Global adaptive air purification system applied to coating equipment

Abstract

The invention discloses a global adaptive air purification system applied to coating equipment, and belongs to the field of coating equipment, the global adaptive air purification system specifically comprises a monitoring and maintenance center, and the monitoring and maintenance center is in communication connection with a peculiar smell gas monitoring unit, an abnormal feature recognition unit and an abnormal analysis unit; according to the invention, the coating equipment is subjected to operation process data acquisition through the peculiar smell gas monitoring unit, an upper layer event and a basic event are set according to the acquired data, an anomaly monitoring model is constructed, the operation state of the coating equipment is judged according to the anomaly monitoring model, and a high-risk anomaly trend and a low-risk anomaly trend are generated. The abnormal feature recognition unit carries out collected data feature extraction recognition on the coating equipment in the high-risk abnormal trend, abnormal events and non-abnormal events are obtained according to feature extraction recognition, operation and maintenance control is carried out on collected data of the corresponding events, and therefore automatic control and intelligent response of the coating equipment are achieved.

Description

Technical Field

[0001] The present invention relates to the field of coating equipment, and in particular to a global adaptability air purification system used in coating equipment. Background Art

[0002] Coating equipment is a machine used to evenly apply coatings, glues, inks and other substances on the surface of various substrates (such as paper, plastic film, metal foil, etc.). It is widely used in printing, packaging, electronics, photovoltaics, automotive and other industries. During the use of coating equipment, the coating will produce a certain odor, which will be emitted into the working environment, causing harm to the environment and the health of personnel.

[0003] In response to the above problems, a Chinese patent provides an air purification device for a coating room (publication number: CN222027100U). The device extracts the odorous gas from the coating equipment through an exhaust device, and purifies the odorous gas through a filter plate to prevent the paint odor from being emitted into the working environment.

[0004] However, in actual use, the above-mentioned technical solutions often adopt a fixed air purification process, which cannot automatically adjust according to real-time environmental changes, lacks comprehensive environmental data monitoring functions, and mainly relies on manual inspection and experience judgment. The feedback mechanism may be relatively lagging, and lacks automated control and intelligent response. Summary of the Invention

[0005] The purpose of the present invention is to provide a global adaptive air purification system for use in coating equipment to solve the technical defects proposed in the background art.

[0006] The object of the present invention can be achieved by the following technical solution: a global adaptive air purification system used in a coating device, comprising a monitoring and maintenance center, wherein the monitoring and maintenance center is communicatively connected to an odor gas monitoring unit, an abnormality feature recognition unit, and an abnormality analysis unit;

[0007] Odor gas monitoring unit, which is used to collect air environment data during the operation of coating equipment, set upper-level events and basic events based on the collected data, and build an abnormality monitoring model. It also judges the operating status of the coating equipment based on the abnormality monitoring model and generates high-risk abnormal trends and low-risk abnormal trends;

[0008] Abnormal feature recognition unit, used to continuously collect data and extract features of coating equipment with high-risk abnormal trends, and identify abnormal events and non-abnormal events based on feature extraction, and perform operation and maintenance control on the collected data of corresponding events;

[0009] The abnormality analysis unit is used to perform multi-device parallel abnormality risk analysis on coating equipment that is in a low-risk abnormal trend, and infer whether the corresponding coating equipment has a high-risk abnormal risk based on the analysis results.

[0010] Preferably, the operation process of the odor gas monitoring unit is as follows:

[0011] Collect the operating time of the coating equipment and continuously monitor the operating time. Construct the historical operating time based on the current status monitoring time and the start time of the operating time. According to the historical operating time, obtain the abnormal time and abnormal point of the coating equipment. According to the abnormal type of the abnormal time and abnormal point, set it as the upper-level event of the abnormal monitoring model;

[0012] According to the cause of the upper-level event corresponding to the abnormal type, the cause type statistics are collected through the collected data of the historical operation period and marked as the basic event.

[0013] Preferably, based on the correlation analysis of basic events, the occurrence relationship between the upper-level events and different basic events is set; the currently constructed basic events are set at the same layer of the anomaly monitoring model, and the next layer is constructed, that is, the fundamental events of the current basic events are collected.

[0014] Preferably, the interior of the coating equipment is divided into different monitoring spaces according to their corresponding functions, and the multiple monitoring spaces are given names A1, A2, A3...An, and the abnormal monitoring model is continuously updated according to the event update of each layer of the abnormal monitoring model at each moment in the historical operation period;

[0015] During the updating process, the sum of the number of types of fundamental events corresponding to the same layer of basic events in each monitoring space and the number of types of events corresponding to the upper layer are collected.

[0016] Preferably, if the sum of the quantities exceeds a set threshold, or the sum of the quantities continues to increase at a rate that exceeds a set speed threshold, the trend is marked as a high-risk abnormal trend; if the sum of the quantities does not exceed the set threshold, and the sum of the quantities continues to increase at a rate that does not exceed the set speed threshold, the trend is marked as a low-risk abnormal trend.

[0017] Preferably, the operation process of the abnormal feature recognition unit is as follows:

[0018] Perform feature recognition and marking of coating equipment with high-risk abnormal trends, analyze and count the basic events collected at each layer of the abnormal monitoring model corresponding to each monitoring space of the marked equipment and the fundamental event data corresponding to the basic events, collect the numerical floating waveform of the data corresponding to the fundamental event in the abnormal monitoring model corresponding to the marked equipment before and after the generation of the upper layer time, and obtain the floating waveform deviation area of ​​the current data based on the floating waveform comparison;

[0019] At the same time, the floating time of the fundamental event is counted, and the floating time waveform is obtained according to the floating of each time point in the running period. Specifically, the floating threshold of the Y-axis of the coordinate system is set. That is, if the current time point floats, the value of the corresponding time point on the Y-axis of the coordinate system is the set floating threshold. If it continues to float, a horizontal line will be drawn on the coordinate system, otherwise the value will be 0 if there is no floating. The floating frequency is inferred based on the floating time waveform.

[0020] Preferably, the operation process of the abnormality analysis unit is as follows:

[0021] The coating equipment with low-risk abnormal trends is marked as cooperative monitoring equipment, and the cooperation type is determined according to the coating structure required by the coating process. During the cooperative operation of the cooperative monitoring equipment, the upper-level events of multiple monitoring spaces in the corresponding cooperative monitoring equipment are collected, and basic event analysis is performed according to the upper-level event type, that is, the basic events of the same type of upper-level events as the upper-level events that have occurred and the upper-level events of the same type of basic events as the upper-level events that have occurred are collected, and marked as the same-as-above non-same-base events and the same-base non-same-as-above events respectively.

[0022] Preferably, the data of the same-base non-same-base events and the same-base non-same-base events corresponding to the cooperative monitoring equipment are collected, and the difference processing is performed between the data and the threshold of the corresponding data to obtain the corresponding deviation value ratio of the real-time collected data value of the corresponding event. If the deviation value ratio exceeds the set threshold and continues to increase, it is inferred that the collected data corresponding to the same-base non-same-base events or the same-base non-same-base events affect the operating status of the cooperative monitoring equipment, and the current collected data type is sent to the monitoring and maintenance center.

[0023] The beneficial effects of the present invention are as follows:

[0024] The present invention analyzes coating equipment anomalies by collecting data in combination with anomaly monitoring model technology. The anomaly monitoring model technology is used to collect the impact of corresponding events in multiple monitoring spaces of the coating equipment. The probability of anomaly occurrence and the impact of the operating status event are determined according to the event type, thereby improving the accuracy of coating equipment data monitoring, effectively improving the availability of collected data, and being able to improve the detection efficiency of the coating equipment, ensuring the accuracy of anomaly prediction within the operating cycle, and improving the work efficiency of operation and maintenance control.

[0025] At the same time, the impact of collected data is accurately assessed by extracting and identifying features. Combined with the impact of the collected data itself and its impact on the coating equipment, the targeted operation and maintenance control of the coating equipment is improved. When an abnormality occurs, it can be quickly traced according to the abnormal monitoring model. It can not only quickly locate which monitoring space in the coating equipment the abnormality is in, but also perform targeted control based on the traceability results, fundamentally reducing the impact of the abnormality and improving the operating efficiency of the air purification system in the coating equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The present invention will be further described below with reference to the accompanying drawings;

[0027] Figure 1 is a system block diagram of the present invention;

[0028] Figure 2 This is a flow chart of the steps for implementing the odor gas monitoring unit of the present invention;

[0029] Figure 3 This is a flowchart of the steps for implementing the abnormal feature recognition unit in the present invention. DETAILED DESCRIPTION

[0030] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0031] See also Figure 1 - Figure 3 As shown, this embodiment is a global adaptive air purification system used in coating equipment, including a monitoring and maintenance center, which is communicatively connected to an odor gas monitoring unit, an abnormality feature recognition unit, and an abnormality analysis unit;

[0032] The odor gas monitoring unit is used to collect air environment data during the operation of the coating equipment, set upper-level events and basic events based on the collected data, and build an abnormality monitoring model. The operating status of the coating equipment is judged based on the abnormality monitoring model, and high-risk abnormal trends and low-risk abnormal trends are generated. The high-risk abnormal trend indicates that the air in the coating equipment is abnormal, while the low-risk abnormal trend indicates that the air purification system in the coating equipment is operating normally;

[0033] It should be noted that the operation process of the odor gas monitoring unit is as follows:

[0034] Collect the operating time of the coating equipment and continuously monitor the operating time. Construct the historical operating time based on the current status monitoring time and the start time of the operating time. According to the historical operating time, obtain the abnormal time and abnormal point of the coating equipment. According to the abnormal type of the abnormal time and abnormal point, set it as the upper-level event of the abnormal monitoring model;

[0035] Data collection is accomplished through multiple gas sensors fixedly installed in the coating equipment.

[0036] At the same time, the upper-level event is also a basic event, because the abnormality of the coating equipment will affect other cooperating equipment. However, this scenario is the status monitoring of the coating equipment, so it is calibrated as an upper-level event; specifically, the abnormality type such as uneven coating.

[0037] According to the cause of the upper-level event corresponding to the abnormal type, the cause type statistics are collected through the collected data of the historical operation period and marked as the basic event, such as the fan running but not exhausting or the fan not running.

[0038] Based on the correlation analysis of basic events, an occurrence relationship is set between the upper-level event and different basic events, specifically an OR relationship. If multiple basic events correspond to a single basic event affecting the generation of the upper-level event and cannot exist at the same time, the corresponding relationship is OR. For example, if the fan is running but not exhausting air or the fan is not running, it is an OR relationship.

[0039] The currently constructed basic event is set at the same layer of the anomaly monitoring model, and the next layer is constructed, that is, the root event is collected for the current basic event, where the upper-layer event and the root event are in an AND relationship, that is, the generation of the root event affects the generation of the basic event; it should be explained that the basic events between the upper-layer event and the root event are not unique according to the actual scenario number, and the anomaly monitoring model is jointly constructed.

[0040] Through the abnormal monitoring model technology, the corresponding events of multiple monitoring spaces of the coating equipment are affected and collected. The probability of abnormal occurrence and the event affecting the operating status are determined according to the event type, which improves the accuracy of coating equipment data monitoring, effectively improves the availability of collected data, and can improve the detection efficiency of coating equipment, ensure the accuracy of abnormality prediction within the operation cycle, and improve the work efficiency of operation and maintenance control.

[0041] At the same time, the interior of the coating equipment is divided into different monitoring spaces according to their corresponding functions, and multiple monitoring spaces are given names A1, A2, A3...An, where n is a positive integer greater than 0. At the same time, the abnormal monitoring model is continuously updated according to the event update of each layer of the abnormal monitoring model at each moment in the historical operation period, and during the update process, the sum of the number of types of fundamental events corresponding to the same layer of basic events in each monitoring space and the number of types of events corresponding to the upper layer are collected;

[0042] If the sum of the quantities exceeds the set threshold, or the sum of the quantities continues to increase at a rate exceeding the set speed threshold, the trend will be marked as a high-risk abnormal trend; if the sum of the quantities does not exceed the set threshold, and the sum of the quantities continues to increase at a rate not exceeding the set speed threshold, the trend will be marked as a low-risk abnormal trend.

[0043] It should be noted that the monitoring space is specifically set as a spherical space with a radius of 20 cm around the structure required for the coating equipment to operate. The required structure refers to structures such as the coating nozzle and the drive motor. That is, continuous data collection and monitoring of the surrounding environment when each required structure is in use is carried out. When an abnormality occurs inside the coating equipment, it can be quickly traced according to the abnormality monitoring model to quickly locate the abnormal position of the coating equipment.

[0044] Abnormal feature recognition unit, used to continuously collect data and extract features of coating equipment with high-risk abnormal trends, and identify abnormal events and non-abnormal events based on feature extraction, and perform operation and maintenance control on the collected data of corresponding events;

[0045] Combining the impact of the collected data itself and its impact on the coating equipment, the pertinence of the operation and maintenance control of the coating equipment is improved, and when an abnormality occurs, it can be quickly traced according to the abnormal monitoring model. It can not only quickly locate which monitoring space in the coating equipment the abnormality is in, but also carry out targeted control based on the traceability results, fundamentally reducing the impact of the abnormality and improving the operating efficiency of the coating equipment.

[0046] The operation process of the abnormal feature recognition unit is as follows:

[0047] Perform feature recognition and marking of coating equipment with high-risk abnormal trends, analyze and count the basic events collected at each layer of the abnormal monitoring model corresponding to each monitoring space of the marked equipment and the fundamental event data corresponding to the basic events, collect the numerical floating waveform of the data corresponding to the fundamental event in the abnormal monitoring model corresponding to the marked equipment before and after the generation of the upper layer time, and obtain the floating waveform deviation area of ​​the current data based on the floating waveform comparison;

[0048] At the same time, the floating time of the fundamental event is counted, and the floating time waveform is obtained according to the floating of each time point in the running period. Specifically, the floating threshold of the Y-axis of the coordinate system is set. That is, if the current time point floats, the value of the corresponding time point on the Y-axis of the coordinate system is the set floating threshold. If it continues to float, a horizontal line will be drawn on the coordinate system, otherwise the value will be 0 if there is no floating. The floating frequency is inferred based on the floating time waveform.

[0049] After the upper-level event occurs, the floating waveform deviation area of ​​the basic event is collected, and the floating frequency is obtained according to the floating moment waveform. If the floating waveform deviation area of ​​the basic event during the period of the upper-level event exceeds the set area threshold, or the floating frequency obtained according to the floating moment waveform exceeds the set floating frequency threshold, the current basic event is regarded as an abnormal event with a high-risk abnormal trend; and the parameter corresponding to the basic event exceeding the threshold floating is used as the operation and maintenance control trend of the coating equipment;

[0050] If the area of ​​the floating waveform deviation of the basic event during the period of the upper-level event does not exceed the set area threshold, and the floating frequency obtained based on the floating waveform exceeds the set floating frequency threshold, the current basic event will be regarded as a non-abnormal event with a high-risk abnormal trend; and the collected data of non-abnormal events will not be given priority as operation and maintenance control detection data;

[0051] After the abnormal feature identification is completed, the data type of the corresponding abnormal event and the numerical floating trajectory of the corresponding type of data in the corresponding operation and maintenance abnormality will be synchronized to the monitoring and maintenance center. The monitoring and maintenance center will store them and issue early warnings based on the data floating trajectory of the abnormal event. When necessary, the set threshold will be adjusted and updated according to the floating value.

[0052] It should be explained that the set thresholds are threshold values ​​manually set by those skilled in the art based on historical operational anomaly scenarios; if the set thresholds are not suitable for the current scenario, the thresholds are updated to ensure accurate data detection in the current scenario;

[0053] The abnormality analysis unit is used to perform multi-device parallel abnormality risk analysis on coating equipment that is in a low-risk abnormal trend. Based on the analysis results, it is inferred whether the corresponding coating equipment has a high-risk abnormal risk, so as to avoid the inability to ensure that the coating equipment is still in a low-risk abnormal trend under the coordinated operation scenario during operation and maintenance control. Through coordinated operation monitoring, the accuracy of status monitoring of the coating equipment is effectively improved, the efficiency of monitoring and operation and maintenance control is improved, the abnormality rate of the coating equipment is minimized, and the occurrence of abnormalities is effectively avoided through timely operation and maintenance.

[0054] The operation process of the abnormal analysis unit is as follows:

[0055] The coating equipment with low-risk abnormal trends is marked as cooperative monitoring equipment, and the cooperation type is determined according to the coating structure required by the coating process. During the cooperative operation of the cooperative monitoring equipment, the upper-level events of multiple monitoring spaces in the corresponding cooperative monitoring equipment are collected, and basic event analysis is performed according to the upper-level event type, that is, the basic events of the same type of upper-level events as the upper-level events that have occurred and the upper-level events of the same type of basic events as the upper-level events that have occurred are collected, and marked as the same-as-above non-same-base events and the same-base non-same-as-above events respectively.

[0056] Collect the data of the same-base non-same-base events and the same-base non-same-base events corresponding to the monitoring equipment, and perform difference processing on them with the threshold of the corresponding data to obtain the corresponding deviation value ratio of the real-time collected data value of the corresponding event. If the deviation value ratio exceeds the set threshold and continues to increase, it is inferred that the collected data corresponding to the same-base non-same-base events or the same-base non-same-base events affect the operating status of the monitoring equipment, and the current collected data type is sent to the monitoring and maintenance center.

[0057] In summary, the odor gas monitoring unit is used to collect data on the operation process of the coating equipment. Upper-level events and basic events are set based on the collected data, and an abnormal monitoring model is constructed. The operating status of the coating equipment is judged based on the abnormal monitoring model, and high-risk abnormal trends and low-risk abnormal trends are generated. The abnormal feature recognition unit extracts and identifies features of the collected data of the coating equipment with high-risk abnormal trends. Based on the feature extraction and recognition, abnormal events and non-abnormal events are obtained, and the collected data of the corresponding events are used for operation and maintenance control.

[0058] The abnormality analysis unit conducts multi-device abnormality risk analysis on coating equipment that is in a low-risk abnormal trend. Based on the analysis, it infers whether there is a risk of operational failure when the coating equipment with a low-risk abnormal trend is running together, thereby achieving comprehensive monitoring of the internal environmental data of the coating equipment. At the same time, the feedback mechanism is faster, realizing automated control and intelligent response of the coating equipment.

[0059] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A global adaptive air purification system used in coating equipment, including a monitoring and maintenance center, characterized in that: The monitoring and maintenance center is communicatively connected to an odor gas monitoring unit, an abnormality feature recognition unit, and an abnormality analysis unit; Odor gas monitoring unit, which is used to collect air environment data during the operation of coating equipment, set upper-level events and basic events based on the collected data, and build an abnormality monitoring model. It also judges the operating status of the coating equipment based on the abnormality monitoring model and generates high-risk abnormal trends and low-risk abnormal trends; Abnormal feature recognition unit, used to continuously collect data and extract features of coating equipment with high-risk abnormal trends, and identify abnormal events and non-abnormal events based on feature extraction, and perform operation and maintenance control on the collected data of corresponding events; The abnormality analysis unit is used to perform multi-device parallel abnormality risk analysis on coating equipment that is in a low-risk abnormal trend, and infer whether the corresponding coating equipment has a high-risk abnormal risk based on the analysis results.

2. The global adaptive air purification system used in coating equipment according to claim 1, characterized in that: The operation process of the odor gas monitoring unit is as follows: Collect the operating time of the coating equipment and continuously monitor the operating time. Construct the historical operating time based on the current status monitoring time and the start time of the operating time. According to the historical operating time, obtain the abnormal time and abnormal point of the coating equipment. According to the abnormal type of the abnormal time and abnormal point, set it as the upper-level event of the abnormal monitoring model; According to the cause of the upper-level event corresponding to the abnormal type, the cause type statistics are collected through the collected data of the historical operation period and marked as the basic event.

3. The global adaptive air purification system used in coating equipment according to claim 2, characterized in that: According to the correlation analysis of basic events, the occurrence relationship between upper-level events and different basic events is set; the currently constructed basic events are set at the same layer of the anomaly monitoring model, and the next layer is constructed, that is, the fundamental events of the current basic events are collected.

4. The global adaptive air purification system used in coating equipment according to claim 3, characterized in that: The coating equipment is divided into different monitoring spaces according to their corresponding functions, and multiple monitoring spaces are named A1, A2, A3...An. At the same time, the abnormal monitoring model is continuously updated according to the event updates of each layer of the abnormal monitoring model at each moment in the historical operation period; During the updating process, the sum of the number of types of fundamental events corresponding to the same layer of basic events in each monitoring space and the number of types of events corresponding to the upper layer are collected.

5. The global adaptive air purification system used in coating equipment according to claim 4, characterized in that: If the sum of the quantities exceeds the set threshold, or the sum of the quantities continues to increase at a rate exceeding the set speed threshold, the trend will be marked as a high-risk abnormal trend; If the sum of the quantities does not exceed the set threshold, and the rate at which the sum of the quantities continues to increase does not exceed the set speed threshold, the trend is marked as a low-risk abnormal trend.

6. The global adaptive air purification system used in coating equipment according to claim 1, characterized in that: The operation process of the abnormal feature recognition unit is as follows: Perform feature recognition and marking of coating equipment with high-risk abnormal trends, analyze and count the basic events collected at each layer of the abnormal monitoring model corresponding to each monitoring space of the marked equipment and the fundamental event data corresponding to the basic events, collect the numerical floating waveforms of the data corresponding to the fundamental events in the abnormal monitoring model corresponding to the marked equipment before and after the generation of the upper layer time, and obtain the floating waveform deviation area of ​​the current data based on the floating waveform comparison; At the same time, the floating time of the fundamental event is counted, and the floating time waveform is obtained according to the floating of each time point in the running period. Specifically, the floating threshold of the Y-axis of the coordinate system is set. That is, if the current time point floats, the value of the corresponding time point on the Y-axis of the coordinate system is the set floating threshold. If it continues to float, a horizontal line will be drawn on the coordinate system, otherwise the value will be 0 if there is no floating. The floating frequency is inferred based on the floating time waveform.

7. The global adaptive air purification system used in coating equipment according to claim 1, characterized in that: The operation process of the abnormal analysis unit is as follows: The coating equipment with low-risk abnormal trends is marked as cooperative monitoring equipment, and the cooperation type is determined according to the coating structure required by the coating process. During the cooperative operation of the cooperative monitoring equipment, the upper-level events of multiple monitoring spaces in the corresponding cooperative monitoring equipment are collected, and basic event analysis is performed according to the upper-level event type, that is, the basic events of the same type of upper-level events as the upper-level events that have occurred and the upper-level events of the same type of basic events as the upper-level events that have occurred are collected, and marked as the same-as-above non-same-base events and the same-base non-same-as-above events respectively.

8. The global adaptive air purification system used in coating equipment according to claim 7, characterized in that: Collect the data of the same-base non-same-base events and the same-base non-same-base events corresponding to the monitoring equipment, and perform difference processing on them with the threshold of the corresponding data to obtain the corresponding deviation value ratio of the real-time collected data value of the corresponding event. If the deviation value ratio exceeds the set threshold and continues to increase, it is inferred that the collected data corresponding to the same-base non-same-base events or the same-base non-same-base events affect the operating status of the monitoring equipment, and the current collected data type is sent to the monitoring and maintenance center.

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