Node optimization method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to a node optimization method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: collecting energy through a micro-energy collection front end, and storing the energy to a micro-energy storage module; under the condition that the energy in the micro-energy storage module meets the preset energy requirement, energy is supplied through the micro-energy storage module; reading sensor data, and sending the sensor data to a client through a radio frequency module under the condition that the sensor data is detected to be abnormal; and under the condition that the sensor data detection is normal, performing aggregation and compression processing on the sensor data until the processed accumulated data volume reaches a preset threshold value, and sending the processed data to a client through a radio frequency module. By adopting the method, the survivability of the node in an unstable energy supply environment can be improved.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a node optimization method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Art

[0002] As IoT nodes are widely used in environmental monitoring, industrial sensing, smart agriculture and other fields, their long-term deployment faces severe energy challenges. Many nodes rely on environmental energy (such as solar energy, vibration energy, radio frequency energy, etc.), which are usually weak (milliwatt level) and unstable (such as day and night light fluctuations or intermittent energy harvesting).

[0003] In related technologies, due to low energy utilization efficiency, nodes frequently crash or have limited functions. These problems significantly shorten the node lifespan and increase maintenance costs. Summary of the Invention

[0004] Based on this, it is necessary to provide a node optimization method, device, computer equipment, computer-readable storage medium and computer program product that can reduce energy loss in response to the above technical problems.

[0005] In a first aspect, the present application provides a node optimization method, comprising:

[0006] Collect energy through the micro energy collection front end and store the energy in the micro energy storage module;

[0007] When the energy in the micro energy storage module meets the preset energy demand, energy is supplied through the micro energy storage module;

[0008] Read sensor data and send it to the client via the RF module if the sensor data detects an anomaly.

[0009] When the sensor data detection is normal, the sensor data is aggregated and compressed until the cumulative amount of processed data reaches the preset threshold, and the processed data is sent to the client through the radio frequency module.

[0010] In one embodiment, the method further comprises:

[0011] During the process of storing energy in the micro energy storage module, obtaining a voltage signal from the micro energy collection module, and determining the energy storage state of the micro energy storage module according to the voltage signal;

[0012] When the energy in the micro energy storage module exceeds a preset energy threshold, increasing the operating frequency of the microcontroller;

[0013] When the energy in the micro energy storage module does not exceed a preset energy threshold, the operating frequency of the microcontroller is reduced.

[0014] In one embodiment, the method further comprises:

[0015] After the microcontroller is awakened, the sensor switch is turned on, and after receiving the sensor data, the sensor switch is turned off;

[0016] When sensor data is detected to be abnormal or the accumulated sensor data volume reaches a preset threshold, the RF module switch is turned on. After ensuring that the sensor data is sent successfully, the RF module switch is turned off.

[0017] In one embodiment, the aggregating and compressing the sensor data includes:

[0018] De-duplicate and normalize sensor data of the same type or the same monitoring period to obtain aggregated sensor data;

[0019] The aggregated sensor data is compressed using a lightweight compression algorithm to obtain processed data.

[0020] In one embodiment, the micro energy harvesting front end includes at least one of a solar panel or a piezoelectric sensor.

[0021] In one embodiment, the abnormal data determination process includes:

[0022] The sensor data is determined to be abnormal data when at least one of the following occurs: the sensor data exceeds a preset normal data range; the change in the sensor data within a preset time period exceeds the normal change range; the difference between the sensor data and historical sensor data reaches a preset level; or the sensor data does not meet the current environmental conditions.

[0023] In a second aspect, the present application further provides a node optimization device, comprising:

[0024] A storage module is used to collect energy through a micro energy collection front end and store the energy in a micro energy storage module;

[0025] an energy supply module, configured to supply energy through the micro energy storage module when the energy in the micro energy storage module meets a preset energy requirement;

[0026] The sending module is used to read sensor data and send the sensor data to the client through the radio frequency module when the sensor data detects an abnormality;

[0027] The sending module is also used to aggregate and compress the sensor data when the sensor data detection is normal, until the accumulated data volume after processing reaches a preset threshold, and send the processed data to the client through the radio frequency module.

[0028] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0029] Collect energy through the micro energy collection front end and store the energy in the micro energy storage module;

[0030] When the energy in the micro energy storage module meets the preset energy demand, energy is supplied through the micro energy storage module;

[0031] Read sensor data and send it to the client via the RF module if the sensor data detects an anomaly.

[0032] When the sensor data detection is normal, the sensor data is aggregated and compressed until the cumulative amount of processed data reaches the preset threshold, and the processed data is sent to the client through the radio frequency module.

[0033] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0034] Collect energy through the micro energy collection front end and store the energy in the micro energy storage module;

[0035] When the energy in the micro energy storage module meets the preset energy demand, energy is supplied through the micro energy storage module;

[0036] Read sensor data and send it to the client via the RF module if the sensor data detects an anomaly.

[0037] When the sensor data detection is normal, the sensor data is aggregated and compressed until the cumulative amount of processed data reaches the preset threshold, and the processed data is sent to the client through the radio frequency module.

[0038] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0039] Collect energy through the micro energy collection front end and store the energy in the micro energy storage module;

[0040] When the energy in the micro energy storage module meets the preset energy demand, energy is supplied through the micro energy storage module;

[0041] Read sensor data and send it to the client via the RF module if the sensor data detects an anomaly.

[0042] When the sensor data detection is normal, the sensor data is aggregated and compressed until the cumulative amount of processed data reaches the preset threshold, and the processed data is sent to the client through the radio frequency module.

[0043] The above-mentioned node optimization method, apparatus, computer equipment, computer-readable storage medium, and computer program product first collect energy through a micro-energy collection front end and store the energy in a micro-energy storage module; when the energy in the micro-energy storage module meets the preset energy demand, energy is supplied through the micro-energy storage module; sensor data is read, and when the sensor data detects an abnormality, the sensor data is sent to the client through the radio frequency module; when the sensor data detects normality, the sensor data is aggregated and compressed until the accumulated processed data volume reaches a preset threshold, and the processed data is sent to the client through the radio frequency module. In this way, the corresponding energy supply is only carried out on the basis of meeting the preset conditions, which reduces unnecessary energy consumption, achieves global optimization, and thus improves the survivability of the node in an unstable functional environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 A diagram illustrating an application environment of a node optimization method in one embodiment;

[0046] Figure 2 A schematic flow chart of a node optimization method according to an embodiment;

[0047] Figure 3 is a schematic structural diagram of a node in one embodiment;

[0048] Figure 4 1 is a schematic diagram of a startup process of a microcontroller in one embodiment;

[0049] Figure 5 A schematic flow chart of a node optimization method according to another embodiment;

[0050] Figure 6 is a structural block diagram of a node optimization device in one embodiment;

[0051] Figure 7 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0053] The node method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. The terminal 102 communicates with the server 104 via a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car devices, projection devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. The server 104 can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0054] In an exemplary embodiment, Figure 2 As shown, a node optimization method is provided, which is applied to Figure 1 The following describes the terminal in the example.

[0055] In one embodiment, IoT nodes rely on ambient energy for power supply in actual implementation, but this ambient energy is usually weak and unstable, which causes frequent node downtime or limited functionality. The node optimization method of the present application is specifically applied to a terminal.

[0056] In one of the implementations, the specific structure of the IoT node is as follows Figure 3As shown, it specifically includes a micro energy collection front end, a micro energy storage module, a microcontroller, a radio frequency module, and a sensor. The micro energy storage module also includes an energy storage element for storing energy. The terminal is the microcontroller, and energy and signals are transmitted between the microcontroller and other modules. Specifically, the following steps 202 to 208 are included. Among them:

[0057] Step 202: Collect energy through the micro energy collection front end and store the energy in the micro energy storage module.

[0058] The micro energy collection front end is at least one of a solar panel or a piezoelectric sensor.

[0059] Exemplarily, the microcontroller collects energy such as solar energy, vibration energy, and radio frequency energy through a micro energy collection front end, and stores the collected energy in a micro energy storage module.

[0060] Step 204 : When the energy in the micro energy storage module meets the preset energy requirement, energy is supplied through the micro energy storage module.

[0061] Optionally, the micro energy storage module continuously receives energy from the microcontroller. When the preset energy requirement is reached, that is, when the energy is sufficient, the switch Q1 between the micro energy storage module and the microcontroller is turned on, and the microcontroller controls the micro energy storage module to supply energy to the node. The specific flow chart is as follows: Figure 4 shown.

[0062] Step 206 , reading the sensor data. If the sensor data detects an abnormality, the sensor data is sent to the client via the radio frequency module.

[0063] Exemplarily, the microcontroller is awakened from sleep mode and started, the switch Q3 between the microcontroller and the sensor is turned on, the micro energy storage module provides energy for the sensor, the microcontroller reads the sensor data, and detects the sensor data. When the sensor data detects an abnormality, the switch Q2 between the microcontroller and the RF module is turned on, the micro energy storage module provides energy for the RF module, and the sensor data of the detected abnormality is sent to the client through the RF module.

[0064] Step 208: If the sensor data detection is normal, the sensor data is aggregated and compressed until the accumulated amount of processed data reaches a preset threshold, and the processed data is sent to the client through the radio frequency module.

[0065] Optionally, when the sensor data detection is normal, the sensor data is aggregated and compressed until the cumulative amount of processed data reaches a preset threshold, and the switch Q2 between the RF module is opened. The micro energy storage module provides energy to the RF module, and the processed data is sent to the client through the RF module.

[0066] In the above node optimization method, energy is collected through a micro-energy collection front-end and stored in a micro-energy storage module. When the energy in the micro-energy storage module meets the preset energy requirements, energy is supplied through the micro-energy storage module. Sensor data is read, and if the sensor data detects an abnormality, the sensor data is sent to the client via the radio frequency module. If the sensor data detects normality, the sensor data is aggregated and compressed until the accumulated processed data volume reaches a preset threshold, at which point the processed data is sent to the client via the radio frequency module. In this way, energy supply is only carried out when the preset conditions are met, reducing unnecessary energy consumption, achieving global optimization, and thus improving the node's survivability in unstable functional environments.

[0067] In an exemplary embodiment, the node optimization method also includes: obtaining a voltage signal from the micro-energy collection module during the process of storing energy in the micro-energy storage module, and determining the energy storage state of the micro-energy storage module based on the voltage signal; when the energy in the micro-energy storage module exceeds a preset energy threshold, increasing the operating frequency of the microcontroller; when the energy in the micro-energy storage module does not exceed the preset energy threshold, reducing the operating frequency of the microcontroller.

[0068] In actual implementation, while the micro-energy storage module is storing energy, the microcontroller acquires the voltage signal from the micro-energy harvesting module in real time through a pin and determines the energy storage status of the micro-energy storage module based on the voltage signal. If the energy in the micro-energy storage module exceeds a preset energy threshold, the microcontroller increases its operating frequency, i.e., reduces its sleep time. If the energy in the micro-energy storage module does not exceed the preset energy threshold, the microcontroller decreases its operating frequency, i.e., increases its sleep time. The preset energy threshold can be set by the user according to actual circumstances and is not limited in this embodiment of the present application.

[0069] In the above embodiment, the energy storage state is monitored in real time, thereby dynamically adjusting the sleep strategy of the microcontroller, thereby improving energy utilization efficiency.

[0070] In an exemplary embodiment, the node optimization method further includes: after the microcontroller is awakened, turning on the sensor switch, and turning off the sensor switch after receiving the sensor data; when the sensor data detects an abnormality or the accumulated sensor data volume reaches a preset threshold, turning on the RF module switch, and turning off the RF module switch after ensuring that the sensor data is sent successfully.

[0071] In actual implementation, after the energy storage in the micro-energy storage module is sufficient, the microcontroller is awakened and the sensor switch Q3 is turned on. The micro-energy storage module provides energy for the sensor, and the microcontroller closes the sensor switch Q3 after receiving the sensor data. When the sensor data detects an abnormality or the accumulated data volume of the sensor reaches a preset threshold, the RF module switch Q2 is turned on. After ensuring that the sensor data is sent successfully, the RF module switch Q2 is turned off.

[0072] In the above embodiment, the microcontroller collaboratively controls the on-demand power supply of the sensor and the RF module to avoid wasting energy when the module is idle, thereby significantly improving the survivability, reliability and long-term stability of the node in a weak or unstable power supply environment.

[0073] In an exemplary embodiment, sensor data is aggregated and compressed, including: deduplication and normalization of sensor data of the same type or the same monitoring period to obtain aggregated sensor data; and compression of the aggregated sensor data using a lightweight compression algorithm to obtain processed data.

[0074] In actual implementation, sensor data of the same type or within the same monitoring period is deduplicated and normalized to obtain aggregated sensor data. The aggregated sensor data is then compressed using a lightweight compression algorithm to obtain processed data. The method for compressing the aggregated sensor data may also be other compression methods, which are not limited in this embodiment of the application.

[0075] In the above embodiment, by aggregating and compressing the sensor data without causing sensor data anomalies, data batch processing is achieved, which effectively reduces overall power consumption and extends node life.

[0076] In an exemplary embodiment, the micro energy harvesting front end includes at least one of a solar panel or a piezoelectric sensor.

[0077] In actual implementation, the micro-energy collection front end includes at least one of a solar panel or a piezoelectric sensor, and can also be other devices capable of collecting micro-energy, such as a hotspot module, which is not limited in this application.

[0078] In the above embodiment, energy is collected by various micro-energy devices, so that the endurance of the node is extended.

[0079] In an exemplary embodiment, the abnormal data judgment process includes: determining that the sensor data is abnormal data when at least one of the following occurs: the sensor data exceeds a preset normal data range; the change in the sensor data within a preset time period exceeds the normal change range; the difference between the sensor data and historical sensor data reaches a preset degree; or the sensor data does not meet the current environmental conditions.

[0080] In actual implementation, when at least one of the following conditions occurs: the amount of change in sensor data within a preset time period exceeds the normal range of change; the difference between sensor data and historical sensor data reaches a preset level; or the sensor data does not meet the current environmental conditions, the sensor data is considered abnormal data.

[0081] For example, if the change trend of the data collected by the sensor data belongs to urban data, but the actual node is located in the suburbs, then the sensor data is abnormal data.

[0082] In the above embodiment, the node operation is made more stable by detecting abnormal sensor data.

[0083] To explain the node optimization method in this application in detail, an embodiment is given below. The specific flow chart is as follows: Figure 5 As shown, illustratively, this application describes a node optimization method in a specific scenario.

[0084] First, the microcontroller collects solar energy, vibration energy, radio frequency energy and other energies through the micro energy collection front end, and stores the collected energy in the micro energy storage module.

[0085] The micro energy storage module continuously receives energy from the microcontroller. When the preset energy requirement is reached, that is, when there is sufficient energy, the switch Q1 between the micro energy storage module and the microcontroller is turned on. The microcontroller controls the micro energy storage module to supply energy to the node, thereby waking up the microcontroller.

[0086] The microcontroller is awakened from sleep mode and starts up, turning on the switch Q3 between the microcontroller and the sensor. The micro-energy storage module provides energy for the sensor. The microcontroller reads the sensor data and detects the sensor data. If the sensor data detects an abnormality, the switch Q2 between the microcontroller and the RF module is turned on. The micro-energy storage module provides energy for the RF module and sends the abnormal sensor data to the client through the RF module.

[0087] When the sensor data detection is normal, the sensor data is aggregated and compressed until the cumulative amount of processed data reaches the preset threshold. The switch Q2 between the RF module is turned on, the micro energy storage module provides energy to the RF module, and the processed data is sent to the client through the RF module.

[0088] This application significantly improves the energy efficiency and stability of IoT nodes through the coordinated optimization of dynamic energy harvesting and intelligent sleep strategies. Real-time monitoring of energy storage status and dynamic adjustment of sleep strategies, combined with on-demand power supply and data batch processing mechanisms, effectively reduces overall power consumption and extends node life. Furthermore, real-time processing of abnormal data and optimized scheduling of RF modules further enhance system reliability, enabling long-term stable operation even in weak or unstable power supply environments.

[0089] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0090] Based on the same inventive concept, the present application also provides a node optimization device for implementing the node optimization method mentioned above. The solution provided by this device is similar to the solution described in the method described above. Therefore, the specific limitations in one or more node optimization device embodiments provided below can be found in the above-mentioned limitations on the node optimization method and will not be repeated here.

[0091] In an exemplary embodiment, Figure 6 As shown, a node optimization device is provided, including: a storage module 601, an energy supply module 602 and a sending module 603, wherein:

[0092] The storage module is used to collect energy through the micro energy collection front end and store the energy in the micro energy storage module.

[0093] The energy supply module is used to supply energy through the micro energy storage module when the energy in the micro energy storage module meets the preset energy demand.

[0094] The sending module is used to read sensor data and send the sensor data to the client through the radio frequency module when the sensor data detects an abnormality.

[0095] The sending module is also used to aggregate and compress the sensor data when the sensor data detection is normal, until the accumulated data volume after processing reaches a preset threshold, and send the processed data to the client through the radio frequency module.

[0096] In some embodiments, the device further includes a determination module for obtaining a voltage signal from the micro energy collection module during the process of storing energy in the micro energy storage module, and determining an energy storage state of the micro energy storage module based on the voltage signal;

[0097] When the energy in the micro energy storage module exceeds a preset energy threshold, increasing the operating frequency of the microcontroller;

[0098] When the energy in the micro energy storage module does not exceed a preset energy threshold, the operating frequency of the microcontroller is reduced.

[0099] In some embodiments, the device further comprises a control module, configured to turn on a sensor switch after the microcontroller is awakened, and turn off the sensor switch after receiving sensor data;

[0100] When sensor data is detected to be abnormal or the accumulated sensor data volume reaches a preset threshold, the RF module switch is turned on. After ensuring that the sensor data is sent successfully, the RF module switch is turned off.

[0101] In some embodiments, the apparatus further includes a processing module for performing deduplication and normalization processing on sensor data of the same type or the same monitoring period to obtain aggregated sensor data;

[0102] The aggregated sensor data is compressed using a lightweight compression algorithm to obtain processed data.

[0103] In some embodiments, the micro energy harvesting front end includes at least one of a solar panel or a piezoelectric sensor.

[0104] In some embodiments, the device also includes a judgment module for determining that the sensor data is abnormal data when at least one of the following situations occurs: the sensor data exceeds a preset normal data range; the change in the sensor data within a preset time period exceeds the normal change range; the difference between the sensor data and historical sensor data reaches a preset degree; or the sensor data does not meet the current environmental conditions.

[0105] Each module in the node optimization device described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0106] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface, a display unit and an input device. The processor, memory and input / output interface are connected via a system bus, and the communication interface, display unit and input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store sensor data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a node optimization method is implemented.

[0107] The display unit of the computer device is used to produce a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, a keypad, a trackball, or a touchpad provided on the computer device housing, or an external keyboard, touchpad, or mouse.

[0108] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0109] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0110] Collect energy through the micro energy collection front end and store the energy in the micro energy storage module;

[0111] When the energy in the micro energy storage module meets the preset energy demand, energy is supplied through the micro energy storage module;

[0112] Read sensor data and send it to the client via the RF module if the sensor data detects an anomaly.

[0113] When the sensor data detection is normal, the sensor data is aggregated and compressed until the cumulative amount of processed data reaches the preset threshold, and the processed data is sent to the client through the radio frequency module.

[0114] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0115] Collect energy through the micro energy collection front end and store the energy in the micro energy storage module;

[0116] When the energy in the micro energy storage module meets the preset energy demand, energy is supplied through the micro energy storage module;

[0117] Read sensor data and send it to the client via the RF module if the sensor data detects an anomaly.

[0118] When the sensor data detection is normal, the sensor data is aggregated and compressed until the cumulative amount of processed data reaches the preset threshold, and the processed data is sent to the client through the radio frequency module.

[0119] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0120] Collect energy through the micro energy collection front end and store the energy in the micro energy storage module;

[0121] When the energy in the micro energy storage module meets the preset energy demand, energy is supplied through the micro energy storage module;

[0122] Read sensor data and send it to the client via the RF module if the sensor data detects an anomaly.

[0123] When the sensor data detection is normal, the sensor data is aggregated and compressed until the cumulative amount of processed data reaches the preset threshold, and the processed data is sent to the client through the radio frequency module.

[0124] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0125] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0126] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0127] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A node optimization method, characterized in that: Applied to a microcontroller; the method comprises: Collect energy through the micro energy collection front end and store the energy in the micro energy storage module; When the energy in the micro energy storage module meets the preset energy demand, energy is supplied through the micro energy storage module; Read sensor data and send it to the client via the RF module if the sensor data detects an anomaly. When the sensor data detection is normal, the sensor data is aggregated and compressed until the cumulative amount of processed data reaches the preset threshold, and the processed data is sent to the client through the radio frequency module.

2. The method according to claim 1, characterized in that The method further comprises: During the process of storing energy in the micro energy storage module, obtaining a voltage signal from the micro energy collection module, and determining the energy storage state of the micro energy storage module according to the voltage signal; When the energy in the micro energy storage module exceeds a preset energy threshold, increasing the operating frequency of the microcontroller; When the energy in the micro energy storage module does not exceed a preset energy threshold, the operating frequency of the microcontroller is reduced.

3. The method according to claim 1, characterized in that The method further comprises: After the microcontroller is awakened, the sensor switch is turned on, and after receiving the sensor data, the sensor switch is turned off; When sensor data is detected to be abnormal or the accumulated sensor data volume reaches a preset threshold, the RF module switch is turned on. After ensuring that the sensor data is sent successfully, the RF module switch is turned off.

4. The method according to claim 1, wherein The aggregating and compressing the sensor data includes: De-duplicate and normalize sensor data of the same type or the same monitoring period to obtain aggregated sensor data; The aggregated sensor data is compressed using a lightweight compression algorithm to obtain processed data.

5. The method according to claim 1, wherein The micro energy harvesting front end includes at least one of a solar panel or a piezoelectric sensor.

6. The method according to claim 1, characterized in that The abnormal data judgment process includes: The sensor data is determined to be abnormal data when at least one of the following occurs: the sensor data exceeds a preset normal data range; the change in the sensor data within a preset time period exceeds the normal change range; the difference between the sensor data and historical sensor data reaches a preset level; or the sensor data does not meet the current environmental conditions.

7. A node optimization device, characterized in that: The device comprises: A storage module is used to collect energy through a micro energy collection front end and store the energy in a micro energy storage module; an energy supply module, configured to supply energy through the micro energy storage module when the energy in the micro energy storage module meets a preset energy requirement; The sending module is used to read sensor data and send the sensor data to the client through the radio frequency module when the sensor data detects an abnormality; The sending module is also used to aggregate and compress the sensor data when the sensor data detection is normal, until the accumulated data volume after processing reaches a preset threshold, and send the processed data to the client through the radio frequency module.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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