Intelligent water treatment monitoring device and system

Abstract

The invention relates to the technical field of factory distributed control, in particular to an intelligent water treatment monitoring device and system. The standby detection head carries out calibration and fault detection on the main detection head every preset time; the video data acquisition module acquires a monitoring video of the water quality analyzer; the abnormal behavior analysis module is used for detecting abnormal behaviors of personnel on the water quality analyzer in the monitoring video; the exception marking module performs exception marking on the data acquired after the exception time point when the exception behavior occurs or the monitoring data is exceptional; and the notification module notifies the personnel to go to the position of the corresponding factory for processing based on the factory data when the abnormal mark appears. The water quality analyzer is monitored in real time, and meanwhile, the video data acquisition module is arranged to monitor the environment where the water quality analyzer is located, so that the possibility of human intervention is reduced, and the authenticity of acquired data and the monitoring efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of factory distributed control, and in particular to an intelligent water treatment monitoring device and system. Background Art

[0002] Chemical plant water treatment and discharge refers to the process of treating the wastewater generated during the chemical production process and discharging it into the environment in accordance with relevant regulations and standards. In order to ensure that the discharged wastewater does not have a negative impact on the environment, chemical companies must comply with strict national or local environmental protection laws and regulations and implement effective wastewater treatment measures.

[0003] The existing monitoring method is to install an online water quality analyzer inside the factory to monitor the quality parameters of the discharged water in real time to ensure that it meets the emission standards. In addition to online monitoring, manual sampling and analysis are also required at a prescribed frequency to verify the accuracy of the data. Ultimately, the treatment process parameters are automatically adjusted based on the monitoring results to ensure stable and standard emissions.

[0004] However, the above-mentioned method of regular manual inspection is not conducive to obtaining real data, while irregular inspection places high demands on personnel, increases the burden on employees, and thus reduces the effectiveness of monitoring. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent water treatment monitoring device and system, which aims to monitor the environment of the water quality analyzer by real-time monitoring of the water quality analyzer while also providing a video data acquisition module, thereby reducing the possibility of human intervention and improving the authenticity of the acquired data and the monitoring efficiency.

[0006] To achieve the above-mentioned object, in a first aspect, the present invention provides an intelligent water treatment monitoring device, including a water quality analyzer, a spare detection head, a video data acquisition module, an identity binding module, an abnormal behavior analysis module, an abnormal marking module and a notification module;

[0007] The water quality analyzer is used to obtain factory monitoring data;

[0008] The standby detection head is used to calibrate and detect faults of the main detection head at preset intervals;

[0009] The video data acquisition module is used to acquire the monitoring video of the water quality analyzer;

[0010] The identity binding module is used to bind the surveillance video, monitoring data and factory identity to obtain factory data;

[0011] The abnormal behavior analysis module is used to detect abnormal behavior of personnel on the water quality analyzer in the monitoring video;

[0012] The abnormal marking module is used to mark the data acquired after the abnormal time point as abnormal when abnormal behavior occurs or abnormal monitoring data occurs;

[0013] The notification module is used to notify personnel to go to the location of the corresponding factory for processing based on factory data when an abnormal mark appears.

[0014] Wherein, the water quality analyzer includes a main detection head, a calibration unit, a filtering unit, a data acquisition unit and a first storage unit;

[0015] The calibration unit is used to calibrate each sensor;

[0016] The filtering unit is used to filter the collected water sample for detection;

[0017] The data acquisition unit is used to introduce the water sample into the main detection head for detection to obtain monitoring data;

[0018] The first storage unit is used to store all monitoring data in a database.

[0019] Wherein, the spare detection head comprises a detection head body, a detection cycle setting unit, an activation unit, a moving unit, a measuring unit and an adjustment unit;

[0020] The cycle setting unit is used to set the calibration and fault detection cycle of the main detection head;

[0021] The activation unit is used to trigger the activation instruction of the standby detection head when the detection cycle is reached;

[0022] The moving unit is used to move the standby head body to a preset position when the main detection head has no measurement task;

[0023] The measuring unit is used for collecting water sample data through the detection head body and comparing it with the detection value of the main detection head to obtain a deviation curve;

[0024] The adjustment unit is used to adjust the output curve of the main detection head according to the deviation curve or to issue a fault warning when the deviation value of the deviation curve is greater than a preset value.

[0025] Wherein, the video data acquisition module includes an address allocation unit, a recording unit and a second storage unit;

[0026] The address allocation unit is used to allocate a static IP address to each camera;

[0027] The recording unit is used to continuously record the monitoring video at the water quality analyzer;

[0028] The second storage unit is used to store the monitoring video in a database.

[0029] Wherein, the identity binding module includes an identification code allocation unit, a time synchronization unit and an association unit;

[0030] The identification code allocation unit is used to allocate an identification code to each factory and its water quality analyzer using a unique identifier;

[0031] The time synchronization unit is used to synchronize the water quality analyzer and the recording unit to the same time source;

[0032] The association unit is used to associate the monitoring video, the monitoring data and the identification code to obtain associated factory data.

[0033] Wherein, the abnormal behavior analysis module includes an image extraction unit, a posture recognition unit and an alarm unit;

[0034] The image extraction unit is used to extract image frames from the monitoring video at a preset frequency to obtain a detection image group;

[0035] The posture recognition unit is used to calculate the real-time posture key points of the human body in the detection image group using a deep learning model;

[0036] The alarm unit is used to detect real-time posture key points in real time based on the behavior recognition model of historical posture key points, and trigger an abnormal behavior alarm when the detected behavior characteristics exceed a set threshold.

[0037] Wherein, the abnormal marking module includes an alarm acquisition unit and a marking unit;

[0038] The alarm acquisition unit is used to acquire abnormal behavior alarm information generated by the alarm unit;

[0039] The marking unit is used to mark all data subsequently acquired with an abnormal label starting from the abnormal time point based on the abnormal behavior alarm information.

[0040] In the second aspect, the present invention also provides an intelligent water treatment monitoring system, in which the water quality analyzer is equipped with sensor technology, which can accurately obtain and analyze water quality data from the factory, such as key indicators such as pH value, dissolved oxygen, chemical oxygen demand (COD), and biological oxygen demand (BOD). These data are crucial for assessing the health of water bodies and provide a scientific basis for subsequent decision-making. The backup detection head will automatically activate at a preset time interval to calibrate the main detection head to ensure measurement accuracy, and at the same time check whether there are any mechanical or electrical faults. This mechanism greatly improves the stability and reliability of the system. The video data acquisition module captures the images captured by the cameras installed around the water quality analyzer. In this way, changes in the environment around the instrument can be recorded, and visual evidence support can be provided for subsequent disputes. The identity binding module combines all the collected information with the identity information of a specific factory. This association allows each data point to be traced back to a specific source, thereby achieving more accurate data management and responsibility definition. The abnormal behavior analysis module uses artificial intelligence algorithms to automatically detect abnormal operations performed by personnel on the water quality analyzer in the surveillance video, such as unauthorized contact, tampering with instrument settings, etc. This helps to promptly discover potential safety hazards or violations. Once abnormal behavior is detected or the monitoring data shows a deviation from the normal range, the abnormal marking module will mark all data obtained after that moment as "abnormal". This approach not only facilitates post-review, but also facilitates the rapid location of the problem. Finally, when any of the above links triggers an abnormal mark, the notification module will be activated and send an alarm message to relevant personnel according to pre-set rules. The alarm content usually contains the specific location of the affected factory and an overview of the current problems, so that staff can take quick action to go to the site to solve the problem.

[0041] In summary, this intelligent water treatment monitoring device combines modern sensor technology, video monitoring, data analysis, instant messaging and other advanced technologies to build a comprehensive and efficient water quality management platform. Its application not only improves the efficiency and accuracy of water quality monitoring, but also strengthens the prevention and response capabilities of environmental pollution incidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 It is a structural diagram of an intelligent water treatment monitoring device of the present invention.

[0044] Figure 2 It is a structural diagram of the water quality analyzer of the present invention.

[0045] Figure 3 It is a structural diagram of the spare detection head of the present invention.

[0046] Figure 4 It is a structural diagram of the video data acquisition module of the present invention.

[0047] Figure 5 It is a structural diagram of the identity binding module of the present invention.

[0048] Figure 6 It is a structural diagram of the abnormal behavior analysis module of the present invention.

[0049] Figure 7 It is a structural diagram of the abnormal marking module of the present invention.

[0050] Water quality analyzer 101, spare detection head 102, video data acquisition module 103, identity binding module 104, abnormal behavior analysis module 105, notification module 106, main detection head 107, calibration unit 108, filtering unit 109, data acquisition unit 110, first storage unit 111, detection head body 112, detection cycle setting unit 113, activation unit 114, moving unit 115, measurement unit 116, adjustment unit 117, address allocation unit 118, recording unit 119, second storage unit 120, identification code allocation unit 121, time synchronization unit 122, association unit 123, image extraction unit 124, gesture recognition unit 125, alarm unit 126, alarm acquisition unit 127, marking unit 128, abnormal marking module 129. DETAILED DESCRIPTION

[0051] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0052] First embodiment

[0053] See also Figure 1 to Figure 7The present invention provides an intelligent water treatment monitoring device, including a water quality analyzer 101, a spare detection head 102, a video data acquisition module 103, an identity binding module 104, an abnormal behavior analysis module 105, an abnormal marking module 129 and a notification module 106; the water quality analyzer 101 is used to obtain factory monitoring data; the spare detection head 102 is used to calibrate and detect faults on the main detection head 107 at preset intervals; the video data acquisition module 103 is used to obtain the monitoring video of the water quality analyzer 101; the identity binding module 104 is used to bind the monitoring video, monitoring data and factory identity to obtain factory data; the abnormal behavior analysis module 105 is used to detect abnormal behavior of personnel on the water quality analyzer 101 in the monitoring video; the abnormal marking module 129 is used to abnormally mark the data obtained after the abnormal time point when abnormal behavior occurs or abnormal monitoring data occurs; the notification module 106 is used to notify personnel to go to the corresponding factory location for processing based on factory data when abnormal marking occurs.

[0054] In this embodiment, the water quality analyzer 101 is equipped with sensor technology, which can accurately obtain and analyze water quality data from the factory, such as pH value, dissolved oxygen, chemical oxygen demand (COD), biological oxygen demand (BOD) and other key indicators. These data are crucial for evaluating the health status of water bodies and provide a scientific basis for subsequent decision-making.

[0055] Considering the aging or failure of equipment that may occur during long-term operation, the system is equipped with a backup detection head 102. This component will automatically activate at preset time intervals to calibrate the main detection head 107 to ensure measurement accuracy and check for any mechanical or electrical failures. This mechanism greatly improves the stability and reliability of the system.

[0056] The video data acquisition module 103 captures the images captured by the cameras installed around the water quality analyzer 101. In this way, the changes in the environment around the instrument can be recorded, and visual evidence support can also be provided for possible disputes in the future.

[0057] The identity binding module 104 combines all the collected information with the identity information of a specific factory. This association allows each data point to be traced back to a specific source, thereby achieving more accurate data management and responsibility definition.

[0058] The abnormal behavior analysis module 105 can automatically detect abnormal operations performed by personnel on the water quality analyzer 101 in the surveillance video using artificial intelligence algorithms, such as unauthorized contact, tampering with instrument settings, etc. This helps to promptly discover potential safety hazards or violations.

[0059] Once abnormal behavior is detected or monitoring data shows deviation from the normal range, the abnormal marking module 129 will mark all data obtained after that moment with an "abnormal" label. This approach not only facilitates post-event review, but also provides convenience for quickly locating the problem.

[0060] Finally, when any of the above links triggers an abnormal flag, the notification module 106 will be activated to send an alarm message to relevant personnel according to pre-set rules. The alarm content usually includes the specific location of the affected factory and an overview of the current problem, so that the staff can take quick action to go to the site to solve the problem.

[0061] In summary, this intelligent water treatment monitoring device combines modern sensor technology, video monitoring, data analysis, instant messaging and other advanced technologies to build a comprehensive and efficient water quality management platform. Its application not only improves the efficiency and accuracy of water quality monitoring, but also strengthens the prevention and response capabilities of environmental pollution incidents.

[0062] The water quality analyzer 101 includes a main detection head 107, a calibration unit 108, a filtering unit 109, a data acquisition unit 110 and a first storage unit 111; the calibration unit 108 is used to calibrate each sensor; the filtering unit 109 is used to filter the collected water sample for detection; the data acquisition unit 110 is used to introduce the water sample into the main detection head 107 for detection to obtain monitoring data; the first storage unit 111 is used to store all monitoring data in a database.

[0063] The main detection head 107 is the core part of the water quality analyzer 101. The main detection head 107 integrates various types of sensors for measuring and analyzing the concentration or content of different parameters in the water sample. These sensors may include but are not limited to pH sensors, conductivity sensors, turbidity sensors, temperature sensors, etc., which can provide comprehensive information about the chemical composition and physical properties of the water body.

[0064] In order to ensure the accuracy and consistency of the measurement results, the calibration unit 108 plays a vital role in the system. It can automatically calibrate each sensor regularly and adjust the response curve of the sensor by using a known standard solution to eliminate errors caused by the passage of time or environmental changes.

[0065] Considering that the actual water sample may contain impurities, suspended particles or other interfering substances, the filtration unit 109 is designed to pre-treat the collected water sample. This process helps to remove insoluble substances that may affect the accuracy of the sensor reading, ensuring that the water sample entering the main detection head 107 is as pure as possible, thereby improving the overall measurement accuracy.

[0066] The data acquisition unit 110 guides the pre-treated water sample to the main detection head 107 and starts the corresponding detection program. It not only controls the speed and direction of the water flow, but also coordinates the workflow of each sensor to ensure that each measurement can be carried out smoothly. After completing the detection, the data acquisition unit 110 collects the data from all sensors and formats it into a form that is easy to understand and analyze.

[0067] The first storage unit 111 safely stores all monitoring data obtained by the data acquisition unit 110 into an internal database. This database can not only store a large amount of historical records to support subsequent trend analysis and problem tracing, but also can be connected to other external systems (such as cloud platforms) to achieve data sharing and remote access.

[0068] The spare detection head 102 includes a detection head body 112, a detection cycle setting unit 113, an activation unit 114, a moving unit 115, a measuring unit 116 and an adjustment unit 117; the cycle setting unit is used to set the calibration and fault detection cycle of the main detection head 107; the activation unit 114 is used to trigger the activation instruction of the spare detection head 102 when the detection cycle is reached; the moving unit 115 is used to move the spare head body to a preset position when the main detection head 107 currently has no measurement task; the measuring unit 116 is used for the detection head body 112 to collect water sample data, and compare it with the detection value of the main detection head 107 to obtain a deviation curve; the adjustment unit 117 is used to adjust the output curve of the main detection head 107 according to the deviation curve or to issue a fault warning when the deviation value of the deviation curve is greater than a preset value.

[0069] As the core part of the spare detection head 102, the detection head body 112 is equipped with the same type of sensor as the main detection head 107 to ensure that it can seamlessly replace the main detection head 107 for water quality analysis when necessary.

[0070] The detection cycle setting unit 113 allows the user to flexibly set the calibration and fault detection cycle of the main detection head 107 according to actual conditions. By adjusting these cycles, the maintenance plan can be optimized, unnecessary downtime can be reduced, and the main detection head 107 can always be ensured to be in the best working condition. The setting unit usually includes a graphical interface or a remote configuration interface to facilitate management and adjustment by operators.

[0071] When the preset detection cycle is reached, the activation unit 114 will automatically trigger the activation instruction of the backup detection head 102. This process is fully automated and does not require manual intervention, thereby improving the response speed and efficiency of the system. The activation unit 114 will also check the working status of the current main detection head 107. If an abnormality is found, the backup detection head 102 will be activated first for emergency calibration or troubleshooting.

[0072] In order not to affect the normal operation of the main detection head 107, the moving unit 115 will move the spare detection head 102 body to a preset position when the main detection head 107 has no measurement task. This preset position is usually carefully selected to ensure that the spare detection head 102 has enough space to operate without interfering with other equipment.

[0073] Once the standby detection head 102 is activated and moved to a suitable position, the measurement unit 116 starts to work. It is responsible for collecting water sample data and comparing it with the detection value of the main detection head 107 in the same time period. Through this comparison, a deviation curve between the two can be obtained, which helps to evaluate the accuracy of the main detection head 107.

[0074] Based on the deviation curve obtained from the measurement unit 116, the adjustment unit 117 will fine-tune the output curve of the main detection head 107 to compensate for any detection errors. If the deviation value displayed by the deviation curve exceeds the preset safety threshold, the adjustment unit 117 will immediately issue a fault warning signal. At this time, the system will notify the relevant personnel to take further measures, such as replacing the sensor, performing a depth calibration, or conducting a comprehensive overhaul. In addition, the adjustment unit 117 may also record all adjustment logs for future review and improvement.

[0075] The video data acquisition module 103 includes an address allocation unit 118, a recording unit 119 and a second storage unit 120; the address allocation unit 118 is used to allocate a static IP address to each camera; the recording unit 119 is used to continuously record the monitoring video at the water quality analyzer 101; the second storage unit 120 is used to store the monitoring video in a database.

[0076] The address allocation unit 118 allocates a unique static IP address to each camera connected to the system. This process ensures the uniqueness of each camera in the network for easy management and identification. Static IP addresses not only simplify network configuration, but also improve the stability and security of the system and avoid connection problems caused by dynamic IP address changes.

[0077] Network configuration management also provides flexible network configuration options, supports multiple network protocols (such as TCP / IP, UDP, etc.), and can adapt to different network environments (LAN, WAN or Internet). In addition, it can also seamlessly connect with network devices such as routers and switches to achieve efficient network communication and data transmission.

[0078] The recording unit 119 continuously records the monitoring video at the water quality analyzer 101. In order to ensure the video quality, the recording unit 119 is equipped with a high-performance encoder and decoder, which can compress the high-definition video into a format suitable for storage and transmission while maintaining clarity and fluency.

[0079] The main responsibility of the second storage unit 120 is to safely store the recorded surveillance video in the database. In order to cope with the storage requirements of massive video data, the storage unit usually adopts a distributed storage architecture with high scalability and redundant backup capabilities to ensure that data will not be lost due to hardware failure.

[0080] The identity binding module 104 includes an identification code allocation unit 121, a time synchronization unit 122 and an association unit 123; the identification code allocation unit 121 is used to allocate an identification code to each factory and its water quality analyzer 101 using a unique identifier;

[0081] The time synchronization unit 122 is used to synchronize the water quality analyzer 101 and the recording unit 119 to the same time source; the association unit 123 is used to associate the monitoring video and the monitoring data with the identification code to obtain associated factory data.

[0082] The identification code allocation unit 121 allocates a unique identification code (UID) to each factory and its water quality analyzer 101. These UIDs can be generated based on international standards (such as UUID) or custom algorithms to ensure that they are unique globally. In this way, each factory and its equipment can be accurately identified and distinguished. In order to better manage and classify, the identification code may adopt a multi-level coding system, such as including information such as regional code, enterprise code, equipment type code, etc. This not only simplifies the data query process, but also facilitates statistical analysis and trend research.

[0083] The time synchronization unit 122 synchronizes the water quality analyzer 101 and the recording unit 119 to the same high-precision time source (such as an NTP server or a GPS clock). This measure ensures that the timestamps recorded by all devices are consistent and accurate, eliminating time deviations caused by time zone differences or local clock drift.

[0084] The association unit 123 associates data from different sources (monitoring videos, monitoring data) with corresponding identification codes to form a complete "factory data" record. This involves an efficient data integration platform that can handle a large amount of heterogeneous data and organize it into a form that is easy to retrieve and analyze.

[0085] The abnormal behavior analysis module 105 includes an image extraction unit 124, a posture recognition unit 125 and an alarm unit 126; the image extraction unit 124 is used to extract image frames from the monitoring video at a preset frequency to obtain a detection image group; the posture recognition unit 125 is used to use a deep learning model to calculate the real-time posture key points of the human body in the detection image group; the alarm unit 126 is used to detect the real-time posture key points in real time based on the behavior recognition model of the historical posture key points, and trigger an abnormal behavior alarm when the detected behavior characteristics exceed the set threshold.

[0086] The main task of the image extraction unit 124 is to extract image frames from the continuously recorded monitoring video at a preset frequency (e.g., every second or every few seconds) to form a series of static pictures, i.e., a detection image group. This on-demand extraction method not only reduces the amount of data to be processed later, but also ensures the capture of key moments.

[0087] The posture recognition unit 125 uses deep learning models (such as convolutional neural networks CNN, recurrent neural networks RNN, etc.) to calculate the real-time posture key points of the human body in the detection image group. These models are trained with a large amount of labeled data and can accurately identify various parts of the human body (such as head, shoulders, elbows, knees, etc.) and generate detailed skeleton diagrams. In order to cope with the complex situation where multiple people appear in the monitoring screen at the same time, the posture recognition unit 125 integrates multi-posture tracking technology. This technology can distinguish different individuals and continuously track their movement changes, maintaining stable recognition performance even in the case of partial occlusion or rapid movement.

[0088] The alarm unit 126 is based on a pre-trained behavior recognition model, which has established a library of difference features between normal behavior patterns and abnormal behavior patterns by learning a large amount of historical posture key point data. When a new real-time posture key point is input, the model can quickly determine whether it conforms to the known abnormal behavior pattern. The alarm unit 126 will perform real-time detection on each newly captured posture key point to evaluate whether the current behavior feature exceeds the set threshold. This process involves complex mathematical operations and pattern matching, but thanks to the development of modern computing technology, it can be completed in milliseconds. Once the detected behavior feature exceeds the set threshold, the alarm unit 126 will immediately trigger an abnormal behavior alarm. The alarm form can be diversified, including but not limited to sound alarms, flashing lights, SMS notifications, email reminders, etc. In addition, the system will automatically record the time, location and specific behavior features of the abnormal event to facilitate subsequent investigation and evidence collection.

[0089] The abnormal marking module 129 includes an alarm acquisition unit 127 and a marking unit 128; the alarm acquisition unit 127 is used to obtain the abnormal behavior alarm information generated by the alarm unit 126; the marking unit 128 is used to mark all subsequently acquired data with abnormal labels starting from the abnormal time point based on the abnormal behavior alarm information.

[0090] The alarm acquisition unit 127 acquires abnormal behavior alarm information in real time from the system's alarm unit 126. This includes but is not limited to the time and location of the alarm, the specific equipment or personnel involved, and the behavioral characteristics that triggered the alarm. In order to ensure the timeliness and accuracy of information transmission, the alarm acquisition unit 127 usually adopts a highly reliable communication protocol (such as MQTT, HTTP / HTTPS, etc.).

[0091] When the alarm acquisition unit 127 receives the abnormal behavior alarm information, the marking unit 128 will first determine the exact time point when the abnormal behavior occurred. This time point is the basis for subsequent data marking and must be as accurate as possible. By parsing the timestamp in the alarm information and combining the time synchronization mechanism within the system, the marking unit 128 can accurately locate the moment when the abnormal event occurred.

[0092] Starting from the determined abnormal time point, the marking unit 128 will mark all related data (including monitoring videos, water quality monitoring data, etc.) obtained subsequently with abnormal labels. This process involves real-time interception and processing of data streams to ensure that each new data entering the system can be correctly marked. The marking method can be to add specific fields to the metadata, or to directly modify the file name or storage path to facilitate user retrieval and filtering.

[0093] In addition to marking new data, the marking unit 128 also supports retrospective marking of historical data within a period of time before the abnormal time point. This approach helps to fully understand the development process of the abnormal event and provide more information for the root cause analysis of the problem. The time window of the retrospective mark can be flexibly set according to the actual situation. It is generally recommended to cover several key periods before and after the abnormal event occurs.

[0094] In summary, the abnormal marking module 129 realizes efficient management of abnormal behavior alarm information and accurate data labeling through the close cooperation of its alarm acquisition unit 127 and marking unit 128. This not only improves the efficiency of abnormal event processing, but also provides a solid foundation for subsequent data analysis and decision support.

[0095] Second embodiment

[0096] The present invention also provides an intelligent water treatment monitoring system, comprising the intelligent water treatment monitoring device.

[0097] In this embodiment, the core of the system is the above-mentioned intelligent water treatment monitoring device, which includes but is not limited to a water quality analyzer 101, a spare detection head 102, a video data acquisition module 103, an identity binding module 104, an abnormal behavior analysis module 105, and a notification module 106. These components work together to ensure the automation and intelligence of the entire process from water quality data collection to abnormal event response.

[0098] In addition, you can also set up a remote monitoring and management system and a cloud service platform. The remote monitoring and management system is connected via the Internet or a dedicated network, and users can access the system anytime and anywhere to view real-time water quality data, historical records, and monitoring videos. In addition, the module supports multi-user authority management and log auditing, which enhances the security and traceability of the system.

[0099] The cloud service platform uses cloud computing technology. The system provides flexible data storage and computing resources, supports large-scale deployment and cross-regional collaboration. Users can choose different service levels according to actual needs and enjoy efficient and stable cloud support.

[0100] What is disclosed above is only a preferred embodiment of the present invention, and it certainly cannot be used to limit the scope of rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiment and equivalent changes made according to the claims of the present invention still fall within the scope of the invention.

Claims

1. An intelligent water treatment monitoring device, characterized in that: It includes a water quality analyzer, a spare detection head, a video data acquisition module, an identity binding module, an abnormal behavior analysis module, an abnormal marking module and a notification module; The water quality analyzer is used to obtain factory monitoring data; The standby detection head is used to calibrate and detect faults of the main detection head at preset intervals; The video data acquisition module is used to acquire the monitoring video of the water quality analyzer; The identity binding module is used to bind the surveillance video, monitoring data and factory identity to obtain factory data; The abnormal behavior analysis module is used to detect abnormal behavior of personnel on the water quality analyzer in the monitoring video; The abnormal marking module is used to mark the data acquired after the abnormal time point as abnormal when abnormal behavior occurs or abnormal monitoring data occurs; The notification module is used to notify personnel to go to the location of the corresponding factory for processing based on factory data when an abnormal mark appears.

2. An intelligent water treatment monitoring device as claimed in claim 1, characterized in that: The water quality analyzer comprises a main detection head, a calibration unit, a filtering unit, a data acquisition unit and a first storage unit; The calibration unit is used to calibrate each sensor; The filtering unit is used to filter the collected water sample for detection; The data acquisition unit is used to introduce the water sample into the main detection head for detection to obtain monitoring data; The first storage unit is used to store all monitoring data in a database.

3. An intelligent water treatment monitoring device as claimed in claim 2, characterized in that: The spare detection head includes a detection head body, a detection cycle setting unit, an activation unit, a moving unit, a measuring unit and an adjustment unit; The cycle setting unit is used to set the calibration and fault detection cycle of the main detection head; The activation unit is used to trigger the activation instruction of the standby detection head when the detection cycle is reached; The moving unit is used to move the standby head body to a preset position when the main detection head has no measurement task; The measuring unit is used for collecting water sample data through the detection head body and comparing it with the detection value of the main detection head to obtain a deviation curve; The adjustment unit is used to adjust the output curve of the main detection head according to the deviation curve or to issue a fault warning when the deviation value of the deviation curve is greater than a preset value.

4. An intelligent water treatment monitoring device as claimed in claim 3, characterized in that: The video data acquisition module includes an address allocation unit, a recording unit and a second storage unit; The address allocation unit is used to allocate a static IP address to each camera; The recording unit is used to continuously record the monitoring video at the water quality analyzer; The second storage unit is used to store the monitoring video in a database.

5. An intelligent water treatment monitoring device as claimed in claim 4, characterized in that: The identity binding module includes an identification code allocation unit, a time synchronization unit and an association unit; The identification code allocation unit is used to allocate an identification code to each factory and its water quality analyzer using a unique identifier; The time synchronization unit is used to synchronize the water quality analyzer and the recording unit to the same time source; The association unit is used to associate the monitoring video, the monitoring data and the identification code to obtain associated factory data.

6. An intelligent water treatment monitoring device as claimed in claim 5, characterized in that: The abnormal behavior analysis module includes an image extraction unit, a posture recognition unit and an alarm unit; The image extraction unit is used to extract image frames from the monitoring video at a preset frequency; Obtain a detection image group; The posture recognition unit is used to calculate the real-time posture key points of the human body in the detection image group using a deep learning model; The alarm unit is used to detect real-time posture key points in real time based on the behavior recognition model of historical posture key points, and trigger an abnormal behavior alarm when the detected behavior characteristics exceed a set threshold.

7. An intelligent water treatment monitoring device as claimed in claim 6, characterized in that: The abnormal marking module includes an alarm acquisition unit and a marking unit; The alarm acquisition unit is used to acquire abnormal behavior alarm information generated by the alarm unit; The marking unit is used to mark all data subsequently acquired with an abnormal label starting from the abnormal time point based on the abnormal behavior alarm information.

8. An intelligent water treatment monitoring system, characterized in that: An intelligent water treatment monitoring device comprising the device described in any one of claims 1 to 7.

Images