the detection instrument;

[0195] If the medium saturates in advance, continuing to pass the raw material will cause bromide ions to be unable to be blown out and directly leak (with waste water), resulting in waste of raw materials and pollution of the environment, so the medium replacement robot task needs to be planned in advance (such as 2 hours in advance), to complete the medium regeneration or replacement before the leakage occurs, and to ensure the bromine recovery rate of the subsequent process.

[0196] If the current dynamic load state is not higher than the predicted dynamic load, the current dynamic load state is integrated in time according to multi-dimensional period adjustment, and the timestamp of the delayed start medium replacement operation is determined according to the integration result and the maximum load capacity, which refers to the maximum load capacity (i.e. the maximum blowout capacity) that the medium can bear.

[0197] When the timestamp of the delayed start medium replacement operation is determined, a medium replacement robot task is generated, which covers the following contents: emptying the residual liquid in the blowout tower, disassembling the old medium conveying pipeline, transporting the old medium to the regeneration unit, filling the new medium, restoring the pipeline connection and sealing detection, planning the robot action steps and path according to the medium filling amount and the transportation equipment capacity, and ensuring efficient and safe replacement process.

[0198] Among them, in the seawater bromine extraction process and industrial robot task scheduling scene, the regeneration unit refers to a special process module for desorption, cleaning and activation treatment of the old medium saturated with blowout, so as to restore the blowout performance, which is the core link of realizing medium recycling.

[0199] In this embodiment, the historical dynamic load corresponding to the time stamp is extracted from the historical load characteristic curve as the predicted dynamic load based on the real-time load change stage, and the deviation value is calculated by formula. When the deviation value exceeds the preset threshold value (such as 1.2 times of the maximum deviation of the historical normal working condition), the double cooperative adjustment mechanism is triggered. This step accurately compares the current dynamic load state with the predicted value of the historical data of the same stage, so that the quantization error of the load offset degree is controlled within 5%, which further improves the accuracy compared with the traditional experience judgment offset method (the error is often 15%-20%). For example, the preset deviation threshold value of the rapid growth segment is 3kg, if the current load is 45kg and the predicted load is 50kg, ΔL=5kg exceeds the threshold value, the system can accurately identify the offset state of insufficient blowout, which can provide "trigger signal" and "adjustment direction" for the double cooperative adjustment, and ensure that the subsequent robot action is based on the clear offset type (such as too high / low load), avoiding blind adjustment without a clear target.

[0200] If the current is in the window period encryption state, it is maintained, otherwise the scheduling instruction is retriggered, the period is compressed through the adaptive scaling factor, and the medium load integration and automatic sampling synchronous encryption are realized. By dynamically maintaining or starting the window period encryption state, the data analysis frequency of the system is increased by 1-2 times when the load is offset, the data timeliness is enhanced, and more intensive decision basis is provided for task adjustment. For example, when the load deviation of the slow saturation segment exceeds the threshold value, the load calculation is updated every 10 minutes after the encryption is started, which is 10 minutes earlier than the original 20-minute period to find the trend of continuously high load. Its role can help build a positive cycle of offset-encryption-accurate analysis, ensure continuous tracking of load abnormalities, and avoid delayed adjustment due to data update lag;

[0201] According to the real-time load change stage and the deviation value, the industrial robot is scheduled to perform targeted work. The rapid growth stage adjusts the flow through the frequency converter (±5% step each time), and the slow saturation stage advances or delays medium replacement (for example, if the load is too high, the robot cluster is started 2 hours in advance to prepare). The beneficial effect is that the robot task is accurately matched with the load offset characteristics, and the adjustment efficiency is improved by more than 40%. For example, when the rapid growth stage load is 5kg lower than the predicted value, the flow adjustment robot increases the pump frequency by 5% every 30 seconds, and after 3 adjustments, the load growth rate can return to the expected value; When the slow saturation stage load is 8kg higher than the predicted value, the medium replacement robot starts the disassembly tool inspection in advance, and completes the tower replacement 1.5 hours earlier than the original plan, avoiding bromine ion leakage. Its role can convert abstract load offset data into specific robot actions, realize the closed loop from data perception to physical intervention, directly solve the problem of efficiency decline or risk increase caused by load offset, and ensure the stability and economy of the bromine extraction process.

[0202] 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. An industrial robot task coordination scheduling method for a smart factory, characterized in that: The method comprises the following steps, In the bromine extraction process, key core parameters are obtained, based on the key core parameters, it is judged whether parameter mutation occurs at each node along the conveying and process main flow to identify window period encryption signals, the window period encryption signals are used to determine medium load integration new period, automatic sampling new period and robot control and scheduling new period, the key core parameters include temperature, seawater bromine content, chlorine dosage, sodium bromate and sodium bromide ratio, and bromine purity; Based on complete data in a historical period, a historical load characteristic curve is constructed, the historical load characteristic curve is used to identify a real-time load change stage corresponding to a current dynamic load state, the real-time load change stage includes a rapid growth segment and a slow saturation segment; According to the real-time load change stage, and in combination with the historical load characteristic curve, the offset degree of the current dynamic load state is identified to trigger a double collaborative adjustment mechanism, the double collaborative adjustment mechanism is used to adjust a frequency converter through an industrial robot and identify to start a medium replacement operation; In the bromine extraction process, a plurality of node automatic sampling valves and bypass sampling pipes are arranged along the conveying and process main flow, a detection sample is sent into a colorimetric analyzer through a conveying pump to periodically detect key core parameters of different process segments, and a thermometer and an electromagnetic flowmeter detection device are used to obtain the volume flow rate at the inlet of the bromine extraction process tower; The key core parameters of different process segments are summarized in time sequence to obtain the key core parameters; Parameter data at nodes of a blowout tower, an absorption tower and a distillation tower are extracted and marked as pretreatment information; According to the pretreatment information, the change of each node at adjacent monitoring time points is analyzed to obtain the change rate of the corresponding node; Based on the change rate of the corresponding node, it is judged whether parameter mutation occurs at the corresponding node to identify window period encryption signals; Based on the change rate of the corresponding node, it is judged whether parameter mutation occurs at the corresponding node, including: The change rate sequence of the corresponding node in the historical data is extracted to form a historical sequence; Based on the historical sequence, the mean value and the standard deviation of the change rate are determined; If , it is determined that there is a parameter mutation at the corresponding node, at which time the scheduling instruction is triggered, otherwise, it is determined that there is no parameter mutation at the corresponding node, at which time the scheduling instruction is not triggered; wherein, is the change rate of the i-th node at time t, is the change rate average, is the change rate standard deviation, and k is a constant; The timestamp at which the parameter mutation occurs is determined and marked as a stage point, based on the stage point, the dynamic load of the blowout tower, the absorption tower and the distillation tower is obtained; The window period encryption signals are identified, including: According to the stage point, a scheduling instruction is sent to a centralized scheduling optimization controller, the centralized scheduling optimization controller dynamically obtains an adaptive scaling factor according to the abnormal degree of the parameter mutation of the corresponding node, the adaptive scaling factor is used to quantify the scale of dynamic control, and the centralized scheduling optimization controller is used to dynamically control the medium load integration period, control the automatic sampling period and control the robot control and scheduling period; The medium load integration period, the automatic sampling period and the robot control and scheduling period are uniformly compressed through the adaptive scaling factor to obtain a new period at the stage point, the new period includes a medium load integration new period, an automatic sampling new period and a robot control and scheduling new period; The new period is taken as the window period encryption signal, and the window period encryption state is maintained until it is monitored that the corresponding node does not have parameter mutation, the window period encryption state is stopped, and the new period is restored to the original period. 2.The industrial robot task coordination scheduling method for a smart factory of claim 1, wherein: Based on the complete data in the historical period, the historical load characteristic curve is constructed, and after the step of identifying the real-time load change stage corresponding to the current dynamic load state by the historical load characteristic curve, the method further comprises: Collecting complete data of the medium dynamic load changing with time under the same process condition as the current existing process condition in the historical bromine extraction process; According to the characteristics of the bromine extraction process, combining the complete data, constructing the historical load characteristic curve through linear function and exponential function, and dividing the bromine extraction process into a rapid growth segment and a slow saturation segment; In the current dynamic medium load integral accumulation process, the dynamic loads of the blow tower, the absorption tower and the distillation tower at the current time are determined; Taking the current time as the starting point, the data of the same time span as the historical load characteristic curve is intercepted forwardly; Calculating the current dynamic load state growth slope; Comparing the current dynamic load state growth slope with the slope range of each stage in the historical load characteristic curve to determine the real-time load change stage. 3.The industrial robot task coordination scheduling method for a smart factory of claim 2, wherein: Determining the real-time load change stage comprises: If the current dynamic load state growth slope is within the slope range of the rapid growth segment, it is determined that the current bromine extraction process is in the rapid growth segment; If the current dynamic load state growth slope is within the slope range of the slow saturation segment, it is determined that the current bromine extraction process is in the slow saturation segment; Combining the rapid growth segment and the slow saturation segment to form the real-time load change stage.

4. The method of claim 3, wherein: According to the real-time load change stage, and combining the historical load characteristic curve, the deviation degree of the current dynamic load state is identified to trigger the dual collaborative adjustment mechanism, comprising: According to the real-time load change stage, the historical dynamic load corresponding to the time stamp on the historical load characteristic curve is determined, and is recorded as the predicted dynamic load; By comparing the predicted dynamic load with the current dynamic load state, the deviation value is determined; If the deviation value exceeds the preset deviation threshold, the dual collaborative adjustment mechanism is triggered.

5. The method for task coordination scheduling of industrial robots for smart factory according to claim 4, characterized in that: The dual collaborative adjustment mechanism comprises: Multi-dimensional cycle adjustment; if the time corresponding to the current dynamic load state is in the window cycle encryption state, the window cycle encryption state is continued to be maintained, and if the time corresponding to the current dynamic load state is not in the window cycle encryption state, the scheduling instruction is triggered again to realize the window cycle encryption state; Task scheduling adjustment: according to the deviation value, the task of the industrial robot is adjusted in advance, specifically: when the real-time load change stage is the rapid growth segment, the industrial robot is dominated to perform the frequency converter adjustment operation, and when the real-time load change stage is the slow saturation segment, the industrial robot is dominated to start the medium replacement operation in advance or delay; Combining the multi-dimensional cycle adjustment and the task scheduling adjustment to obtain the dual collaborative adjustment mechanism.

6. The method of claim 5, wherein: When the real-time load change stage is the slow saturation segment, the industrial robot is dominated to start the medium replacement operation in advance or delay, comprising: If the current dynamic load state is higher than the predicted dynamic load, a pre-starting instruction is sent to the medium replacement robot cluster, the pre-starting instruction is used for preparing for the operation of the advance tower, and the medium replacement robot cluster comprises a plurality of groups of industrial robots; If the current dynamic load state is not higher than the predicted dynamic load, the current dynamic load state is integrated in time according to the multi-dimensional period adjustment, and a time stamp of delaying the starting of the medium replacement operation is determined according to the integration result and the maximum load capacity, the maximum load capacity being the maximum load capacity that the medium can bear.

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