Intelligent inspection and maintenance system for robot in sewage treatment plant and inspection and maintenance robot

Abstract

The invention discloses an intelligent robot inspection and maintenance system for a sewage treatment plant. The intelligent robot inspection and maintenance system comprises a data transceiving module A, a central processing unit A, a data analysis and processing module A, a data storage module A and a robot terminal, the data analysis processing module A comprises a map correction module, an inspection route making module, an inspection operation customization module and a deep learning prediction module; the robot terminal comprises a robot and a robot control system; the robot is provided with a power supply system, a walking system, a data acquisition system and an inspection operation system. And the robot control system comprises a data transceiving module B, a central processing unit B, a data analysis processing module B and a data storage module B. According to the invention, the inspection route can be intelligently set according to the specific conditions of the treatment process and the operation equipment of the sewage treatment plant, the inspection robot is utilized to carry out intelligent analysis according to the on-site collected data, and simple daily maintenance work and hidden danger investigation operation are further carried out.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent inspection and maintenance of sewage treatment plants, and in particular relates to an intelligent inspection and maintenance system and an inspection and maintenance robot for sewage treatment plants. Background Art

[0002] Large-scale sewage treatment plants, as key facilities for urban wastewater treatment, are complex and massive in scale. They typically comprise pretreatment units, biological treatment units, advanced treatment units, sludge treatment units, and a range of supporting ancillary facilities. Equipment such as screen scrubbers and grit chamber scrapers in the pretreatment unit are primarily responsible for intercepting and removing larger solid impurities and sand particles from the wastewater, laying the foundation for subsequent treatment steps. The aeration tanks and activated sludge systems in the biological treatment unit utilize microbial metabolism to decompose and transform organic pollutants in the wastewater. Filtration, disinfection, and other process equipment in the advanced treatment unit further enhance water quality, ensuring it meets discharge standards or reuse requirements. Equipment such as sludge thickeners and dewatering units reduce and stabilize the sludge generated during the sewage treatment process. Large-scale sewage treatment plants typically have a daily processing capacity exceeding 100,000 cubic meters, with some ultra-large plants reaching a processing capacity of 1 million cubic meters or even higher. Such a large-scale sewage treatment volume places extremely high demands on the stable operation and efficient management of the equipment, and also places higher demands on the inspection and maintenance work of the operation and maintenance personnel.

[0003] Due to their large scale, complex processes, and numerous equipment, large-scale sewage treatment plants present numerous inspection challenges. These challenges include their vast footprint, long inspection routes, and the numerous equipment and structures that require inspection, resulting in a heavy inspection workload. Furthermore, treatment plants typically utilize a variety of treatment processes and equipment, including pumps, fans, screens, aeration equipment, and sludge treatment equipment, each requiring different inspection priorities and standards. Furthermore, the inspection process requires recording a vast amount of data, posing a significant challenge in effectively recording, organizing, and rapidly analyzing this data to identify potential hazards. To this end, large-scale sewage treatment plants have begun exploring the use of inspection robots to assist with inspections and reduce workload. However, existing inspection systems and robots can only conduct inspections and collect data along pre-set routes, rendering them incapable of performing simple routine maintenance operations on equipment. Furthermore, they lack intelligent analysis and processing of field-collected data, hindering the timely identification of potential hazards. Summary of the Invention

[0004] In response to the above-mentioned shortcomings, the present invention discloses a sewage treatment plant robot intelligent inspection and maintenance system and an inspection and maintenance robot, which can intelligently set inspection routes according to the treatment process and specific conditions of the operating equipment of the sewage treatment plant, use the inspection robot to perform intelligent analysis based on the on-site collected data, and further carry out simple daily maintenance work and hidden danger investigation operations.

[0005] The present invention is achieved by adopting the following technical solutions: A sewage treatment plant robot intelligent inspection and maintenance system, comprising a data transceiver module A, a central processing unit A, a data analysis and processing module A, a data storage module A, and a robot terminal; The user connects to the data transceiver module A through a touch screen or a computer to input instructions or query data information to the sewage treatment plant robot intelligent inspection and maintenance system; the data transceiver module A exchanges data with the robot terminal through wireless communication, and the wireless communication method includes any one of Wi-Fi, cellular network, Bluetooth and Zigbee; the data transceiver module A receives and obtains Beidou satellite remote sensing data and navigation data; The central processing unit A is connected to the data transceiver module A, the data analysis and processing module A, and the data storage module A respectively, for coordinating their work; The data analysis and processing module A includes a map correction module, an inspection route formulation module, an inspection operation customization module, and a deep learning prediction module; the map correction module analyzes the remote sensing data and navigation data obtained regularly and updates the sewage treatment plant topographic map in a timely manner; the deep learning prediction module analyzes and processes the existing inspection data to predict potential danger points or areas; the inspection route formulation module makes corrections based on the preset inspection route and in combination with the latest sewage treatment plant topographic map, and then adds inspection points and inspection areas to the inspection route based on the potential danger points or areas predicted by the deep learning prediction module to obtain the final inspection route; the inspection operation customization module configures corresponding inspection operation instructions according to the inspection route, and sends the inspection operation instructions and the inspection route into an inspection workflow to the robot terminal through the data transceiver module A; The data storage module A is used to store various data information received and generated during operation by the sewage treatment plant robot intelligent inspection and maintenance system; The robot terminal includes a robot and a robot control system; the robot is provided with a power supply system, a walking system, a data acquisition system and an inspection operating system; the power supply system is used to power various devices in the robot terminal; the walking system is used to move the robot terminal up and down, forward and backward, left and right; the data acquisition system includes an optical camera for taking pictures and video, an infrared camera, a sensor component, a field sensor connector for connecting to receive field sensor data, and a drone; the inspection operating system includes a detection mechanical arm with a water quality detection probe provided at the front end, a clamping arm for clamping debris, a valve operating arm for clamping a valve handwheel and driving it to rotate, a cleaning mechanical arm with a backflush pipe provided at the front end, and the pipe mouth of the backflush pipe is sleeved with an electric rotating brush, the backflush pipe is connected to a conduit provided along the cleaning mechanical arm, and the conduit is connected to a micro air pump; The robot control system includes a data transceiver module B, a central processing unit B, a data analysis and processing module B, and a data storage module B; the data transceiver module B is used to exchange data with the data transceiver module A, and is used to exchange data with the power supply system, the walking system, the data acquisition system, and the inspection operation system; the central processing unit B is respectively connected to the data transceiver module B, the data analysis and processing module B, and the data storage module B to coordinate their work; The data analysis and processing module B includes a data parsing module and a data analysis and early warning module. The data parsing module parses the received inspection workflow and the central processing unit B executes the various instructions in the inspection workflow in sequence. The data analysis and early warning module includes a numerical comparison module and an image comparison module. The numerical comparison module is used to compare the collected data information with the preset normal data information. If the collected data information does not meet the preset requirements, an alarm message will be prompted. The image comparison module is used to convert the collected image information into data information and then compare it with the preset normal data information. If the collected data information does not meet the preset requirements, an alarm message will be prompted. The data storage module B is used to store various data information received by the robot terminal and generated during operation.

[0006] Furthermore, the preset inspection route includes the following process units: pretreatment unit, biological treatment unit, deep treatment unit, sludge treatment unit; the preset inspection route includes the following equipment: screen cleaner, sand settling tank scraper, aeration fan, pump equipment, biological reactor agitator, filter backwash equipment, ultraviolet disinfector, and chlorination equipment.

[0007] Furthermore, the following data information is collected in the pretreatment unit: a picture or image of the bars of the screen cleaner, the current of the screen cleaner, a picture or image of the entire grit chamber, and a picture or image of the chain of the grit chamber scraper; the following data information is collected in the biological treatment unit: the dissolved oxygen concentration in the aeration tank, the pressure of the sludge return pump and the residual sludge pump, and a picture or image of the sludge; the following data information is collected in the deep treatment unit: the turbidity and pressure difference of the inlet and outlet water of the filtration equipment, the working parameters of the disinfection equipment, and the ultraviolet intensity (for ultraviolet disinfectors); the following data information is collected in the sludge treatment unit: the working parameters of the sludge thickener and the sludge dewatering machine.

[0008] Furthermore, the deep learning prediction module is trained and analyzed based on the collected and acquired historical data information, and the data information includes sewage flow, water quality indicators including chemical oxygen demand COD, biochemical oxygen demand BOD5, ammonia nitrogen, total phosphorus, total nitrogen, pH value, sludge concentration, sludge age, dissolved oxygen, operating parameters of aeration equipment (air volume, air pressure, power, etc.), water pump flow, pressure, head, current, voltage, etc., inlet and outlet water turbidity, pressure difference, backwash cycle, etc. of the filtration equipment, disinfection dosage, disinfection time, and ultraviolet intensity (for ultraviolet disinfectors) of the disinfection equipment.

[0009] Furthermore, the sensor assembly includes a temperature sensor, a humidity sensor, a flue gas sensor, a gas sensor, a particulate matter sensor, an atmospheric pressure sensor, a wind speed and direction sensor, a radiation sensor, a sound sensor, and an infrared thermometer.

[0010] Furthermore, the inspection operation customization module pre-sets the following work processes: water quality detection operation process, stain cleaning operation process, debris cleaning operation process and valve adjustment operation process; the inspection operating system of the robot terminal performs corresponding operations according to the instructions in the above work processes.

[0011] A patrol and maintenance robot includes the above-mentioned robot terminal, wherein the robot is a humanoid robot, the humanoid robot includes a torso, a robot control system and a power supply system are arranged inside the torso, a walking system is installed at the lower part of the torso, and patrol operating systems are installed on both sides of the torso, a sensor assembly and a field sensor connector for connecting to receive field sensor data are installed at the front part of the torso, an optical camera and an infrared camera for taking pictures and video are installed at the upper part of the torso, and a drone platform and a drone are installed on the back of the torso.

[0012] Furthermore, the field sensor joint moves under the drive of the electric telescopic rod.

[0013] Compared with the existing technology, this technical solution has the following beneficial effects: 1. The sewage treatment plant robot intelligent inspection and maintenance system described in the present invention aims to solve the problems existing in the inspection of large sewage treatment plants, such as long inspection routes, multiple inspection equipment, large amount of data collection, and heavy inspection workload. It analyzes and processes the collected data through a map correction module, an inspection route formulation module, an inspection operation customization module, a deep learning prediction module, etc., and intelligently optimizes the inspection route and inspection time to obtain the inspection workflow. The inspection workflow is then sent to the inspection and maintenance robot and it is controlled to perform inspections, replacing workers to perform most of the inspection work, reducing the inspection workload, improving the inspection work efficiency, and promptly investigating and resolving hidden dangers to ensure the normal operation of each unit and equipment in the sewage treatment plant.

[0014] 2. The present invention configures a humanoid robot to perform inspection work on the basis of the inspection and maintenance system. The robot has flexible walking ability, which is conducive to mobile inspection in the factory area where various equipment and pipelines are installed. The various sensors it carries can not only collect various equipment operating parameters, but also obtain image information through image acquisition devices such as cameras, which is conducive to timely discovery of hidden dangers in the working environment. At the same time, the humanoid robot is equipped with multiple robotic arms, which can complete simple maintenance operations according to preset instructions, such as detection work, equipment cleaning, debris cleaning and valve adjustment, which is conducive to maintaining the normal operation of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a connection diagram of the sewage treatment plant robot intelligent inspection and maintenance system of the present invention.

[0016] Figure 2 This is a workflow diagram of the sewage treatment plant robot intelligent inspection and maintenance system described in the present invention. DETAILED DESCRIPTION

[0017] The present invention is further illustrated by the following examples, which are not intended to limit the present invention. Specific experimental conditions and methods not specified in the following examples are conventional methods well known to those skilled in the art.

[0018] Embodiment 1: A sewage treatment plant robot intelligent inspection and maintenance system, which includes a data transceiver module A, a central processing unit A, a data analysis and processing module A, a data storage module A, and a robot terminal; a user connects to the data transceiver module A through a touch screen or a computer to input instructions or query data information to the sewage treatment plant robot intelligent inspection and maintenance system; the data transceiver module A interacts with the robot terminal through wireless communication, and the wireless communication method includes any one of Wi-Fi, cellular network, Bluetooth and Zigbee; the data transceiver module A receives and obtains remote sensing data and navigation data from Beidou satellites; the central processing unit A is respectively connected to the data transceiver module A, the data analysis and processing module A, and the data storage module A to coordinate their work; the data analysis and processing module A includes a map correction module, an inspection route formulation module, a patrol ... Inspection operation customization module, deep learning prediction module; the map correction module analyzes the remote sensing data and navigation data obtained regularly and updates the sewage treatment plant topographic map in time; the deep learning prediction module analyzes and processes the existing inspection data to predict potential danger points or areas; the inspection route formulation module makes corrections based on the preset inspection route and in combination with the latest sewage treatment plant topographic map, and then adds inspection points and inspection areas to the inspection route based on the potential danger points or areas predicted by the deep learning prediction module to obtain the final inspection route; the inspection operation customization module configures the corresponding inspection operation instructions according to the inspection route, and sends the inspection operation instructions and the inspection route into an inspection workflow through the data transceiver module A to the robot terminal; the data storage module A is used to store various data information received by the sewage treatment plant robot intelligent inspection and maintenance system and generated during operation; The robot terminal includes a robot and a robot control system; the robot is provided with a power supply system, a walking system, a data acquisition system and an inspection operating system; the power supply system is used to power various devices in the robot terminal; the walking system is used to move the robot terminal up and down, forward and backward, left and right; the data acquisition system includes an optical camera for taking pictures and video, an infrared camera, a sensor component, a field sensor connector for connecting to receive field sensor data, and a drone; the inspection operating system includes a detection robotic arm with a water quality detection probe at the front end, a clamping arm for clamping debris, a valve operating arm for clamping the valve handwheel and driving it to rotate, a cleaning robotic arm with a backblowing pipe at the front end and the pipe mouth of the backblowing pipe is sleeved with an electric rotating brush, the backblowing pipe is connected to a conduit arranged along the cleaning robotic arm, and the conduit is connected to a micro air pump; a water sample extraction needle and a gas extraction needle are provided on the detection robotic arm for collecting water samples and gas samples.

[0019] The robot control system includes a data transceiver module B, a central processing unit B, a data analysis and processing module B, and a data storage module B; the data transceiver module B is used to exchange data with the data transceiver module A, and is used to exchange data with the power system, the walking system, the data acquisition system, and the inspection operating system; the central processing unit B is respectively connected to the data transceiver module B, the data analysis and processing module B, and the data storage module B to coordinate their work; the data analysis and processing module B includes a data parsing module and a data analysis and early warning module. The data parsing module parses the received inspection workflow and then the central processing unit B executes the various instructions in the inspection workflow in sequence; the data analysis and early warning module includes a numerical comparison module and an image comparison module. The numerical comparison module is used to compare the collected data information with the preset normal data information. If the collected data information does not meet the preset requirements, an alarm message will be prompted. The image comparison module is used to convert the collected image information into data information, and then compare it with the preset normal data information. If the collected data information does not meet the preset requirements, an alarm message will be prompted; the data storage module B is used to store various data information received by the robot terminal and generated during operation.

[0020] Example 2: The difference between the sewage treatment plant robot intelligent inspection and maintenance system described in this example and that described in Example 1 is that the preset inspection route includes the following process units: pretreatment unit, biological treatment unit, deep treatment unit, and sludge treatment unit; the preset inspection route includes the following equipment: screen cleaner, grit chamber scraper, aeration fan, pump equipment, biological reactor agitator, filter tank backwash equipment, ultraviolet disinfector, and chlorination equipment; the following data information is collected in the pretreatment unit: a picture or image of the bars of the screen cleaner, the current and vibration frequency of the screen cleaner, a picture or image of the entire grit chamber, and a picture or image of the chain of the grit chamber scraper; the following data information is collected in the biological treatment unit: the dissolved oxygen concentration in the aeration tank, the flow, pressure, and head of the sludge return pump and the residual sludge pump, and a picture or image of the sludge; the following data information is collected in the deep treatment unit: the turbidity of the inlet and outlet water of the filtration equipment and the pressure difference, the working parameters and ultraviolet intensity of the disinfection equipment; the following data information is collected in the sludge treatment unit: the sludge thickener and the sludge The working parameters of the mud dewatering machine; the deep learning prediction module is trained and analyzed based on the data information collected in the past, and the data information includes sewage flow, water quality indicators including chemical oxygen demand COD, biochemical oxygen demand BOD5, ammonia nitrogen, total phosphorus, total nitrogen, pH value, sludge concentration, sludge age, dissolved oxygen, aeration equipment operating parameters (air volume, air pressure, power, etc.), water pump flow, pressure, head, current, voltage, etc., filtration equipment inlet and outlet water turbidity, pressure difference, backwash cycle, etc., disinfection equipment disinfection dosage, disinfection time, ultraviolet intensity (for ultraviolet disinfector); the sensor component includes a temperature sensor, a humidity sensor, a flue gas sensor, a gas sensor, a particulate matter sensor, an atmospheric pressure sensor, a wind speed and direction sensor, a radiation sensor, and a sound sensor; the inspection operation customization module is pre-set with the following work processes: water quality detection operation process, stain cleaning operation process, debris cleaning operation process and valve adjustment operation process; the inspection operating system of the robot terminal performs corresponding operations according to the instructions in the above work processes.

[0021] Example 3: A patrol and maintenance robot, which includes the robot terminal described in Example 2, wherein the robot is a humanoid robot, the humanoid robot includes a torso, a robot control system and a power supply system are arranged inside the torso, a walking system is installed at the lower part of the torso, and patrol operating systems are installed on both sides of the torso, a sensor assembly and a field sensor connector for connecting to receive field sensor data are installed at the front part of the torso, an optical camera and an infrared camera for taking pictures and video are installed at the upper part of the torso, and a drone platform and a drone are installed on the back of the torso; the field sensor connector moves under the drive of an electric telescopic rod.

[0022] The specific steps for carrying out inspection and maintenance of a sewage treatment plant using the sewage treatment plant robot intelligent inspection and maintenance system described in Example 2 and the inspection and maintenance robot described in Example 3 are as follows: S1. The user inputs instructions to the data transceiver module A through a computer to set a preset inspection route for the inspection route formulation module; the preset inspection route includes the following process units: pretreatment unit, biological treatment unit, deep treatment unit, sludge treatment unit; the preset inspection route includes the following equipment: screen decontamination machine, sand settling tank scraper, aeration fan, pump equipment, biological reactor agitator, filter backwash equipment, ultraviolet disinfector, chlorination equipment; the pretreatment unit collects the following data information: the bar picture of the screen decontamination machine, the grid The current of the screen scrubber, an overall picture of the grit chamber, and a picture of the chain of the grit chamber scraper; the following data is collected in the biological treatment unit: dissolved oxygen concentration in the aeration tank, pressure of the sludge return pump and excess sludge pump, and a picture of the sludge; the following data is collected in the deep treatment unit: turbidity and pressure difference of the inlet and outlet water of the filtration equipment, operating parameters of the disinfection equipment, and ultraviolet intensity (for ultraviolet disinfectors); the following data is collected in the sludge treatment unit: concentration ratio of the sludge concentrator, sludge moisture content, and operating parameters of the sludge dewatering machine; The deep learning prediction module is trained and analyzed based on the collected and acquired historical data information, and the data information includes sewage flow, water quality indicators including chemical oxygen demand COD, biochemical oxygen demand BOD5, ammonia nitrogen, total phosphorus, total nitrogen, pH value, sludge concentration, sludge age, dissolved oxygen, operating parameters of aeration equipment (air volume, air pressure, power, etc.), water pump flow, pressure, head, current, voltage, etc., turbidity of inlet and outlet water of filtration equipment, pressure difference, backwash cycle, etc., disinfection dosage, disinfection time, ultraviolet intensity (for ultraviolet disinfector) of disinfection equipment; at the same time, the inspection and maintenance robot is placed in the standby area for charging; the deep learning prediction module constructs a deep learning prediction model based on long short-term memory network and generative adversarial network, firstly standardizes the collected and acquired data, and removes outliers, and then constructs a long short-term memory network, wherein the long short-term memory network includes an input layer, an LSTM layer, a fully connected layer and an output layer, and the input layer is used for Input time series data, the LSTM layer is designed with 3 layers, the number of units in each layer is between 64 and 128, and the activation function adopts ReLU. The fully connected layer is added after the LSTM layer for mapping to the prediction target, and the output layer is used to output the prediction result; then a GAN network is constructed, in which a generator is composed of a fully connected layer and a convolutional layer, and a random noise vector or the output of the LSTM network is used as input. At the same time, a discriminator is composed of a fully connected layer and a convolutional layer, and the real time series data and the data generated by the generator are used as input to determine whether the input data is real data or generated data; then the LSTM network is trained separately until it reaches a certain performance in the prediction task, and then the output of the LSTM is used as a condition to generate synthetic data that is as close as possible to the real data distribution, and the discriminator is used to discriminate between the real data and the generated data, and feedback is provided to the generator, and the generator and the discriminator are trained alternately and iteratively until the GAN reaches a Nash equilibrium; S2. The map correction module analyzes the latest remote sensing data and navigation data to form a topographic map of the sewage treatment plant. The inspection route formulation module corrects the preset inspection route and the topographic map of the sewage treatment plant to obtain an inspection route. Then, the inspection operation customization module configures corresponding inspection operation instructions according to the inspection route, and sends the inspection operation instructions and inspection route into an inspection workflow to the robot terminal through the data transceiver module A. S3. After the robot terminal receives the inspection workflow, the data parsing module parses the received inspection workflow, and then the central processor B executes the instructions in the inspection workflow in sequence, starting the inspection and maintenance robot to start the inspection; S4. During the inspection process, the inspection and maintenance robot collects data information through the data acquisition system and transmits it to the data analysis and processing module A through the data transceiver module B. At the same time, the data analysis and early warning module in the data analysis and processing module B compares and analyzes the collected data information. If the data information exceeds the normal range, an alarm message will be prompted. Among them, the drone is used to take low-altitude photos of large areas such as aeration tanks and grit chambers. The drone can be used to patrol and take photos of specific areas according to the inspection process or the instructions in the data analysis and processing module B. If the gas particle concentration in the data analyzed by the data analysis and processing module B exceeds the alarm value, the data analysis module B will prompt Alarm information, at the same time, the central processing unit B retrieves the corresponding emergency operation process in the data storage module B or the data storage module A, sends the emergency operation process to the inspection and maintenance robot, moves to the ventilation fan switch and starts the ventilation fan for ventilation; for example, the inspection and maintenance robot collects infrared thermal imaging data and on-site noise data of the aeration fan, and compares the normal infrared thermal imaging data and on-site noise data through the data analysis and processing module B. If overtemperature or excessive noise occurs, the corresponding emergency operation process is retrieved, and the inspection and maintenance robot is controlled to move to the aeration fan control switch and stop the aeration fan in use and start the standby aeration fan; S5. During the inspection process, the inspection and maintenance robot follows the inspection workflow, with the robot control system sending instructions to the inspection operating system to execute the water quality detection operation process, the stain cleaning operation process, the debris cleaning operation process, and the valve adjustment operation process. Alternatively, based on the alarm information, the data analysis and early warning module selects any one or more of the water quality detection operation process, the stain cleaning operation process, the debris cleaning operation process, and the valve adjustment operation process according to pre-set rules; S6. After the inspection is completed, the inspection and maintenance robot returns to the standby area to charge and wait for the next inspection instruction; at the same time, the deep learning prediction module analyzes and processes the collected data information to predict the areas where hidden dangers exist. The inspection route formulation module re-formulates the inspection route for the next inspection based on the predicted hidden danger areas, or increases the number of inspections and formulates a special inspection route with the hidden danger areas as the inspection targets.

[0023] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A sewage treatment plant robot intelligent inspection and maintenance system, characterized by: It includes data transceiver module A, central processor A, data analysis and processing module A, data storage module A, and robot terminal; The user connects to the data transceiver module A through a touch screen or a computer to input instructions or query data information to the sewage treatment plant robot intelligent inspection and maintenance system; the data transceiver module A exchanges data with the robot terminal through wireless communication, and the wireless communication method includes any one of Wi-Fi, cellular network, Bluetooth and Zigbee; the data transceiver module A receives and obtains Beidou satellite remote sensing data and navigation data; The central processing unit A is connected to the data transceiver module A, the data analysis and processing module A, and the data storage module A respectively, for coordinating their work; The data analysis and processing module A includes a map correction module, an inspection route planning module, an inspection operation customization module, and a deep learning prediction module. The map correction module analyzes the regularly acquired remote sensing data and navigation data to timely update the sewage treatment plant topographic map. The deep learning prediction module analyzes and processes the existing inspection data to predict potential risk points or areas. The inspection route formulation module modifies the preset inspection route based on the latest sewage treatment plant topographic map, and then adds inspection points and inspection areas to the inspection route based on the potential danger points or areas predicted by the deep learning prediction module to obtain the final inspection route; The inspection operation customization module configures the corresponding inspection operation instructions according to the inspection route, and combines the inspection operation instructions and the inspection route into an inspection workflow and sends it to the robot terminal through the data transceiver module A; The data storage module A is used to store various data information received and generated during operation by the sewage treatment plant robot intelligent inspection and maintenance system; The robot terminal includes a robot and a robot control system; the robot is provided with a power supply system, a walking system, a data acquisition system and an inspection operating system; the power supply system is used to power various devices in the robot terminal; the walking system is used to move the robot terminal up and down, forward and backward, left and right; the data acquisition system includes an optical camera for taking pictures and video, an infrared camera, a sensor component, a field sensor connector for connecting to receive field sensor data, and a drone; the inspection operating system includes a detection mechanical arm with a water quality detection probe provided at the front end, a clamping arm for clamping debris, a valve operating arm for clamping a valve handwheel and driving it to rotate, a cleaning mechanical arm with a backflush pipe provided at the front end, and the pipe mouth of the backflush pipe is sleeved with an electric rotating brush, the backflush pipe is connected to a conduit provided along the cleaning mechanical arm, and the conduit is connected to a micro air pump; The robot control system includes a data transceiver module B, a central processing unit B, a data analysis and processing module B, and a data storage module B; the data transceiver module B is used to exchange data with the data transceiver module A, and is used to exchange data with the power supply system, the walking system, the data acquisition system, and the inspection operation system; the central processing unit B is respectively connected to the data transceiver module B, the data analysis and processing module B, and the data storage module B to coordinate their work; The data analysis and processing module B includes a data parsing module and a data analysis and early warning module. The data parsing module parses the received inspection workflow and then the central processing unit B executes the various instructions in the inspection workflow in sequence. The data analysis and early warning module includes a numerical comparison module and an image comparison module. The numerical comparison module is used to compare the collected data information with the preset normal data information. If the collected data information does not meet the preset requirements, an alarm message will be prompted. The image comparison module is used to convert the collected image information into data information and then compare it with the preset normal data information. If the collected data information does not meet the preset requirements, an alarm message will be prompted. The data storage module B is used to store various data information received by the robot terminal and generated during operation.

2. The sewage treatment plant robot intelligent inspection and maintenance system according to claim 1 is characterized by: The preset inspection route includes the following process units: pretreatment unit, biological treatment unit, deep treatment unit, and sludge treatment unit; The preset inspection route The equipment includes: screen cleaner, sand scraper for grit chamber, aeration fan, pump equipment, biological reactor agitator, filter backwash equipment, ultraviolet disinfector, and chlorination equipment.

3. The sewage treatment plant robot intelligent inspection and maintenance system according to claim 1 is characterized by: The following data information is collected in the pretreatment unit: a picture or image of the bars of the screen cleaner, the current of the screen cleaner, a picture or image of the entire grit chamber, and a picture or image of the chain of the grit chamber scraper; the following data information is collected in the biological treatment unit: the dissolved oxygen concentration in the aeration tank, the pressure of the sludge return pump and the residual sludge pump; the following data information is collected in the deep treatment unit: the turbidity and pressure difference of the inlet and outlet water of the filtration equipment, the working parameters and ultraviolet intensity of the disinfection equipment; the following data information is collected in the sludge treatment unit: the working parameters of the sludge thickener and the sludge dewatering machine.

4. The sewage treatment plant robot intelligent inspection and maintenance system according to claim 1 is characterized by: The deep learning prediction module is trained and analyzed based on the collected and acquired historical data information, and the data information includes sewage flow, water quality indicators including chemical oxygen demand COD, biochemical oxygen demand BOD5, ammonia nitrogen, total phosphorus, total nitrogen, pH value, sludge concentration, sludge age, dissolved oxygen, aeration equipment operating parameters, water pump flow, pressure, head, current, voltage, etc., inlet and outlet water turbidity, pressure difference, backwash cycle, etc. of the filtration equipment, disinfection dosage, disinfection time, and ultraviolet intensity of the disinfection equipment.

5. The sewage treatment plant robot intelligent inspection and maintenance system according to claim 1 is characterized by: The sensor assembly includes a temperature sensor, a humidity sensor, a smoke sensor, a gas sensor, a particulate matter sensor, an atmospheric pressure sensor, a wind speed and direction sensor, a radiation sensor, a sound sensor, and an infrared thermometer.

6. The sewage treatment plant robot intelligent inspection and maintenance system according to claim 1 is characterized by: The inspection operation customization module pre-sets the following workflows: water quality detection operation process, stain cleaning operation process, debris cleaning operation process and valve adjustment operation process; the inspection operating system of the robot terminal performs corresponding operations according to the instructions in the above workflows.

7. A patrol and maintenance robot, characterized in that: It includes the robot terminal as described in claim 1, wherein the robot is a humanoid robot, the humanoid robot includes a torso, a robot control system and a power supply system are arranged inside the torso, a walking system is installed at the lower part of the torso, and inspection operating systems are installed on both sides of the torso, a sensor assembly and a field sensor connector for connecting to receive field sensor data are installed at the front part of the torso, an optical camera and an infrared camera for taking pictures and video are installed at the upper part of the torso, and a drone platform and a drone are installed on the back of the torso.

8. According to the inspection and maintenance robot, it is characterized in that: The on-site sensor joint moves under the drive of the electric telescopic rod.

Images