A printing and dyeing drainage state analysis method and system based on sensing data

Abstract

The application relates to the technical field of flood prevention drainage, and discloses a printing and dyeing drainage state analysis method and system based on sensing data. The method deploys group sensors with equal intervals in a printing and dyeing drainage pipeline, obtains drainage data, and generates a curve; the upstream curve is matched with the middlestream / downstream data as a template, time difference discrete values are calculated to identify sensing abnormalities; according to the average value of the upstream data, an execution instruction is matched, and the drainage treatment equipment is controlled in stages to continuously work, to rotate and patrol in multiple sets or to start all together, corresponding pop-up prompts, early warnings or alarms are given. Meanwhile, through multi-sensor data fusion, environmental interference is corrected, database parameters are dynamically optimized to adapt to a production cycle, pipeline undercurrent is identified in combination with detection particles, and equipment parameters are monitored to give a fault warning. The method improves the monitoring accuracy of printing and dyeing drainage and the intelligentization of equipment scheduling, and enhances the abnormal early warning capability.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of printing and dyeing drainage, in particular to a printing and dyeing drainage state analysis method and system based on sensing data. BACKGROUND

[0002] The printing and dyeing industry produces a large amount of industrial wastewater with complex components, high colority and large water fluctuation in the production process. If such wastewater is not properly treated, it will not only cause serious environmental pollution, but also affect the production continuity due to problems such as pipeline blockage and equipment failure. Therefore, accurate monitoring and efficient control of the printing and dyeing drainage state are the key to ensuring that enterprises meet environmental standards and reduce operational risks. However, the current drainage monitoring of printing and dyeing enterprises mainly relies on traditional single-point sensors or manual inspection mode, which cannot adapt to the special working conditions of printing and dyeing wastewater, and has problems such as insufficient monitoring accuracy and delayed response.

[0003] The limitations of the traditional monitoring method mainly include three aspects: first, it is easily disturbed by the environment. The dye attached to the printing and dyeing drainage pipeline, fiber entanglement and pipeline corrosion can cause distortion of the single-point sensor data. Local fluctuations caused by differences in pipeline structure are often misjudged as real abnormalities. Second, the equipment scheduling lacks flexibility. The drainage treatment equipment is usually operated with fixed parameters, which cannot be dynamically adjusted according to the drainage state, resulting in insufficient treatment capacity during peak periods and energy waste during low periods. Third, the abnormal identification capability is weak. It is difficult to distinguish between structural interference and real hidden dangers by relying on single parameter threshold judgment, and hidden problems such as pipeline undercurrent and early equipment failure are difficult to discover in time.

[0004] In addition, the periodic characteristics of printing and dyeing production, such as the peak / valley of batch production and the difference in drainage of different processes, make it impossible for the monitoring model with fixed parameters to dynamically adapt to long-term changes. For example, pipeline fouling can gradually change the water flow characteristics. If the sensor correction parameters are not updated synchronously, it will cause cumulative monitoring errors and affect the accuracy of decision-making. Therefore, it is a core requirement to develop a printing and dyeing drainage state analysis method that has accurate monitoring, intelligent scheduling, abnormal early warning and self-adaptability to solve the current industry problems. SUMMARY

[0005] In order to realize the monitoring of the printing and dyeing drainage state, the application provides a printing and dyeing drainage state analysis method and system based on sensing data.

[0006] In the first aspect, the application provides a printing and dyeing drainage state analysis method based on sensing data, which adopts the following technical scheme:

[0007] A printing and dyeing drainage state analysis method based on sensing data, comprising the following steps:

[0008] The printing and dyeing drainage data is obtained based on a preset printing and dyeing drainage sensor, the printing and dyeing drainage sensor is classified into multiple groups, each group of the printing and dyeing drainage sensor is located at different positions on the same printing and dyeing drainage pipeline at equal intervals; the printing and dyeing drainage data of each group is recorded periodically, and the printing and dyeing drainage data with drainage state information of each group is processed into a printing and dyeing drainage data curve;

[0009] The printing and dyeing drainage data curve corresponding to the upstream position or the downstream position of the printing and dyeing drainage pipeline is taken as a basic template, and similar intervals are matched with other printing and dyeing drainage data curves, the time interval of the matched similar intervals is extracted, and the time difference between the time intervals of the printing and dyeing drainage data curves corresponding to adjacent positions is calculated; the time dispersion value of multiple time differences is calculated;

[0010] If the time dispersion value is greater than a preset dispersion reference value, a sensor abnormality prompt is performed; otherwise, the content average value of the printing and dyeing drainage data of the upstream position of the printing and dyeing drainage pipeline is calculated, and an execution instruction is matched from a preset execution instruction library according to the content average value;

[0011] In response to the execution instruction, multiple drainage treatment devices are controlled to work: if the printing and dyeing drainage data is located in a preset first range, a single drainage treatment device is controlled to work continuously; and a pop-up prompt is performed; if the printing and dyeing drainage data is located in a preset second range, multiple drainage treatment devices are controlled to work in a round-robin manner, wherein the number of the drainage treatment devices working simultaneously is greater than one; and a pre-warning prompt is performed; if the printing and dyeing drainage data is located in a preset third range, all the drainage treatment devices are controlled to work simultaneously, and an alarm prompt is performed;

[0012] Wherein, the value in the first range is less than the value in the second range, and the value in the second range is less than the value in the third range.

[0013] By adopting the technical scheme, the sensors distributed at intervals in groups are deployed in the printing and dyeing drainage pipeline, real-time drainage data are acquired and processed to generate a curve, time difference discrete values are calculated by matching the upstream curve with the middlestream / downstream data as a template, sensor abnormalities can be accurately identified, such as sensor deviation caused by corrosion of printing and dyeing wastewater and monitoring error caused by pipeline fouling, an execution instruction is matched according to the average value of the upstream data, and the printing and dyeing wastewater treatment equipment is controlled in stages, i.e., a single device is continuously started, multiple devices are patrolled in turns, or all devices are started, and corresponding pop-up prompts, early warnings or alarms are given. The printing and dyeing drainage pipeline is accurately monitored and abnormal diagnosis is achieved, and misjudgment of the printing and dyeing wastewater treatment caused by sensor failure is avoided. The printing and dyeing wastewater treatment equipment is controlled in stages, and the fluctuation of the printing and dyeing wastewater discharge is adapted, such as the difference in water quantity between the peak and trough periods of production, the processing efficiency of the equipment is improved, and the energy consumption is reduced. The automatic decision-making level of the printing and dyeing wastewater treatment is improved, manual intervention is reduced, sudden situations such as pipeline blockage and sudden increase in water quantity are quickly responded to, and the risk of wastewater overflow is reduced.

[0014] Optionally, the step of acquiring the printing and dyeing drainage data based on the preset printing and dyeing drainage sensor further includes the following substeps:

[0015] The printing and dyeing drainage sensor is a plurality of liquid level sensors, and the liquid level sensor generates liquid level data;

[0016] A corresponding fluctuation gain is matched from a preset pipeline fluctuation database according to the position data of the liquid level sensor;

[0017] A liquid level reference range is matched according to the fluctuation gain;

[0018] A liquid level comprehensive value is calculated from a plurality of the liquid level height values;

[0019] If the liquid level comprehensive value is located outside the preset liquid level reference range, a sensor abnormality prompt is given; otherwise, the liquid level comprehensive value is taken as the printing and dyeing drainage data.

[0020] By adopting the technical scheme, for liquid level monitoring of the printing and dyeing drainage pipeline, a fluctuation gain is matched from a pipeline fluctuation database based on the position of the sensor, such as considering liquid level fluctuation caused by pipeline slope and valve switching, and an abnormality is identified by comparing the liquid level comprehensive value with the reference range. The difference in terrain between the printing and dyeing workshop and the pipeline connection section, and the impact of water flow in the pipeline, such as liquid level fluctuation in the instant of dye vat drainage, are eliminated, and the liquid level data reflects the true drainage state. The liquid level comprehensive value is calculated by fusing data of multiple sensors, data distortion caused by dye adhesion and corrosion of a single-point sensor is avoided, sensor abnormalities are promptly prompted, and the reliability of liquid level monitoring is ensured.

[0021] Optionally, the step of acquiring the printing and dyeing drainage data based on the preset printing and dyeing drainage sensor further includes the following substeps:

[0022] The printing and dyeing drainage sensor is a plurality of flow rate sensors, and the flow rate sensors generate flow rate data;

[0023] According to the position data of the flow rate sensors, a corresponding nozzle gain is matched from a preset pipe nozzle database;

[0024] According to the flow rate data and the nozzle gain, an actual flow rate value is calculated;

[0025] A flow rate comprehensive value is calculated from a plurality of the actual flow rate values;

[0026] If the flow rate comprehensive value is outside a preset flow rate reference range, a sensor abnormality prompt is performed; otherwise, the flow rate comprehensive value is taken as the printing and dyeing drainage data.

[0027] By adopting the above technical solution, for flow rate monitoring of printing and dyeing drainage, the sensor position is matched with a nozzle gain from a pipe nozzle database, flow rate mutations caused by structures such as pipe elbows and valves are calculated, actual flow rate values are fused into a flow rate comprehensive value. The influence of structural differences of printing and dyeing drainage pipes, such as the nozzle effect of different workshop drainage outlets merging into the main pipe, on flow rate monitoring is corrected, and flow rate changes caused by structures are avoided to be misjudged as water quantity abnormalities; errors caused by single flow rate sensors being wound by fiber impurities and dye deposition are filtered, and the authenticity of the flow rate comprehensive value is ensured through multi-data fusion.

[0028] Optionally, the following sub-steps are further included:

[0029] The time when the printing and dyeing drainage data is in the first range is calculated as a first time, the time when the printing and dyeing drainage data is in the second range is calculated as a second time, and the time when the printing and dyeing drainage data is in the third range is calculated as a third time;

[0030] If the first time is the longest, a ratio of the first time and the second time is calculated as a first time ratio, and values in the pipe fluctuation database or the pipe nozzle database are adjusted according to a positive correlation between a multiplication result of the first time ratio and a preset slow gain value;

[0031] Otherwise, a ratio of the second time and the third time is calculated as a second time ratio, and values in the pipe fluctuation database or the pipe nozzle database are adjusted according to an inverse correlation between a multiplication result of the second time ratio and a preset fast gain value.

[0032] By adopting the technical scheme, parameters of a pipeline fluctuation database or a pipeline orifice database are dynamically adjusted based on printing and dyeing drainage data in different ranges of duration. When a low liquid level / low flow rate state lasts for a long time, a gain value is positively adjusted to improve sensitivity to slight changes, so as to avoid lag in monitoring of a low load scene caused by solidification of database parameters; when a medium-high liquid level / high flow rate state accounts for a high proportion, parameters are reversely adjusted to accelerate response speed, so as to adapt to a scene with large drainage and rapid changes; self-iterative optimization of the database parameters is realized, so that the monitoring model is more suitable for periodic drainage characteristics of printing and dyeing production, and long-term errors caused by static parameters are reduced.

[0033] Optionally, the method further comprises the following sub-steps:

[0034] A plurality of detection particles are put into the upstream;

[0035] A particle sensor is arranged downstream, which can measure velocity data of the output detection particles;

[0036] The plurality of velocity data are sorted from large to small;

[0037] Velocity data ranked in the front and reaching a preset percentage are extracted, and an average value of the extracted velocity data is calculated as a flow rate comparison value;

[0038] The flow rate comprehensive value associated with the position of the particle sensor is obtained;

[0039] A flow rate difference value of the flow rate comprehensive value and the flow rate comparison value is calculated;

[0040] If an absolute value of the flow rate difference value is greater than a preset reference absolute value, a pipeline dark flow early warning prompt is given.

[0041] By adopting the technical scheme, by putting detection particles and cooperating with a downstream particle sensor, a comparison is made between actual measured flow rate of the particles and the flow rate comprehensive value monitored by the sensor, a pipeline dark flow is identified, such as wastewater leakage caused by pipeline damage and convergence of an unmonitored branch pipeline. The traditional flow rate sensor is compensated for a monitoring blind area of a complex flow state of a printing and dyeing drainage pipeline, and hidden water flow abnormalities are accurately found; velocity data of particles ranked in the front are extracted, impurity interference is filtered, the flow rate comparison value is ensured to reflect a real dark flow velocity, and a difference value is used for early warning to timely prompt pipeline structure hazards; problems such as wastewater loss and incomplete treatment caused by a dark flow are avoided.

[0042] Optionally, the method further comprises the following steps:

[0043] The detection particles are injection molded spheres suspended in printing and dyeing wastewater, a spherical metal core is arranged in the injection molded sphere, the particle sensor is a metal detector, and the metal detector is used to detect a moving speed of the metal core to obtain the flow rate data.

[0044] Alternatively, the detection particle is an injection molded sphere suspended in the printing and dyeing wastewater, a spherical magnetic core is arranged in the injection molded sphere, the particle sensor is a magnetic field detector, at least two particle sensors are arranged, and the distance between the two particle sensors is a preset detection distance. The flow rate data is calculated based on the time difference between the detection of the same detection particle by the two particle sensors and the detection distance.

[0045] By adopting the above technical scheme, the detection particle suitable for the printing and dyeing wastewater is designed, the injection molded sphere is wrapped around the metal / magnetic core, and the corresponding particle sensor is matched. The injection molded sphere is resistant to the corrosion of the printing and dyeing wastewater, and has a regular shape, so that the particles can stably move with the water flow and truly reflect the flow rate; the metal / magnetic core improves the detection sensitivity of the sensor, resists the interference of impurities such as dyes and fibers, and ensures the reliability of the speed data; the double magnetic field detectors calculate the flow rate through the time difference, eliminate the position error of the single sensor, and further improve the accuracy of the dark current monitoring.

[0046] Alternatively, the method further comprises the following steps:

[0047] Obtaining the rotation speed data, voltage data and current data of the drainage treatment equipment;

[0048] Calculating the speed falling edge of the rotation speed data, the voltage falling edge of the voltage data and the current rising edge of the current data;

[0049] Matching the first time of the speed falling edge, the second time of the voltage falling edge and the third time of the current rising edge;

[0050] If the time difference between any two of the first time, the second time and the third time is within a preset minimum unit time range, a drainage treatment equipment fault warning is given.

[0051] By adopting the above technical scheme, the rotation speed, voltage and current data of the drainage treatment equipment are monitored, the time correlation of the falling edge / rising edge is analyzed, and the equipment failure is warned. The multi-parameter collaborative monitoring can accurately capture early signs of equipment failure, such as mechanical jamming and motor aging; avoid false alarms of single parameter monitoring, such as voltage fluctuation alone, which may be a power grid problem rather than an equipment failure, and improve the accuracy of fault identification; realize automatic warning during unattended period in the printing and dyeing workshop, facilitate timely maintenance of the equipment, and reduce the interruption of wastewater treatment caused by equipment downtime.

[0052] Alternatively, the method further comprises the following sub-steps:

[0053] Calculating a speed component according to the value of the speed falling edge and a preset speed reference value;

[0054] a voltage component is calculated according to the value of the voltage falling edge and a preset voltage reference value;

[0055] a current component is calculated according to the value of the current rising edge and a preset current reference value;

[0056] an equipment abnormality quantity is calculated according to the speed component, the voltage component and the current component;

[0057] if the equipment abnormality quantity is greater than a preset abnormality reference quantity, a drainage treatment equipment fault alarm prompt is performed.

[0058] By using the above technical solution, the comprehensive abnormality quantity of the speed component, the voltage component and the current component is calculated, the fault degree of the drainage treatment equipment is quantitatively evaluated and graded alarm is performed. The multi-parameter change is standardized as the equipment abnormality quantity, the quantitative extraction of the fault characteristics is realized, the judgment deviation caused by the absolute value difference of the parameters is avoided; the graded response is realized by setting the abnormality reference value, the early warning is performed for the slight abnormality and the alarm is performed for the serious fault, the unnecessary shutdown maintenance is reduced, and the serious fault is ensured to be handled in time; the quantitative basis is provided for the equipment maintenance, such as the fault severity degree reflected by the abnormality quantity, the operation and maintenance efficiency is improved, and the equipment loss is reduced.

[0059] Optionally, the method further comprises the following steps:

[0060] according to the data sorting position of the printing and dyeing drainage data in the first range from small to large, the rotation speed of a single drainage treatment equipment is positively correlated adjusted; the smaller the data sorting position of the printing and dyeing drainage data in the first range, the smaller the rotation speed of the drainage treatment equipment; the greater the data sorting position of the printing and dyeing drainage data in the first range, the greater the rotation speed of the drainage treatment equipment;

[0061] according to the data sorting position of the printing and dyeing drainage data in the second range from small to large, the time interval of the drainage treatment equipment for round-patrol work is inversely correlated adjusted; the smaller the data sorting position of the printing and dyeing drainage data in the second range, the greater the time interval of the drainage treatment equipment for round-patrol work; the greater the data sorting position of the printing and dyeing drainage data in the second range, the smaller the time interval of the drainage treatment equipment for round-patrol work;

[0062] according to the data sorting position of the printing and dyeing drainage data in the third range from small to large, the total power of all the drainage treatment equipment is positively correlated adjusted; the smaller the data sorting position of the printing and dyeing drainage data in the third range, the smaller the total power of all the drainage treatment equipment; the greater the data sorting position of the printing and dyeing drainage data in the third range, the greater the total power of all the drainage treatment equipment.

[0063] By adopting the technical scheme, the rotating speed, the wheel tour interval or the total power of the wastewater treatment equipment is dynamically adjusted according to the sorting position of the printing and dyeing wastewater data in each range. In a low-load scenario, the rotating speed of a single device is adjusted in a positive correlation with the data sorting, the on-demand processing is realized, and the energy consumption is reduced; in a medium-load scenario, the wheel tour interval is adjusted in an inverse correlation, the higher the wastewater data, the more frequent the wheel tour, the load of multiple devices is balanced, and the dye deposition and wear caused by the continuous operation of a single device are avoided; in a high-load scenario, the total power is adjusted in a positive correlation with the data sorting, the processing capacity is matched with the wastewater volume, for example, the full power is operated during the production peak period, and the wastewater backlog is avoided; the fine and adaptive device scheduling is realized, the processing efficiency, the energy consumption control and the device life are taken into account, and the dynamic wastewater demand of the printing and dyeing production is adapted.

[0064] In a second aspect, the printing and dyeing wastewater state analysis system based on sensing data is provided, and the following technical scheme is adopted:

[0065] The printing and dyeing wastewater state analysis system based on sensing data comprises a processor, and the processor executes the steps of the printing and dyeing wastewater state analysis method based on sensing data according to any one of the preceding aspects.

[0066] In summary, the present application has at least one of the following beneficial technical effects:

[0067] By deploying the sensors at equal intervals in groups, correcting multiple data, and identifying abnormal sensors, the monitoring errors caused by factors such as dye adhesion, fiber interference and pipeline corrosion of printing and dyeing wastewater are effectively filtered, and the liquid level, flow rate and other data truly reflect the wastewater state.

[0068] Based on the hierarchical response mechanism of the wastewater data, the single device is continuously operated, the multiple devices are operated in a wheel tour mode, and the total power is dynamically adjusted in combination with the data sorting, so that the periodic wastewater fluctuation of the printing and dyeing production such as the water volume difference between the peak period and the valley period can be adapted, the energy consumption and the processing efficiency can be balanced, the device idle or overload can be reduced, and the device life can be prolonged.

[0069] Through dynamic iterative optimization of database parameters, dark flow identification and multi-parameter collaborative diagnosis of device faults, the system can independently adapt to the complex scenarios of printing and dyeing wastewater, accurately warn the pipeline structure hazards, device faults and sensor abnormalities, reduce the cost of manual intervention, and improve the emergency response speed. BRIEF DESCRIPTION OF DRAWINGS

[0070] Figure 1 It is a step diagram of a printing and dyeing wastewater state analysis method based on sensing data.

[0071] Figure 2 It is a step diagram of printing and dyeing wastewater sensors being multiple liquid level sensors and printing and dyeing wastewater data being obtained based on the preset printing and dyeing wastewater sensors.

[0072] Figure 3 The printing and dyeing drainage sensor is a plurality of flow rate sensors, and the printing and dyeing drainage data is acquired based on a preset printing and dyeing drainage sensor acquisition printing and dyeing drainage data step chart. DETAILED DESCRIPTION

[0073] Embodiments of the present application are described in detail below with reference to examples of embodiments shown in the accompanying drawings.

[0074] In the description of the present specification, the description of the terms "certain embodiments", "one embodiment", "some embodiments", "illustrative embodiments", "example", "specific example" or "some examples" means that the specific features, structures, materials or characteristics described in connection with the described embodiments or examples are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the described specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0075] The printing and dyeing drainage state analysis method based on sensing data disclosed in the embodiments of the present application, with reference to Figure 1 , includes the following steps:

[0076] In the main drainage pipeline and branch pipeline of the printing and dyeing plant area, preset printing and dyeing drainage sensors are deployed, including liquid level sensors, flow rate sensors and water quality sensors; water quality sensors such as pH sensors, turbidity sensors, etc. The printing and dyeing drainage sensors are divided into three groups according to the pipeline direction: the upstream group near the dye vat drainage port, the middle group in the middle section of the pipeline, and the downstream group near the inlet of the sewage treatment station. Each group of printing and dyeing drainage sensors is distributed at an interval of 5-8 meters along the pipeline to ensure coverage of the key nodes of the pipeline.

[0077] Collect sensor data of each group through industrial bus at a fixed period (such as every 10 seconds), and clean the data: eliminate abnormal values obviously beyond the physical range, such as negative values displayed by the liquid level sensor, fill in missing values caused by signal interruption, and use the average value of the previous and next five cycles for interpolation. Process the effective information (such as liquid level height, water flow rate) reflecting the drainage state in each group of data into time-value curves. For example, the liquid level curve of the upstream group takes time as the horizontal axis and liquid level height (unit: meters) as the vertical axis, directly presenting the change trend of the drainage state.

[0078] The printing and dyeing drainage data curve of the middle group or the downstream group is selected as the basic template, the downstream group is preferentially selected because it is less affected by local disturbance, and the dynamic time warping (DTW) algorithm is used to match the similar interval of the curve with the upstream group and other branch pipes. For example, if the downstream group has a liquid level rising interval at 8:00-8:10, the time interval of the upstream group in the same trend is matched by the DTW algorithm, such as 7:58-7:68, and the time difference of the similar interval of the two groups of curves is extracted, and the time difference here is 2 minutes.

[0079] The time difference of all adjacent position curves is calculated, such as the upstream group and the middle group, the middle group and the downstream group, and the dispersion value of multiple time differences is calculated by using the standard deviation formula: if the five adjacent time differences are 120 seconds, 130 seconds, 110 seconds, 140 seconds and 125 seconds, the standard deviation is about 10.8 seconds, that is, the time dispersion value.

[0080] A preset dispersion reference value is set according to the length of the pipeline, such as 15 seconds, if the calculated time dispersion value (10.8 seconds) is less than the reference value, it is determined that the sensor is normal; if a certain group of sensors causes data delay due to dye adhesion, the time dispersion value increases to 20 seconds, the system triggers an abnormal sensor prompt, and displays “upstream group No. 2 sensor is abnormal, please check” on the monitoring terminal.

[0081] When the sensor is normal, the content average value of all effective data of the upstream group is calculated, such as liquid level average value = (h1+h2+h3) / 3, h1-h3 is the liquid level value of the three sensors of the upstream group. The preset execution instruction library includes three instructions:

[0082] The first instruction corresponds to the first range, liquid level <0.3 meters, flow rate <0.5 m / s;

[0083] The second instruction corresponds to the second range, 0.3 meters≤liquid level <0.6 meters, 0.5 m / s≤flow rate <1.0 m / s;

[0084] The third instruction corresponds to the third range, liquid level≥0.6 meters, flow rate≥1.0 m / s.

[0085] According to the matching instruction of the average value of the upstream group, for example, the average value shows that the liquid level is 0.4 meters and the flow rate is 0.7 m / s, and the second instruction is matched.

[0086] In response to the matched execution instruction, multiple drainage treatment equipment (such as grating machines, lifting pumps and adjusting pool mixers) are controlled:

[0087] If the first instruction (low load state) is triggered, control 1 lifting pump to work continuously, the rotating speed is set to 60% of the rated rotating speed, and the monitoring terminal pops up a prompt “current drainage load is low, equipment is running normally”;

[0088] If the second instruction (medium load state) is triggered, control 3 lifting pump wheels to run at patrol, each for 10 minutes, switch once, at the same time, 2 run, start the grid machine high frequency operation (50 Hz), and send a pre-warning prompt (yellow light flickering) through the audible and visual alarm;

[0089] If the third instruction (high load state) is triggered, control all 5 lifting pumps to run at full power, open the emergency regulating pool water inlet valve, and the monitoring terminal sends an alarm prompt, red light + buzzer, and sends a short message to the mobile phone of the operation and maintenance personnel.

[0090] Through the above steps, the closed-loop management and control of printing and dyeing wastewater from monitoring to treatment is realized, the high-load wastewater is quickly responded during the production peak period (such as 9:00-12:00), the equipment energy consumption is optimized during the night low load, and the stability and economy of printing and dyeing wastewater treatment are improved.

[0091] Referring to Figure 2 , based on the step of obtaining printing and dyeing wastewater data by the preset printing and dyeing wastewater sensor, the following sub-steps are further included:

[0092] A plurality of liquid level sensors are arranged in the key positions of the printing and dyeing wastewater pipeline, such as 1 meter downstream of the dye vat drainage outlet, pipeline slope change points, and 3 meters inside the valve. The sensor shell is made of 316L stainless steel material to resist the corrosiveness of printing and dyeing wastewater. The sensor generates liquid level data (unit: meters) in real time. For example, when the dye vat is drained, the sensor can capture a liquid level fluctuation of 0.1-0.8 meters, and transmit it to the central control system through the 4G / NB-IoT module.

[0093] A pipeline fluctuation database is preset, which stores fluctuation characteristic parameters at different positions: according to the pipeline slope (such as 0.5°, 1°, 2°), the valve type (gate valve, ball valve), and whether it is close to the drainage outlet, the corresponding fluctuation gain (value range 0.8-1.2) is generated in advance. For example:

[0094] The sensor close to the dye vat drainage outlet is greatly impacted by instantaneous drainage, and the fluctuation gain is matched to 1.2;

[0095] The sensor with a slope of 0.5° in the flat section of the pipeline matches the fluctuation gain of 1.0;

[0096] The sensor within 5 meters behind the valve is disturbed by the valve opening and closing, and the fluctuation gain is matched to 1.1.

[0097] The central control system automatically matches the corresponding fluctuation gain from the pipeline fluctuation database according to the GPS position data (error ≤0.5 meters) of the liquid level sensor.

[0098] Calculate the liquid level reference range according to the fluctuation gain: take the historical average liquid level of the position (the average value of the same period in the past 30 days) as the reference value, and the reference range is [reference value × (1- fluctuation gain × 0.1), reference value × (1+ fluctuation gain × 0.1)]. For example:

[0099] The historical average liquid level of a certain sensor is 0.3 meters, and the matching fluctuation gain is 1.2, so the reference range is [0.3 × (1-1.2 × 0.1), 0.3 × (1+1.2 × 0.1)] = [0.264 meters, 0.336 meters].

[0100] Calculate the comprehensive value of multiple liquid level sensors using the weighted average method: give higher weight (0.2-0.3) to sensors near key nodes (such as drainage outlets, valves), and give lower weight (0.1-0.15) to middle section sensors, and the total weight is 1. For example, the liquid level values of 3 sensors are 0.28 meters (weight 0.3), 0.30 meters (weight 0.4), and 0.32 meters (weight 0.3), then the liquid level comprehensive value = 0.28 × 0.3 + 0.30 × 0.4 + 0.32 × 0.3 = 0.30 meters.

[0101] If the liquid level comprehensive value (0.30 meters) is within the reference range (0.264-0.336 meters), it will be used as valid printing and dyeing drainage data; if the comprehensive value is 0.35 meters (above the upper limit) at a certain time, the monitoring system will immediately issue a sensor anomaly prompt; for example, mark the group of sensors red on the monitoring interface and send a short message to the operation and maintenance personnel: "the downstream group liquid level comprehensive value is abnormal, suspected sensor failure or pipe blockage".

[0102] Through this embodiment, the liquid level monitoring error caused by terrain and equipment disturbance in the printing and dyeing drainage pipeline can be effectively eliminated, ensuring that the data truly reflects the drainage state and providing a reliable basis for subsequent drainage treatment equipment scheduling.

[0103] Referring to Figure 3 , in the step of obtaining printing and dyeing drainage data based on the preset printing and dyeing drainage sensor, the following sub-steps are further included:

[0104] Deploy multiple electromagnetic flow rate sensors at key structural nodes (such as bends, reducers, and workshop drainage outlet junctions) and straight sections of the printing and dyeing drainage pipeline. The sensor probe is made of polytetrafluoroethylene material to avoid dye adhesion and fiber entanglement. The sensor is installed along the pipeline axis with an insertion depth of 1 / 3 of the pipeline diameter, ensuring that the probe is completely immersed in wastewater and is not disturbed by the pipeline wall.

[0105] The flow rate sensor generates raw flow rate data (unit: m / s) at a frequency of 5 times per second. For example, at a pipeline bend, the sensor may collect fluctuating values such as 1.2 m / s and 1.3 m / s, and the data is transmitted in real time to the data processing terminal through industrial Ethernet.

[0106] A preset pipe nozzle database is built based on pipe CAD drawings and field mapping, which stores the structural parameters of each location and the corresponding nozzle gain:

[0107] Straight section of the pipe, no structural mutation, nozzle gain is 1.0, no correction is needed;

[0108] At the 90° elbow, the flow rate changes suddenly due to the change in direction, the nozzle gain is 0.85, and the measured flow rate is usually high, which needs to be corrected downward;

[0109] Variable diameter section, such as reducing from DN300 to DN200, nozzle gain is 0.7, flow rate is high due to the reduction of cross section, correction range is larger;

[0110] Multiple vehicle drainage outlets converge, water flow is disturbed violently, nozzle gain is 0.9, local turbulence leads to fluctuation of measured value, slight correction.

[0111] The data processing terminal matches its location information through the installation coordinates of the sensor (accurate to 0.1 meters), and retrieves the corresponding nozzle gain from the nozzle database. For example, the sensor located 2 meters downstream of the DN300 to DN200 variable diameter section matches the nozzle gain of 0.7.

[0112] The formula "actual flow rate value = original flow rate data × nozzle gain" is used for correction. For example:

[0113] The original flow rate of the variable diameter section sensor is 1.5 m / s, the nozzle gain is 0.7, then the actual flow rate value = 1.5 × 0.7 = 1.05 m / s;

[0114] The original flow rate of the straight section sensor is 0.8 m / s, the nozzle gain is 1.0, then the actual flow rate value = 0.8 × 1.0 = 0.8 m / s.

[0115] The corrected data can eliminate the systematic error caused by the pipe structure, such as the false high flow rate at the elbow is corrected to the true value, avoiding misjudgment as water volume increases suddenly.

[0116] Select the actual flow rate values of 6 sensors along the pipeline, use the median average method to calculate the comprehensive flow rate value, eliminate 1 maximum value and 1 minimum value, then take the average, reduce the interference of extreme values. For example, the actual flow rate values of 6 sensors at a certain time are 0.9 m / s, 1.05 m / s, 0.8 m / s, 1.2 m / s, 0.95 m / s, 1.1 m / s, after eliminating 0.8 m / s and 1.2 m / s, the comprehensive value = (0.9 + 1.05 + 0.95 + 1.1) ÷ 4 = 1.0 m / s.

[0117] A preset flow rate reference range is set according to the pipeline design flow rate, such as 0.5-1.5 m / s, if the flow rate comprehensive value (1.0 m / s) is within the range, it is regarded as effective printing and dyeing drainage data; if the comprehensive value is reduced to 0.3 m / s (lower than the lower limit) at some moment due to the fiber winding of the sensor, the system immediately triggers an abnormal sensing prompt, the monitoring terminal displays "abnormal comprehensive value of flow rate in the middle reach, it is suggested to check the winding condition of the sensor probe", and automatically marks the group data as a to-be-verified state.

[0118] Through the embodiment, the interference of the pipeline structure on the flow rate monitoring can be accurately corrected, the real water flow state is ensured, and reliable basis is provided for predicting pipeline blockage (flow rate sudden drop) and adjusting the load of the drainage treatment equipment.

[0119] The embodiment is aimed at parameter optimization of the pipeline fluctuation database and the pipeline socket database, gain value self-iteration adjustment is realized by analyzing the duration of printing and dyeing drainage data in different ranges, and the specific process is as follows:

[0120] The system automatically counts the distribution time of printing and dyeing drainage data (liquid level comprehensive value or flow rate comprehensive value) in the past 24 hours at 00:00 every morning:

[0121] The first time (t1): the cumulative length of time when the data is in the first range (liquid level <0.3 m or flow rate <0.5 m / s), for example, 10 hours of non-production period (22:00-8:00 the next day);

[0122] The second time (t2): the cumulative length of time when the data is in the second range (0.3 m≤liquid level <0.6 m or 0.5 m / s≤flow rate <1.0 m / s), for example, 2 hours of early shift preparation stage (8:00-10:00);

[0123] The third time (t3): the cumulative length of time when the data is in the third range (liquid level≥0.6 m or flow rate≥1.0 m / s), for example, 8 hours of production peak (10:00-18:00).

[0124] By comparing t1, t2 and t3, the current data distribution characteristics are determined: if t1 (10 hours) is the longest, it belongs to the low load dominant scene; otherwise (such as t3 is the longest), it belongs to the medium and high load dominant scene.

[0125] When t1 is the longest, the first time ratio k1=t1 / t2 (such as 10 / 2=5) is calculated. The preset slow gain value g1=0.02, the adjustment coefficient Δ1=k1×g1=5×0.02=0.1 is calculated.

[0126] The gain value in the pipeline fluctuation database or the pipeline socket database is positively adjusted:

[0127] The original gentle section sensor with a fluctuation gain of 1.0 is adjusted to 1.0 x (1 + Δ1) = 1.1;

[0128] The original elbow sensor with a bayonet gain of 0.85 is adjusted to 0.85 x (1 + Δ1) = 0.935.

[0129] After adjustment, the database is more sensitive to subtle changes in low-range data (such as liquid level from 0.25 meters to 0.28 meters), avoiding small-scale abnormalities during non-production periods.

[0130] When t1 is not the longest (such as t3 is the longest), calculate the second time ratio k2 = t2 / t3 (such as 2 / 8 = 0.25). The preset fast gain value g2 = 0.04, calculate the adjustment coefficient Δ2 = k2 x g2 = 0.25 x 0.04 = 0.01.

[0131] Reverse adjustment of gain values in the database:

[0132] The original fluctuation gain of the drain sensor is 1.2, and after adjustment, it is 1.2 x (1 - Δ2) = 1.188;

[0133] The original bayonet gain of the variable diameter section sensor is 0.7, and after adjustment, it is 0.7 x (1 - Δ2) = 0.693.

[0134] After adjustment, the response speed of the database parameters to medium and high range data is improved, for example, when the flow rate suddenly increases from 0.9 m / s to 1.1 m / s during the production peak period, the system can quickly identify and trigger the corresponding processing instructions.

[0135] To avoid excessive parameter deviation, set the upper and lower limits of the gain value adjustment, the fluctuation gain is 0.8-1.5, the bayonet gain is 0.6-1.2, and the boundary value is automatically taken when exceeding the range. After each adjustment, the system generates a log record of the adjustment time, original gain value, adjustment coefficient and new value, for example: "2023-10-0100:00, the middle stream elbow sensor bayonet gain is adjusted from 0.85 to 0.935, the adjustment coefficient is 0.1 (because k1 = 5)".

[0136] Through this embodiment, the database parameters can dynamically adapt to the periodic fluctuations of printing and dyeing production, improve the monitoring sensitivity during low load, and accelerate the response speed during high load, reducing the parameter solidification error in long-term operation.

[0137] This embodiment realizes accurate identification of dark current in printing and dyeing drainage pipeline by detecting particles and sensor cooperation, and makes up for the blind area of traditional flow rate monitoring, the specific process is as follows:

[0138] Select the diameter of 5-8 mm injection molding sphere as the detection particles, the sphere density is close to the density of printing and dyeing wastewater (1.02-1.05 g / cm³), which ensures that it is suspended in water and moves with water flow; the surface of the sphere is sprayed with corrosion-resistant coating, and a neodymium iron boron magnetic core (remanence strength ≥1200 mT) with a diameter of 2 mm is packaged inside to improve the detection sensitivity.

[0139] An automatic feeding device is set upstream of the main pipe of printing and dyeing wastewater, 3 meters downstream of the workshop wastewater collection port, and the detection particles are fed at regular intervals (such as 9:00 and 15:00). The number of single feeding is 20-30, which is adjusted according to the diameter of the pipe. For DN300 pipe, 25 particles are fed, and the interval of particle feeding is 5 seconds per particle to avoid mutual collision affecting the motion trajectory.

[0140] Two magnetic field detectors, i.e. particle sensors, are installed on the outer wall of the pipe 50 meters downstream of the feeding point along the water flow direction at an interval of 3 meters. The detector uses a Hall element array to identify the magnetic field change when the magnetic particles pass through, with a sampling frequency of 1 kHz and a positioning accuracy of ±0.05 meters.

[0141] When the detection particles flow through the first detector, the system records the time stamp t1; when they flow through the second detector, the time stamp t2 is recorded; the speed data of a single particle (unit: m / s) is calculated by the formula "particle speed=3 meters / (t2-t1)". For example, the time difference of a certain particle passing through the two detectors is 4.5 seconds, and its speed is 3 / 4.5≈0.67 m / s.

[0142] The system collects the speed data of all detection particles, such as 25 particles generating 25 groups of speed values, which are sorted from large to small: assuming the data is 0.82 m / s, 0.79 m / s, 0.75 m / s, 0.67 m / s...0.31 m / s; some particles have low speed due to fiber attachment.

[0143] The preset percentage is 30%, i.e. the first 30% of high speed data is extracted, and the first 8 of 25 data is extracted, 25×30%=7.5, rounded up to 8, and the average value is calculated as the flow rate comparison value: (0.82+0.79+0.75+0.73+0.71+0.69+0.68+0.67)÷8≈0.72 m / s. This value can filter out low speed anomalies caused by particle interference, reflecting the true water flow speed.

[0144] The flow rate comprehensive value associated with the position of the downstream particle sensor is retrieved, and the data corrected by the flow rate sensor, such as 0.55 m / s, is calculated. The flow rate difference value is equal to the flow rate comprehensive value minus the flow rate comparison value, which is 0.55-0.72=-0.17 m / s, and its absolute value is 0.17 m / s.

[0145] The preset reference absolute value is 0.1 m / s, which is set according to the fluctuation range of the pipeline design flow rate. Since 0.17 m / s>0.1 m / s, the system determines that there is a hidden current; if the pipeline is locally damaged to cause part of the water flow to leak, the sensor monitoring value is lower than the actual flow rate, the pipeline hidden current early warning prompt is triggered immediately: the monitoring terminal displays "abnormal flow rate difference is detected at the downstream 50 meters, suspected pipeline hidden current, suggest checking the pipeline integrity", and the early warning position is marked in the pipeline electronic map, and a repair work order is pushed to the operation and maintenance system.

[0146] Through the embodiment, hidden currents caused by corrosion perforation, joint loosening and the like of the printing and dyeing wastewater pipeline can be effectively identified, environmental protection risks and water resource waste caused by wastewater leakage are avoided, and the embodiment is especially suitable for pipeline safety monitoring of old factory sites.

[0147] The embodiment designs two detection particle and sensor adaptation schemes for the special environment of printing and dyeing wastewater, to ensure the accuracy and anti-interference of flow rate measurement, and the specific schemes are as follows:

[0148] Scheme one: metal core detection particle and metal detector combination

[0149] The detection particle adopts a polypropylene injection molded sphere with a diameter of 6 mm (density 1.03 g / cm³), and the surface of the sphere is treated by polytetrafluoroethylene coating (thickness 0.2 mm), which can resist the corrosion of printing and dyeing wastewater with pH value of 3-11, and the surface is smooth and not easy to attach dye or fiber. A 304 stainless steel metal core (containing iron-nickel alloy components) with a diameter of 3 mm is packaged in the sphere, and the metal core and the sphere are concentrically arranged to ensure the stability of the particle barycenter and the rolling deviation of the water flow.

[0150] The particle sensor adopts a high-frequency metal detector (working frequency 100 kHz), which is installed on the outer wall of the printing and dyeing wastewater pipeline, 1 / 3 pipe diameter height away from the bottom of the pipeline, the distance between the detector probe and the outer wall of the pipeline is ≤5 mm, and the detection range is a circular area with a diameter of 150 mm. When the metal core particle flows through the detection area, the detector outputs a pulse signal, and records the time point of signal triggering; by setting two metal detectors at an interval of 5 meters along the water flow direction, the time difference Δt of the same particle passing through is calculated, such as the time of the particle passing through the first detector is t1, the time of the particle passing through the second detector is t2, Δt=t2-t1, and the flow rate data=5m / Δt. For example, when Δt=8 seconds, the flow rate=5 / 8=0.625 m / s.

[0151] The scheme is suitable for the scene with a pipeline diameter ≤500 mm, the metal core has strong anti-interference performance on dyes and auxiliaries, and is especially suitable for flow rate measurement of high-color printing and dyeing wastewater.

[0152] Scheme two: magnetic core detection particle and magnetic field detector combination

[0153] The detection particle is a polyethylene injection sphere with a diameter of 8 mm (density 1.04 g / cm³), a sphere wall thickness of 1 mm, an inner package diameter of 4 mm of a neodymium iron boron permanent magnetic core, a magnetic force strength of 800-1000 Gauss, and a magnetic core surface coated with an epoxy resin insulation layer to prevent electrochemical reaction with chloride ions in the printing and dyeing wastewater. The sphere shape is designed to be streamlined (two end radii of 2 mm), reducing water flow resistance and ensuring that the flow error is less than or equal to 5%.

[0154] The particle sensor uses a double-channel magnetic field detector, each detector containing 3 groups of Hall elements arranged in an equilateral triangle to locate the passing position of the magnetic particles. Two magnetic field detectors are installed along the pipeline axis direction at an interval of 4 meters, with a preset detection distance of 4 meters. When the same magnetic particle passes through the first detector, the system records the time t3; when it passes through the second detector, the time t4 is recorded, and the time difference At' = t4-t3 is calculated, and the flow rate data = 4m / At'. For example, when At' = 6 seconds, the flow rate = 4 / 6 ≈ 0.667 m / s.

[0155] To avoid signal interference from adjacent particles, the detection particle release interval is set to 10 seconds per particle, and the single release quantity is not more than 1 / 5 of the ratio of the pipeline cross-sectional area to the particle cross-sectional area; for example, for a DN600 pipeline, the single release is ≤15.

[0156] Scheme one is suitable for printing and dyeing wastewater containing more magnetic impurities, such as denim dyeing and finishing wastewater, which can filter background interference by adjusting the detector frequency; scheme two is suitable for high flow rate pipelines (≥1.0 m / s), and the time difference calculation of the double detector can eliminate the response delay error of a single sensor; the injection spheres of the two schemes have corrosion resistance, and the density is close to that of the printing and dyeing wastewater, ensuring that the particle motion state is consistent with the water flow, providing a reliable speed reference for the identification of the dark current.

[0157] Through this embodiment, the combination of the detection particle and the sensor can adapt to the complex composition and flow state of the printing and dyeing wastewater, providing accurate data support for the calculation of the flow rate ratio, and improving the reliability of the pipeline dark current monitoring.

[0158] This embodiment is aimed at key equipment in the printing and dyeing wastewater treatment process, such as the lifting pump, the grating machine, and the stirrer, and realizes early fault warning through multi-parameter collaborative monitoring to avoid the paralysis of the drainage system caused by sudden equipment shutdown. The specific process is as follows:

[0159] A sensor module is installed in the motor control box of the drainage treatment equipment, including:

[0160] Hall speed sensor: installed at the end of the motor output shaft, sampling frequency 100 Hz, real-time acquisition of equipment speed data (unit: r / min), such as the normal speed of the lifting pump being 1450 r / min;

[0161] Voltage sensor: connected in series in the motor power supply circuit, measurement range 0-380V, collect the average value of three-phase voltage (unit: V);

[0162] Current sensor: connected through the current transformer, measurement range 0-50A, collect the average value of three-phase current (unit: A).

[0163] Sensor data is transmitted to the device monitoring system through the industrial bus, with a storage period of 1 second per record, ensuring the capture of transient parameter changes.

[0164] The monitoring system performs trend analysis on the collected speed, voltage, and current data, extracting key features:

[0165] Speed falling edge: refers to the starting time when the speed data continuously decreases from the stable value, for example, the speed of the booster pump decreases from 1450 r / min to 1300 r / min, the first time of the falling edge is recorded as t1, such as 10:05:23.120;

[0166] Voltage falling edge: refers to the starting time when the voltage data deviates from the rated value (such as 380V) and continuously decreases, for example, the voltage decreases from 380V to 360V, the second time of the falling edge is recorded as t2, such as 10:05:23.125;

[0167] Current rising edge: refers to the starting time when the current data abnormally increases from the normal range (such as 15A), for example, the current increases from 15A to 20A, the third time of the rising edge is recorded as t3, such as 10:05:23.130.

[0168] The determination of feature edges needs to meet the "consistent trend of data change for 3 consecutive periods (3 seconds)", to avoid misjudgment due to transient interference.

[0169] Calculate the time difference of the three feature edges:

[0170] Δt1=|t2-t1| (time difference between voltage falling edge and speed falling edge);

[0171] Δt2=|t3-t1| (time difference between current rising edge and speed falling edge);

[0172] Δt3=|t3-t2| (time difference between current rising edge and voltage falling edge).

[0173] The preset minimum unit time range is 0-0.5 seconds, set according to the device response delay characteristics, if the above three time differences meet Δt≤0.5 seconds (such as Δt1=0.005 seconds, Δt2=0.010 seconds, Δt3=0.005 seconds), it is determined that the abnormal changes of the three parameters have strong correlation, indicating that the device has mechanical or electrical fault risk.

[0174] When the time correlation condition is met, the monitoring system immediately performs:

[0175] A warning window pops up on the device operation interface, displaying "Promote Pump 1 detects simultaneous occurrence of speed drop, voltage drop, and current rise, suspected mechanical jam, please check immediately";

[0176] Send a warning message to the operation personnel's mobile phone, including the device number, abnormal parameter value, and timestamp;

[0177] Automatically record the parameter curve 10 minutes before the failure as a basis for fault diagnosis.

[0178] If only a single parameter is abnormal, such as voltage drop but normal speed and current, it is determined as external disturbance such as power grid fluctuation, no warning is triggered, only abnormal log is recorded.

[0179] This embodiment can accurately identify early device failures such as bearing wear causing jamming and motor winding aging through multi-parameter time correlation analysis, reducing false alarm rate compared to traditional single parameter alarm, and ensuring stable operation of printing and dyeing wastewater treatment equipment during unattended period.

[0180] On the basis of fault warning, this embodiment realizes accurate assessment of fault degree through quantitative calculation of device abnormal quantity, providing basis for hierarchical processing, the specific process is as follows:

[0181] Extract the speed falling edge value, voltage falling edge value, and current rising edge value of the wastewater treatment equipment such as printing and dyeing wastewater lifting pump:

[0182] Speed falling edge value: refers to the change quantity from stable value to falling edge time of speed, that is, Δv = rated speed - falling edge time speed (unit: r / min), for example, rated speed 1450 r / min, falling edge time speed 1300 r / min, then Δv = 150 r / min;

[0183] Voltage falling edge value: refers to the change quantity from rated value to falling edge time of voltage, that is, Δu = rated voltage - falling edge time voltage (unit: V), for example, rated voltage 380V, falling edge time voltage 350V, then Δu = 30V;

[0184] Current rising edge value: refers to the change quantity from normal peak value to rising edge time of current, that is, Δi = rising edge time current - normal peak current (unit: A), for example, normal peak current 18A, rising edge time current 25A, then Δi = 7A.

[0185] Pre-set reference value (based on device factory parameters and historical operation data):

[0186] Speed reference value v0 = 200 r / min (maximum allowable speed drop);

[0187] Voltage reference value u0 = 50 V (maximum allowable voltage drop);

[0188] Current reference value i0 = 10 A (maximum allowable current rise).

[0189] Calculate each parameter component using the standardization formula, and normalize the change in different dimensions:

[0190] Speed component: Sv = Δv / v0, for example, 150 / 200 = 0.75;

[0191] Voltage component: Su = Δu / u0, for example, 30 / 50 = 0.6;

[0192] Current component: Si = Δi / i0, for example, 7 / 10 = 0.7.

[0193] The component value ranges from 0 to 1, and the closer to 1 indicates that the parameter deviates from the normal state more significantly.

[0194] Calculate the device anomaly A using the weighted sum formula: A = 0.4 × Sv + 0.3 × Su + 0.3 × Si; the weights are set according to the impact of the parameters on the device failure, and the speed drop is more sensitive to mechanical failure, so the weight is the highest.

[0195] For example: A = 0.4 × 0.75 + 0.3 × 0.6 + 0.3 × 0.7 = 0.3 + 0.18 + 0.21 = 0.69.

[0196] The preset anomaly reference value A0 = 0.8, calibrated according to the device critical failure data:

[0197] If A ≤ 0.8 (such as 0.69 above), it is determined to be a slight anomaly, maintaining the fault warning state, and prompting "There is a potential fault in the device, please repair within 24 hours";

[0198] If A > 0.8, trigger the fault alarm, for example, when Δv = 180 r / min, Δu = 40 V, Δi = 9 A, Sv = 0.9, Su = 0.8, Si = 0.9, A = 0.4 × 0.9 + 0.3 × 0.8 + 0.3 × 0.9 = 0.36 + 0.24 + 0.27 = 0.87 > 0.8, the system immediately executes:

[0199] Sound and light alarm (red warning light flashing + buzzer ringing);

[0200] Automatically switch the device to standby pump operation (if it is a redundant system);

[0201] Generate an emergency work order on the operation and maintenance platform, label the anomaly value and the proportion of each component, such as "Current component accounts for 31%, suspected motor overload".

[0202] Update the reference value every month according to the equipment maintenance record: if the actual allowable speed of a batch of booster pumps decreases by 180 r / min due to impeller wear, adjust v0 to 180 r / min to ensure that the component calculation fits the aging state of the equipment. At the same time, increase the monitoring frequency of equipment with abnormal quantity exceeding 0.6 from 1 second / time to 0.5 seconds / time to capture subtle changes.

[0203] Through this embodiment, the equipment failure can be upgraded from "qualitative warning" to "quantitative evaluation", and the operation and maintenance personnel can prioritize the processing of serious failures (such as A=0.95 equipment over A=0.7 equipment) according to the size of the abnormal quantity, reducing unnecessary downtime, while providing data support for fault location, such as a device Si proportion of 40%, which can focus on checking the motor winding.

[0204] This embodiment is aimed at drainage treatment equipment under different load scenarios, and the sorting position of printing and dyeing drainage data is used to achieve fine adjustment, balancing processing efficiency and energy cost, and the specific process is as follows:

[0205] The system statistically analyzes the collected printing and dyeing drainage data (liquid level comprehensive value or flow rate comprehensive value) by day and divides it into intervals:

[0206] The first range: 0.1-0.3 meters (liquid level) or 0.2-0.5 m / s (flow rate), sorted from small to large as 10 positions (positions 1 to 10), for example, position 1 corresponds to 0.1 meters, and position 10 corresponds to 0.3 meters;

[0207] The second range: 0.3-0.6 meters (liquid level) or 0.5-1.0 m / s (flow rate), also sorted into 10 positions (positions 1 to 10), with position 1 corresponding to 0.3 meters and position 10 corresponding to 0.6 meters;

[0208] The third range: 0.6-1.0 meters (liquid level) or 1.0-1.5 m / s (flow rate), sorted into 10 positions (positions 1 to 10), with position 1 corresponding to 0.6 meters and position 10 corresponding to 1.0 meters.

[0209] Each position corresponds to a specific numerical threshold, for example, position 5 in the first range corresponds to 0.2 meters ((0.3-0.1) x 5 / 10 + 0.1).

[0210] When the printing and dyeing drainage data is in the first range, control a single drainage treatment equipment (such as a pool mixer) to adjust the speed positively according to the sorting position:

[0211] The preset speed adjustment range is 300-600 r / min (30%-60% of the rated speed of the equipment);

[0212] Adjustment formula: actual speed = 300 + (ranking position - 1) x 33.3 (about 33.3 r / min per gear);

[0213] Example: data is located in the first range position 3 (corresponding to 0.16 meters), the actual speed = 300 + 2 x 33.3 ≈ 367 r / min; position 10 (0.3 meters), the speed = 300 + 9 x 33.3 ≈ 600 r / min.

[0214] Through this adjustment, low liquid level data (positions 1-3) in the non-production period (such as night) corresponds to low-speed operation, reducing energy consumption; the production preparation stage in the early morning (positions 7-10) corresponds to medium-speed operation, activating processing capacity in advance.

[0215] When the data is located in the second range, the round-robin interval of 3 drainage treatment devices (such as filter pumps) is adjusted inversely:

[0216] The preset round-robin interval range is 5-15 minutes (single running time of each device);

[0217] Adjustment formula: actual interval = 15 - (ranking position - 1) x 1.11 (about 1.11 minutes per gear);

[0218] Example: data is located in the second range position 2 (corresponding to 0.33 meters), actual interval = 15 - 1 x 1.11 ≈ 13.9 minutes; position 9 (0.57 meters), interval = 15 - 8 x 1.11 ≈ 6.1 minutes.

[0219] High ranking position (such as positions 8-10) corresponds to short interval, device round-robin more frequently, to avoid single device due to continuous running resulting in dye deposition; low ranking position (such as positions 1-3) corresponds to long interval, reducing device start-stop loss.

[0220] When the data is located in the third range, the total power of 5 drainage treatment devices (such as lifting pumps) is adjusted positively:

[0221] The preset total power range is 15-30 kW, the single rated power is 6 kW, and the total power of 5 is 30 kW;

[0222] Adjustment formula: actual total power = 15 + (ranking position - 1) x 1.67 (about 1.67 kW per gear);

[0223] Example: data is located in the third range position 4 (corresponding to 0.72 meters), total power = 15 + 3 x 1.67 ≈ 20 kW, which can be allocated as 3 full power + 2 half power; position 10 (1.0 meters), total power = 30 kW, 5 full power operation.

[0224] The adjustment ensures that high liquid level data during production peak period (such as noon period) corresponds to full load treatment, avoiding wastewater backlog; high range low order position (such as position 1-3) corresponds to medium power, balancing energy consumption and treatment demand.

[0225] Set safety threshold: first range speed not less than 300r / min, prevent equipment from stopping to cause dye deposition; third range total power not more than 30kW (avoid circuit overload). The system analyzes and adjusts the effect every day, if the treatment is not timely in a certain gear, such as wastewater overflow in position 10 of the second range, automatically correct the interval parameters of this gear, such as from 5 minutes to 4 minutes, realize dynamic adaptation.

[0226] The embodiment of the application further discloses a printing and dyeing wastewater state analysis system based on sensing data, comprising a processor, wherein the processor executes the steps of the printing and dyeing wastewater state analysis method based on sensing data according to any one of the above.

[0227] Although the embodiments of the application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the application.

Claims

1. A method of analyzing a printing and dyeing water discharge state based on sensing data, characterized by, The method comprises the following steps: obtaining printing and dyeing drainage data based on preset printing and dyeing drainage sensors, the printing and dyeing drainage sensors being classified into multiple groups, each group of the printing and dyeing drainage sensors being located at different positions on the same printing and dyeing drainage pipeline at equal intervals; recording the printing and dyeing drainage data of each group at a fixed period, and processing the printing and dyeing drainage data of each group with drainage state information into a printing and dyeing drainage data curve; taking the printing and dyeing drainage data curve corresponding to the position at the downstream of the printing and dyeing drainage pipeline as a basic template, matching similar intervals of other printing and dyeing drainage data curves, extracting time intervals of the matched similar intervals, calculating time differences between the time intervals of the printing and dyeing drainage data curves corresponding to adjacent positions; calculating a time dispersion value of multiple time differences; if the time dispersion value is greater than a preset dispersion reference value, an abnormal sensor prompt is given; otherwise, calculating a content average value of the printing and dyeing drainage data at the upstream of the printing and dyeing drainage pipeline, and matching an execution instruction from a preset execution instruction library according to the content average value; in response to the execution instruction, multiple drainage treatment devices are controlled to work: if the printing and dyeing drainage data is within a preset first range, a single drainage treatment device is controlled to work continuously; and a pop-up prompt is given; if the printing and dyeing drainage data is within a preset second range, multiple drainage treatment devices are controlled to work in a round-robin manner, wherein the number of simultaneously working drainage treatment devices is greater than one; and a pre-warning prompt is given; if the printing and dyeing drainage data is within a preset third range, all drainage treatment devices are controlled to work simultaneously, and an alarm prompt is given; wherein the value within the first range is less than the value within the second range, and the value within the second range is less than the value within the third range. In the step of obtaining printing and dyeing drainage data based on preset printing and dyeing drainage sensors, the following sub-steps are further included:

2. The method of claim 1, wherein the method is characterized by, the printing and dyeing drainage sensors are multiple liquid level sensors, and the liquid level sensors generate liquid level data; a corresponding fluctuation gain is matched from a preset pipeline fluctuation database according to position data of the liquid level sensors; a liquid level reference range is matched according to the fluctuation gain; a liquid level comprehensive value is calculated from multiple liquid level height values; if the liquid level comprehensive value is outside a preset liquid level reference range, an abnormal sensor prompt is given; otherwise, the liquid level comprehensive value is taken as the printing and dyeing drainage data. In the step of obtaining printing and dyeing drainage data based on preset printing and dyeing drainage sensors, the following sub-steps are further included:

3. The method of claim 1, wherein the method is characterized by, the printing and dyeing drainage sensors are multiple flow rate sensors, and the flow rate sensors generate flow rate data; a corresponding orifice gain is matched from a preset pipeline orifice database according to position data of the flow rate sensors; actual flow rate values are calculated according to the flow rate data and the orifice gain; a flow rate comprehensive value is calculated from multiple actual flow rate values; if the flow rate comprehensive value is outside a preset flow rate reference range, an abnormal sensor prompt is given; otherwise, the flow rate comprehensive value is taken as the printing and dyeing drainage data. the following sub-steps are further included:

4. The method of claim 2 or 3, wherein the method is characterized by, ​ The time at which the printing and dyeing drainage data is calculated to be within the first range is a first time, the time at which the printing and dyeing drainage data is calculated to be within the second range is a second time, and the time at which the printing and dyeing drainage data is calculated to be within the third range is a third time; If the first time is the longest, a first time ratio is calculated as a ratio of the first time and the second time, and a value in the pipeline fluctuation database or the pipeline socket database is adjusted according to a positive correlation between a multiplication result of the first time ratio and a preset slow gain value; Otherwise, a second time ratio is calculated as a ratio of the second time and the third time, and the value in the pipeline fluctuation database or the pipeline socket database is adjusted according to an inverse correlation between a multiplication result of the second time ratio and a preset fast gain value.

5. The method of analyzing the printing and dyeing effluent state based on sensing data according to claim 3, wherein, Further comprising the following sub-steps: A plurality of detection particles are put upstream; A particle sensor is arranged downstream, which can measure velocity data of the output detection particles; The plurality of velocity data is sorted from large to small; Velocity data arranged in the front and reaching a preset percentage is extracted, and an average value of the extracted velocity data is calculated as a flow rate contrast value; A flow rate comprehensive value associated with the position of the particle sensor is obtained; A flow rate difference value of the flow rate comprehensive value and the flow rate contrast value is calculated; If the absolute value of the flow rate difference value is greater than a preset reference absolute value, a pipeline dark current early warning prompt is given.

6. The method of analyzing the printing and dyeing effluent state based on sensing data according to claim 5, wherein, The method further comprises the following steps: The detection particles are injection molded spheres suspended in printing and dyeing wastewater, a spherical metal core is arranged in the injection molded spheres, and the particle sensor is a metal detector used to detect the moving speed of the metal core to obtain the flow rate data; Alternatively, the detection particles are injection molded spheres suspended in printing and dyeing wastewater, a spherical magnetic core is arranged in the injection molded spheres, the particle sensor is a magnetic field detector, at least two particle sensors are arranged at a preset detection distance, and the flow rate data is calculated based on the time difference of the two particle sensors detecting the same detection particle and the detection distance.

7. The method of analyzing printing and dyeing effluent state based on sensing data according to claim 1, wherein, The method further comprises the following steps: Obtain the rotational speed data, voltage data and current data of the drainage treatment equipment; Calculate the speed falling edge of the rotational speed data, the voltage falling edge of the voltage data and the current rising edge of the current data; Match the first time of the speed falling edge, the second time of the voltage falling edge and the third time of the current rising edge; If the time difference between any two of the first time, the second time and the third time is within a preset minimum unit time range, a drainage treatment equipment fault early warning prompt is given.

8. The method of claim 7, wherein the method is characterized by, Further comprising the following sub-steps: A speed component is calculated according to the value of the speed falling edge and a preset speed reference value; A voltage component is calculated according to the value of the voltage falling edge and a preset voltage reference value; A current component is calculated according to the value of the current rising edge and a preset current reference value; An equipment abnormality amount is calculated according to the speed component, the voltage component and the current component; If the device abnormality quantity is greater than a preset abnormality reference quantity, a drainage treatment device fault alarm prompt is performed.

9. The method of analyzing printing and dyeing effluent state based on sensing data according to claim 1, wherein, Further comprising the following sub-steps: According to the data sorting position of the printing and dyeing drainage data in the first range from small to large, positively correlate to adjust the rotating speed of a single said drainage treatment device; the smaller the data sorting position of the printing and dyeing drainage data in the first range, the smaller the rotating speed of the drainage treatment device; the greater the data sorting position of the printing and dyeing drainage data in the first range, the greater the rotating speed of the drainage treatment device; According to the data sorting position of the printing and dyeing drainage data in the second range from small to large, inversely correlate to adjust the time interval of the drainage treatment device for round-patrol work; the smaller the data sorting position of the printing and dyeing drainage data in the second range, the greater the time interval of the drainage treatment device for round-patrol work; the greater the data sorting position of the printing and dyeing drainage data in the second range, the smaller the time interval of the drainage treatment device for round-patrol work; According to the data sorting position of the printing and dyeing drainage data in the third range from small to large, positively correlate to adjust the total power of all said drainage treatment devices; the smaller the data sorting position of the printing and dyeing drainage data in the third range, the smaller the total power of all said drainage treatment devices; the greater the data sorting position of the printing and dyeing drainage data in the third range, the greater the total power of all said drainage treatment devices.

10. A printing and dyeing effluent condition analysis system based on sensing data, characterized by, A processor is included, in which the steps of the printing and dyeing drainage state analysis method based on sensing data as claimed in any one of claims 1-9 are executed.

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