Urban intelligent inspection system

Abstract

The invention provides an urban intelligent inspection system. The urban intelligent inspection system comprises a central processing module, an environmental sanitation module and a stray animal management module, the inspection area is divided into a plurality of inspection blocks, and the inspection blocks comprise a plurality of streets and communities; a plurality of inspection devices are deployed in each inspection block and are used for collecting inspection data in real time; the central processing module is used for analyzing and processing the inspection data of each inspection block transmitted by the environmental sanitation module and the stray animal management module, and generating decision support information according to analysis and processing results; the environmental sanitation module is used for evaluating the cleaning condition of a street and monitoring air quality, water quality and noise indexes in real time; the stray animal management module is used for monitoring and managing stray animals and recording the activity area, behavior pattern and number of the stray animals, thereby improving the efficiency and accuracy of urban inspection, promoting the improvement of the urban environment, improving the animal welfare, and enhancing the public safety and health.

Description

Technical Field

[0001] The present invention relates to the field of intelligent patrol inspection management, and in particular to an urban intelligent patrol inspection system. Background Art

[0002] In the context of rapid urbanization, urban management faces unprecedented challenges. The traditional urban management model relies on manual inspections and paper records, which is not only inefficient but also difficult to respond to problems in real time. With the development of technology, especially the rise of big data and artificial intelligence technology, new solutions have been provided for urban management. However, how to effectively integrate these technologies and build an efficient and intelligent urban inspection system has become an urgent problem to be solved. In addition, the management of stray animals in cities faces many challenges. The disorderly reproduction and random movement of stray animals have brought difficulties to urban management and also posed a threat to urban sanitation. In addition, stray animals may disrupt urban order, cause inconvenience to pedestrians, and even pose safety hazards. At present, the management of stray animals is still at the level of manual capture, manual registration, and sheltering, which requires a lot of manpower and material costs, and the coverage of stray animals is also limited, and the management effect is not ideal. Therefore, an urban intelligent inspection system architecture that can solve the above technical problems is proposed, which is of great significance and value to the inspection management of cities. Summary of the invention

[0003] In order to solve the technical problems in the above background, the present invention provides an urban intelligent inspection system, including a central processing module, a sanitation module and a stray animal management module; The inspection area is divided into several inspection blocks, which include several streets and communities; Several inspection devices are deployed in each inspection area to collect inspection data in real time; The central processing module is used to analyze and process the inspection data of each inspection block transmitted by the sanitation module and the stray animal management module, and generate decision support information based on the analysis and processing results; The sanitation module is used to assess the cleanliness of streets and monitor air quality, water quality and noise indicators in real time; The stray animal management module is used to monitor and manage stray animals, and record the activity areas, behavior patterns and numbers of stray animals.

[0004] In the preferred solution, the inspection equipment includes inspection robots, drones and cameras; Inspection robots and drones work together. Drones conduct large-scale rapid inspections in the air, using high-definition cameras and infrared thermal imaging equipment to conduct long-distance observations and identify objects within the area. Inspection robots confirm and process anomalies discovered by drones at close range and enter dangerous or hard-to-reach areas for detailed inspections.

[0005] In a preferred embodiment, the sanitation module includes a cleanliness level assessment model; The cleanliness hierarchical assessment model is used to conduct hierarchical assessments from streets to inspection blocks and then to cities. Specifically: The YOLO model detects the amount and type of garbage in street images, and then calculates a cleanliness index or score based on the amount and type of garbage data; The cleanliness evaluation result of the entire inspection block is obtained by averaging or weighted averaging the cleanliness indexes of all streets in the inspection block; By summarizing the cleanliness assessment results of all inspection blocks in the area, the cleanliness level of the entire city can be obtained.

[0006] In a preferred solution, a food station is set up at a specific location in each inspection block to place animal food, assist stray animals, attract stray animals to come for food, centrally manage and monitor the activities of stray animals, and count the number of stray animals.

[0007] In the preferred solution, a charging station is also provided at a specific location of each inspection block for charging the inspection equipment; Food stations and charging stations serve as stop nodes for inspection equipment. The inspection path of the inspection equipment is planned by analyzing the location coordinates and possible connection nodes contained in each stop node.

[0008] In the preferred scheme, the inspection device judges based on the power threshold. When the power required for the inspection device to return to the charging station minus the current power of the inspection device is less than a preset threshold or when the power consumption required for the inspection robot to go to the next stop node, the power consumption required for the next stop node to work, and the power consumption required from the next stop node to the charging station total exceeds the difference between the current power of the inspection robot and the threshold, a charging warning is issued and the inspection robot moves to the charging station to ensure that the inspection robot can be charged in time and continue to work.

[0009] In a preferred solution, the charging station is set up near the food station, and the inspection robot performs monitoring and power replenishment at the same time during the period when it stops at the stop node.

[0010] In the preferred solution, during the inspection process, the inspection equipment identifies stray animal images, locks stray animals as monitoring targets, and collects relevant data on stray animals, including the number, health status and behavior patterns of stray animals. Based on the collected relevant data, combined with historical data and historical laws, the trend of changes in the number of stray animals in the future is predicted, and further decisions are made based on the predicted data. The inspection equipment also monitors the behavior and physical condition of stray animals. When an abnormality is detected, an alarm is sent to the central processing module, and a video of the environment around the stray animal or pet is taken and sent to the central processing module; The inspection equipment regularly collects images of stray animals that appear in the community during a specific period of time, and counts the number of stray animals in the specific period of time through the central processing module; When the number of stray animals exceeds the set threshold, capture and sterilization measures will be implemented or stray animal welfare organizations will be contacted; Identify the sex of stray animals and predict the probability of mating and reproduction in a specific period of time in the future by counting the number of males and females separately; When the predicted mating and fertility probabilities exceed the set thresholds, stray animal population control measures are taken.

[0011] In the preferred solution, the inspection equipment uses the ST-GCN model, an action classification algorithm based on a graph convolutional neural network, to identify abnormal behaviors of stray animals. When abnormal behaviors are detected, an alarm will sound to alert nearby personnel to be vigilant, and an alarm will be sent to the central processing module to provide corresponding processing decisions.

[0012] In a preferred solution, the stray animal management module includes a food station management unit, which is used to manage the food supply in the food station and prevent the food from being randomly placed by irrelevant personnel or foreign objects from appearing in the food station due to other reasons through real-time monitoring; By installing temperature and humidity sensors and cameras in the food station, the environmental conditions and food status of the food station can be monitored in real time, and the temperature and humidity data can be visualized and the warning thresholds of the temperature and humidity data can be configured; During the inspection process, the inspection equipment stops at the food station to collect temperature and humidity data. When the temperature and humidity data exceed the safe range, an alarm is automatically triggered; Clean and replenish food in the food station regularly.

[0013] The beneficial effects of the present invention are: (1) The present invention can cover a larger area through automated inspection equipment, improve the efficiency and frequency of inspections, and reduce the need for manual inspections.

[0014] (2) By using technologies such as high-definition cameras, infrared thermal imaging, and the YOLO model, the system can more accurately collect and analyze data related to sanitation and stray animals, thereby improving the accuracy of decision-making.

[0015] (3) The system can monitor the urban environment and the status of stray animals in real time, and can also quickly respond to the sanitation conditions of urban streets. The cleanliness level evaluation model of the sanitation module can promote the improvement of urban cleanliness and promote the continuous improvement of the urban environment through a quantitative cleanliness index.

[0016] (4) The establishment of food stations not only helps to centrally manage and monitor stray animals, but also provides food assistance to stray animals and improves animal welfare. The system can also identify abnormal behaviors of stray animals, discover and deal with potential safety hazards caused by stray animals, and issue alarms in a timely manner. The central processing module generates decision support information based on the collected data, improves public safety, and facilitates urban management.

[0017] (5) The system sets up intelligent charging stations to ensure that inspection equipment can be charged in time when the power is low, reducing energy waste and improving the efficiency of equipment use.

[0018] (6) The food station management unit can ensure the adequacy and quality of food supply, while preventing contamination by foreign matter and ensuring the food safety of stray animals. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the system structure of the present invention.

[0020] Figure 2 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0021] like Figure 1~2 The present invention provides an urban intelligent inspection system, comprising a central processing module, a sanitation module and a stray animal management module; The inspection area is divided into several inspection blocks, which include several streets and communities; Several inspection devices are deployed in each inspection area to collect inspection data in real time; The central processing module is used to analyze and process the inspection data of each inspection block transmitted by the sanitation module and the stray animal management module, and generate decision support information based on the analysis and processing results; The sanitation module is used to assess the cleanliness of streets and monitor air quality, water quality and noise indicators in real time; The stray animal management module is used to monitor and manage stray animals, and record the activity areas, behavior patterns and numbers of stray animals.

[0022] Preferably, the inspection equipment includes an inspection robot, a drone and a camera; Inspection robots and drones work together. Drones conduct large-scale rapid inspections in the air, using high-definition cameras and infrared thermal imaging equipment to conduct long-distance observations and identify objects within the area. Inspection robots confirm and process anomalies discovered by drones at close range and enter dangerous or hard-to-reach areas for detailed inspections.

[0023] Preferably, the sanitation module includes a cleanliness hierarchy assessment model; The cleanliness hierarchical assessment model is used to conduct hierarchical assessments from streets to inspection blocks and then to cities. Specifically: The YOLO model detects the amount and type of garbage in street images, and then calculates a cleanliness index or score based on the amount and type of garbage data; The cleanliness evaluation result of the entire inspection block is obtained by averaging or weighted averaging the cleanliness indexes of all streets in the inspection block; By summarizing the cleanliness assessment results of all inspection blocks in the area, the cleanliness level of the entire city can be obtained.

[0024] Preferably, a food station is set up at a specific location of each inspection block for placing animal food, assisting stray animals, and centrally managing and monitoring the activities of stray animals and counting the number of stray animals by attracting stray animals to come for food.

[0025] Preferably, a charging station is also provided at a specific location of each inspection block for charging the inspection equipment; Food stations and charging stations serve as stop nodes for inspection equipment. The inspection path of the inspection equipment is planned by analyzing the location coordinates and possible connection nodes contained in each stop node.

[0026] Preferably, the inspection device judges based on the power threshold, and when the power required for the inspection device to return to the charging station minus the current power of the inspection device is less than a preset threshold or when the power consumption required for the inspection robot to go to the next stop node, the power consumption required for the next stop node to work, and the power consumption required from the next stop node to the charging station total exceeds the difference between the current power of the inspection robot and the threshold, a charging warning is issued and the inspection robot moves to the charging station to ensure that the inspection robot can be charged in time and continue to work.

[0027] Preferably, the charging station is set up near the food station, and the inspection robot performs monitoring and power replenishment at the same time during the period when it stops at the stop node.

[0028] Preferably, during the inspection process, the inspection equipment identifies stray animal images, locks on stray animals as monitoring targets, and collects relevant data on stray animals, including the number, health status, and behavior patterns of stray animals. Based on the collected relevant data, combined with historical data and historical rules, the trend of changes in the number of stray animals in the future is predicted, and further decisions are made based on the predicted data. The inspection equipment also monitors the behavior and physical condition of stray animals. When an abnormality is detected, an alarm is sent to the central processing module, and a video of the environment around the stray animal or pet is taken and sent to the central processing module; The inspection equipment regularly collects images of stray animals that appear in the community during a specific period of time, and counts the number of stray animals in the specific period of time through the central processing module; When the number of stray animals exceeds the set threshold, capture and sterilization measures will be implemented or stray animal welfare organizations will be contacted; Identify the sex of stray animals and predict the probability of mating and reproduction in a specific period of time in the future by counting the number of males and females separately; When the predicted mating and fertility probabilities exceed the set thresholds, stray animal population control measures are taken.

[0029] Preferably, the inspection equipment uses the ST-GCN model, an action classification algorithm based on a graph convolutional neural network, to identify abnormal behaviors of stray animals. When abnormal behaviors are detected, an alarm will sound to alert nearby personnel to be vigilant, and an alarm will be sent to the central processing module to provide corresponding processing decisions.

[0030] Preferably, the stray animal management module includes a food station management unit, which is used to manage the food supply in the food station and prevent the food from being randomly placed by irrelevant personnel or foreign objects from appearing in the food station due to other reasons through real-time monitoring; By installing temperature and humidity sensors and cameras in the food station, the environmental conditions and food status of the food station can be monitored in real time, and the temperature and humidity data can be visualized and the warning thresholds of the temperature and humidity data can be configured; During the inspection process, the inspection equipment stops at the food station to collect temperature and humidity data. When the temperature and humidity data exceed the safe range, an alarm is automatically triggered; Clean and replenish food in the food station regularly.

[0031] The following are other embodiments of the present invention: Example 1 An urban intelligent inspection system includes several inspection block subsystems; The inspection block subsystem is used to inspect the corresponding inspection blocks, including the central processing module, sanitation module and stray animal management module.

[0032] It also includes inspection equipment; Inspection equipment includes inspection robots, drones and cameras.

[0033] Preferably, inspection robots and drones can work together to improve the efficiency and coverage of inspections. Drones can conduct large-scale rapid inspections in the air, using high-definition cameras, infrared thermal imaging and other equipment for long-distance observation and identification of objects within the area; inspection robots can confirm and process anomalies found by drones at close range, and enter dangerous or hard-to-reach areas for detailed inspections.

[0034] Preferably, a food station is set up at a specific location in each inspection block. Generally, food stations are set up in areas where stray animals frequently move around. Through data analysis during multiple inspections, areas where stray animals appear more frequently are marked, such as one of the main areas where stray animals forage near garbage cans. Studies have shown that stray cats spend 85% of their time within a 100-meter radius of feeding points, and the higher the density of feeding points, the smaller the range of stray cats' activities.

[0035] Preferably, the stray animal management module includes a food station supervision unit for monitoring and managing the food station, selecting a suitable location to set up the food station, monitoring the use of the food station, and ensuring food supply.

[0036] Preferably, the food station and the charging station are used as the stop nodes of the inspection robot, each stop node contains the position coordinates and possible connection nodes, and the inspection path is planned. The specific implementation steps are: Set the start point, end point, and locations of all feeding stations; Start the SLAM algorithm and build an environment map; Use the A* algorithm to search for the shortest path from the starting point to each food station; Optimize routes based on feedback from drones and inspection robots to ensure coverage and efficiency; Insert charging station nodes in the path to optimize the endurance of the inspection robot; Inspection robots and drones perform tasks according to the planned paths; Dynamically adjust paths and task allocations based on environmental changes and task requirements.

[0037] Preferably, based on the power threshold, a charging warning is issued when the power required for the inspection robot to return to the charging station and the current power of the inspection robot is less than a preset threshold (or: when the power consumption required for the inspection robot to go to the next stop node, the power consumption required for the work of the next stop node and the power consumption required from the next stop node to the charging station total exceeds the difference between the current power of the inspection robot and the threshold), to ensure that the inspection robot can be charged in time and continue to work.

[0038] Preferably, the charging station can be arranged near the stop node, and the inspection robot can work while charging at the stop node.

[0039] Preferably, during the inspection process, the inspection robots and drones identify images of stray animals, lock on stray animals as monitoring targets, and collect relevant data on stray animals, including the number, health status, and behavior patterns of stray animals. Based on the collected data, combined with historical data and historical rules, the trend of changes in the number of stray animals in the future is predicted, and further decisions are made based on the predicted data.

[0040] Preferably, the inspection equipment also monitors the behavior and physical condition of stray animals, and when an abnormality is detected, sends an alarm to the central processing module, and takes a video of the surrounding environment of the stray animal or pet and sends it to the central processing module; Preferably, the data collection module regularly collects images of stray animals that appear in the community within a specific period of time, and the central processing module counts the number of stray animals within the specific period of time; When the number of stray animals exceeds the set threshold, capture and sterilization measures will be implemented or stray animal welfare agencies will be contacted.

[0041] Preferably, the central processing module is also used to identify the sex of stray animals, and to predict the probability of mating and reproduction in a specific future period by counting the number of males and females respectively; When the predicted mating and fertility probabilities exceed the set thresholds, stray animal population control measures are taken.

[0042] Preferably, the inspection equipment uses the ST-GCN model, an action classification algorithm based on a graph convolutional neural network, to identify abnormal behaviors of stray animals. When abnormal behaviors are detected, an alarm will sound to remind nearby personnel to be vigilant and try to avoid approaching stray animals. An alarm will also be sent to the central processing module to provide corresponding processing decisions.

[0043] Preferably, a food station monitoring and management unit may be provided to manage the food supply in the food station and, through real-time monitoring, prevent the presence of foreign matter in the food station due to arbitrarily placing food therein by irrelevant personnel or due to other reasons.

[0044] Example 2 (Management of stray animals and pets in community inspection areas) Each community in the residential area is regarded as an inspection block. The inspection blocks with communities as inspection objects are collectively referred to as community inspection blocks, and the corresponding inspection block system is called community inspection block system. It includes central processing module, sanitation module and stray animal management module.

[0045] The stray animal management module in the community inspection block system also includes a pet protection and management unit.

[0046] In order to distinguish stray cats and dogs from pet cats and dogs, pet cats and dogs can be marked, and smart cameras and machine learning algorithms can be used to distinguish between stray animals and pets.

[0047] During the inspection process, the inspection equipment uses cameras to capture images of pets and suspected stray animals, and sends the images of pets and suspected stray animals to the community inspection block system. Using pet facial and nose print recognition technology, the captured images are analyzed to identify the identity of the pet. For animals that cannot be identified, further analysis of the animal's wearing characteristics and behavior patterns is used to determine whether it is a stray animal.

[0048] The pet image and corresponding pet information are recorded in the community inspection block system, and the pet information is bound to the pet owner information; Images of stray animals and corresponding status information will also be recorded in the community inspection block system.

[0049] The inspection equipment also monitors the behavior and physical condition of stray animals and pets. When an abnormality is detected, an alarm is sent to the central processing module, and a video of the environment around the stray animals or pets is taken and sent to the central processing module; When an abnormality is detected in a pet, the pet owner is contacted through the pet owner information bound to the pet information.

[0050] Example 3 (Management of food stations) Monitoring and management of food stations in the city's intelligent inspection system: In order to effectively monitor and manage the food station, ensure sufficient food supply and avoid waste and spoilage, and prevent foreign matter contamination, a food station monitoring and management unit is set up, which is specifically used for: By installing temperature and humidity sensors, cameras and other equipment, the environmental conditions and food status of the food station can be monitored in real time; Configure warning thresholds for parameters such as temperature and humidity. Once they exceed the safe range, an alarm will be automatically triggered. Deploy cameras for 24 / 7 monitoring to observe the situation inside and outside the food station in real time, including food storage, environmental conditions and personnel activities. Use AI video analysis technology to conduct intelligent monitoring of food stations, identify possible foreign body contamination, such as illegal intrusion, fireworks and other safety hazards, and issue alarms in time; RFID, sensors, and intelligent image recognition technology are used to establish a rapid registration system, automatic sampling system, etc. to automatically collect food station operation data and ensure data accuracy.

[0051] Historical data is automatically stored on the cloud platform, and historical curve trend charts are generated to dynamically display environmental changes. Monitoring data and alarm history can be exported.

[0052] The specific work flow of the monitoring and management unit of the food station is as follows: 1. Collect data such as temperature, humidity, and food inventory at the food station through sensors and cameras; 2. The system analyzes the collected data to monitor whether the environment and food status of the food station are normal; 3. When the monitored data exceeds the preset safety threshold, the system automatically triggers an early warning and notifies the management personnel; 4. Use AI video analysis technology to analyze the video stream of the food station in real time, identify foreign objects and abnormal behavior, and issue an alarm when anomalies are detected 5. The system records the in-and-out information of food to ensure the accuracy and timeliness of food supply; 6. All monitoring data and operation logs are stored on the cloud platform, generating historical trend charts and reports for management personnel to analyze and make decisions; 7. Clean and inspect food stations regularly to ensure food quality and environmental hygiene and prevent contamination by foreign matter.

[0053] Preferably, the above-mentioned AI video analysis technology can specifically adopt a video inference network model based on the Transformer architecture, take video frames as elements of the input sequence, capture the dynamic relationship between frames through a multi-head self-attention mechanism, thereby learning the dynamic features in the video and processing information in the spatial and temporal dimensions at the same time.

[0054] When non-related personnel are identified in the input video clips as moving around the food station, the alarm mechanism is triggered to remind staff to pay attention to the activities of the non-related personnel. If malicious feeding of stray animals or other suspicious behaviors are found, appropriate measures will be taken in a timely manner.

[0055] Example 4 (Animal Population Trend Prediction and Behavior Prediction) Quantity forecast As a centralized activity area for stray animals, the feeding station is convenient for collecting and counting the number and behavior data of stray animals. By observing and recording at the feeding station, data can be obtained more efficiently, improving the accuracy and efficiency of statistics.

[0056] In order to accurately count the number, it is necessary to collect the total number of stray animals that appear around the food stations within a specific period of time.

[0057] Collect and summarize the time patterns of stray animals’ appearance, such as peak hours of the day.

[0058] Collect characteristic data of stray animals, including age, gender and breeding status, to facilitate more detailed data analysis.

[0059] Preferably, the following methods are suitable for identifying the sex, age and breeding status of stray cats and dogs: Gender identification method: For stray cats and dogs, sex identification can be done by observing their reproductive organs: Observation of reproductive organs: The distance between the anus and the opening of the external genital organs of a female cat is closer (about 1 cm), and the opening of the external genital organs is triangular or flat, similar to the "!" symbol; while the distance between the anus and the opening of the external genital organs of a male cat is farther (about 2 cm), and the opening of the genital organs is rounder, similar to a colon ":".

[0060] Physical characteristics: Male dogs usually have more obvious testicles, while female dogs do not have this feature. For kittens, the gender can be determined by observing whether there are two furry testicles. Age identification method: The age of stray cats and dogs can be determined by the following methods: Teeth observation: For cats, their age can be judged by observing the growth and wear of their teeth. For example, cats have no milk teeth within 2 weeks of age, the first pair of milk incisors begin to grow at 2-3 weeks of age, premolars grow at 4-6 weeks of age, 26 milk teeth grow at 2 months of age, and they enter the tooth replacement period at 3-8 months of age. After 8 months of age, all teeth are replaced with permanent teeth.

[0061] Weight and hair: For dogs, a rough estimate can be made by looking at their weight and external features such as the glossiness of their hair and skin condition. For example, puppies gain weight rapidly, while adult dogs' weight is relatively stable.

[0062] Methods for identifying breeding status: The breeding status can be identified by the following methods: Observation of reproductive organs: For female cats and dogs, you can determine whether they are in estrus or pregnant by observing the swelling, secretions and other characteristics of the reproductive organs.

[0063] Behavioral observations: The behavior of female cats and dogs will change during pregnancy or lactation, such as increased maternal behavior and protective behavior towards their pups.

[0064] Use the above characteristics to predict future trends in animal population changes: By collecting data on the sex, age, and breeding status of stray cats and dogs, it is possible to predict future trends in animal populations: Sex ratio: By counting the number of males and females, it is possible to predict the probability of mating and reproduction in a specific period of time in the future. For example, if there are more female animals in a certain area, then more reproduction is likely to occur without intervention.

[0065] Age structure: By analyzing the proportion of animals of different ages, it is possible to predict the growth potential of a population. For example, if the proportion of young animals (breeding age) is high, then the population has a greater potential for growth.

[0066] Reproductive status: By monitoring the reproductive status of animals, future birth rates can be predicted. For example, if a large number of animals are found to be pregnant, then a large number of newborns can be expected in the future.

[0067] Statistics on the speed and amount of food consumption at food stations indirectly reflect changes in the number of stray animals.

[0068] Through video surveillance and image recognition technology, the health status of stray animals can be assessed, such as whether they are injured, sick or pregnant.

[0069] Monitor stray animals for behavioral patterns, such as unusual behavior, which may indicate health or environmental issues.

[0070] Statistical methods: Block sampling: Divide the city into a series of inspection blocks, randomly select sample blocks, count the number of stray animals in these blocks, and then calculate the total number of stray animals in the entire city.

[0071] Sampling rate estimation method: The total number of stray animals in the entire city is estimated by dividing the number of stray animals in the sample block by the sampling rate.

[0072] Example 5 (Cleanliness Level Evaluation) Establish a hierarchical cleanliness model to conduct hierarchical assessment from community subsystem to regional system and then to central management system; The YOLO model detects the amount and type of garbage in street images, and then calculates a cleanliness index or score based on the amount and type of garbage data; By averaging or weighted averaging the cleanliness index of all streets within a block, an assessment of the cleanliness of the entire block can be obtained; By aggregating the cleanliness assessments of all blocks within the area, the cleanliness level of the entire city is obtained.

[0073] Example 6 (Example of inspection path of inspection robot) Two patrol robots move in parallel and towards each other, with a certain distance between them, so that stray animals in the area between them can be identified by at least one patrol robot. The patrol robot on the dominant side of the strip area (called A) counts and records every stray animal identified in the strip area (the area between the patrol robot and the stray animals and the other patrol robot), and the other patrol robot (called B) calls the number for verification to prevent A from ignoring stray animals outside its sight. When reaching the end of each strip area, the patrol robot on the dominant side (A) returns along the original route, while the other patrol robot (B) moves to the dominant side of the second strip area. In the second strip area, B counts and records at this time, and A is responsible for marking the edge position of the previous strip area and reminding B of any hidden stray animals. Repeat the above process until the statistics of the entire area are completed.

[0074] Preferably, the above process can also be applied to the cleaning condition assessment module, and the inspection robot identifies and records the road garbage in the same manner as the above solution.

[0075] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An urban intelligent inspection system, characterized by: It includes central processing module, sanitation module and stray animal management module; The inspection area is divided into several inspection blocks, which include several streets and communities; Several inspection devices are deployed in each inspection area to collect inspection data in real time; The central processing module is used to analyze and process the inspection data of each inspection block transmitted by the sanitation module and the stray animal management module, and generate decision support information based on the analysis and processing results; The sanitation module is used to assess the cleanliness of streets and monitor air quality, water quality and noise indicators in real time; The stray animal management module is used to monitor and manage stray animals, and record the activity areas, behavior patterns and numbers of stray animals.

2. According to claim 1, a city intelligent inspection system is characterized by: Inspection equipment includes inspection robots, drones and cameras; Inspection robots and drones work together. Drones conduct large-scale rapid inspections in the air, using high-definition cameras and infrared thermal imaging equipment to conduct long-distance observations and identify objects within the area. The inspection robots will confirm and process anomalies discovered by drones at close range and enter dangerous or hard-to-reach areas for detailed inspections.

3. According to claim 1, a city intelligent inspection system is characterized by: The sanitation module includes a cleanliness hierarchy assessment model; The cleanliness hierarchical assessment model is used to conduct hierarchical assessments from streets to inspection blocks and then to cities. Specifically: The YOLO model detects the amount and type of garbage in street images, and then calculates a cleanliness index or score based on the amount and type of garbage data; The cleanliness evaluation result of the entire inspection block is obtained by averaging or weighted averaging the cleanliness indexes of all streets in the inspection block; By summarizing the cleanliness assessment results of all inspection blocks in the area, the cleanliness level of the entire city can be obtained.

4. According to claim 1, a city intelligent inspection system is characterized by: There are food stations at specific locations in each patrol area to place animal food, help stray animals, attract stray animals to come for food, centrally manage and monitor the activities of stray animals, and count the number of stray animals; The food station serves as a stopover node for inspection equipment.

5. According to claim 1, the city intelligent inspection system is characterized by: There are also charging stations at specific locations in each inspection block for charging inspection equipment; The charging station serves as a stop node for the inspection equipment. The inspection path of the inspection equipment is planned by analyzing the location coordinates and possible connection nodes contained in each stop node.

6. According to claim 1, the city intelligent inspection system is characterized by: The inspection device judges based on the power threshold. When the power required for the inspection device to return to the charging station minus the current power of the inspection device is less than the preset threshold or when the power consumption required for the inspection robot to go to the next stop node, the power consumption required for the next stop node to work, and the power consumption required from the next stop node to the charging station total exceeds the difference between the current power of the inspection robot and the threshold, a charging warning is issued and the inspection robot moves to the charging station to ensure that the inspection robot can be charged in time and continue to work.

7. According to claim 5, a city intelligent inspection system is characterized by: The charging station is set up near the food station. The inspection robot monitors and replenishes power at the same time when it stops at the stop node.

8. According to claim 1, the city intelligent inspection system is characterized by: During the inspection process, the inspection equipment identifies stray animal images, locks on stray animals as monitoring targets, and collects relevant data on stray animals, including the number, health status, and behavior patterns of stray animals. Based on the collected relevant data, combined with historical data and historical laws, the trend of changes in the number of stray animals in the future is predicted, and further decisions are made based on the predicted data; The inspection equipment also monitors the behavior and physical condition of stray animals. When an abnormality is detected, an alarm is sent to the central processing module, and a video of the environment around the stray animal or pet is taken and sent to the central processing module; The inspection equipment regularly collects images of stray animals that appear in the community during a specific period of time, and counts the number of stray animals in the specific period of time through the central processing module; When the number of stray animals exceeds the set threshold, capture and sterilization measures will be implemented or stray animal welfare organizations will be contacted; Identify the sex of stray animals and predict the probability of mating and reproduction in a specific period of time in the future by counting the number of males and females separately; When the predicted mating and fertility probabilities exceed the set thresholds, stray animal population control measures are taken.

9. The urban intelligent inspection system according to claim 1 is characterized in that: The inspection equipment uses the ST-GCN model, an action classification algorithm based on a graph convolutional neural network, to identify abnormal behaviors of stray animals. When abnormal behaviors are detected, an alarm will sound to alert nearby pedestrians to be vigilant and send an alarm to the central processing module to provide corresponding processing decisions.

10. The urban intelligent inspection system according to claim 1 is characterized in that: The stray animal management module includes a food station management unit, which is used to manage the food supply in the food station and prevent the food from being randomly placed by irrelevant personnel or foreign objects from appearing in the food station due to other reasons through real-time monitoring; By installing temperature and humidity sensors and cameras in the food station, the environmental conditions and food status of the food station can be monitored in real time, and the temperature and humidity data can be visualized and the warning thresholds of the temperature and humidity data can be configured; During the inspection process, the inspection equipment stops at the food station to collect temperature and humidity data. When the temperature and humidity data exceed the safe range, an alarm is automatically triggered; Clean and replenish food in the food station regularly.

Images