Electric power tool state intelligent perception diagnosis method and system based on star flash communication

Abstract

The invention provides an electric power tool state intelligent perception diagnosis method based on star flash communication, and the method comprises the steps: S1, collecting the state data of an electric power tool in real time through a perception module disposed in the electric power tool; s2, transmitting the state data to a gateway in a low-delay and high-reliability manner by using a star flash communication module; s3, processing the state data through an intelligent analysis and diagnosis module, and generating a diagnosis result based on a preset threshold value or an intelligent algorithm model trained by historical data; and S4, if the diagnosis result is that abnormity exists, early warning information is pushed to the operator handheld terminal through an early warning and feedback module. The invention further provides a corresponding system. According to the invention, intelligent sensing and diagnosis can be rapidly and accurately carried out on the state of the electric power tool, and the safety of the electric power tool is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the operation and maintenance technical field of power systems, in particular to a power tool state intelligent perception and diagnosis method and system based on star flash communication. BACKGROUND

[0002] In the power industry, the safe and reliable use of power tools is crucial. Traditional power tool management relies on manual periodic inspection and simple records, making it difficult to monitor their status in real time and accurately. For example, key power tools such as insulating gloves and electroscopes are difficult to detect performance degradation or potential failure in daily use. Once a failure occurs during operation, it will pose a serious threat to the safety of workers and even cause a power outage.

[0003] Existing wireless communication technologies have many shortcomings when used for power tool state monitoring. For example, Bluetooth communication has a short communication distance and weak anti-interference ability; Wi-Fi has high power consumption and large time delay, which cannot meet the demand for real-time and reliable monitoring of power tool state. With the development of intelligent power systems, there is an urgent need for an advanced communication and monitoring technology to realize intelligent perception and self-diagnosis of power tool state and improve the safety and reliability of power operation. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a power tool state intelligent perception and diagnosis method and system based on star flash communication, which can quickly and accurately perceive and diagnose the state of power tools, improving the safety of power tools.

[0005] As one aspect of the present application, a power tool state intelligent perception and diagnosis method based on star flash communication is provided, which includes the following steps:

[0006] S1: Real-time acquisition of power tool state data by a perception module configured in the power tool, the operating state data including at least one of insulation resistance, detection accuracy, and mechanical performance parameters;

[0007] S2: Transmission of the state data to the gateway in a low-latency and high-reliability manner using a star flash communication module;

[0008] S3: Processing of the state data by an intelligent analysis and diagnosis module, determining whether the power tool has potential failure or performance degradation based on a preset threshold or an intelligent algorithm model trained from historical data, and generating a diagnosis result;

[0009] S4: If the diagnosis result is abnormal, push the warning information to the worker's handheld terminal through the warning and feedback module, and send the fault report containing the fault type, location, and severity to the management end.

[0010] Preferably, in the step S2, the star flash communication module adopts SLE mode for power tool state data transmission, and the transmission period is 50 ms; a self-defined application layer protocol is adopted during data transmission, and the data frame format comprises a frame header, a power tool type, a state code, a data length, sensor data and a CRC check field; wherein the state code comprises 0x01 normal, 0x02 abnormal and 0x03 low power.

[0011] Preferably, in the step S1, the sensing module comprises one or more sensors integrated on the power tool; the sensors comprise at least one of an insulation resistance sensor embedded in an insulating glove, a detection accuracy sensor in an electroscope and a torque sensor of an electric tool, and the sensors are connected to the built-in low-power star flash communication module through a micro wire and are powered by a button cell.

[0012] Preferably, in the step S3, the intelligent analysis and diagnosis module adopts an LSTM neural network model, trains based on historical state data, predicts potential failure risks of the power tool, and pushes a risk warning to a management end 24 hours in advance when the prediction accuracy is more than 80%.

[0013] Preferably, in the step S4, the warning and feedback module issues a warning through a gateway local sound and light alarm module in a public network-free scene, the sound and light alarm module has a volume of ≥85 dB and an LED flashing frequency of 2 Hz, and the gateway is provided with a backup battery with a power-off endurance of ≥8 hours.

[0014] As another aspect of the application, an intelligent power tool state sensing and diagnosis system based on star flash communication is also provided, which comprises:

[0015] A sensing module configured in the power tool for collecting state data of the power tool in real time, wherein the running state data comprises at least one of an insulation resistance, a detection accuracy and a mechanical performance parameter;

[0016] A star flash communication module arranged at the power tool end and the gateway end for transmitting the state data in a low-latency and high-reliability manner;

[0017] An intelligent analysis and diagnosis module arranged at the gateway or the cloud end for analyzing the state data, judging whether the power tool has potential failure or performance degradation based on a preset threshold or a historical data trained intelligent algorithm model, and generating a diagnosis result;

[0018] A warning and feedback module for pushing a warning information to a handheld terminal of an operator and sending a fault report containing a fault type, a location and a severity to a management end when it is determined that there is an abnormality in the diagnosis result;

[0019] Gateway, integrated SLE / SLB dual-mode communication module, 4G CAT1 communication module, for the state data, supporting local data caching and offline alarm.

[0020] Preferably, the SLE low latency mode and SLB large bandwidth mode dual-mode communication are adopted by the SLE / SLB dual-mode communication module; the working frequency band is 24.05-24.25GHz, the communication time delay in the SLE mode is not more than 20ms, and the adaptive code rate adjustment of 125kbps to 48Mbps is supported; the module adopts a low-power design at the power tool end, supports a sleep-wake mechanism, and the normal sleep power consumption is not higher than 26μA.

[0021] Preferably, the sensing module includes one or more sensors integrated on the power tool; the sensors include at least one of an insulation resistance sensor embedded in an insulating glove, a detection accuracy sensor in an electroscope, and a torque sensor of an electric tool, and the sensors are connected to the built-in low-power SLE / SLB dual-mode communication module through a micro wire and are powered by a button cell.

[0022] Preferably, the gateway adopts an industrial-grade embedded host, the CPU is ARM Cortex-A53, 4 cores 1.5GHz, integrates an H363 type SLE / SLB dual-mode communication module and a 4G CAT1 full-network module, the SLE / SLB dual-mode communication module is a 5dBi omnidirectional antenna with a coverage radius of ≥200m, the local cache stores 7 days of data, and real-time alarm can still be realized when the network is disconnected.

[0023] Preferably, the intelligent analysis and diagnosis module adopts an LSTM neural network model, is trained based on historical state data, predicts potential failure risks of the power tool, and when the prediction accuracy is more than 80%, pushes a risk early warning to a management end 24 hours in advance.

[0024] The sound and light alarm module of the early warning and feedback module has a volume of ≥85dB, an LED flickering frequency of 2Hz, a gateway power module supporting AC 220V / DC 12V dual input, a built-in 10Ah backup battery, and a power outage endurance of ≥8 hours.

[0025] The embodiment of the present application has the following beneficial effects:

[0026] The present application provides a power tool state intelligent sensing and diagnosis method and system based on SLE / SLB dual-mode communication, which can quickly and accurately intelligently sense and diagnose the state of the power tool, and improves the safety of the power tool. The present application realizes real-time and accurate monitoring of the state of the power tool, overcomes the limitations of traditional manual inspection by collecting data in real time through sensors, and can timely discover potential failures and performance degradation of the power tool.

[0027] In the present application, by adopting star flash communication technology, the timeliness, integrity and stability of power tool state data transmission are ensured by using its low latency, high reliability, strong anti-interference ability, relatively low power consumption and support for high concurrency, meeting the demand of real-time monitoring.

[0028] In the present application, intelligent analysis and self-diagnosis capability are provided, the collected data are deeply analyzed through intelligent algorithm, the power tool state can be quickly judged and early warning information can be generated, so that the operation personnel and the management end can take measures in time, the safety accidents caused by power tool failure are reduced, and the whole life cycle management level of the power tool is improved.

[0029] In the present application, the early warning mode is various and the information is detailed, the operation personnel are pushed to remind, and the management end is sent a failure report, so that the maintenance, replacement and other operations of the power tool can be arranged in time, and the safety and efficiency of power operation are improved. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to these drawings without creative labor.

[0031] Figure 1 The main flowchart of one embodiment of the power tool state intelligent perception diagnosis method based on star flash communication provided by the present application is shown in the figure.

[0032] Figure 2 The application environment diagram of the method provided by the present application is shown in the figure.

[0033] Figure 3 The layout diagram of the insulating glove sensor related to the present application is shown in the figure.

[0034] Figure 4 The hardware block diagram of the perception module related to the present application is shown in the figure.

[0035] Figure 5 The hardware block diagram of the gateway related to the present application is shown in the figure.

[0036] Figure 6 The structure diagram of one embodiment of the power tool state intelligent perception diagnosis system based on star flash communication provided by the present application is shown in the figure. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application will be further described in detail below with reference to the drawings.

[0038] like Figure 1 The diagram shown illustrates the main flowchart of an embodiment of an intelligent sensing and diagnostic method for the status of power equipment based on star-flash communication provided by the present invention. (In conjunction with...) Figures 2 to 5 As shown, in this embodiment, the method includes the following steps:

[0039] S1: Real-time status data of the power tool is collected by a sensing module configured in the power tool. The status data includes at least one of insulation resistance, detection accuracy, and mechanical performance parameters.

[0040] Preferably, in step S1, the sensing module includes one or more sensors integrated into the power tool; the sensors include at least one of an insulation resistance sensor embedded in an insulating glove, a detection accuracy sensor in an electroscope, and a torque sensor of the power tool, and the sensors are connected to a built-in low-power starlight communication module via microwires and are powered by a button battery. Figure 2 As shown, corresponding sensors can also be deployed in items such as safety helmets, safety belts, wiring harnesses, gas detectors, and work clothes.

[0041] Taking insulating gloves as an example, during the production process or later modifications, insulation resistance sensors are distributed in key areas inside and outside the glove, such as the palm and other easily worn areas. The sensors are connected to a built-in StarFlash communication module via microwires. This module features a low-power design and can be powered by a small button battery, ensuring stable operation over extended periods. Figure 3 As shown, the sensor electrodes in the insulating glove are designed with a dual-electrode layout to form a detection circuit. Specifically, two independent electrodes are integrated on the sensor module:

[0042] Inner electrode: Made of flexible conductive material (such as conductive fabric or thin copper foil), it fits the inner layer of the glove (the side that contacts the hand) to ensure close contact with the inner layer of the glove.

[0043] External electrode: Also made of flexible conductive material, it is sewn onto the outer layer of the glove (the side in contact with the external environment), located on the outer surface of the corresponding position in the palm. The external electrode is flush with the outer surface of the glove and does not protrude, to avoid damage to the electrode due to wear or scratches.

[0044] The insulating medium between the two electrodes is the insulating material (natural rubber or nitrile rubber) in the palm of the glove, forming a detection path of "inner electrode → insulating material → outer electrode".

[0045] The inner and outer electrodes are isolated from each other. Specifically, the inner and outer electrodes are isolated from the main circuit of the sensor through the insulating layer of the sensor shell (such as polyimide film), which prevents the electrodes from conducting directly and ensures that the object being measured is only the insulating material of the glove.

[0046] The hardware block diagram of the sensing module can be found in the reference diagram. Figure 4 As shown.

[0047] S2: The status data is transmitted to the gateway in a low-latency, high-reliability manner using the StarFlash communication module;

[0048] In step S2, the StarFlash communication module uses SLE mode to transmit the status data of the power equipment, with a transmission period of 50ms. When transmitting data, a custom application layer protocol is used, and the data frame format includes: frame header, power equipment type, status code, data length, sensor data, and CRC check field. The status codes include 0x01 normal, 0x02 abnormal, and 0x03 low battery.

[0049] Both the power equipment and the gateway device are equipped with StarScan communication units. The StarScan communication module on the power equipment side (e.g., using the BS21 StarScan module, with a communication distance of 100 meters, average power consumption less than 10mA, and sleep-keeping power consumption less than 26uA) is responsible for packaging the data collected by the sensors and transmitting it to the gateway in a low-latency, highly reliable manner via StarScan communication technology (operating frequency band 24.05-24.25GHz, SLE mode latency ≤20ms). StarScan communication does not rely on the public network; through direct communication between the gateway and the power equipment side (coverage radius ≥100 meters), it can still transmit data in real time even in scenarios without 4G signals.

[0050] S3: The status data is processed by the intelligent analysis and diagnosis module. Based on the intelligent algorithm model trained by the preset threshold or historical data, the system determines whether there are potential faults or performance degradation in the power equipment and generates diagnostic results.

[0051] In step S3, the intelligent analysis and diagnosis module uses an LSTM neural network model, trained based on historical state data, to predict potential fault risks of power tools. When the prediction accuracy exceeds 80%, a risk warning is pushed to the management terminal 24 hours in advance.

[0052] In a specific example, the intelligent analysis and diagnosis module employs a high-performance processor and intelligent algorithms. The algorithms are trained based on a large amount of historical data and fault cases, enabling rapid analysis of received data from electrical equipment. For instance, by comparing real-time insulation resistance values ​​with historical data and safety thresholds, it can determine whether insulating gloves are at risk of insulation performance degradation; based on the trend of changes in the accuracy of the voltage detector, it can predict whether an inaccurate detection fault is imminent. When a potential fault or performance degradation is detected, an early warning message is immediately generated.

[0053] S4: If the diagnosis result indicates that there is an abnormality, push the warning information to the operator's handheld terminal through the warning and feedback module, and send a fault report containing the fault type, location and severity to the management terminal.

[0054] In step S4, the warning and feedback module issues a warning through the local sound and light alarm module of the gateway in the absence of a public network. The sound and light alarm module has a volume of ≥85dB, an LED flashing frequency of 2Hz, and the gateway has a built-in backup battery with a power outage duration of ≥8 hours.

[0055] Understandably, once the intelligent analysis and diagnosis module determines that there is a problem with the electrical tools, the early warning and feedback module will quickly issue warnings through various means. For example, it will push notifications to the operator's handheld terminal and simultaneously send a detailed fault report to the electrical tool management terminal, including information such as the fault type, location, and severity. Based on the feedback information, the management terminal can promptly arrange maintenance, replacement, and other operations for the electrical tools.

[0056] In this invention, the gateway uses an industrial-grade embedded host (e.g., CPU: ARM Cortex-A53, 4 cores 1.5GHz), integrating a StarSpark SLE / SLB dual-mode communication module (model: H363) and a 4G CAT1 communication module (supporting full network compatibility). The StarSpark antenna uses a 5dBi omnidirectional antenna, installed at a high point in the work site, with a coverage radius of ≥200 meters. The gateway performs logic judgment and alarm locally. The 4G CAT1 is only used to upload data to the cloud when online. When the network is down, the gateway locally caches 7 days of data, meeting the requirement of "real-time alarm even without a public network". The alarm module adopts an integrated sound and light design (volume ≥85dB, LED flashing frequency 2Hz). The power module supports AC 220V / DC 12V dual input, has a built-in 10Ah backup battery, and provides ≥8 hours of battery life after power failure. The hardware block diagram of the gateway can be found here. Figure 5 As shown.

[0057] Furthermore, in this embodiment of the invention, the communication protocol is implemented using the StarSpark protocol stack, as detailed below:

[0058] Physical layer: It adopts the unique OFDM+MIMO technology of StarSpark, with an operating frequency band of 24.05-24.25GHz, modulation method of QPSK / 16QAM / 64QAM, and code rate supports adaptive adjustment from 125kbps to 48Mbps.

[0059] Data Link Layer: Enables dual-mode switching between StarFlash SLE (Low Latency) and SLB (High Bandwidth). In the power tool status monitoring scenario, the default mode is SLE with a latency of ≤20ms; in the data upload scenario, it automatically switches to SLB mode with a throughput of ≥10Mbps.

[0060] Application layer protocol: Defines the format of power tool status data frame: [Frame header (2B)][Power tool type (1B)][Status code (1B)][Data length (1B)][Sensor data (nB)][CRC check (2B)], where the status code is defined as: 0x01 = Normal, 0x02 = Abnormal, 0x03 = Low power.

[0061] Offline scenario data processing mechanism: In offline scenarios (no 4G signal), the gateway stores alarm data to local Flash (capacity 16GB), and uploads it to the cloud in batches after the network is restored. The data caching time is ≥7 days.

[0062] To further understand the details of the method provided by this invention, the following is an example of intelligent sensing and self-diagnosis of the status of an electrical tool (insulating glove):

[0063] Hardware connection: The star flash module on the wrist of the insulating glove establishes an SLE connection with the gateway (address: 0x2001). The data collected by the insulation resistance sensor is reported to the gateway through the star flash at a period of 50ms.

[0064] Logical judgment process: After receiving the insulation resistance value of the insulated glove, the gateway compares it with the safety threshold using the intelligent analysis and diagnosis module. When the insulation resistance value approaches the safety threshold, an early warning is triggered.

[0065] C code:

[0066]

[0067] Warning Implementation: The gateway issues a voice alarm, "The insulation performance of the insulated gloves has deteriorated. Please replace them!", the LED yellow light flashes, and the event is sent to the platform via 4G, and a text message is sent to the person in charge of the operation.

[0068] Cloud platform data interaction implementation methods

[0069] Data interface definition: A RESTful API interface is used, and the data format is JSON.

[0070]

[0071] }

[0072] Alarm data upload frequency: real-time upload (delay ≤ 3 seconds), historical data is summarized and uploaded at 10-minute intervals.

[0073] Big data analytics capabilities: Establishing state models for electrical equipment and using LSTM neural networks to analyze historical data and predict potential failure risks.

[0074] Python code:

[0075] #Risk Prediction Model Fragment

[0076] model = Sequential()

[0077] model.add(LSTM(64,input_shape=(timesteps,features)))

[0078] model.add(Dense(32,activation='relu'))

[0079] model.add(Dense(1,activation='sigmoid'))

[0080] model.compile(loss='binary_crossentropy',optimizer='adam')

[0081] When the prediction accuracy is greater than 80%, a risk warning will be sent to the management system 24 hours in advance.

[0082] In this embodiment, low power consumption optimization is achieved in the following manner.

[0083] Power consumption control at the electrical appliance end: A sleep-wake mechanism is adopted. Under normal conditions, the star-flash module is in sleep mode (power consumption ≤ 0.26mA), and wakes up every 10 seconds to receive gateway commands. The sensor dynamically samples; when the electrical appliance is stationary, the sensor sampling frequency drops to 1Hz, and automatically increases to 100Hz when it is working.

[0084] Gateway power management: The StarScan module supports dynamic power adjustment, with a transmit power of 10dBm at close range (<50 meters), 15dBm at medium range (50-100 meters), and 20dBm at long range (>100 meters). During non-working hours (23:00-6:00), it enters power-saving mode, with the CPU frequency reduced to 800MHz and the alarm module going into sleep mode, reducing power consumption by 60%.

[0085] It is understood that the method provided by this invention has the following advantages compared with the prior art:

[0086] Compared to traditional methods of managing electrical tools, this invention enables real-time, dynamic monitoring, greatly improving the timeliness and accuracy of fault detection. Compared to existing monitoring schemes based on other wireless communication technologies, the low latency and high reliability of StarScan communication ensure efficient and stable data transmission, effectively avoiding misjudgments of faults caused by data loss and delays.

[0087] This invention offers excellent scalability. On one hand, sensor types can be flexibly added or replaced according to the characteristics and needs of different power tools, enabling the monitoring of more performance parameters of these tools. For example, for power tools, motor current and temperature sensors can be added to monitor their operating status. On the other hand, with the continuous development and upgrading of StarFlash communication technology, this system can easily integrate new technologies to improve overall performance and functionality.

[0088] From an economic perspective, early detection of electrical equipment malfunctions reduces losses from power accidents caused by such malfunctions, lowers equipment maintenance and replacement costs, and improves the efficiency of power operations, thus bringing significant economic benefits. From a social perspective, it effectively protects the lives of power workers and reduces the adverse impact of power accidents on social production and daily life, thus possessing important social benefits.

[0089] like Figure 6 The diagram shows a structural schematic of an embodiment of an intelligent sensing and diagnostic system for the status of power tools based on star-flash communication provided by the present invention. In this embodiment, the intelligent sensing and diagnostic system for the status of power tools 1 includes at least:

[0090] Sensing module 1, configured in the power tool, is used to collect the status data of the power tool in real time. The operating status data includes at least one of insulation resistance, detection accuracy, and mechanical performance parameters. In a specific example, sensing module 1 includes one or more sensors integrated on the power tool. The sensors include at least one of insulation resistance sensor embedded in an insulating glove, detection accuracy sensor in an electroscope, and torque sensor of the power tool. The sensors are connected to a built-in low-power star-flash communication module via microwires and are powered by a button battery.

[0091] The Star Flash Communication Module 2 is installed at the power tool end and the gateway end, and is used to transmit the status data in a low-latency and high-reliability manner.

[0092] In a specific example, the StarScan communication module 2 adopts dual-mode communication with SLE low latency mode and SLB high bandwidth mode; its operating frequency band is 24.05-24.25GHz, the communication latency in SLE mode is no more than 20ms, and it supports adaptive bit rate adjustment from 125kbps to 48Mbps; the module adopts a low power consumption design at the power tool end, supports a sleep-wake mechanism, and the normal sleep power consumption is no more than 26μA.

[0093] The intelligent analysis and diagnosis module 3 is set on the gateway or cloud and is used to analyze the status data, determine whether there are potential faults or performance degradation in the power equipment based on the intelligent algorithm model trained by the preset threshold or historical data, and generate diagnostic results.

[0094] In a specific example, the intelligent analysis and diagnosis module 3 uses an LSTM neural network model, trained based on historical state data, to predict potential fault risks of electrical equipment. When the prediction accuracy exceeds 80%, a risk warning is pushed to the management terminal 24 hours in advance.

[0095] The early warning and feedback module 4 is used to push early warning information to the operator's handheld terminal when the diagnosis results indicate that there is an abnormality, and to send a fault report containing the fault type, location and severity to the management terminal.

[0096] In a specific example, the sound and light alarm module of the warning and feedback module 4 has a volume of ≥85dB, an LED flashing frequency of 2Hz, a gateway power module that supports AC 220V / DC 12V dual input, a built-in 10Ah backup battery, and a power outage duration of ≥8 hours.

[0097] In this embodiment of the invention, a StarSignal SLE / SLB dual-mode communication module and a 4G CAT1 communication module are integrated into the gateway for the status data, supporting local data caching and offline alarms. Specifically, the gateway adopts an industrial-grade embedded host with an ARM Cortex-A53 CPU (4 cores, 1.5GHz), integrating an H363-type StarSignal dual-mode communication module and a 4G CAT1 full-network module. The StarSignal antenna is a 5dBi omnidirectional antenna with a coverage radius of ≥200 meters, locally caching 7 days of data, and still providing real-time alarms during network outages.

[0098] For more details, please refer to and combine with the above. Figures 1 to 5 The description of that will not be repeated here.

[0099] In practical applications, the method and system provided by this invention can be applied to the following situations:

[0100] Firstly, it can be applied to power operation and maintenance. In daily operation and maintenance work such as power line inspection and substation equipment maintenance, the technology of this invention can be applied to various electrical tools used by operators. By monitoring the status of electrical tools in real time, operational safety can be ensured and maintenance efficiency improved. For example, if electrical tools such as voltage detectors and insulating rods used by inspection personnel show signs of malfunction, timely warnings can be issued, avoiding misjudgments or safety accidents caused by tool failures.

[0101] Secondly, it can also be applied to power emergency repair operations. In emergency power repair scenarios, ensuring the reliability of electrical tools is crucial. This invention can quickly diagnose the status of repair tools, such as the power status of emergency lighting equipment and the mechanical performance of repair tools, providing a guarantee for the smooth progress of repair work. For example, if the torque sensor of an electric wrench used by repair personnel detects an abnormal torque output, it will promptly report this to the repair personnel and command center via satellite communication, allowing for timely tool replacement or adjustment of the work plan.

[0102] In addition, it can also be applied to the production and quality inspection of electrical tools. Before products leave the factory, manufacturers of electrical tools can use this invention's system to conduct comprehensive performance testing and simulated usage tests. By monitoring product performance parameters in real time, potential quality problems can be identified promptly, thus improving product quality.

[0103] Further applications will not be elaborated here.

[0104] Implementing the embodiments of the present invention has the following beneficial effects:

[0105] This invention provides a method and system for intelligent sensing and diagnosis of the status of electrical tools based on star-flash communication. It can quickly and accurately sense and diagnose the status of electrical tools, improving their safety. This invention achieves real-time and precise monitoring of the status of electrical tools, overcoming the limitations of traditional manual inspection by collecting data in real time through sensors. It can promptly detect potential faults and performance degradation in electrical tools.

[0106] In this invention, by employing StarFlash communication technology, leveraging its characteristics of low latency, high reliability, strong anti-interference capability, relatively low power consumption, and support for high concurrency, the timeliness, integrity, and stability of power tool status data transmission are ensured, thus meeting the needs of real-time monitoring.

[0107] This invention features intelligent analysis and self-diagnosis capabilities. Through intelligent algorithms, it performs in-depth analysis of collected data, enabling rapid assessment of the status of electrical tools and generating early warning information. This allows operators and managers to take timely measures, reducing safety accidents caused by electrical tool malfunctions and improving the overall lifecycle management level of electrical tools.

[0108] In this invention, the early warning methods are diverse and the information is detailed. They not only push reminders to the operators, but also send fault reports to the management end, which facilitates the timely arrangement of maintenance, replacement and other operations of electrical tools and improves the safety and efficiency of power operations.

[0109] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0110] The above description is merely a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. Therefore, any equivalent variations made in accordance with the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for intelligent sensing and diagnosis of the status of power tools based on star-flash communication, characterized in that, Includes the following steps: S1: Real-time status data of the power tool is collected by a sensing module configured in the power tool. The status data includes at least one of insulation resistance, detection accuracy, and mechanical performance parameters. S2: The status data is transmitted to the gateway in a low-latency, high-reliability manner using the StarFlash communication module; S3: The status data is processed by the intelligent analysis and diagnosis module. Based on the intelligent algorithm model trained by the preset threshold or historical data, the system determines whether there are potential faults or performance degradation in the power equipment and generates diagnostic results. S4: If the diagnosis result indicates that there is an abnormality, push the warning information to the operator's handheld terminal through the warning and feedback module, and send a fault report containing the fault type, location and severity to the management terminal.

2. The method according to claim 1, characterized in that, In step S2, the StarFlash communication module uses SLE mode to transmit the status data of electrical tools; when transmitting data, a custom application layer protocol is used, and the data frame format includes: frame header, electrical tool type, status code, data length, sensor data and CRC check field; wherein the status code includes 0x01 normal, 0x02 abnormal, and 0x03 low battery.

3. The method according to claim 2, characterized in that, In step S1, the sensing module includes one or more sensors integrated on the power tool; the sensors include at least one of an insulation resistance sensor embedded in an insulating glove, a detection accuracy sensor in an electroscope, and a torque sensor of the power tool, and the sensors are connected to a built-in low-power star flash communication module via microwires and are powered by a button battery.

4. The method according to claim 3, characterized in that, In step S3, the intelligent analysis and diagnosis module uses an LSTM neural network model, trained based on historical state data, to predict potential fault risks of power tools. When the prediction accuracy exceeds 80%, a risk warning is pushed to the management terminal 24 hours in advance.

5. The method according to claim 4, characterized in that, In step S4, the warning and feedback module issues a warning through the local sound and light alarm module of the gateway in the absence of a public network. The sound and light alarm module has a volume of ≥85dB, an LED flashing frequency of 2Hz, and the gateway has a built-in backup battery with a power outage duration of ≥8 hours.

6. A smart sensing and diagnostic system for the status of power tools based on star-flash communication, characterized in that, include: A sensing module, configured in an electrical tool, is used to collect the status data of the electrical tool in real time. The operating status data includes at least one of insulation resistance, detection accuracy, and mechanical performance parameters. The StarScan communication module is installed at the power equipment end and the gateway end to transmit the status data in a low-latency and highly reliable manner; The intelligent analysis and diagnosis module, set on the gateway or cloud, is used to analyze the status data, and based on the intelligent algorithm model trained on the preset threshold or historical data, to determine whether the power equipment has potential faults or performance degradation, and generate diagnostic results. The early warning and feedback module is used to push early warning information to the operator's handheld terminal when the diagnostic results indicate that there is an abnormality, and to send a fault report containing the fault type, location and severity to the management terminal.

7. The system according to claim 6, characterized in that, The StarFlash communication module adopts dual-mode communication with SLE low latency mode and SLB high bandwidth mode; it supports adaptive bit rate adjustment from 125kbps to 48Mbps; the module adopts a low power consumption design at the power tool end and supports a sleep-wake mechanism.

8. The system according to claim 7, characterized in that, The sensing module includes one or more sensors integrated into the power tool; the sensors include at least one of an insulation resistance sensor embedded in an insulating glove, a detection accuracy sensor in an electroscope, and a torque sensor of a power tool, and the sensors are connected to a built-in low-power starlight communication module via microwires and are powered by a button battery.

9. The system according to claim 8, characterized in that, The gateway uses an industrial-grade embedded host, integrating a StarFlash dual-mode communication module and a 4G CAT1 full-network module. The StarFlash antenna is a 5dBi omnidirectional antenna with a coverage radius of ≥200 meters. It locally caches 7 days of data and can still provide real-time alarms when the network is down.

10. The system according to claim 9, characterized in that, The intelligent analysis and diagnosis module uses an LSTM neural network model, trained based on historical state data, to predict potential fault risks of electrical equipment. When the prediction accuracy exceeds 80%, a risk warning is pushed to the management terminal at a predetermined time.

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