An intelligent management system for power tools

Abstract

The present application relates to the technical field of electric tool management, and specifically relates to an intelligent management system for electric tools, comprising a device multi-dimensional state acquisition and characteristic vector construction module, which acquires operating parameters and constructs a characteristic vector through an MCU; a tool health assessment and risk identification module, which outputs a health score and an early warning level and identifies a modification behavior; a self-adaptive OTA upgrade and configuration strategy recommendation module, which triggers an update when an anomaly or modification occurs; an electronic fence and disconnection locking module, which tracks a position and triggers locking when crossing a boundary or being disconnected; and a smartphone terminal control response module, which displays a state and issues a control instruction. The present application constructs a closed-loop system of intelligent management and safety cooperation of electric tools through device multi-dimensional state sensing, health scoring and modification identification, self-adaptive upgrade and configuration recommendation, spatial behavior safety locking and user interaction feedback mechanisms.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric tool management, and particularly relates to an intelligent management system for electric tools. BACKGROUND

[0002] Under the background of wide application of intelligent electric tools, performance, safety and user experience have become the focus of industrial manufacturing and individual operation scenes. With the enhancement of device interconnection capability, multi-dimensional information such as tool operation data, component state and user usage preference is gradually perceived and uploaded, which provides a data basis for carrying out health assessment, remote configuration and safety control. Especially in the system in which chargers, batteries and tool mainframes cooperatively operate, realizing full-process digital monitoring and control has become an important direction to improve the intelligent level of devices.

[0003] However, most of the existing electric tools only have basic data acquisition and firmware upgrade functions, lack of deep assessment of component state and active identification of abnormal behavior, and cannot effectively judge the risk of modification or push personalized configuration schemes. At the same time, the safety problems such as fence disengagement and disconnection rely on manual positioning and disposal, and lack of automatic response mechanism. User interaction is often limited to passive information viewing, and lacks integrated control capability of OTA upgrade, parameter adjustment and fence setting. SUMMARY

[0004] The present application provides an intelligent management system for electric tools, which collects multi-dimensional state parameters in real time through an MCU chip, constructs an operation feature vector, realizes component health assessment and modification suspiciousness analysis, and automatically recommends firmware version and configuration scheme in abnormal state, combines with electronic fence mechanism to realize boundary tracking and disconnection locking, and realizes user decision-making closed-loop control through a mobile phone terminal, which significantly improves the safety, intelligent level and user operation efficiency of tool operation.

[0005] An intelligent management system for electric tools, comprising a device multi-dimensional state acquisition and feature vector construction module, a tool health assessment and risk identification module, an adaptive OTA upgrade and configuration strategy recommendation module, an electronic fence and disconnection locking module, and an intelligent mobile phone terminal control response module, wherein;

[0006] The device multi-dimensional state acquisition and feature vector construction module periodically acquires device operation state parameters through the MCU chips deployed in the electric tool body, the battery and the charger, including temperature, current, voltage, charge and discharge cycle count, component connection state, Bluetooth communication signal strength and firmware version information, and constructs a device operation state feature vector based on a preset feature extraction rule;

[0007] The tool's health assessment and risk identification module inputs the device's operating status feature vector into the health assessment model and outputs the health scores and warning levels of each component of the current device. The warning levels include normal, mild abnormality, and severe abnormality. At the same time, it combines the component replacement records, abnormal signal behavior, and firmware version information differences in the device's operating status feature vector to calculate the degree of suspicion of modification and identify whether there is any unauthorized replacement, repair, or modification behavior.

[0008] When the warning level is mild or severe, or when modification behavior is detected, the adaptive OTA upgrade and configuration strategy recommendation module automatically triggers the OTA update judgment process, calculates the optimal firmware version and security patch combination applicable to the current device, and recommends parameter configuration schemes based on usage scenarios and user habits, including torque limit, speed curve, and startup delay, and outputs upgrade instructions and parameter configuration schemes.

[0009] The electronic fence and disconnection locking module uses the device's Bluetooth or wireless communication unit to continuously track the device's geographical location and signal reception frequency, constructs a sequence of device behavior trajectories, and matches the sequence of device behavior trajectories with the electronic fence model set by the user. If the device leaves the fence or is continuously disconnected for more than a preset time threshold, the locking algorithm is triggered and a locking command is output.

[0010] The smartphone-side control response module receives the health score, modification suspicion level, parameter configuration scheme and locking command uploaded by the device, displays them visually, and allows users to confirm OTA upgrades, accept or adjust configuration suggestions, set the electronic fence range and view historical status trajectories. Finally, the user's decision results are encoded into control commands and sent to the device for execution.

[0011] Optionally, the device multi-dimensional state acquisition and feature vector construction module includes:

[0012] Operating status parameter acquisition: MCU chips deployed in the power tool body, battery, and charger periodically collect equipment operating status parameters, including temperature sensors to collect the temperature of key components. Real-time current is obtained through the current detection unit. The current voltage is obtained through the voltage detection unit. Record the number of charge and discharge cycles. Read component connection status Detect Bluetooth communication signal strength Read current firmware version information ;

[0013] Data preprocessing: The collected equipment operating status parameters are preprocessed, including normalization and missing value imputation;

[0014] Operational status feature vector construction: Based on the preprocessed device operational status parameters, multi-dimensional attributes are combined into a device operational status feature vector according to a predefined order. .

[0015] Optionally, the tool's health assessment and risk identification module includes:

[0016] Health status assessment: Based on the generated equipment operating status feature vector, the health score of each component is calculated through the health assessment model, and the warning level is divided according to the health score, including normal, mild abnormality and severe abnormality.

[0017] Risk identification and modification detection: By comparing the current equipment operating status feature vector with the factory parameter library and historical operating records, the frequency of component replacement, signal stability, and firmware version information differences are analyzed. When abnormal feature patterns are detected, it is determined that the equipment has been replaced, repaired or modified without authorization.

[0018] Optionally, the health status assessment includes:

[0019] Health score calculation: Based on the device's operating status feature vector, according to a set weight vector. Perform a weighted average and output the current component's health score. ;

[0020] Warning level classification: Health score The warning level is determined by comparing the data with a preset risk level threshold. When, it is marked as normal, when When, it is marked as a mild abnormality, when When, it is marked as a serious anomaly, among which, This is the lower limit threshold of normal. This is the lower limit threshold for mild abnormalities.

[0021] Optionally, the risk identification and modification detection includes:

[0022] Firmware version consistency verification: based on the current firmware version information in the device's operating state feature vector. Compared with the recorded factory firmware version baseline value A comparison is performed to determine if there are any version changes that have not been recorded via OTA. If a firmware version difference is found and there is no upgrade record, it is marked as a firmware anomaly, indicated as:

[0023] ;

[0024] in, This is a boolean value indicating an firmware version error.

[0025] Component replacement behavior analysis: By accessing the equipment maintenance logs, the number of times critical components (battery, chassis control unit) were replaced within a predetermined time period (30 days) is statistically analyzed. ,like The threshold for determining the frequency of replacement is exceeded. (set as) This is considered suspicious behavior and is indicated as follows:

[0026] ;

[0027] in, To replace the abnormal Boolean value;

[0028] Signal stability analysis: Calculate the signal fluctuation rate based on the Bluetooth communication signal strength in the device's operating state feature vector. ,when Exceeding the signal fluctuation judgment threshold When this occurs, it is marked as a signal anomaly;

[0029] Risk assessment output: Calculate the suspicion level of modification by combining abnormal Boolean values ​​for firmware version, abnormal Boolean values ​​for replacement, and signal volatility. and with risk assessment threshold (Set to 0.5) for comparison, when If so, it is determined that the equipment has been replaced, repaired, or modified without authorization.

[0030] Optionally, the adaptive OTA upgrade and configuration strategy recommendation module includes:

[0031] OTA upgrade judgment and firmware version adaptation: When the device has a warning level of mild or severe abnormality, or when modification behavior is detected, the OTA upgrade judgment process is automatically started. The current firmware version information is compared with the historical records and security patch list in the firmware version database to identify whether there are security vulnerabilities, compatibility issues or version backwardness. Based on the device type, hardware configuration and current operating status, the optimal version combination is matched and an upgrade instruction including firmware version identifier and security patch information is generated.

[0032] Configuration strategy generation and parameter distribution: Based on the current load of the device, motor temperature rise, task execution cycle, user's historical usage behavior and current task scenario, the optimal configuration strategy is generated. Combining user preference data and the current working mode of the device, the optimal configuration strategy is automatically recommended, including combinations of torque limit, speed curve and start-up delay. The parameter configuration scheme is constructed and distributed to the device along with the upgrade command.

[0033] Optionally, the OTA upgrade determination and firmware version adaptation includes:

[0034] Version difference analysis and risk identification: Read current firmware version information and the set of historical versions recorded in the firmware version database. and its corresponding security patch status set Perform a difference comparison and calculate the version risk factor score. ,when When this happens, the device is marked as needing an upgrade. The threshold for version risk assessment;

[0035] Optimal version combination matching and upgrade instruction generation: Retrieves the firmware version from the database and matches the current device type. Current hardware configuration Current running status label The optimal firmware version for full compatibility and its corresponding security patches and generate upgrade instructions. .

[0036] Optionally, the configuration strategy generation and parameter distribution include:

[0037] Behavioral tag fusion: Collects the current load of the device, motor temperature rise, and task execution cycle, and combines them with the user's historical usage behavior sequence. Extract behavioral preference tags This includes operating frequency, switching sensitivity, and extreme operating time periods, and generates a fused sensing vector. ;

[0038] Task scenario matching and strategy space reduction: based on the current task context label This includes scenarios involving heavy loads, hill starts, and speed-limited areas, matching similar scenario instances from the historical configuration strategy library. The search space is reduced based on a similarity weight function, while retaining the candidate configuration strategy set. ;

[0039] Generation parameter configuration scheme: Comprehensive fusion of perception vectors Behavioral preference tags and candidate configuration strategy set The optimal parameter combination scheme is determined by using a multi-objective optimization model, and a parameter configuration scheme is constructed. This includes torque limiting, speed curve shape, and start-up delay;

[0040] Build and distribute command packages: Configure parameter schemes With the generated upgrade instructions The components are integrated and packaged to form the final instruction package. The data is sent to the target device via the edge control unit.

[0041] Optionally, the electronic fence and disconnection locking module includes:

[0042] Continuous acquisition of device location information and signal frequency: The device periodically acquires its current location and signal reception frequency through its wireless communication unit, forming tracking record points. The location acquisition results are recorded as follows: , , These are latitude and longitude coordinates, and the signal receiving frequency sequence is as follows: And generate a continuous behavior trajectory sequence. , represented as:

[0043] ;

[0044] Matching behavioral trajectories with electronic fence models: Matching continuous behavioral trajectory sequences of devices with preset electronic fence models. Perform spatial matching and communication state analysis; if a time interval exists... Equipment location If the signal reception frequency is lower than the set disconnection threshold, it is determined that the device has left the fence. (Set to 0.2) is considered as a loss of contact. Let the continuous loss of contact time window be... ,like If so, it is determined to be a continuous loss of contact, among which, The threshold for continuous loss of contact;

[0045] Trigger the locking mechanism and output a command: When the device's current position exceeds the fence boundary ( )or At that time, output a lock command. .

[0046] Optionally, the smartphone control response module receives the following:

[0047] Multidimensional diagnostic information reception and visualization: Receives health scores, modification suspicion, parameter configuration schemes and lock commands uploaded from the device, and displays them in a graphical and visual way in the form of charts, indicator dashboards or warning prompts;

[0048] User interaction confirmation and control decision: Users perform interactive operations through the mobile interface, including confirming or rejecting OTA remote upgrades, accepting or manually modifying the parameter configuration schemes suggested by the system, setting or modifying the geographical range and disconnection threshold of the electronic fence, and viewing the historical status trajectory of the device.

[0049] Control command encoding and issuance: The decision-making behavior formed during user interaction is encoded into a set of structured control commands and sent to the device through the wireless communication unit. Triggering operations include: initiating OTA updates, applying new parameter configurations, activating or updating electronic fence rules, and responding to locking or unlocking mechanisms.

[0050] The beneficial effects of this invention are:

[0051] This invention embeds MCU chips into the power tool body, battery, and charger to collect temperature, current, voltage, charge / discharge cycle count, Bluetooth signal strength, and firmware version information in real time. This constructs a multi-dimensional feature vector of the device's operating status, enabling quantifiable representation from underlying operating parameters to overall health status. Combined with a health scoring model and risk identification mechanism, it can accurately determine the health level of device components and identify potential modifications such as firmware anomalies, frequent component replacements, or abnormal signal fluctuations. This forms a closed-loop health diagnosis system covering the entire lifecycle of the device, effectively improving device reliability and proactive maintenance.

[0052] This invention, by constructing an adaptive OTA upgrade and configuration strategy recommendation module, can automatically trigger an upgrade judgment process when mild or severe anomalies are detected or modification risks are discovered. Based on the device type, hardware configuration, and operating status, it matches the optimal firmware version and security patch combination. At the same time, by combining the user's historical behavior sequence and task scenario tags, it dynamically generates a fusion perception vector and a candidate strategy set. Using a multi-objective optimization model, it calculates the optimal parameter configuration scheme, including torque limit, speed curve shape, and start-up delay, to achieve coordinated adaptive adjustment of device performance and user preferences. This mechanism significantly improves the operating safety, energy efficiency, and personalized adaptability of power tools.

[0053] This invention, through an electronic fence and a disconnection locking module, can continuously track the geographical location and signal status of devices based on Bluetooth or wireless communication units, construct a sequence of device behavior trajectories, and compare it with the electronic fence model in real time. When the device exceeds the set area or continuously loses connection for more than a threshold, a locking command is automatically triggered, ensuring the security and controllability of the device in terms of asset management and anti-theft. At the same time, the smartphone terminal control response module realizes the visualization of health status, modification risks, and configuration strategies, supports users to confirm OTA upgrades, adjust parameter configurations, or set fence ranges online, and transmits the decision results back to the device for execution in real time. Thus, a cloud-device-human collaborative closed-loop intelligent management system is constructed, comprehensively improving the intelligent level of operation and maintenance of power tools and the user experience. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Fig. 1 This is a schematic diagram of the system functional modules according to an embodiment of the present invention;

[0056] Fig. 2 This is a schematic diagram of the tool health assessment and risk identification module in an embodiment of the present invention. Detailed Implementation

[0057] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. Those skilled in the art may employ other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0058] like Figs. 1-2 As shown, an intelligent management system for power tools includes a multi-dimensional equipment status acquisition and feature vector construction module, a tool health assessment and risk identification module, an adaptive OTA upgrade and configuration strategy recommendation module, an electronic fence and disconnection locking module, and a smartphone control response module, wherein;

[0059] The device multi-dimensional status acquisition and feature vector construction module periodically collects device operating status parameters, including temperature, current, voltage, charge and discharge cycle count, component connection status, Bluetooth communication signal strength and firmware version information, through MCU chips deployed in the power tool body, battery and charger, and constructs device operating status feature vectors based on preset feature extraction rules;

[0060] The tool health assessment and risk identification module inputs the device operating status feature vector into the health assessment model and outputs the health score and warning level of each component of the current device. The warning level includes normal, mild abnormality and severe abnormality. At the same time, it combines the component replacement records, abnormal signal behavior and firmware version information differences in the device operating status feature vector to calculate the degree of suspicion of modification and identify whether there is unauthorized replacement, repair or modification.

[0061] When the warning level is mild or severe, or when modification behavior is detected, the adaptive OTA upgrade and configuration strategy recommendation module automatically triggers the OTA update judgment process, calculates the optimal firmware version and security patch combination applicable to the current device, and recommends parameter configuration schemes based on usage scenarios and user habits, including torque limit, speed curve, and startup delay, and outputs upgrade instructions and parameter configuration schemes.

[0062] The electronic fence and disconnection locking module uses the device's Bluetooth or wireless communication unit to continuously track the device's geographical location and signal reception frequency, constructs a sequence of device behavior trajectories, and matches the sequence of device behavior trajectories with the electronic fence model set by the user. If the device leaves the fence or is continuously disconnected for more than a preset time threshold, the locking algorithm is triggered and a locking command is output.

[0063] The smartphone-based control response module receives health scores, modification suspicion levels, parameter configuration schemes, and lock commands uploaded by the device, displays them visually, and allows users to confirm OTA upgrades, accept or adjust configuration suggestions, set electronic fence ranges, and view historical status trajectories. Finally, the user's decision results are encoded into control commands and sent to the device for execution.

[0064] The device multidimensional status acquisition and feature vector construction module includes:

[0065] Operating status parameter acquisition: MCU chips deployed in the power tool body, battery, and charger periodically collect equipment operating status parameters, including temperature sensors to collect the temperature of key components. Real-time current is obtained through the current detection unit. The current voltage is obtained through the voltage detection unit. Record the number of charge and discharge cycles. Read component connection status , This indicates a normal connection; the Bluetooth communication signal strength is being checked. Read current firmware version information ;

[0066] Data preprocessing: The collected equipment operating status parameters are preprocessed, including normalization and missing value imputation, specifically including:

[0067] (1) The collected equipment operating status parameters are normalized using min-max normalization, and are expressed as follows:

[0068] ;

[0069] in, For the collected equipment operating status parameters, These are the normalized equipment operating status parameters. , These are the upper and lower limits of the historically acceptable range for this dimension, respectively.

[0070] (2) Missing values ​​are filled using the sliding window mean method, as shown below:

[0071] ;

[0072] in, For the first Fill in the value in time. For window size, For the front Historical valid values ​​at each point in time;

[0073] Operational status feature vector construction: Based on the preprocessed device operational status parameters, multi-dimensional attributes are combined into a device operational status feature vector according to a predefined order. , represented as:

[0074] ;

[0075] in, , , , These are the normalized temperature, current, voltage, and Bluetooth communication signal strength, respectively. For normalized charge / discharge cycle counts, This is the design lifespan value. This is a hash mapping function for firmware version information.

[0076] The tool's health assessment and risk identification module includes:

[0077] Health status assessment: Based on the generated equipment operating status feature vector, the health score of each component is calculated through the health assessment model, and the warning level is divided according to the health score, including normal, mild abnormality and severe abnormality.

[0078] Risk identification and modification detection: By comparing the current equipment operating status feature vector with the factory parameter library and historical operating records, the frequency of component replacement, signal stability, and firmware version information differences are analyzed. When abnormal feature patterns are detected, it is determined that the equipment has been replaced, repaired or modified without authorization.

[0079] Health status assessment includes:

[0080] Health score calculation: Based on the device's operating status feature vector, according to a set weight vector. Perform a weighted average and output the current component's health score. , represented as:

[0081] ;

[0082] in, For the normalized first Each device's operating status parameters The index weighting coefficient, The total number of operating status parameters of the equipment participating in the scoring;

[0083] Warning level classification: Health score The warning level is determined by comparing the data with a preset risk level threshold. When, it is marked as normal, when When, it is marked as a mild abnormality, when When, it is marked as a serious anomaly, among which, This is the lower limit threshold of normal. This is the lower limit threshold for mild abnormalities.

[0084] Risk identification and modification inspection include:

[0085] Firmware version consistency verification: based on the current firmware version information in the device's operating state feature vector. Compared with the recorded factory firmware version baseline value A comparison is performed to determine if there are any version changes that have not been recorded via OTA. If a firmware version difference is found and there is no upgrade record, it is marked as a firmware anomaly, indicated as:

[0086] ;

[0087] in, This is a boolean value indicating an firmware version error.

[0088] Component replacement behavior analysis: By accessing the equipment maintenance logs, the number of times critical components (battery, chassis control unit) were replaced within a predetermined time period (30 days) is statistically analyzed. ,like The threshold for determining the frequency of replacement is exceeded. (set as) This is considered suspicious behavior and is indicated as follows:

[0089] ;

[0090] in, To replace the abnormal Boolean value;

[0091] Signal stability analysis: Calculate the signal fluctuation rate based on the Bluetooth communication signal strength in the device's operating state feature vector. ,when Exceeding the signal fluctuation judgment threshold When this occurs, it is marked as a signal abnormality, as indicated by:

[0092] ;

[0093] in, , The first , The strength of the Bluetooth communication signal collected this time. This represents the total number of sampling points;

[0094] ;

[0095] in, , These represent the mean and standard deviation of signal volatility under normal communication scenarios, respectively. Confidence coefficient;

[0096] Risk assessment output: Calculate the suspicion level of modification by combining abnormal Boolean values ​​for firmware version, abnormal Boolean values ​​for replacement, and signal volatility. and with risk assessment threshold (Set to 0.5) for comparison, when When it is determined that the equipment has been replaced, repaired, or modified without authorization, it will be indicated as follows:

[0097] ;

[0098] in, , , These are the corresponding weighting coefficients.

[0099] The adaptive OTA upgrade and configuration strategy recommendation module includes:

[0100] OTA upgrade judgment and firmware version adaptation: When the device has a warning level of mild or severe abnormality, or when modification behavior is detected, the OTA upgrade judgment process is automatically started. The current firmware version information is compared with the historical records and security patch list in the firmware version database to identify whether there are security vulnerabilities, compatibility issues or version backwardness. Based on the device type, hardware configuration and current operating status, the optimal version combination is matched and an upgrade instruction including firmware version identifier and security patch information is generated.

[0101] Configuration strategy generation and parameter distribution: Based on the current load of the device, motor temperature rise, task execution cycle, user's historical usage behavior and current task scenario, the optimal configuration strategy is generated. Combining user preference data and the current working mode of the device, the optimal configuration strategy is automatically recommended, including combinations of torque limit, speed curve and start-up delay. The parameter configuration scheme is constructed and distributed to the device along with the upgrade command.

[0102] OTA upgrade determination and firmware version compatibility include:

[0103] Version difference analysis and risk identification: Read current firmware version information and the set of historical versions recorded in the firmware version database. and its corresponding security patch status set Perform a difference comparison and calculate the version risk factor score. ,when When this happens, the device is marked as needing an upgrade. The threshold for version risk assessment is expressed as:

[0104] ;

[0105] in, , , These are the corresponding scoring weights. This represents the degree of difference between the version number of the current version and the latest version (normalized value). To check if the current version contains any unpatched security vulnerabilities, To determine if the current version is incompatible with the device hardware or operating scenario;

[0106] Optimal version combination matching and upgrade instruction generation: Retrieves the firmware version from the database and matches the current device type. Current hardware configuration Current running status label The optimal firmware version for full compatibility and its corresponding security patches and generate upgrade instructions. Specifically, it includes:

[0107] (1) Candidate version filtering: Filter all versions that match the current device type from the firmware version database. Matching version candidate set , represented as:

[0108] ;

[0109] in, For the first firmware version and its security patches, For the first Device types that firmware version compatibility supports;

[0110] (2) Adaptation score calculation: For each version combination in the version candidate set, calculate the compatibility score with the current hardware configuration. and running status label Adaptability rating , represented as:

[0111] ;

[0112] in, This represents the attribute similarity function. , The first Hardware configuration and runtime status templates adapted to each candidate version , These are the corresponding weight coefficients;

[0113] (3) Optimal combination selection and upgrade instruction generation: Select the combination with the highest compatibility score from the version candidate set as the optimal firmware version and its patch, and generate upgrade instructions. , represented as:

[0114] ;

[0115] .

[0116] Configuration strategy generation and parameter distribution include:

[0117] Behavioral tag fusion: Collects the current load of the device, motor temperature rise, and task execution cycle, and combines them with the user's historical usage behavior sequence. Extract behavioral preference tags This includes operating frequency, switching sensitivity, and extreme operating time periods, and generates a fused sensing vector. Specifically, it includes:

[0118] (1) Current behavior status data acquisition: Current load of the acquisition device Motor temperature rise and task execution cycle And construct the current behavior state vector. , represented as:

[0119] ;

[0120] (2) Retrieve and process user history usage behavior sequences This sequence is a user operation record sorted by time, including a sequence of operation timestamps. And the corresponding task control events (start / stop, mode switching, speed adjustment), based on the user's historical usage behavior sequence. Build user behavior preference tags , represented as:

[0121] ;

[0122] in, For operating frequency, This represents the total number of operations. The length of the behavior observation window. To switch sensitivity, This is the period of extreme operation;

[0123] (3) Fusion perception vector generation: The current behavior state vector is generated. User behavior preference tags Perform splicing and fusion to construct a fused perception vector. , represented as:

[0124] ;

[0125] Task scenario matching and strategy space reduction: based on the current task context label This includes scenarios involving heavy loads, hill starts, and speed-limited areas, matching similar scenario instances from the historical configuration strategy library. The search space is reduced based on a similarity weight function, while retaining the candidate configuration strategy set. , represented as:

[0126] ;

[0127] in, For the first Scenario tags for each configuration strategy The minimum similarity threshold, The cosine similarity function;

[0128] Generation parameter configuration scheme: Comprehensive fusion of perception vectors Behavioral preference tags and candidate configuration strategy set The optimal parameter combination scheme is determined by using a multi-objective optimization model, and a parameter configuration scheme is constructed. This includes torque limitation, speed curve shape, and start-up delay, expressed as:

[0129] ;

[0130] in, Configure a scheme for candidate parameters. For the deviation from user preferences, This is a function to measure the deviation from the current performance state of the equipment. , These are the corresponding weight coefficients;

[0131] The deviation from user preferences is expressed as follows:

[0132] ;

[0133] in, For the first Several candidate parameter configuration options, including torque limit, speed curve shape, and start-up delay. These are behavioral preference tags, including the user's desired torque, speed profile, and latency values. For the first Torque limit values ​​for each candidate configuration strategy, This is a user-preferred torque limit reference value. For the first The speed curve shape parameter of each candidate configuration strategy For the user's preferred velocity curve shape parameters, For the first The startup delay time of each candidate configuration strategy For the user's preferred startup delay time, , , These are the weight coefficients for the corresponding user preference dimensions;

[0134] The deviation from the current performance state of the device is expressed as:

[0135] ;

[0136] in, For the current load level, The optimal velocity curve shape threshold is derived from the current temperature rise trend. The recommended startup latency is based on the current task cycle and thermal recovery time. , , These are the weighting factors for each performance dimension;

[0137] The optimal velocity curve shape threshold derived from the current temperature rise trend Represented as:

[0138] ;

[0139] in, This is a reference value for the standard speed curve shape (the ideal speed curve shape when the equipment is unloaded and has no temperature rise). The maximum safe load threshold, This represents the current motor temperature rise rate. The maximum allowable rate of temperature rise threshold, As a load suppression factor, It is a factor that inhibits the rising temperature trend;

[0140] Build and distribute command packages: Configure parameter schemes With the generated upgrade instructions The components are integrated and packaged to form the final instruction package. The data is sent to the target device via the edge control unit.

[0141] The electronic fence and disconnection locking module includes:

[0142] Continuous acquisition of device location information and signal frequency: The device periodically acquires its current location and signal reception frequency through its wireless communication unit, forming tracking record points. The location acquisition results are recorded as follows: , , These are latitude and longitude coordinates, and the signal receiving frequency sequence is as follows: And generate a continuous behavior trajectory sequence. , represented as:

[0143] ;

[0144] Matching behavioral trajectories with electronic fence models: Matching continuous behavioral trajectory sequences of devices with preset electronic fence models. Perform spatial matching and communication state analysis; if a time interval exists... Equipment location If the signal reception frequency is lower than the set disconnection threshold, it is determined that the device has left the fence. (Set to 0.2) is considered as a loss of contact. Let the continuous loss of contact time window be... ,like If so, it is determined to be a continuous loss of contact, among which, The threshold for continuous loss of contact is expressed as:

[0145] ;

[0146] Among them, when hour Otherwise, it is 0. The number of most recent consecutive sampling points. The sampling period;

[0147] Electronic fence model Defined as the set of boundary points of a certain region ;

[0148] Trigger the locking mechanism and output a command: When the device's current position exceeds the fence boundary ( )or At that time, output a lock command. .

[0149] The smartphone control response module receives the following:

[0150] Multidimensional diagnostic information reception and visualization: Receive health scores, modification suspicion, parameter configuration schemes and lock commands uploaded from the device, and display them in a graphical and visual way in the form of charts, indicator dashboards or warning prompts to enhance the user's perception of the current status of the device;

[0151] User interaction confirmation and control decision: Users perform interactive operations through the mobile interface, including confirming or rejecting OTA remote upgrades, accepting or manually modifying the parameter configuration schemes suggested by the system, setting or modifying the geographical range and disconnection threshold of the electronic fence, and viewing the historical status trajectory of the device.

[0152] Control command encoding and issuance: The decision-making behavior formed during user interaction is encoded into a set of structured control commands and sent to the device through the wireless communication unit. Triggering operations include: initiating OTA updates, applying new parameter configurations, activating or updating electronic fence rules, and responding to locking or unlocking mechanisms.

[0153] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0154] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An intelligent management system for power tools, characterized in that, It includes a multi-dimensional device status acquisition and feature vector construction module, a tool health assessment and risk identification module, an adaptive OTA upgrade and configuration strategy recommendation module, an electronic fence and disconnection locking module, and a smartphone control response module, among which; The device multidimensional status acquisition and feature vector construction module periodically collects device operating status parameters, including temperature, current, voltage, charge and discharge cycle count, component connection status, Bluetooth communication signal strength and firmware version information, through MCU chips deployed in the power tool body, battery and charger, and constructs device operating status feature vectors based on preset feature extraction rules. The tool health assessment and risk identification module inputs the device operating status feature vector into the health assessment model and outputs the health score and warning level of each component of the current device. The warning level includes normal, mild abnormality and severe abnormality. Combining the component replacement records, abnormal signal behavior and firmware version information differences in the device operating status feature vector, the module calculates the degree of suspicion of modification and identifies whether there is unauthorized replacement, repair or modification behavior. When the warning level is mild or severe, or when modification behavior is detected, the adaptive OTA upgrade and configuration strategy recommendation module automatically triggers the OTA update judgment process, calculates the optimal firmware version and security patch combination applicable to the current device, and recommends parameter configuration schemes based on usage scenarios and user habits, including torque limit, speed curve, and startup delay, and outputs upgrade instructions and parameter configuration schemes. The electronic fence and disconnection locking module continuously tracks the device's geographical location and signal reception frequency using the device's Bluetooth or wireless communication unit, constructs a sequence of device behavior trajectories, and matches this sequence with the user-defined electronic fence model. If the device leaves the fence or remains disconnected for more than a preset time threshold, the locking algorithm is triggered, and a locking command is output, specifically including: Continuous acquisition of device location information and signal frequency: The device periodically acquires the current location and signal reception frequency through the wireless communication unit to form tracking record points. Among them, the location acquisition result is recorded as the longitude and latitude respectively, and the signal reception frequency sequence is , and generates a continuous behavior trajectory sequence , which is expressed as: Matching the behavior trajectory with the electronic fence model: Matching the continuous behavior trajectory sequence of the device with the preset electronic fence model for spatial matching and communication status analysis. If there is a moment when the device location , it is determined to be out of the fence. When the signal reception frequency is lower than the set loss-of-contact threshold , it is regarded as loss of contact. Let the continuous loss-of-contact time window be . If , it is determined to be continuous loss of contact. Among them, is the continuous loss-of-contact threshold; Trigger the locking mechanism and output an instruction: When the current location of the device exceeds the fence boundary or , output the locking instruction The smartphone-side control response module receives the following: Multidimensional diagnostic information reception and visualization: Receives health scores, modification suspicion, parameter configuration schemes and lock commands uploaded from the device, and displays them in a graphical and visual way in the form of charts, indicator dashboards or warning prompts; User interaction confirmation and control decision: Users perform interactive operations through the mobile interface, including confirming or rejecting OTA remote upgrades, accepting or manually modifying the parameter configuration schemes suggested by the system, setting or modifying the geographical range and disconnection threshold of the electronic fence, and viewing the historical status trajectory of the device. Control command encoding and issuance: The decision-making behavior formed in user interaction is encoded into a set of structured control commands and sent to the device through the wireless communication unit. Triggering operations include: initiating OTA update, applying new parameter configuration, activating or updating electronic fence rules, and responding to locking or unlocking mechanisms. The smartphone-side control response module receives the health score, modification suspicion level, parameter configuration scheme and locking command uploaded by the device, displays them visually, and allows users to confirm OTA upgrades, accept or adjust configuration suggestions, set the electronic fence range and view historical status trajectories. Finally, the user's decision results are encoded into control commands and sent to the device for execution.

2. The intelligent management system for power tools according to claim 1, characterized in that, The device multidimensional state acquisition and feature vector construction module includes: Operating status parameter acquisition: Periodically acquire the operating status parameters of the device through the MCU chips deployed in the power tool body, battery, and charger. Among them, the temperature of key components of the device is acquired through temperature sensors. , obtain the real-time current through the current detection unit , obtain the current voltage through the voltage detection unit , record the charge-discharge cycle count , read the component connection status , detect the Bluetooth communication signal strength , read the current firmware version information Data preprocessing: Preprocess the acquired device operating status parameters, including normalization and missing value filling; Operational status feature vector construction: Based on the preprocessed device operational status parameters, multi-dimensional attributes are combined into a device operational status feature vector according to a predefined order. .

3. The intelligent management system for power tools according to claim 2, characterized in that, The tool's health assessment and risk identification module includes: Health status assessment: Based on the generated equipment operating status feature vector, the health score of each component is calculated through the health assessment model, and the warning level is divided according to the health score, including normal, mild abnormality and severe abnormality. Risk identification and modification detection: By comparing the current equipment operating status feature vector with the factory parameter library and historical operating records, the frequency of component replacement, signal stability, and firmware version information differences are analyzed. When abnormal feature patterns are detected, it is determined that the equipment has been replaced, repaired or modified without authorization.

4. The intelligent management system for power tools according to claim 3, characterized in that, The health status assessment includes: Health score calculation: Based on the feature vector of the device operating state, perform weighted average according to the set weight vector and output the health score of the current component Early warning level division: Compare the health score with the preset risk level threshold to divide the early warning level. When , it is marked as normal. When , it is marked as slightly abnormal. When , it is marked as severely abnormal. Among them, is the normal lower limit threshold, is the slightly abnormal lower limit threshold.

5. The intelligent management system for power tools according to claim 4, characterized in that, The risk identification and modification detection include: Firmware version consistency check: Based on the current firmware version information in the device operation status feature vector , compare with the recorded factory firmware version baseline value , determine whether there is a version change behavior that is not recorded by OTA. If a firmware version difference is found and there is no upgrade record, mark it as firmware anomaly, expressed as: Among them, is the firmware version anomaly boolean value; Component replacement behavior analysis: By calling the device maintenance log, count the replacement times of key components within a predetermined time , if exceeds the set replacement frequency determination threshold , judge it as a suspicious behavior, expressed as: Among them, is the replacement anomaly boolean value; Signal stability analysis: Calculate the signal volatility based on the Bluetooth communication signal strength in the device operating state feature vector , when exceeds the signal fluctuation determination threshold , it is marked as signal abnormality; Risk assessment output: Calculate the suspicion level of modification by combining abnormal Boolean values ​​for firmware version, abnormal Boolean values ​​for replacement, and signal volatility. and with risk assessment threshold When comparing, The equipment was determined to have been replaced, repaired, or modified without authorization.

6. The intelligent management system for power tools according to claim 5, characterized in that, The adaptive OTA upgrade and configuration strategy recommendation module includes: OTA upgrade judgment and firmware version adaptation: When the device has a warning level of mild or severe abnormality, or when modification behavior is detected, the OTA upgrade judgment process is automatically started. The current firmware version information is compared with the historical records and security patch list in the firmware version database to identify whether there are security vulnerabilities, compatibility issues or version backwardness. Based on the device type, hardware configuration and current operating status, the optimal version combination is matched and an upgrade instruction including firmware version identifier and security patch information is generated. Configuration strategy generation and parameter distribution: Based on the current load of the device, motor temperature rise, task execution cycle, user's historical usage behavior and current task scenario, the optimal configuration strategy is generated. Combining user preference data and the current working mode of the device, the optimal configuration strategy is automatically recommended, including combinations of torque limit, speed curve and start-up delay. The parameter configuration scheme is constructed and distributed to the device along with the upgrade command.

7. The intelligent management system for power tools according to claim 6, characterized in that, The OTA upgrade determination and firmware version adaptation include: Version difference analysis and risk identification: Read current firmware version information and the set of historical versions recorded in the firmware version database. and its corresponding security patch status set Perform a difference comparison and calculate the version risk factor score. ,when When this happens, the device is marked as needing an upgrade. The threshold for version risk assessment; Optimal version combination matching and upgrade instruction generation: Retrieves the firmware version from the database and matches the current device type. Current hardware configuration Current running status label The optimal firmware version for full compatibility and its corresponding security patches and generate upgrade instructions. .

8. The intelligent management system for power tools according to claim 7, characterized in that, The configuration strategy generation and parameter distribution include: Behavioral tag fusion: Collects the current load of the device, motor temperature rise, and task execution cycle, and combines them with the user's historical usage behavior sequence. Extract behavioral preference tags This includes operating frequency, switching sensitivity, and extreme operating time periods, and generates a fused sensing vector. ; Task scenario matching and strategy space reduction: based on the current task context label This includes scenarios involving heavy loads, hill starts, and speed-limited areas, matching similar scenario instances from the historical configuration strategy library. The search space is reduced based on a similarity weight function, while retaining the candidate configuration strategy set. ; Generation parameter configuration scheme: Comprehensive fusion of perception vectors Behavioral preference tags and candidate configuration strategy set The optimal parameter combination scheme is determined by using a multi-objective optimization model, and a parameter configuration scheme is constructed. This includes torque limiting, speed curve shape, and start-up delay; Build and distribute command packages: Configure parameter schemes With the generated upgrade instructions The components are integrated and packaged to form the final instruction package. The data is sent to the target device via the edge control unit.

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