Intelligent management system for electric tool

Abstract

The invention relates to the technical field of electric tool management, in particular to an intelligent management system for an electric tool, and the system comprises an equipment multi-dimensional state collection and feature vector construction module which collects operation parameters through an MCU and constructs feature vectors; the tool health assessment and risk identification module outputs a health score and an early warning level and identifies a refitting behavior; the self-adaptive OTA upgrading and configuration strategy recommendation module triggers updating during abnormity or refitting; the electronic fence and the disconnection locking module track the position, cross the border or disconnect to trigger locking; and the smart phone end controls the response module to display the state and issue a control instruction. According to the invention, a closed-loop system for intelligent management and safety collaboration of the electric tool is constructed through equipment multi-dimensional state perception, health scoring and modification identification, adaptive upgrading and configuration recommendation, space 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 health assessment and risk identification module inputs the equipment operating state feature vector into a health assessment model, outputs the health score and early warning level of each component of the current equipment, the early warning level includes normal, mild abnormality, severe abnormality, at the same time, combined with the component replacement record, signal abnormal behavior and firmware version information difference in the equipment operating state feature vector, the refitting suspicious degree is calculated, whether there is unauthorized replacement, maintenance or refitting behavior is identified;

[0008] The adaptive OTA upgrade and configuration strategy recommendation module automatically triggers the OTA update judgment process when the early warning level is mild abnormality or severe abnormality, or when the refitting behavior is detected, calculates the optimal firmware version and security patch combination applicable to the current equipment, and recommends a parameter configuration scheme based on the use scenario and user habit, including torque limit, speed curve, start delay, outputs the upgrade instruction and parameter configuration scheme;

[0009] The electronic fence and disconnection locking module uses the device Bluetooth or wireless communication unit to continuously track the device geographic position and signal receiving frequency, constructs a device behavior trajectory sequence, and matches the device behavior trajectory sequence 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, triggers the locking algorithm, and outputs the execution locking instruction;

[0010] The smart phone end control response module receives the health score, refitting suspicious degree, parameter configuration scheme and locking instruction uploaded by the device, visualizes and displays, and allows the user to confirm the OTA upgrade, accept or adjust the configuration suggestion, set the electronic fence range and view the historical state trajectory, finally encodes the user decision result as a control instruction and sends it to the device end for execution.

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

[0012] Operating state parameter acquisition: through the MCU chips deployed in the electric power tool body, battery and charger, the device operating state parameters are periodically acquired, wherein the temperature of the key components of the device is acquired through the temperature sensor , the real-time current is acquired through the current detection unit , the current voltage is acquired through the voltage detection unit , the charge and discharge cycle count is recorded , the component connection state is read , the Bluetooth communication signal strength is detected , and the current firmware version information is read ;

[0013] Data preprocessing: the acquired device operating state parameters are preprocessed, including normalization and missing value filling;

[0014] Running state feature vector construction: based on the pre-processed device running state parameters, combine multi-dimensional attributes into a device running state feature vector in a predefined order .

[0015] Optionally, the tool health assessment and risk identification module comprises:

[0016] Health state assessment: based on the generated device running state feature vector, calculate the health score of each component through the health assessment model, and divide the early warning level according to the health score, including normal, mild abnormality, and serious abnormality;

[0017] Risk identification and modification detection: by comparing the current device running state feature vector with the factory parameter library and historical operation records, analyze the component replacement frequency, signal stability, and firmware version information difference, and when detecting abnormal feature mode, determine that the device has unauthorized replacement, maintenance, or modification behavior.

[0018] Optionally, the health state assessment comprises:

[0019] Health score calculation: based on the device running state feature vector, weighted average is performed according to the set weight vector to output the health score of the current component ;

[0020] Early warning level division: compare the health score with the preset risk level threshold value to divide the early warning level, when , mark as normal, when , mark as mild abnormality, and when , mark as serious abnormality, wherein, is the lower threshold value of normal, is the lower threshold value of mild abnormality.

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

[0022] Firmware version consistency check: based on the current firmware version information in the device running state feature vector , compare with the recorded factory firmware version reference value , judge whether there is an OTA-recorded version change behavior, if the firmware version difference is found and no upgrade record is found, mark as firmware abnormality, represented as:

[0023] ;

[0024] wherein, is the firmware version abnormality Boolean value;

[0025] Component replacement behavior analysis: by calling the device maintenance log, the number of replacements of key components (battery, body control unit) within a predetermined time (30 days) is counted , if the replacement frequency exceeds the set replacement frequency determination threshold (set to ), it is determined that the behavior is suspicious, and is represented as:

[0026] ;

[0027] wherein is the replacement abnormality Boolean value;

[0028] Signal stability analysis: based on the Bluetooth communication signal strength in the device operating state feature vector, the signal fluctuation rate is calculated , when the signal fluctuation rate exceeds the signal fluctuation determination threshold , it is marked as signal abnormality;

[0029] Risk determination output: combining the firmware version abnormality Boolean value, the replacement abnormality Boolean value, and the signal fluctuation rate, the modification suspiciousness is calculated , and compared with the risk determination threshold (set to 0.5), when , it is determined that the device has unauthorized replacement, repair, or modification behavior.

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

[0031] OTA upgrade determination and firmware version adaptation: when the device has a mild abnormality or a serious abnormality warning level, or a modification behavior is detected, an OTA upgrade determination process is automatically started, the current firmware version information is compared with the historical records and security patch list in the firmware version database, it is identified whether there is a security vulnerability, compatibility problem or version lag, and according to the device type, hardware configuration and current operating state, the optimal version combination is matched, and an upgrade instruction including firmware version identification and security patch information is generated;

[0032] Configuration strategy generation and parameter delivery: according to the device current load, motor temperature rise, task execution period, user's historical use behavior and current task scene, the optimal configuration strategy is generated, combined with user preference data and device current working mode, the combination scheme including torque limit, speed curve, start delay is automatically recommended, the parameter configuration scheme is constructed, and the upgrade instruction is delivered to the device together.

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

[0034] Version difference analysis and risk identification: read the 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 the out-of-contact locking module comprise:

[0042] Device position information and signal frequency continuous collection: the device periodically collects the current position and signal receiving frequency through the wireless communication unit, forming a tracking record point, wherein the position collection result is recorded as , , respectively, the signal receiving frequency sequence is , and a continuous behavior trajectory sequence is generated, which is expressed as:

[0043] ;

[0044] Behavior trajectory and electronic fence model matching: the device continuous behavior trajectory sequence is matched with the preset electronic fence model in space and the communication state is analyzed, if the device position at time exists, it is determined that the device is out of the fence, when the signal receiving frequency is lower than the set out-of-contact threshold (set as 0.2), it is considered as out of contact, the continuous out-of-contact time window is set as , if , it is determined that the device is continuously out of contact, wherein is the continuous out-of-contact threshold;

[0045] Triggering the locking mechanism and outputting the instruction: when the device current position is out of the fence boundary or , the locking instruction is outputted.

[0046] Optionally, the smart phone terminal control response module receives:

[0047] Multi-dimensional diagnostic information receiving and visualized presentation: receiving the health score, the refitting suspicious degree, the parameter configuration scheme and the locking instruction uploaded from the device end, and performing interface visualized display in the form of charts, index instrument panels or warning prompts;

[0048] User interactive confirmation and control decision: the user performs interactive operation through the mobile phone terminal interface, including confirming or rejecting the OTA remote upgrade, accepting the system suggested parameter configuration scheme or manual modification, setting or modifying the geographic range and the out-of-contact threshold of the electronic fence, and viewing the device historical state trajectory;

[0049] Control instruction coding and issuing: the decision behavior formed in the user interaction is coded as a structured control instruction set, and is sent to the device end through the wireless communication unit, triggering the operation including: starting the OTA update, applying the new parameter configuration, activating or updating the electronic fence rule, responding to the locking or unlocking mechanism.

[0050] The beneficial effects of the present application are:

[0051] The present application, by embedding MCU chips in the body of the electric power tool, battery and charger, real-time collects temperature, current, voltage, charge and discharge cycle count, Bluetooth signal strength and firmware version information, constructs a multi-dimensional device running state feature vector, realizes the quantifiable characterization from the bottom layer running parameter to the overall health state, combines the health score model and the risk identification mechanism, can accurately determine the health level of the device components, and identify the potential modification behavior such as firmware exception, frequent component replacement or signal fluctuation exception, form a health diagnosis closed loop facing the whole life cycle of the device, and effectively improve the reliability and maintenance initiative of the device.

[0052] The present application, by constructing an adaptive OTA upgrade and configuration strategy recommendation module, can automatically trigger the upgrade judgment process when detecting mild or severe abnormalities or finding modification risks, match the optimal firmware version and security patch combination according to the device type, hardware configuration and running state, at the same time, combine the user historical behavior sequence and task scene label, dynamically generate the fusion perception vector and candidate strategy set, use the multi-objective optimization model to calculate the optimal parameter configuration scheme, including torque limit, speed curve shape and start delay, realize the collaborative adaptive adjustment of device performance and user preference, which significantly improves the running safety, energy efficiency and personalized adaptation ability of the electric power tool.

[0053] The present application, through the electronic fence and disconnection locking module, can continuously track the geographic location and signal state of the device based on Bluetooth or wireless communication unit, construct the device behavior trajectory sequence and compare it with the electronic fence model in real time, automatically trigger the locking instruction when the device exceeds the set area or is continuously disconnected for more than a threshold, ensure the safety and controllability of the device in the dimensions of asset management and theft prevention, at the same time, the smart phone end control response module realizes the visual display of health state, modification risk and configuration strategy, supports the user to confirm OTA upgrade, adjust parameter configuration or set fence range online, and returns the decision result to the device end for execution in real time, thereby constructing a cloud-end-human collaborative closed loop intelligent management system, and comprehensively improving the operation intelligent level and user experience of the electric power tool. BRIEF DESCRIPTION OF DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only illustrate the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0055] Fig. 1 It is a schematic diagram of the system function module of the embodiment of the present application.

[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 intelligent mobile phone end control response module receives the health score, modification suspiciousness, parameter configuration scheme and locking instruction uploaded by the equipment, performs visual display, and allows the user to confirm OTA upgrade, accept or adjust the configuration suggestion, set the electronic fence range and view the historical state trajectory, and finally encodes the user decision result into a control instruction and sends it to the equipment end for execution.

[0064] The equipment multi-dimensional state acquisition and feature vector construction module includes:

[0065] Running state parameter acquisition: through the MCU chips deployed in the body of the electric power tool, the battery and the charger, the equipment running state parameters are periodically acquired, wherein the temperature of the key components of the equipment is acquired through the temperature sensor , the real-time current is obtained through the current detection unit , the current voltage is obtained through the voltage detection unit , the charge and discharge cycle count is recorded , the component connection state is read , indicates that the connection is normal, the Bluetooth communication signal strength is detected , and the current firmware version information is read ;

[0066] Data preprocessing: the acquired equipment running state parameters are preprocessed, including normalization and missing value filling, specifically including:

[0067] (1) The acquired equipment running state parameters are normalized by using the minimum-maximum standardization, which is represented as:

[0068] ;

[0069] wherein, is the acquired equipment running state parameter, is the normalized equipment running state parameter, , are respectively the preset upper and lower limits of the historical acceptable range of this dimension;

[0070] (2) The missing values are filled by using the sliding window mean method, which is represented as:

[0071] ;

[0072] wherein, is the filling value at the moment, is the window size, is the historical valid value at the previous time point;

[0073] Running state feature vector construction: based on the pre-processed device running state parameters, combine multi-dimensional attributes into device running state feature vector according to pre-defined order , denoted as:

[0074] ;

[0075] wherein, , , , are normalized temperature, current, voltage, Bluetooth communication signal strength respectively, is the normalized charge and discharge cycle count, is the life design value, is the firmware version information hash mapping function.

[0076] Tool health assessment and risk identification module includes:

[0077] Health status assessment: based on the generated device running state feature vector, calculate the health score of each component through the health assessment model, and divide the warning level according to the health score, including normal, mild abnormality, serious abnormality;

[0078] Risk identification and modification detection: by comparing the current device running state feature vector with the factory parameter library and historical operation records, analyze the component replacement frequency, signal stability, firmware version information difference, when detecting abnormal feature mode, determine that the device has unauthorized replacement, repair or modification behavior.

[0079] Health status assessment includes:

[0080] Health score calculation: based on the device running state feature vector, weighted average according to the set weight vector , output the health score of the current component , denoted as:

[0081] ;

[0082] wherein, is the normalized th device running state parameter, is the index weight coefficient, is the total number of device running state parameters participating in scoring;

[0083] Warning level division: compare the health score with the preset risk level threshold, divide the warning level, when , mark as normal, when , mark as mild abnormality, when When the value is greater than the normal upper threshold value, the value is marked as a severe abnormality, and the value is represented as: is a normal lower threshold value, is a mild abnormality lower threshold value.

[0084] Risk identification and modification detection includes:

[0085] Firmware version consistency check: based on the current firmware version information in the device running state feature vector , and the recorded factory firmware version reference value Compare and determine whether there is a version change behavior without OTA record. If a firmware version difference is found and no upgrade record is found, it is marked as a firmware abnormality, represented as:

[0086] ;

[0087] wherein, is a firmware version abnormality Boolean value;

[0088] Component replacement behavior analysis: by calling the device maintenance log, the number of replacements of key components (battery, body control unit) within a predetermined time (30 days) is counted If exceeds the set replacement frequency determination threshold value (set to ), it is determined as a suspicious behavior, represented as:

[0089] ;

[0090] wherein, is a replacement abnormality Boolean value;

[0091] Signal stability analysis: based on the Bluetooth communication signal strength in the device running state feature vector, the signal fluctuation rate is calculated When exceeds the signal fluctuation determination threshold value , it is marked as a signal abnormality, represented as:

[0092] ;

[0093] wherein, , are the Bluetooth communication signal strengths collected for the , time, is the total number of sampling points;

[0094] ;

[0095] wherein, , respectively the mean and standard deviation of signal fluctuation rate under normal communication scenario, is the confidence coefficient;

[0096] Risk determination output: combined with firmware version abnormality Boolean value, replacement abnormality Boolean value and signal fluctuation rate, calculate the refitting suspicious degree , and compared with the risk determination threshold (set as 0.5), when , it is determined that there is unauthorized replacement, repair or refitting behavior of the device, which is represented as:

[0097] ;

[0098] wherein, , , respectively the corresponding weight coefficient.

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

[0100] OTA upgrade determination and firmware version adaptation: when the device appears mild abnormality or serious abnormality warning level, or detects refitting behavior, automatically start OTA upgrade judgment process, compare the current firmware version information with the historical record and security patch list in the firmware version database, identify whether there is security vulnerability, compatibility problem or version lag situation, and match the optimal version combination according to the device type, hardware configuration and current running state, generate upgrade instruction including firmware version identification and security patch information;

[0101] Configuration strategy generation and parameter delivery: according to the current load of the device, motor temperature rise, task execution period, user's historical use behavior and current task scene, generate the optimal configuration strategy, combine user preference data and current working mode of the device, automatically recommend the combination scheme including torque limit, speed curve, start delay, build parameter configuration scheme, and deliver to the device together with the upgrade instruction.

[0102] OTA upgrade determination and firmware version adaptation includes:

[0103] Version difference analysis and risk identification: read the current firmware version information , and compare with the historical version set recorded in the firmware version database and the corresponding security patch state set , calculate the version risk factor score , when , mark the device as upgrade state, wherein, is the version risk judgment threshold, represented as:

[0104] ;

[0105] wherein, , , are the corresponding score weights, is the version number difference (normalized numerical value) between the current version and the latest version, is whether the current version has unpatched security vulnerabilities, is whether the current version is incompatible with the device hardware or running scenario;

[0106] Optimal version combination matching and upgrade instruction generation: retrieve from the firmware version database the optimal firmware version that is fully compatible with the current device type , current hardware configuration , current running state label , and its corresponding security patch , and generate an upgrade instruction , specifically including: (1) Candidate version screening: screen from the firmware version database all version candidates that match the current device type

[0107] , represented as:

[0108] ;

[0109] wherein, is the th firmware version and its security patch, is the device type to which the th firmware version is adapted;

[0110] (2) Adaptation score calculation: for each version combination in the version candidate set, calculate the adaptation score with the current hardware configuration and running state label , represented as:

[0111] ;

[0112] wherein, represents the attribute similarity function, , are the hardware configuration and running state template adapted by the th candidate version, , are the corresponding weight coefficients;

[0113] ​​(3) Optimal combination selection and upgrade instruction generation: selecting the combination with the highest adaptation score from the version candidate set as the optimal firmware version and its patches, and generating an upgrade instruction , is expressed as:

[0114] ;

[0115] .

[0116] Configuration policy generation and parameter delivery includes:

[0117] Behavior label fusion: collecting the current load of the device, motor temperature rise, and task execution period, while combining the user historical use behavior sequence , extracting the behavior preference label , including operation frequency, switching sensitivity, and limit operation period, and generating a fusion perception vector , specifically including:

[0118] (1) Current behavior state data collection: collecting the current load , motor temperature rise , and task execution period of the device, and constructing a current behavior state vector , expressed as:

[0119] ;

[0120] (2) Retrieving and processing user historical use behavior sequence , which is a time-ordered user operation record including operation timestamp sequence and corresponding task control events (start-stop, mode switching, speed regulation), based on the user historical use behavior sequence , constructing a user behavior preference label , expressed as:

[0121] ;

[0122] wherein, is the operation frequency, is the total number of operations, is the behavior observation window length, is the switching sensitivity, is the limit operation period;

[0123] (3) Fusion perception vector generation: concatenating and fusing the current behavior state vector and the user behavior preference label to construct a fusion perception vector , expressed as:

[0124] ;

[0125] Task scenario matching and strategy space reduction: according to the current task context label , including load scenario, ramp start, speed limit area, match similar scenario instance in historical configuration strategy library , and reduce the search space according to the similarity weight function, and retain the candidate configuration strategy set , denoted as:

[0126] ;

[0127] Among them, is the scene label of the first configuration strategy, is the minimum similarity threshold, is the cosine similarity function;

[0128] Generate parameter configuration scheme: comprehensive fusion of perception vector , behavior preference label and candidate configuration strategy set , use multi-objective optimization model to determine the optimal parameter combination scheme, build parameter configuration scheme , including torque limit, speed curve shape and start delay, denoted as:

[0129] ;

[0130] Among them, is the candidate parameter configuration scheme, is the deviation measure function of user preference, is the deviation measure function of the current performance state of the device, , are the corresponding weight coefficients respectively;

[0131] The user preference deviation measure function is denoted as:

[0132] ;

[0133] Among them, is the first candidate parameter configuration scheme, including torque limit, speed curve shape and start delay, is the behavior preference label, including the torque, speed curve and delay value expected by the user, is the torque limit value of the first candidate configuration strategy, is the torque limit reference value of the user preference, is the first a speed curve shape parameter of the candidate configuration strategy, a speed curve shape parameter preferred by the user, a start delay time of the candidate configuration strategy, a start delay time preferred by the user, , , , weight coefficients of the corresponding user preference dimensions, respectively;

[0134] The deviation metric function of the current performance state of the device is represented as:

[0135] ;

[0136] wherein, is the current load level, is the optimal speed curve shape threshold derived from the current temperature rise trend, is the recommended start delay under the current task cycle and thermal balance recovery time, , , weight factors of each performance dimension, respectively;

[0137] The optimal speed curve shape threshold derived from the current temperature rise trend is represented as:

[0138] ;

[0139] wherein, is the standard speed curve shape reference value (ideal speed curve shape when the device is unloaded and has no temperature rise), is the maximum safe load threshold, is the current motor temperature rise rate, is the maximum allowable temperature rise rate threshold, is the load suppression factor, is the temperature rise trend suppression factor;

[0140] The instruction package is constructed and issued: the parameter configuration scheme is integrated and packaged with the generated upgrade instruction to form a final instruction package , which is issued to the target device by the edge control unit.

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

[0142] Continuous collection of device location information and signal frequency: the device periodically collects the current location and signal receiving frequency through the wireless communication unit to form tracking record points, wherein the location collection result is recorded as ,​ , respectively, the signal receiving frequency sequence is , and a continuous behavior trajectory sequence is generated, which is expressed as:

[0143] ;

[0144] Behavior trajectory and electronic fence model matching: the device continuous behavior trajectory sequence is matched with the preset electronic fence model in space and the communication state is analyzed, if the device position at time exists, it is determined that the fence is escaped, when the signal receiving frequency is lower than the set disconnection threshold (set as 0.2), it is regarded as disconnection, the continuous disconnection time window is set as , if , it is determined that the continuous disconnection exists, wherein is the continuous disconnection threshold, which is expressed as:

[0145] ;

[0146] wherein, when , otherwise 0, is the number of the latest continuous sampling points, is the sampling period;

[0147] The electronic fence model is defined as a set of boundary points of a certain area ;

[0148] Triggering the locking mechanism and outputting the instruction: when the current position of the device is out of the fence boundary or , the locking instruction is outputted.

[0149] The smart phone end control response module receives the information including:

[0150] Multi-dimensional diagnostic information receiving and visualized presentation: receiving the health score, the refitting suspicious degree, the parameter configuration scheme and the locking instruction uploaded from the device end, and performing the interface visualized display in the form of charts, index instrument panels or warning prompts, so as to enhance the user's perception of the current state of the device;

[0151] User interactive confirmation and control decision: the user performs the interactive operation through the mobile phone end interface, including confirming or rejecting the OTA remote upgrade, accepting the system suggested parameter configuration scheme or manual modification, setting or modifying the geographic range and the disconnection threshold of the electronic fence, and viewing the historical state trajectory of the device;

[0152] ​Control instruction encoding and issuing: encode the decision behavior formed in user interaction into a structured control instruction set, and send it to the device end through the wireless communication unit to trigger operations including: starting OTA update, applying new parameter configuration, activating or updating electronic fence rules, responding to locking or unlocking mechanism.

[0153] The present application encompasses any substitutions, modifications, equivalent methods and solutions made on the essence and scope of the present application. In order to make the public have a thorough understanding of the present application, specific details are described in the following preferred embodiments of the present application, and the present application can also be fully understood without the description of these details for those skilled in the art. In addition, in order to avoid unnecessary confusion to the essence of the present application, well-known methods, processes, procedures, elements and circuits, etc. are not described in detail.

[0154] The above is only the preferred embodiment of the present application, and it should be pointed out that for ordinary skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can also be made, which should be considered as the protection scope of the present application.

Claims

1. An intelligent management system for power tools, characterized by, The device multi-dimensional state acquisition and feature vector construction module, the tool health assessment and risk identification module, the adaptive OTA upgrade and configuration strategy recommendation module, the electronic fence and disconnection locking module, and the intelligent mobile phone terminal control response module are included. The device multi-dimensional state acquisition and feature vector construction module periodically acquires device operating state parameters, including temperature, current, voltage, charge and discharge cycle count, component connection state, Bluetooth communication signal strength and firmware version information, through the MCU chips deployed in the electric tool body, battery and charger, and constructs a device operating state feature vector based on a preset feature extraction rule; The tool health assessment and risk identification module inputs the device operating state feature vector into a health assessment model, outputs the health score and warning level of each component of the current device, and calculates the suspiciousness of modification based on the component replacement record, signal abnormal behavior and firmware version information difference in the device operating state feature vector to identify whether there is unauthorized replacement, repair or modification behavior. The adaptive OTA upgrade and configuration strategy recommendation module automatically triggers the OTA update judgment process when the warning level is mild abnormality or severe abnormality, or when modification behavior is detected, calculates the optimal firmware version and security patch combination applicable to the current device, and recommends a parameter configuration scheme based on the use scenario and user habit, including torque limit, speed curve and start delay, and outputs the upgrade instruction and parameter configuration scheme.

2. The intelligent management system for power tools according to claim 1, wherein, The device multi-dimensional state acquisition and feature vector construction module includes: Running state parameter collection: through the deployment in the electric power tool body, the battery and the charger MCU chip, the device running state parameter is collected periodically, wherein the device key component temperature is collected through the temperature sensor , the real-time current is obtained through the current detection unit , the current voltage is obtained through the voltage detection unit , the charge and discharge cycle count is recorded , the component connection state is read , the Bluetooth communication signal strength is detected , the current firmware version information is read ; Data preprocessing: preprocessing the acquired device operating state parameters, including normalization and missing value filling; Running state feature vector construction: based on the pre-processed equipment running state parameters, multi-dimensional attributes are combined into an equipment running state feature vector according to a predefined order .

3. The intelligent management system for power tools of claim 2, wherein, The tool health assessment and risk identification module includes: Health status assessment: based on the generated device operating state 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 device operating state feature vector with the factory parameter library and historical operation record, the component replacement frequency, signal stability and firmware version information difference are analyzed, and when abnormal feature mode is detected, it is determined that the device has unauthorized replacement, repair or modification behavior.

4. The intelligent management system for power tools of claim 3, wherein, The health status assessment includes: Health score calculation: based on the device running state feature vector, according to the set weight vector Weighted average is performed, and the health score of the current component is output ; The early warning level is divided by comparing the health score with a preset risk level threshold value, and the early warning level is divided when the health score is less than the normal lower threshold value, when the health score is greater than or equal to the normal lower threshold value and less than the mild abnormality lower threshold value, when the health score is greater than or equal to the mild abnormality lower threshold value and less than the severe abnormality lower threshold value, and when the health score is greater than or equal to the severe abnormality lower threshold value, wherein the normal lower threshold value is 0.5, the mild abnormality lower threshold value is 0.

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5. The intelligent management system for power tools of claim 4, wherein, The risk identification and modification detection includes: Firmware version consistency check: based on the current firmware version information in the device running state feature vector with the recorded factory firmware version reference value Compare and determine whether there is a version change behavior without OTA record. If firmware version difference is found and no upgrade record is found, it is marked as firmware exception, which is represented as: ； wherein, is a firmware version exception Boolean value; Component replacement behavior analysis: By calling the device maintenance log, the number of replacements of key components within a predetermined time is counted , if the set replacement frequency determination threshold is exceeded , it is determined that the behavior is suspicious, and is indicated as: ； wherein, to replace abnormal Boolean values; Signal stability analysis: based on the Bluetooth communication signal strength in the device running state feature vector, the signal fluctuation rate is calculated When the signal fluctuation determination threshold is exceeded , it is marked as a signal anomaly; Risk determination output: combining firmware version anomaly Boolean, replacement anomaly Boolean, and signal fluctuation rate, calculate refit suspiciousness , and compare with risk determination threshold , when , determine that the device has unauthorized replacement, repair, or refit behavior.

6. The intelligent management system for power tools of claim 5, wherein, The adaptive OTA upgrade and configuration strategy recommendation module includes: OTA upgrade judgment and firmware version adaptation: when the device appears in the warning level of mild abnormality or severe abnormality, or modification behavior is detected, the OTA upgrade judgment process is automatically started, the current firmware version information is compared with the historical record and security patch list in the firmware version database to identify whether there is a security vulnerability, compatibility problem or version lag, and the optimal version combination is matched according to the device type, hardware configuration and current operating state to generate an upgrade instruction including firmware version identification and security patch information; Configuration strategy generation and parameter issuing: According to the current load of the device, the temperature rise of the motor, the task execution period, the historical use behavior of the user and the current task scene, an optimal configuration strategy is generated, and a combination scheme including torque limitation, speed curve and start delay is automatically recommended in combination with user preference data and the current working mode of the device, a parameter configuration scheme is constructed, and the parameter configuration scheme is issued to the device together with the upgrade instruction.

7. The intelligent management system for power tools of claim 6, wherein, The OTA upgrade determination and firmware version adaptation include: Version difference analysis and risk identification: read the current firmware version information , and compare with the historical version set recorded in the firmware version database and the corresponding security patch state set , and calculate the version risk factor score , when , mark the device as an upgrade state, wherein, is the version risk judgment threshold 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 of claim 7, wherein, The configuration strategy generation and parameter issuing include: Behavior tag fusion: current load of the collection device, motor temperature rise, and task execution period, combined with user historical use behavior sequence , extract behavior preference tags , including operation frequency, switching sensitivity, and limit operation period, and generate a fusion perception vector ; Task scenario matching and strategy space reduction: according to the current task context label , including load scenarios, ramp start, speed limit area, matching similar scenario instances in the historical configuration strategy library , and reducing the search space according to the similarity weight function, retaining the candidate configuration strategy set ; Generating parameter configuration scheme: comprehensive fusion of perception vector , behavior preference label And candidate configuration strategy set , determine the optimal parameter combination scheme by using multi-objective optimization model, build parameter configuration scheme , including torque limit, speed curve shape and start delay; Construction instruction encapsulation and delivery: parameter configuration scheme with the generated upgrade instruction Integrated packaging, forming the final instruction package , through the edge control unit to the target device.

9. The intelligent management system for power tools of claim 8, wherein, The electronic fence and disconnection locking module continuously tracks the geographic position and signal receiving frequency of the device by using the Bluetooth or wireless communication unit of the device, constructs a device behavior trajectory sequence, and matches the device behavior trajectory sequence with the electronic fence model set by the user. If the device deviates from the fence or is continuously disconnected for more than a preset time threshold, the locking algorithm is triggered, and a locking instruction is output. Specifically, the locking algorithm includes: Device position information and signal frequency continuous collection: the device periodically collects the current position and signal receiving frequency through the wireless communication unit, forms a tracking record point, wherein the position collection result is recorded as , 、 respectively as the longitude and latitude, the signal receiving frequency sequence is , and a continuous behavior trajectory sequence is generated, which is represented as: ； Behavior trajectory and electronic fence model matching: match the device continuous behavior trajectory sequence with the preset electronic fence model Space matching and communication state analysis are performed, and if there is a device location at a time , it is determined that the device has left the fence, and if the signal receiving frequency is lower than the set disconnection threshold , it is considered to be disconnected. The continuous disconnection time window is set to , and if , it is determined to be continuously disconnected, wherein is the continuous disconnection threshold. Trigger the lock mechanism and output the instruction: when the current location of the device is beyond the fence boundary or , output the lock instruction .

10. The intelligent management system for power tools of claim 9, wherein, The smart phone terminal control response module receives the following information: Multi-dimensional diagnostic information receiving and visual presentation: receiving the health score, modification suspiciousness, parameter configuration scheme and locking instruction uploaded from the device end, and visualizing the information in the form of charts, index instrument panels or warning prompts; User interaction confirmation and control decision: the user performs interactive operations through the mobile phone terminal interface, including confirming or rejecting OTA remote upgrade, accepting the parameter configuration scheme suggested by the system or manually modifying, setting or modifying the geographic range and disconnection threshold of the electronic fence, and viewing the historical state trajectory of the device; Control instruction coding and issuing: the decision behavior formed in the user interaction is coded into a structured control instruction set, and is sent to the device end through the wireless communication unit to trigger operations including starting OTA update, applying new parameter configuration, activating or updating the electronic fence rule, and responding to the locking or unlocking mechanism; The smart phone terminal control response module receives the health score, modification suspiciousness, parameter configuration scheme and locking instruction uploaded from the device, visualizes the information, and allows the user to confirm OTA upgrade, accept or adjust the configuration suggestion, set the electronic fence range and view the historical state trajectory. Finally, the user decision result is coded into a control instruction and sent to the device end for execution.

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