Ultra-low power consumption environment monitoring system
By deploying an artificial intelligence model on the camera device and using the LoRa network to transmit environmental monitoring results, the problems of high power consumption and poor data transmission of monitoring equipment in sparsely populated areas have been solved, realizing a low-power and high-efficiency environmental monitoring system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SEEED TECH
- Filing Date
- 2025-11-24
- Publication Date
- 2026-04-24
AI Technical Summary
In environmental monitoring in sparsely populated areas, existing technologies face problems such as poor communication network coverage, high power consumption of monitoring equipment, low intelligence, and poor flexibility, resulting in poor data transmission capabilities.
An ultra-low power environmental monitoring system is adopted, which includes a camera device with an artificial intelligence model, uses a LoRa network to transmit environmental monitoring results, transmits data over long distances through a LoRaWAN network server, and the terminal device receives and processes the monitoring results.
It enables low-power, high-efficiency data transmission in remote areas, enhances the intelligence and flexibility of environmental monitoring, and reduces equipment costs and power consumption.
Smart Images
Figure CN121916971A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of environmental monitoring technology, and in particular to an ultra-low power environmental monitoring system. Background Technology
[0002] In remote areas such as forests, lakes, wetlands, and deserts, researchers and management departments often need to conduct wildlife monitoring, environmental quality testing, forest fire risk warnings, and water pollution monitoring to obtain continuous and reliable ecological and environmental data. However, due to the often inconvenient transportation and lack of infrastructure in these areas, the application of relevant monitoring methods faces many challenges: poor communication network coverage leading to poor data transmission capabilities, high power consumption and cost of monitoring equipment, low intelligence, and poor flexibility. Summary of the Invention
[0003] In view of this, one objective of the present invention is to provide an ultra-low power environmental monitoring system to improve the situation of high cost and power consumption and poor data transmission capability in related technologies.
[0004] To address the aforementioned technical problems, the embodiments of the present invention provide the following technical solutions: In a first aspect, embodiments of the present invention provide an ultra-low power environment monitoring system, comprising: The camera device, equipped with an artificial intelligence model, is configured to capture and collect environmental image data and process the environmental image data using the artificial intelligence model to obtain environmental monitoring results. The data device, which is connected to the camera device via a first network, includes a power supply battery and is configured to receive environmental monitoring results transmitted by the camera device and provide power to the camera device via the power supply battery; The cloud module, which communicates with the data device via the LoRa network, includes a LoRaWAN gateway and a LoRaWAN network server. The cloud module is configured to receive environmental monitoring results transmitted by the data device through the LoRaWAN gateway and upload the environmental monitoring results to the LoRaWAN network server. The terminal device, which communicates with the data device via a second network and with the cloud module via a LoRa network, is configured to receive environmental monitoring results transmitted by the LoRaWAN network server and / or the data device.
[0005] In some embodiments, the camera device includes a camera, an image processing chip, and a main control unit. The image processing chip is electrically connected to the camera and the main control unit, respectively. An artificial intelligence model is deployed on the image processing chip. The terminal device is also communicatively connected to the camera device and is configured to configure model operating parameters and transmit the model operating parameters to the camera device. The camera is configured to capture environmental image data and transmit the environmental image data to the image processing chip; The main control unit is configured to receive model operation parameters transmitted by the terminal device, transmit the model operation parameters to the image processing chip, and transmit environmental monitoring results to the data device; The image processing chip is configured to receive model operation parameters transmitted by the main control unit, and call the artificial intelligence model according to the model operation parameters to process the environmental image data and obtain environmental monitoring results.
[0006] In some embodiments, the image processing chip is configured to invoke an artificial intelligence model based on model operating parameters to process environmental image data and obtain environmental monitoring results, including: The environmental image data is preprocessed to obtain reference image data; The reference image data is input into the artificial intelligence model, so that the artificial intelligence model can analyze and identify the reference image data under the model's operating parameters to obtain environmental monitoring results.
[0007] In some embodiments, the model operating parameters include a reference type and reference parameters. Reference image data is input into the artificial intelligence model so that the model can parse and identify the reference image data under the model operating parameters to obtain environmental monitoring results, including: Determine the target artificial intelligence model based on the reference type; The parameters for configuring the target artificial intelligence model are for reference only; Input reference image data into the target AI model so that the target AI model can parse and recognize the reference image data under reference parameters to obtain environmental monitoring results.
[0008] In some embodiments, the camera device further includes a storage unit electrically connected to an image processing chip, the storage unit being configured to store environmental image data and environmental monitoring results.
[0009] In some embodiments, the environmental monitoring results include target results and corresponding confidence values for the target results. The storage unit is configured to store environmental image data and environmental monitoring results, including: If the confidence value of the response result is greater than the preset confidence threshold, the environmental image data and environmental monitoring results will be stored in the storage unit. If the confidence value of the response result is less than or equal to the confidence threshold, delete the environmental image data and environmental monitoring results.
[0010] In some embodiments, the terminal device is further configured to configure reference operating information of the data device and transmit the reference operating information to the data device, the reference operating information including a reference operating mode and a reference power supply time; The data device is configured to receive reference operating information transmitted by the terminal device and operate in reference operating mode, power the camera device during the reference power supply time, and receive environmental monitoring results transmitted by the camera device and transmit the environmental monitoring results to the LoRaWAN gateway.
[0011] In some embodiments, the data device is configured to receive reference operating information transmitted by the terminal device and operate in a reference operating mode, supplying power to the camera device during a reference power supply time, including: Analyze the reference work information and extract the reference work mode; Adjust the data device's operating mode to the reference operating mode; In reference operating mode, the current system time of the response data device meets the reference power supply time, and the electrical connection with the camera device is established to provide power to the camera device.
[0012] In some embodiments, the reference operating information further includes a power supply time interval, responding to the current system time of the data device meeting the reference power supply time, and establishing an electrical connection with the camera device to provide power to the camera device, including: If the current system time meets the reference power supply time, the power supply battery will start supplying power to the camera device for the first time. If the power supply termination condition is met, disconnect the electrical connection with the camera device to suspend power supply to the camera device; Timing delay duration; When the response delay reaches the power supply interval, the electrical connection with the camera device is re-established to provide power to the camera device again.
[0013] In some embodiments, the ultra-low power environment monitoring system further includes a model management platform, which is communicatively connected to the camera device and configured to train and manage artificial intelligence models and deploy artificial intelligence models to the camera device.
[0014] The embodiments of the present invention have the following beneficial effects: Unlike related technologies, the ultra-low power environmental monitoring system provided by the embodiments of the present invention includes a camera device, a data device, a cloud module, and a terminal device. The camera device is equipped with an artificial intelligence model and is configured to capture and collect environmental image data and process the environmental image data using the artificial intelligence model to obtain environmental monitoring results. The data device is communicatively connected to the camera device and includes a power supply battery. It is configured to receive the environmental monitoring results transmitted by the camera device and provide power to the camera device through the power supply battery. The cloud module is communicatively connected to the data device based on a LoRa network and includes a LoRaWAN gateway and a LoRaWAN network server. The cloud module is configured to receive the environmental monitoring results transmitted by the data device through the LoRaWAN gateway and upload the environmental monitoring results to the LoRaWAN network server. The terminal device is communicatively connected to the data device and the cloud module based on a LoRa network and is configured to receive the environmental monitoring results transmitted by the LoRaWAN network server and / or the data device.
[0015] This invention deploys an artificial intelligence model on a camera device and processes the environmental image data collected by the camera device using the artificial intelligence model to obtain environmental monitoring results. Then, the environmental monitoring results are transmitted to the cloud module via a data device based on the LoRa network. Thus, the LoRaWAN network server or data device transmits the environmental monitoring results to the terminal device over a long distance with extremely low power consumption via the LoRa network. The LoRa network is low in cost, has strong anti-interference capabilities, and has good data transmission capabilities. Attached Figure Description
[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the prior art or embodiments will be briefly introduced below. Obviously, the drawings described below only show some embodiments of the present invention and should not be considered as limiting the scope of protection. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0017] Figure 1 This is a schematic diagram of the structure of an ultra-low power environment monitoring system provided in some embodiments of the present invention; Figure 2a These are schematic diagrams of the camera device provided in some embodiments of the present invention; Figure 2b This is a flowchart illustrating the execution steps of the image processing chip in a camera device provided in some embodiments of the present invention; Figure 2c yes Figure 2b A schematic diagram of a sub-process of the image processing chip executing step S22 shown in the embodiment; Figure 3These are schematic diagrams of the camera device provided in other embodiments of the present invention; Figure 4 This is a flowchart illustrating the execution steps of the storage unit in a camera device provided in some embodiments of the present invention; Figure 5 This is a flowchart illustrating the execution steps of a data device in an ultra-low power environment monitoring system provided in some embodiments of the present invention; Figure 6 This is a schematic diagram of the structure of an ultra-low power environment monitoring system provided in some other embodiments of the present invention.
[0018] Explanation of reference numerals in the attached figures: 100. Ultra-low power consumption environmental monitoring system; 110. Camera device; 111. Camera; 112. Image processing chip; 113. Main control unit; 114. Storage unit; 120. Data equipment; 121. Power supply battery; 130. Cloud module; 131. LoRaWAN gateway; 132. LoRaWAN network server; 140. Terminal equipment; 150. Model Management Platform. Detailed Implementation
[0019] To make the objectives and advantages of the embodiments of the present invention more readily understood, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. The detailed description of the embodiments of the present invention in the accompanying drawings is not intended to limit the scope of protection claimed by the present invention, but only to illustrate selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0020] It should be noted that, unless there is a conflict, the various technical features involved in the embodiments of the present invention described below can be combined with each other, and all are within the protection scope of the present invention. Furthermore, although functional modules are divided in the device or structural schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. In addition, the terms "first," "second," "third," and other similar expressions used herein do not limit the data or execution order, but are only for illustrative purposes and to distinguish identical or similar items with substantially the same function and effect, and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features.
[0021] Unless otherwise defined, the technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. It should be understood that the term "and / or" as used herein includes any and all combinations of one or more of the listed items.
[0022] Please see Figure 1 , Figure 1 A schematic diagram of the structure of an ultra-low power environment monitoring system provided in some embodiments of the present invention is shown.
[0023] See Figure 1 As shown, the ultra-low power environment monitoring system 100 includes a camera device 110, a data device 120, a cloud module 130, and a terminal device 140. The data device 120 communicates with the camera device 110 via a first network and with the terminal device 140 via a second network. The cloud module 130 communicates with both the data device 120 and the terminal device 140 via a LoRa network. It can be understood that examples of the first and second networks include, but are not limited to, enterprise intranets, local area networks, mobile communication networks, and combinations thereof. LoRa network is a low-power wide area network (LPWAN) based on LoRa modulation technology. Its core function is to achieve ultra-long-distance, low-speed, and extremely low-power data transmission, making it a key technology for connecting "long-distance low-power devices."
[0024] The camera device 110 is equipped with one or more artificial intelligence models, configured to capture and collect environmental image data, and to process the environmental image data using the artificial intelligence models to obtain environmental monitoring results. The artificial intelligence models can be common application models or other models available on the market. After training and parameter adjustments, the artificial intelligence models are used to process environmental image data to identify animals, people, or other monitoring objects within the environmental image data.
[0025] It is understandable that environmental monitoring results can be for animals, people, water bodies, fires, or any other suitable monitoring objects. Different monitoring objects can use the same or different artificial intelligence models to identify and process environmental data to obtain environmental monitoring results.
[0026] The data device 120 includes a power supply battery 121 and is configured to receive environmental monitoring results transmitted by the camera device 110 and provide power to the camera device 110 via the power supply battery 121. After obtaining environmental monitoring results by recognizing and processing environmental image data using an artificial intelligence model, the camera device 110 transmits the environmental monitoring results to the data device 120 via a first network. The data device 120 is also configured to transmit the environmental monitoring results to the cloud module 130 and the terminal device 140 via a LoRa network. In some embodiments, the data device 120 receives power supply configuration information from the terminal device 140 and intermittently provides power to the camera device 110 via the power supply battery 121 according to the power supply configuration information. This enables ultra-low power consumption of the camera device 110 and supports the deployment of the camera device 110 and the data device 120 in remote areas for several years.
[0027] In this embodiment of the invention, the cloud module 130 includes a LoRaWAN gateway 131 and a LoRaWAN network server 132. The cloud module 130 is configured to receive environmental monitoring results transmitted by the data device 120 via the LoRaWAN gateway 131 over the LoRa network, and the LoRaWAN gateway 131 uploads the environmental monitoring results to the LoRaWAN network server 132 over the LoRa network. The LoRaWAN network server 132 is configured to transmit the environmental monitoring results to the terminal device 140 over the LoRa network.
[0028] Specifically, the terminal device 140 is configured to receive environmental monitoring results transmitted by the LoRaWAN network server 132 (i.e., the LoRaWAN network server 132 transmits environmental monitoring results based on the LoRa network) and / or receive environmental monitoring results transmitted by the data device 120 based on a second network (such as a Bluetooth network).
[0029] In this embodiment of the invention, the camera device 110, as an AIoT (Artificial Intelligence of Things) device, is equipped with a camera sensor, communication module, and artificial intelligence model, etc. It is an intelligent terminal combining artificial intelligence and the Internet of Things, possessing the capabilities of perception, analysis, and decision-making. The camera device 110 collects environmental image data, transmits it through the network, and autonomously analyzes it, ultimately achieving intelligent response or control. It should be understood that the camera device 110 is powered by the battery 121, enabling flexible and ultra-low power consumption operation. This allows the camera device 110 and data device 120 to be deployed for several years in remote areas with poor transportation and lack of infrastructure, thereby achieving environmental monitoring in remote areas.
[0030] This invention deploys an artificial intelligence model on a camera device and processes the environmental image data collected by the camera device using the artificial intelligence model to obtain environmental monitoring results. In this way, the environmental monitoring results are transmitted to the cloud module via a data device based on the LoRa network. Thus, the LoRaWAN network server or data device transmits the environmental monitoring results to the terminal device over a long distance with extremely low power consumption via the LoRa network. The LoRa network is low in cost, has strong anti-interference ability, and has good data transmission capability.
[0031] Please see Figure 2a In some embodiments, the camera device 110 includes a camera 111, an image processing chip 112, and a main control unit 113, with the image processing chip 112 electrically connected to both the camera 111 and the main control unit 113. An artificial intelligence model is deployed on the image processing chip 112.
[0032] It should be understood that the terminal device 140 is also connected to the camera device 110 via a third network, and is configured to configure model operating parameters and transmit the model operating parameters to the camera device 110. Examples of third networks include, but are not limited to, corporate intranets, local area networks, mobile communication networks, and combinations thereof.
[0033] Specifically, the camera 111 is configured to capture environmental image data and transmit the environmental image data to the image processing chip 112.
[0034] The main control unit 113 is configured to receive model operation parameters transmitted by the terminal device 140 via a third network, transmit the model operation parameters to the image processing chip 112, and transmit environmental monitoring results to the data device 120 via a first network.
[0035] The image processing chip 112 is configured to receive model operation parameters transmitted by the main control unit 113, call the artificial intelligence model according to the model operation parameters to process environmental image data, perform inference calculations, and obtain environmental monitoring results.
[0036] In some embodiments, the image processing chip 112 may be a Himax-6538 chip or any other suitable chip. The main control unit 113 may be any suitable controller (e.g., an ESP32 microcontroller), responsible for communicating with external devices (relative to the camera device 110), transmitting control commands, and managing the entire workflow of the camera device 110.
[0037] Please see Figure 2b , Figure 2b The diagram illustrates, schematically, the execution steps of the image processing chip in the camera device provided in some embodiments of the present invention.
[0038] See Figure 2bAs shown, in some embodiments, the execution steps of the image processing chip specifically include, but are not limited to, the following steps S21-S22: S21: Preprocess the environmental image data to obtain reference image data.
[0039] In this step, preprocessing includes operations such as noise reduction, resolution scaling, cropping, and image enhancement. The image processing chip runs an edge computing service, which integrates multiple service modules, including image services, model services, and storage services. The image service is responsible for preprocessing the environmental image data acquired by camera 111, including noise reduction, resolution scaling, cropping, and image enhancement, to obtain reference image data. This reference image data is then transmitted to the terminal device 140. The reference image data is the image data obtained after preprocessing the environmental image data.
[0040] It should be understood that the model service is responsible for loading / calling, managing, and executing inference on artificial intelligence models, and supports dynamic switching and hot update mechanisms for multiple models. The storage service is responsible for determining whether to save environmental image data or environmental monitoring results based on user-defined confidence thresholds, in order to reduce the storage of redundant data and save storage space. The image service, model service, and storage service operate collaboratively through the internal software bus of the camera device 110, and interact with the main control unit 113 for data and instructions through the UART interface.
[0041] The main control unit 113 (such as an ESP32 microcontroller) runs system control and communication services, including the following two core services: 1) Control service, which is responsible for the overall operation status management of camera device 110, including artificial intelligence model selection control, model threshold parameter distribution, data storage logic management, and task scheduling. The control service can parse instructions from user interaction or image processing chip 112 and execute corresponding operations.
[0042] 2) Communication service, which manages communication with external devices. On one hand, it establishes a communication connection with terminal device 140 through a third network, receives model configuration instructions input by the user, and uploads the operating status of camera device 110. On the other hand, it communicates with data device 120 through a first network (such as an RS485 wired communication network), sending the inference results (i.e., environmental monitoring results) to data device 120. Thus, the inference results (i.e., environmental monitoring results) can be further uploaded to LoRaWAN network server 132 via LoRa network through LoRa gateway 131.
[0043] S22: Input the reference image data into the artificial intelligence model so that the artificial intelligence model can analyze and identify the reference image data under the model's operating parameters to obtain environmental monitoring results.
[0044] For example, reference image data is input into a selected artificial intelligence model, which then analyzes and identifies the reference image data to obtain environmental monitoring results. Specifically, the image processing chip configures the parameters of the artificial intelligence model according to the model's operating parameters, ensuring that the model's parameters are the same as its operating parameters. This allows the artificial intelligence model to analyze and identify the reference image data under these operating parameters, thus obtaining environmental monitoring results.
[0045] Please see Figure 2c , Figure 2c The illustration shows a sub-process diagram of the execution step S22 of the image processing chip in the camera device provided in some embodiments of the present invention.
[0046] like Figure 2c As shown, in some embodiments, reference image data is input into an artificial intelligence model so that the model can parse and identify the reference image data under the model's operating parameters to obtain environmental monitoring results, specifically including but not limited to the following steps S221-S223: S221: Determine the target artificial intelligence model based on the reference type.
[0047] S222: Configure the parameters of the target artificial intelligence model as reference parameters.
[0048] In this embodiment, the model running parameters include reference type and reference parameters. The reference type refers to the type of artificial intelligence model that needs to be called and run (e.g., a grizzly bear recognition artificial intelligence model, a human recognition artificial intelligence model, etc.), and the reference parameters refer to the parameters of the artificial intelligence model that is called and run (e.g., the confidence threshold of environmental monitoring results, the storage format of environmental monitoring results, etc.).
[0049] For example, the image processing chip analyzes the model's operating parameters, extracts the reference type and reference parameters, determines the AI model corresponding to the reference type as the target AI model based on the reference type, and configures the parameters of the target AI model as reference parameters based on the reference parameters.
[0050] S223: Input reference image data into the target artificial intelligence model so that the target artificial intelligence model can parse and recognize the reference image data under the reference parameters to obtain environmental monitoring results.
[0051] For example, reference image data is input into the target artificial intelligence model. Under reference parameters, the target artificial intelligence model activates neural network units to parse and recognize the reference image data, identify animals, people or other monitoring objects in the reference image data, and obtain environmental monitoring results.
[0052] Please see Figure 3 In some embodiments, the camera device 110 further includes a storage unit 114, which is electrically connected to the image processing chip 112. The storage unit 114 is configured to store environmental image data and environmental monitoring results. When the environmental monitoring results meet preset conditions (such as a confidence level greater than or equal to a preset confidence threshold), the environmental image data captured by the camera 111 and the environmental monitoring results are stored in the storage unit 114, and the environmental image data and environmental monitoring results are transmitted to the data device 120. This reduces the storage and transmission of redundant data, thereby saving storage space and data transmission volume.
[0053] Please see Figure 4 , Figure 4 The diagram illustrates the execution steps of the storage unit in a camera device provided in some embodiments of the present invention.
[0054] like Figure 4 As shown, in some embodiments, storing environmental image data and environmental monitoring results specifically includes, but is not limited to, the following steps S41-S42: S41: If the confidence value of the response result is greater than the preset confidence threshold, the environmental image data and environmental monitoring results are stored in the storage unit.
[0055] S42: If the confidence value of the response result is less than or equal to the confidence threshold, delete the environmental image data and environmental monitoring results.
[0056] In this embodiment, the environmental monitoring results include the target result and the corresponding result confidence value. The target result is the identification result of the monitored object (i.e., whether the monitored object exists in the monitored environment). The result confidence value characterizes the confidence level of the target result. For example, if the target result is that a grizzly bear is detected in the forest, then the result confidence value characterizes the confidence level that a grizzly bear exists in the forest. It is easy to understand that engineers can customize and set the confidence threshold based on experimental data and historical experience, for example, the confidence threshold can be set to 50%. This embodiment of the invention does not impose any limitation on this.
[0057] Specifically, the result confidence value is compared with a preset confidence threshold. When the result confidence value is greater than the preset confidence threshold, it indicates that the target result is credible, and the environmental image data and environmental monitoring results are stored in the storage unit. When the result confidence value is less than or equal to the confidence threshold, it indicates that the target result is unreliable, and the environmental image data and environmental monitoring results are deleted; the storage unit does not store the environmental image data and environmental monitoring results.
[0058] In some embodiments, the terminal device 140 is further configured to configure reference operating information of the data device 120 and transmit the reference operating information to the data device 120. The reference operating information includes a reference operating mode and a reference power supply time. The reference operating mode refers to the operating mode of the data device 120, and the reference power supply time refers to the time during which the data device 120 provides power to the camera device 110.
[0059] Data device 120 is configured to receive reference working information transmitted by terminal device 140 and operate in reference working mode. During the reference power supply time, it supplies power to camera device 110 through power supply battery 121, receives environmental monitoring results transmitted by camera device 110, and transmits the environmental monitoring results to LoRaWAN gateway 131 of cloud module 130 based on LoRa network.
[0060] In other words, the data device 120 receives the reference working information transmitted by the terminal device 140, adjusts and converts its own working mode to the reference working mode, and operates in the reference working mode. When the current system time reaches the reference power supply time, the power supply operation is started, and the power supply battery 121 provides power to the camera device 110, enabling the camera device 110 to capture and collect environmental image data.
[0061] Please see Figure 5 , Figure 5 The diagram illustrates, schematically, the execution steps of a data device in an ultra-low power environment monitoring system provided by some embodiments of the present invention.
[0062] like Figure 5 As shown, in some embodiments, the device receives reference operating information transmitted by the terminal device and operates in a reference operating mode, supplying power to the camera device during the reference power supply time, specifically including but not limited to the following steps S51-S53: S51: Parse the reference working information and extract the reference working mode.
[0063] S52: Adjust the operating mode of the data device to the reference operating mode.
[0064] S53: In reference operating mode, when the current system time of the response data device meets the reference power supply time, the electrical connection with the camera device is established to provide power to the camera device.
[0065] For example, the data device 120 parses the reference working information transmitted by the terminal device 140, extracts the reference working mode and reference power supply time, and adjusts and converts the working mode of the data device 120 to the reference working mode. When working in the reference working mode, it compares whether the current system time of the data device 120 has reached the reference power supply time. When the current system time has reached the reference power supply time, it means that the current system time meets the reference power supply time. The data device 120 connects / conducts the electrical connection between the power supply battery 121 and the camera device 110, thereby providing power to the camera device 110 through the power supply battery 121.
[0066] In this embodiment, power is flexibly supplied to the camera device according to the reference operating mode and reference power supply time, which can save the power consumption of the camera device and improve the battery life of the ultra-low power environment monitoring system.
[0067] In some embodiments, the reference working information also includes a power supply interval, which is the time interval at which the data device provides power to the camera device, for example, a power supply interval of 5 minutes, whereby the data device provides power to the camera device once every 5 minutes.
[0068] Specifically, when the current system time of the response data device meets the reference power supply time, the electrical connection with the camera device is established to provide power to the camera device, including but not limited to the following steps S531-S534: S531: In response to the current system time meeting the reference power supply time, the power supply battery is started to power the camera device for the first time.
[0069] S532: In response to the power supply termination condition being met, disconnect the electrical connection with the camera device to suspend power supply to the camera device.
[0070] S533: Timing delay duration.
[0071] S534: When the response delay time reaches the power supply interval, the electrical connection with the camera device is re-established to provide power to the camera device again.
[0072] Specifically, after the current system time reaches the reference power supply time (i.e., the current system time meets the reference power supply time), the data device activates the power supply battery to initially power the camera device. When the power supply termination condition is detected, the electrical connection between the power supply battery and the camera device is disconnected to pause power supply to the camera device. It is understood that the power supply termination condition includes, but is not limited to, the duration of this power supply to the camera device being greater than or equal to a preset duration threshold, and the number of environmental image data points captured by the camera device this time being greater than or equal to a preset quantity threshold. After pausing power supply to the camera device, the data device uses a built-in timer to calculate the delay duration. When the delay duration reaches the power supply interval (i.e., the timer has calculated the delay duration), the electrical connection between the power supply battery and the camera device is reconnected to provide power to the camera device again, enabling the camera device to capture environmental image data again.
[0073] Please see Figure 6 In some embodiments of the present invention, the ultra-low power environment monitoring system 100 further includes a model management platform 150, wherein the model management platform 150 is communicatively connected to the camera device 110 via a fourth network, and is configured to train and manage multiple artificial intelligence models and deploy artificial intelligence models to the camera device 110. Examples of the fourth network include, but are not limited to, enterprise intranets, local area networks, mobile communication networks, and combinations thereof.
[0074] The model management platform 150 is used for the training, management and configuration of artificial intelligence models. Users can train custom artificial intelligence models on the model management platform 150 and remotely deploy them to the camera device 110 to realize hot updates and selection switching of artificial intelligence models.
[0075] In this embodiment of the invention, the model management platform 150 provides a web-based user interface, allowing users to train, manage versions, evaluate inference performance, and deploy artificial intelligence models with a single click. For example, the model management platform 150 transmits artificial intelligence model deployment instructions to the image processing chip 112 via a UART interface, enabling remote updating and loading of the model.
[0076] In some embodiments, the terminal device 140 provides a mobile interaction method, allowing users to quickly configure the camera device 110 through the terminal device 140, such as selecting an artificial intelligence model, setting a confidence threshold, and monitoring its operating status. In some embodiments, the terminal device 140 communicates with the camera device 110 via Bluetooth or Wi-Fi to achieve local wireless configuration and maintenance of the artificial intelligence model.
[0077] For example, in practical applications, the workflow of the ultra-low power environment monitoring system 100 is roughly as follows: ① The user trains a custom artificial intelligence model through the model management platform 150 and deploys the artificial intelligence model in the camera device 110. Then, the user configures the target artificial intelligence model and confidence threshold parameters through the terminal device 140. The terminal device 140 transmits the artificial intelligence model configuration instructions to the main control unit 113 through a third network (such as Bluetooth network). The main control unit 113 synchronously transmits the configuration instructions to the image processing chip 112.
[0078] ② After the camera device 110 is deployed to the work site, the user can configure the reference power supply time and power supply interval of the camera device 110, the reference working mode of the data device 120 and the model running parameters through the terminal device 140, and transmit these parameters to the data device 120. The data device 120 works according to these parameters and supplies power to the camera device 110 through the power supply battery 121.
[0079] ③ After the camera device 110 is powered on, the main control unit 113 triggers the image service, in which the image service acquires the image input from the camera 111 to obtain environmental image data, and the model service loads the selected artificial intelligence model to process and identify the environmental image data to obtain environmental monitoring results.
[0080] ④ The service performs a confidence threshold judgment on the environmental monitoring results. If the confidence value of the result corresponding to the target result exceeds the set confidence threshold, the operation of saving the environmental image data and environmental monitoring results to the storage unit 114 is triggered.
[0081] ⑤ At the same time, the environmental monitoring results are transmitted to the main control unit 113 via the UART interface. The main control unit 113 schedules and controls the service, and sends the environmental monitoring results to the data device 120 via the communication service based on the first network.
[0082] ⑥ Data device 120 converts the environmental monitoring results into LoRa signals and uploads the environmental monitoring results to LoRaWAN gateway 131. LoRaWAN gateway 131 transmits the environmental monitoring results to LoRaWAN network server 132 based on the LoRa network, and finally transmits the environmental monitoring results to the cloud or terminal device 140.
[0083] ⑦ Users can receive the working status of the camera device 110 and the data device 120, the environmental monitoring results transmitted by the LoRaWAN network server 132 and / or the data device 120 through the terminal device 140, thereby completing the closed loop of on-site operation and maintenance and remote management of the ultra-low power environmental monitoring system 100.
[0084] In summary, the ultra-low power environmental monitoring system provided by this invention deploys an artificial intelligence model on a camera device, processes the environmental image data collected by the camera device using the artificial intelligence model, and obtains environmental monitoring results. Then, the environmental monitoring results are transmitted to a cloud module via a data device using a LoRa network. This allows the LoRaWAN network server or data device to transmit the environmental monitoring results to the terminal device over long distances with extremely low power consumption via the LoRa network. The LoRa network is low-cost, has strong anti-interference capabilities, and excellent data transmission capabilities.
[0085] Those skilled in the art will understand that the embodiments provided by this invention are merely illustrative. The order in which the steps in the methods of the embodiments are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The order can be adjusted, merged, and deleted according to actual needs. Modules or sub-modules, units or sub-units in the apparatus or system of the embodiments can be merged, divided, and deleted according to actual needs. For example, the division of units is only a logical functional division, and there may be other division methods in actual implementation. For another example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed.
[0086] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, and of course, it can also be implemented using hardware. Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. It should be understood that the storage medium can be flash memory, hard disk, optical disk, register, magnetic surface memory, removable disk, CD-ROM, random access memory (RAM), read-only memory (ROM), electrically programmable ROM, and electrically erasable programmable ROM, etc.
[0087] It should be noted that the above embodiments are for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. Those skilled in the art can understand that all or part of the processes of the above embodiments can be implemented by modifying the technical solutions described in the embodiments of the present invention, or by making equivalent substitutions for some of the technical features. It is understood that these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and should be considered as equivalent changes and modifications made based on the embodiments of the present invention, all of which should fall within the scope of the claims of the present invention.
Claims
1. An ultra-low power environmental monitoring system, characterized in that, include: The camera device is equipped with an artificial intelligence model and is configured to capture environmental image data and process the environmental image data using the artificial intelligence model to obtain environmental monitoring results. A data device, which is communicatively connected to the camera device via a first network, includes a power supply battery and is configured to receive environmental monitoring results transmitted by the camera device and provide power to the camera device through the power supply battery; A cloud module, which communicates with the data device via a LoRa network, includes a LoRaWAN gateway and a LoRaWAN network server. The cloud module is configured to receive environmental monitoring results transmitted by the data device through the LoRaWAN gateway and upload the environmental monitoring results to the LoRaWAN network server. The terminal device is configured to receive environmental monitoring results transmitted by the LoRaWAN network server and / or the data device, based on a second network and a communication connection with the data device, and based on a LoRa network and a communication connection with the cloud module.
2. The ultra-low power consumption environment monitoring system according to claim 1, characterized in that, The camera device includes a camera, an image processing chip, and a main control unit. The image processing chip is electrically connected to the camera and the main control unit respectively. The artificial intelligence model is deployed on the image processing chip. The terminal device is also communicatively connected to the camera device and is configured to configure model operating parameters and transmit the model operating parameters to the camera device. The camera is configured to capture environmental image data and transmit the environmental image data to the image processing chip; The main control unit is configured to receive model running parameters transmitted by the terminal device, transmit the model running parameters to the image processing chip, and transmit the environmental monitoring results to the data device; The image processing chip is configured to receive model running parameters transmitted by the main control unit, and call the artificial intelligence model according to the model running parameters to process the environmental image data and obtain environmental monitoring results.
3. The ultra-low power consumption environment monitoring system according to claim 2, characterized in that, The image processing chip is configured to invoke the artificial intelligence model according to the model's operating parameters to process the environmental image data and obtain environmental monitoring results, including: The environmental image data is preprocessed to obtain reference image data; The reference image data is input into the artificial intelligence model, so that the artificial intelligence model can parse and identify the reference image data under the model's operating parameters to obtain the environmental monitoring results.
4. The ultra-low power consumption environment monitoring system according to claim 3, characterized in that, The model operating parameters include reference type and reference parameters. The reference image data is input into the artificial intelligence model so that the model can parse and recognize the reference image data under the specified operating parameters to obtain the environmental monitoring results, including: The target artificial intelligence model is determined based on the reference type; Configure the parameters of the target artificial intelligence model as the reference parameters; The reference image data is input into the target artificial intelligence model so that the target artificial intelligence model can parse and recognize the reference image data under the reference parameters to obtain the environmental monitoring results.
5. The ultra-low power consumption environment monitoring system according to claim 2, characterized in that, The camera device also includes a storage unit electrically connected to the image processing chip, and the storage unit is configured to store the environmental image data and the environmental monitoring results.
6. The ultra-low power consumption environment monitoring system according to claim 5, characterized in that, The environmental monitoring results include target results and corresponding confidence values for the target results. The storage unit is configured to store the environmental image data and the environmental monitoring results, including: If the confidence value of the result is greater than a preset confidence threshold, the environmental image data and the environmental monitoring result are stored in the storage unit. If the confidence value of the result is less than or equal to the confidence threshold, the environmental image data and the environmental monitoring results are deleted.
7. The ultra-low power consumption environment monitoring system according to claim 1, characterized in that, The terminal device is also configured to configure reference operating information of the data device and transmit the reference operating information to the data device, wherein the reference operating information includes a reference operating mode and a reference power supply time. The data device is configured to receive reference operating information transmitted by the terminal device and operate in the reference operating mode, supply power to the camera device during the reference power supply time, receive environmental monitoring results transmitted by the camera device, and transmit the environmental monitoring results to the LoRaWAN gateway.
8. The ultra-low power environmental monitoring system according to claim 7, characterized in that, The data device is configured to receive reference operating information transmitted by the terminal device and operate in the reference operating mode, providing power to the camera device during the reference power supply time, including: Parse the reference working information and extract the reference working mode; Adjust the operating mode of the data device to the reference operating mode; In the reference operating mode, in response to the current system time of the data device meeting the reference power supply time, the electrical connection with the camera device is established to provide power to the camera device.
9. The ultra-low power consumption environment monitoring system according to claim 8, characterized in that, The reference operating information also includes a power supply time interval. The response that the current system time of the data device meets the reference power supply time, and the connection to the camera device to provide power to the camera device, includes: In response to the current system time meeting the reference power supply time, the power supply battery is activated to power the camera device for the first time. If the power supply termination condition is met, the electrical connection with the camera device is disconnected to suspend power supply to the camera device; Timing delay duration; In response to the delay duration reaching the power supply time interval, the electrical connection with the camera device is re-established to provide power to the camera device again.
10. The ultra-low power environmental monitoring system according to any one of claims 1-9, characterized in that, The system also includes a model management platform, which is communicatively connected to the camera device and configured to train and manage the artificial intelligence model and deploy the artificial intelligence model to the camera device.