A handle lock user operation data processing method and system

Abstract

The present application relates to the field of handle lock control system, and specifically discloses a handle lock user operation data processing method and system, which identifies key events in the operation process in the handle lock control system, records the time stamp, and extracts the associated sensor detection parameters. On this basis, the method can collect sensor detection parameter data within a set time period before and after the key event time stamp, generate a context summary containing these data, and finally store the time stamp and context summary in a structured manner. The scheme of the present application innovates the traditional log mechanism of passively recording original data stream into a recording strategy centered on key events with context summary. This strategy ensures the time continuity of the operation log and the integrity of the key information even in an environment with mechanical abnormalities, thereby providing a basis for accurately judging user intent and efficiently diagnosing faults.

Description

Technical Field

[0001] This application relates to the field of handle lock control systems, and more specifically, to a method and system for processing user operation data of handle locks. Background Technology

[0002] In the field of smart home devices, smart door locks, especially handle locks with integrated operating mechanisms, are becoming increasingly popular. These handle locks typically integrate sophisticated sensor systems designed to understand and respond to user actions, thereby improving security while also providing a more convenient user experience. However, in actual long-term use, the performance of these handle lock systems faces several challenges, particularly in accurately recognizing user intent.

[0003] Specifically, a handle lock system is designed to identify user intentions by analyzing data from their manipulation of the handle, thereby controlling the opening and closing of the door lock. During initial deployment, the system's built-in user operation data processing method underwent careful calibration. It acquires raw data from sensors such as angle encoders located at the handle's pivot point and utilizes a set of optimized digital filters designed to effectively suppress common environmental background noise, ensuring that the data collected by the sensors clearly and accurately reflects the user's actual operation of the handle.

[0004] However, with prolonged and frequent use, the mechanical structure of the handle lock, especially the critical load-bearing points at the handle pivot, inevitably experiences a certain degree of wear. This wear leads to unexpected frictional noise or vibration between components. These newly generated noise signals differ significantly from the typical environmental noise designed for the handle lock system in its initial design. This new noise generated by mechanical wear continuously mixes into the raw data stream acquired by the angle encoder, causing discontinuous and chaotic records in the operation log. This greatly complicates subsequent fault diagnosis and behavioral analysis, making it difficult for technicians to accurately trace the root cause of the problem.

[0005] There is currently no effective technical solution to the above problems. Summary of the Invention

[0006] The purpose of this application is to provide a method and system for processing user operation data of handle locks, which aims to solve the technical problems mentioned in the background art, and to transform the existing log mechanism that passively records the raw data stream into a recording strategy centered on key events and accompanied by context summaries, which is beneficial for accurately judging user intentions and diagnosing handle lock faults.

[0007] To solve the above-mentioned technical problems, the solution proposed in this application is as follows:

[0008] As one aspect of this application, a user operation data processing method for handle locks is proposed, which is applied in the handle lock control system used by the handle lock, including:

[0009] The handle lock control system identifies key events during the handle lock operation process, including user operation-related events and system state transition events.

[0010] When the key event is identified, the timestamp of the key event is recorded;

[0011] Extract the sensor detection parameters associated with the key event from the key event;

[0012] Based on the sensor detection parameters associated with the key event, collect sensor detection parameter data within a set time period before and after the timestamp of the key event, and generate a context summary containing the sensor detection parameter data.

[0013] The timestamp and context summary of the key event are stored in a structured manner.

[0014] Furthermore, the user operation-related events include successful unlocking and successful locking.

[0015] The successful unlocking operation completion indication is determined when the angle encoder in the handle lock detects that the handle rotation angle exceeds the preset unlocking threshold and remains there for a set time, triggering an unlocking command in the handle lock control system, and the unlocking operation is considered complete when the bolt of the handle lock pops out; the successful locking operation completion indication is determined when the angle encoder in the handle lock detects that the handle rotation angle has not changed and the signal of the bolt position sensor used to detect whether the bolt of the handle lock is in position changes, indicating that the locking operation is complete.

[0016] Furthermore, a state machine is used in the handle lock control system to store the standby state, pre-activation state, unlocking state, and locked state of the handle lock;

[0017] The system state transition events include the handle lock standby state to handle lock pre-activation state transition event, the handle lock pre-activation state to handle lock unlock state transition event, and the handle lock unlock state to handle lock locked state transition event.

[0018] Furthermore, the sensor detection parameters include angle encoder detection parameters, pressure sensor detection parameters, and timing sensor detection parameters.

[0019] Furthermore, the step of collecting sensor detection parameter data within a set time period before and after the timestamp of the key event based on the sensor detection parameters associated with the key event, and generating a context summary containing the sensor detection parameter data, includes:

[0020] When the handle lock control system is in low power mode, it collects angle encoder detection parameter data and pressure sensor detection parameter data, and performs preliminary analysis on the collected angle encoder detection parameter data and pressure sensor detection parameter data to identify potential signs of abnormal operation.

[0021] When potential signs of abnormal operation are identified, spatial interpolation is used to interpolate the parameter sequence composed of the collected angle encoder detection parameter data and the parameter sequence composed of the pressure sensor detection parameter data to obtain high-density angle encoder detection parameter data and high-density pressure sensor detection parameter data.

[0022] An abnormal operation evidence chain is constructed based on high-density angle encoder detection parameter data and high-density pressure sensor detection parameter data. The abnormal operation evidence chain includes the jamming index, grip strength, and shaking frequency count calculated based on the high-density angle encoder detection parameter data and high-density pressure sensor detection parameter data.

[0023] Based on the high-density angle encoder detection parameter data and the high-density pressure sensor detection parameter data, the context summary information corresponding to the key event is obtained, and diagnostic labels are added to the context summary in combination with the abnormal operation evidence chain.

[0024] Furthermore, the step of continuously monitoring the angle encoder detection parameter data and pressure sensor detection parameter data when the handle lock control system is in low-power mode, and performing preliminary analysis on the obtained angle encoder detection parameter data and pressure sensor detection parameter data to identify potential signs of abnormal operation, specifically includes:

[0025] Periodically record the detection parameter data of the angle encoder and the detection parameter data of the pressure sensor;

[0026] Based on periodically recorded angle encoder detection parameter data, if there is an angle change in the continuous angle encoder sampling values ​​within a cycle, it is judged as a sign of discontinuous movement in the handle lock; and based on periodically recorded pressure sensor detection parameter data, if there is a change in the pressure difference between the continuous pressure sensor sampling values ​​within a cycle, it is judged as a sign of pulsating grip in the handle lock. Both discontinuous movement and pulsating grip in the handle lock are potential signs of abnormal operation.

[0027] Furthermore, the step of constructing an abnormal operation evidence chain based on high-density angle encoder detection parameter data and high-density pressure sensor detection parameter data specifically includes:

[0028] In the acquired high-density angle encoder detection parameter data, the angle change rate between continuous sampling points in the high-density angle encoder detection parameter data is identified and analyzed. The number and duration of the decrease in multiple angle change rates within a set sampling time window are identified, and the lag index is calculated accordingly.

[0029] In the high-density angle encoder detection parameter data, data is extracted from the continuous sampling points in the high-density angle encoder detection parameter data to obtain the sampling angle data sequence. The number of angle direction changes in the sampling angle data sequence is identified and used as the shaking frequency count.

[0030] From the high-density pressure sensor detection parameter data, the rising rate of pressure, falling rate of pressure, maximum pressure peak value and average pressure value of the handle of the lever lock within the set sampling window are identified and obtained, and the grip strength is calculated accordingly.

[0031] The calculated lag index, shaking frequency count, and grip strength are stored in a structured manner and used as a chain of evidence for abnormal operation.

[0032] Furthermore, the user operation data processing method for the handle lock also includes:

[0033] A baseline judgment method is used to identify invalid micro-motion events during the operation of the handle lock, and the timestamp of the invalid micro-motion event is recorded. The invalid micro-motion event indicates a noise event caused by mechanical wear of the handle lock or a micro-motion event caused by unintentional user operation.

[0034] Based on the identified invalid micro-motion events, the number of occurrences or duration of each invalid micro-motion event is recorded within a set time period, and this is used as a summary of the records of invalid micro-motion events.

[0035] Store the timestamp of the invalid micro-motion event and its corresponding record summary.

[0036] Furthermore, the step of recording the timestamp of an invalid micro-motion event when it is detected specifically includes:

[0037] Continuously monitor the operating parameters of the handle lock;

[0038] When the handle of the lever lock is operated and an action occurs, causing the angle encoder to read an angle, the current reading of the angle encoder and the duration are identified;

[0039] Determine whether the current angle encoder reading and duration are less than the preset angle judgment benchmark and time judgment benchmark, and determine whether the current angle encoder reading and duration are within the signal distribution range pre-learned by the handle lock operating system;

[0040] If so, it is determined to be an invalid micro-motion event, and the timestamp of the invalid micro-motion event is recorded.

[0041] As a second aspect of this application, a user operation data processing system for a handle lock is proposed, comprising:

[0042] The event recognition module is used to identify key events during the operation of the handle lock, including user operation-related events and system state transition events.

[0043] The timestamp recording module is used to record the timestamp of the key event when it is identified.

[0044] A sensor detection parameter extraction module is used to extract sensor detection parameters associated with the key event from the key event.

[0045] The context summary generation module is used to collect sensor detection parameter data within a set time period before and after the timestamp of the key event based on the sensor detection parameters associated with the key event, and to statistically summarize the obtained sensor detection parameter data as a context summary containing the sensor detection parameter data of the key event.

[0046] The data storage module is used to structure and store the timestamp and context summary of the key event.

[0047] In summary, the user operation data processing method and system for handle locks disclosed in this application identifies key events during the operation process in the handle lock control system, records their timestamps, and extracts associated sensor detection parameters. Based on this, the method can collect sensor detection parameter data within a set time period before and after the key event timestamps, generate a context summary containing this data, and finally store the timestamps and context summaries in a structured manner. This application's solution innovates the traditional, passive logging mechanism that records raw data streams into a recording strategy centered on key events and accompanied by context summaries. It no longer records all the details of the handle lock operation logs, but instead precisely defines and identifies key turning points in user operations and system states, and rapidly extracts condensed and information-rich summaries from limited sensor data around these turning points. This strategy ensures the temporal continuity of the operation logs and the integrity of key information even in complex environments where data is sparse due to mechanical wear and power consumption is limited, resulting in reduced sampling frequency. This provides a foundation for accurately determining user intent and efficiently diagnosing faults. Attached Figure Description

[0048] Figure 1A flowchart illustrating a user operation data processing method for a handle lock, provided in an embodiment of this application;

[0049] Figure 2 A system structure block diagram of a handle lock user operation data processing system provided in this application embodiment;

[0050] Figure reference numerals: 100, Handle lock user operation data processing system; 101, Event recognition module; 102, Timestamp recording module; 103, Sensor detection parameter extraction module; 104, Context summary generation module; 105, Data storage module. Detailed Implementation

[0051] To better illustrate the present invention, the invention will now be described in further detail with reference to the accompanying drawings.

[0052] It should be understood that, in order to make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. The components of the embodiments of this disclosure described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0053] The following description uses at least one embodiment, in which:

[0054] Firstly, a user operation data processing method for handle locks is proposed, which is applied in the handle lock control system used by handle locks, including:

[0055] Step S1: Identify key events during the operation of the handle lock through the handle lock control system. The key events include user operation-related events and system state transition events.

[0056] Step S2: When the key event is identified, record the timestamp of the key event;

[0057] Step S3: Extract the sensor detection parameters associated with the key event from the key event;

[0058] Step S4: Based on the sensor detection parameters associated with the key event, collect sensor detection parameter data within a set time period before and after the timestamp of the key event, and generate a context summary containing the sensor detection parameter data.

[0059] Step S5: Store the timestamp and context summary of the key event in a structured manner.

[0060] This application aims to more accurately capture and understand user operation intentions by identifying key events, recording timestamps, extracting sensor detection parameters, generating context summaries, and storing them in a structured manner, thereby improving user experience and providing reliable data support for fault diagnosis.

[0061] To make the technical solution of this application easier and clearer to understand, some key terms and implementation environments involved will be explained in detail below.

[0062] The handle lock control system described in this application is an embedded system integrating sensors, a microcontroller, a storage unit, and a communication module. Its main function is to monitor the physical state of the handle lock and user operations, and process and respond according to preset logic. The system typically operates in a low-power mode to extend its service life.

[0063] Critical events refer to discrete events that are of great significance during the operation of the handle lock, serving as clear indications of system state transitions or user operational intentions.

[0064] Sensor detection parameters refer to the physical quantity data collected in real time by various sensors integrated inside the handle lock (such as angle encoders, pressure sensors, timing sensors, etc.). These parameters directly reflect information such as the mechanical motion, force conditions, and operation duration of the handle lock.

[0065] Contextual summary is a concise summary of sensor detection parameter data over a period of time before and after a critical event. It aims to capture operational details and environmental characteristics related to the event for subsequent analysis.

[0066] In this embodiment, the handle lock control system is first configured to identify key events during the operation of the handle lock. These key events are divided into two categories: user operation-related events and system state transition events. User operation-related events typically indicate direct user interaction with the handle lock, such as attempting to unlock or lock. System state transition events reflect changes in the internal logical state of the handle lock, such as switching from a standby state to a pre-activated state. To identify these events, the handle lock control system continuously monitors real-time data streams from its internal sensors. For example, by analyzing data from the angle encoder, the system can determine whether the handle has been turned; by analyzing data from the pressure sensor, the system can determine whether the handle has been gripped. When these sensor data meet preset patterns or thresholds, the corresponding key event is identified.

[0067] Once a critical event is detected, the handle lock control system immediately records a timestamp of that event. This timestamp provides a timeline for subsequent data analysis, allowing all relevant data to be precisely correlated to specific operations or state changes.

[0068] Next, sensor detection parameters associated with the key event are extracted. For example, if an unlocking event is detected, the sensor detection parameters most directly associated with it are the data from the angle encoder and the pressure sensor. This correlation extraction ensures the relevance and effectiveness of subsequent data collection, avoiding interference from irrelevant data.

[0069] Subsequently, based on the sensor detection parameters associated with the critical event, the system collects sensor detection parameter data for a set time period before and after the timestamp of the critical event, and generates a context summary containing the sensor detection parameter data. For example, if the critical event is an unlocking event, the system will not only record the angle encoder and pressure sensor data at the moment of unlocking, but also collect the angle encoder and pressure sensor data for the few seconds before and after unlocking. This data can be used to construct a time series reflecting the complete process of the user's operation.

[0070] Finally, the timestamp and context summary of this key event are stored in a structured format. The purpose of this structured storage is to facilitate subsequent data retrieval, analysis, and utilization.

[0071] The user operation data processing method for handle locks disclosed in this application, through the synergistic effect of the aforementioned steps, enables comprehensive and precise processing of user operation data for handle locks. When a user operates the handle lock, the handle lock control system continuously monitors real-time data streams from multiple sensors, such as angle encoders and pressure sensors. Once a signal conforming to a preset pattern appears in these data streams, such as the handle rotation angle exceeding a certain threshold, the system identifies a "critical event," such as "unlocking operation completed." At the moment this critical event is identified, the system accurately records its timestamp.

[0072] Next, the system determines the sensor detection parameters most directly related to the nature of this critical event. For example, for unlocking operations, data from the angle encoder and pressure sensor are crucial. The system collects all relevant sensor detection parameter data within a preset time period, centered on the timestamp of this critical event. This raw data is then processed to generate a contextual summary, a concise description of the operational characteristics during this period, which may include information such as the speed of handle rotation, angle changes, and grip pressure curves. Finally, the timestamp of this critical event and the generated contextual summary are stored in a structured manner, forming a complete record for subsequent analysis.

[0073] In this way, the proposed solution revolutionizes the traditional, passive logging mechanism that records raw data streams into a logging strategy centered on key events and accompanied by contextual summaries. It no longer records every detail of the handle lock operation, but instead precisely defines and identifies key turning points in user operations and system states. Around these turning points, it rapidly extracts information from limited sensor data, generating condensed and information-rich summaries. This strategy ensures the temporal continuity of the operation logs and the integrity of key information even in complex environments where data is sparse due to mechanical wear and power consumption limits reduce sampling frequency. This provides a foundation for accurately determining user intent and efficiently diagnosing faults.

[0074] Furthermore, the user operation-related events can be further refined and specified.

[0075] First, the user operation-related events include successful unlocking and successful locking operations. The successful unlocking operation is indicated when the angle encoder in the handle lock detects that the handle rotation angle exceeds a preset unlocking threshold and remains there for a set time. In this case, the handle lock control system triggers an unlocking command, and the unlocking operation is considered complete when the bolt of the handle lock pops out. The successful locking operation is indicated when the angle encoder in the handle lock detects that the handle rotation angle has not changed, and the signal from the bolt position sensor (used to detect whether the bolt is in position) changes, thus determining that the locking operation is complete.

[0076] User-operated events are key behaviors that the handle lock control system needs to focus on, directly reflecting the user's interaction with the handle lock. These are defined as successful unlocking and successful locking operations. A successful unlocking operation means that the user rotates the handle until the angle encoder detects a rotation angle that reaches and maintains above a preset unlocking threshold, prompting the handle lock control system to issue an unlocking command, and ultimately the bolt successfully extends, marking the effective execution of the unlocking action. This ensures that only actually completed unlocking actions are recorded as key events. A successful locking operation focuses on the bolt's position. When the angle encoder detects no change in the handle rotation angle, and the bolt position sensor detects that the bolt has correctly reset or extended to its correct position, the locking operation is judged as complete. This avoids misjudgments caused by accidental handle touches or incomplete locking, ensuring the accuracy of locking events.

[0077] Specifically, by monitoring the handle's rotation angle using an angle encoder, combined with preset unlocking thresholds and durations, and the physical feedback from the bolt's ejection, the integrity and success of the unlocking operation are ensured. Simultaneously, by confirming the handle's stationary state using the angle encoder, and by detecting the bolt's position using a bolt position sensor, the effectiveness and accuracy of the locking operation are ensured. This mechanism allows the handle lock control system to filter out invalid or incomplete operations, collecting and storing data only on key events that truly reflect the user's intent, thus laying a solid foundation for subsequent data analysis.

[0078] Second, a state machine is used in the handle lock control system to store the standby state, pre-activation state, unlocking state and locking state of the handle lock;

[0079] The system state transition events include the handle lock standby state to handle lock pre-activation state transition event, the handle lock pre-activation state to handle lock unlock state transition event, and the handle lock unlock state to handle lock locked state transition event.

[0080] In some embodiments described above in this application, the handle lock control system is configured to identify key events during the handle lock operation process, including system state transition events. Specifically, in order to clearly and systematically manage and identify these system state transition events, this application further proposes the following solutions.

[0081] In the handle lock control system, a state machine is used to store the standby state, pre-activation state, unlocked state, and locked state of the handle lock. The system state transition events include the handle lock standby state to handle lock pre-activation state transition event, the handle lock pre-activation state to handle lock unlocked state transition event, and the handle lock unlocked state to handle lock locked state transition event.

[0082] Specifically, the state machine can be understood as an abstract model used to describe the states of the handle lock control system at different operational stages and the transition logic between them. This state machine is designed to accurately reflect the operating modes of the handle lock. Specifically, the standby state refers to the handle lock being in a low-power mode, waiting for user operation or an external wake-up signal; the pre-activated state refers to the handle lock receiving an initial wake-up signal (e.g., user touch) and beginning to prepare to respond but not yet fully unlocked; the unlocked state refers to the handle lock having successfully executed the unlocking command, the bolt being retracted, allowing the handle to turn and open the door; and the locked state refers to the handle lock having successfully executed the locking command, the bolt being extended, and the door being securely locked.

[0083] The system state transition events refer to the behavior of a handle lock control system transitioning from one well-defined operating state to another. For example, a handle lock standby state to handle lock pre-activation state transition event indicates that the system is transitioning from a low-power standby mode to a stage ready to respond to user operations; a handle lock pre-activation state to handle lock unlock state transition event indicates that the system is transitioning from the preparation stage to the actual unlocking stage after the user completes a valid unlocking operation; a handle lock unlock state to handle lock locked state transition event indicates that the system is returning from the unlocked state to the secure locked state after the door is closed and the latch has extended. Identifying these transition events is crucial for accurately recording user operation data and system behavior.

[0084] When the handle lock control system detects that certain conditions are met, the state machine transitions from the current state to the next state, triggering the corresponding system state transition event. For example, when a user touches the handle lock, the system may switch from a standby state to a pre-activated state, at which point the state machine records the "handle lock standby state to handle lock pre-activated state transition event." This state machine-based management approach ensures that the system can clearly track the handle lock's operational trajectory, thus providing a solid foundation for subsequent critical event identification and data processing. By recording these well-defined system state transition events, the context of user operations can be understood more accurately, such as distinguishing whether the user attempted to unlock the lock or simply touched it.

[0085] In this embodiment, preferably, the sensor detection parameters include angle encoder detection parameters, pressure sensor detection parameters, and timing sensor detection parameters.

[0086] Specifically, the angle encoder detection parameters refer to the data about the rotation angle of the handle lock obtained by the angle encoder installed in the handle lock. Its purpose is to capture the dynamic behavior of the user when operating the handle, such as the initial rotation angle, maximum rotation angle, rotation speed, and rotation direction of the handle.

[0087] Pressure sensor detection parameters refer to data obtained by pressure sensors installed on handle locks regarding the force or pressure applied by a user when gripping the handle. The purpose is to quantify the intensity and duration of the user's grip.

[0088] Timing sensor detection parameters refer to data used to measure the time points, durations, or intervals of various events during the operation of a handle lock. The purpose is to provide accurate timestamps for all critical events and sensor data, enabling time-series analysis of user actions and system state transitions.

[0089] In some of the above implementations, a method has been proposed to collect sensor detection parameter data within a set time period before and after the timestamp of a critical event, based on sensor detection parameters associated with that critical event, and to generate a contextual summary containing the sensor detection parameter data. However, in practical applications, simply performing basic data collection and summary generation may be insufficient to effectively identify and diagnose potential abnormal operation signs, thereby limiting the ability to gain a deeper understanding of user behavior and system status, and to provide early warnings of faults.

[0090] Therefore, in this embodiment, the step of collecting sensor detection parameter data within a set time period before and after the timestamp of the key event based on the sensor detection parameters associated with the key event, and generating a context summary containing the sensor detection parameter data, includes:

[0091] Step S41: When the handle lock control system is in low power mode, collect the angle encoder detection parameter data and the pressure sensor detection parameter data, and perform a preliminary analysis on the collected angle encoder detection parameter data and pressure sensor detection parameter data to identify potential abnormal operation signs.

[0092] Step S42: When potential signs of abnormal operation are identified, spatial interpolation is used to interpolate the parameter sequence composed of the collected angle encoder detection parameter data and the parameter sequence composed of the pressure sensor detection parameter data to obtain high-density angle encoder detection parameter data and high-density pressure sensor detection parameter data.

[0093] Step S43: Construct an abnormal operation evidence chain based on high-density angle encoder detection parameter data and high-density pressure sensor detection parameter data, wherein the abnormal operation evidence chain includes the jamming index, grip strength and shaking frequency count calculated based on the high-density angle encoder detection parameter data and high-density pressure sensor detection parameter data.

[0094] Step S44: Based on the high-density angle encoder detection parameter data and the high-density pressure sensor detection parameter data, obtain the context summary information corresponding to the key event, and add diagnostic tags to the context summary in combination with the abnormal operation evidence chain.

[0095] Specifically, when the handle lock control system is in low-power mode, it continuously collects data from the angle encoder and pressure sensor. The angle encoder data reflects the rotation angle of the handle lock, while the pressure sensor data reflects the force applied by the user when gripping the handle. Analyzing this initially collected data aims to identify early signs that may indicate abnormal operation, such as choppy handle rotation or abnormal fluctuations in grip force.

[0096] When initial analysis identifies potential signs of abnormal operation, spatial interpolation is used to interpolate the parameter sequence composed of collected angle encoder and pressure sensor detection parameter data to obtain more refined data for in-depth diagnosis. Spatial interpolation estimates the values ​​of unknown data points using known data points, thereby generating more data points among the original sparse data points and significantly increasing the density of the data sequence. For example, linear interpolation, spline interpolation, or polynomial interpolation methods can be used. Through interpolation, high-density angle encoder and pressure sensor detection parameter data can be obtained, and this high-density data can capture subtle changes and details in the operation process more precisely.

[0097] Based on this, a chain of evidence for abnormal operation can be constructed using these high-density angle encoder and pressure sensor detection parameter data. This chain of evidence comprehensively describes and diagnoses the nature of abnormal operation. Specifically, the lag index is calculated by analyzing the rate of angle change between consecutive sampling points in the high-density angle encoder detection parameter data, identifying the number and duration of decreases in the rate of angle change; it reflects the degree of resistance or lack of smoothness during handle rotation. Grip strength is calculated based on the rate of increase, rate of decrease, maximum pressure peak, and average pressure value of the pressure applied to the handle from the high-density pressure sensor detection parameter data; it characterizes the force characteristics of the user's grip on the handle. The wobbling frequency count is obtained by analyzing the number of angular direction changes in the high-density angle encoder detection parameter data; it reflects the instability or vibration of the handle during operation. These indicators are stored in a structured manner and together constitute strong evidence of abnormal operation.

[0098] Finally, based on the high-density angle encoder and pressure sensor detection parameter data, a contextual summary of the critical event can be obtained. Simultaneously, diagnostic labels are added to the contextual summary by combining it with the constructed chain of evidence for anomalous operations. These diagnostic labels can be predefined categories, such as "slight jamming," "abnormal grip," and "multiple shaking attempts," which intuitively indicate the type and severity of the identified anomalous operation. This allows the contextual summary to not only contain operational data but also possess preliminary diagnostic capabilities.

[0099] The following example illustrates this: Suppose that when a user tries to unlock a handle lock, there is a slight jamming inside the lock cylinder, causing a brief resistance when the handle is turned. In order to overcome the resistance, the user increases the grip on the handle several times in pulses while turning the handle, and the handle also wobbles slightly from side to side during the turning process.

[0100] In this scenario, the handle lock control system, operating in low-power mode, first identifies inconsistencies in motion and pulsating grip patterns in the angle encoder and pressure sensor data through preliminary analysis. These are identified as potential abnormal operation indicators. Subsequently, the system immediately initiates a spatial interpolation process, performing high-density interpolation on the initially collected angle encoder and pressure sensor data to obtain a more refined and continuous sequence of handle rotation angles and grip pressure.

[0101] Based on this high-density data, the system further constructs an evidence chain for abnormal operation. For example, by analyzing high-density angle encoder detection parameter data, it can identify a significant and sustained decrease in the handle's rotation rate within a certain angle range, thus calculating a high sticking index. Simultaneously, by analyzing high-density pressure sensor detection parameter data, multiple pressure peaks can be detected during rotation, and a grip strength reflecting uneven force applied by the user can be calculated. Furthermore, numerous minute changes in the angular direction within the angle encoder detection parameter data are counted, forming a non-zero wobbling frequency count. Finally, these calculated sticking indexes, grip strengths, and wobbling frequency counts are structured and stored, serving as an evidence chain for the abnormal operation in this instance.

[0102] Finally, the system will generate a contextual summary of the unlocking operation based on these high-density sensor detection parameter data. Combined with the aforementioned chain of evidence for the abnormal operation, a diagnostic label will be added to the contextual summary, such as "slight jamming accompanied by pulsating grip and shaking." In this way, even subtle abnormal operations can be accurately captured, quantified, and analyzed by the system to provide a clear diagnosis, thus providing valuable data support for subsequent maintenance, user behavior analysis, or security assessments.

[0103] Furthermore, the step of continuously monitoring the angle encoder detection parameter data and pressure sensor detection parameter data when the handle lock control system is in low-power mode, and performing preliminary analysis on the obtained angle encoder detection parameter data and pressure sensor detection parameter data to identify potential signs of abnormal operation, specifically includes:

[0104] Periodically record the detection parameter data of the angle encoder and the detection parameter data of the pressure sensor;

[0105] Based on periodically recorded angle encoder detection parameter data, if there is an angle change in the continuous angle encoder sampling values ​​within a cycle, it is judged as a sign of discontinuous movement in the handle lock; and based on periodically recorded pressure sensor detection parameter data, if there is a change in the pressure difference between the continuous pressure sensor sampling values ​​within a cycle, it is judged as a sign of pulsating grip in the handle lock. Both discontinuous movement and pulsating grip in the handle lock are potential signs of abnormal operation.

[0106] The periodic recording of angle encoder detection parameter data and pressure sensor detection parameter data refers to the process by which the handle lock control system samples and stores angle encoder detection parameter data and pressure sensor detection parameter data at preset time intervals or frequencies in low-power mode.

[0107] Based on the periodically recorded angle encoder detection parameter data, if there is a sharp change in the angle of the continuous angle encoder sampling values ​​within a cycle, it is judged as a sign of discontinuous movement in the handle lock. Specifically, a sharp change in angle can be understood as a sudden and non-linear change in the rate of angle change of the handle lock during rotation. For example, if the angle encoder detection parameter data shows that the angle reading increases from angle A to angle B and then briefly decreases to angle C, it is initially judged as discontinuous movement.

[0108] Based on periodically recorded pressure sensor data, a change in the pressure difference between consecutive pressure sensor sampling values ​​within a cycle is considered a sign of pulsating grip on the handle lock. Specifically, pulsating grip refers to the user applying intermittent, fluctuating pressure when gripping the handle lock's handle. For example, if the pressure sensor detects a rapid increase in pressure from a baseline value (less than 50 units) to a higher value (more than 200 units) within a short period (e.g., within 50 milliseconds), followed by a rapid decrease, this is preliminarily identified as pulsating grip.

[0109] The aforementioned signs of discontinuous movement and pulsating grip are considered potential signs of abnormal operation and require further analysis and diagnosis.

[0110] Specifically, when the handle of a lever lock exhibits discontinuous movement during operation—for example, if the angle encoder's measured parameters show non-smooth angular transitions—this may indicate that the user is encountering resistance. Similarly, if the pressure sensor's measured parameters show continuous pressure differential changes, indicating a pulsating grip, this may suggest hesitation, tentative movements, or atypical force patterns when gripping the handle. By identifying these early, subtle anomalies, the system can provide early warnings and attention to potential abnormal operations, offering a preliminary basis for subsequent in-depth analysis and diagnosis.

[0111] In this embodiment, the aforementioned step of constructing an abnormal operation evidence chain based on high-density angle encoder detection parameter data and high-density pressure sensor detection parameter data can be further refined, specifically including:

[0112] In the acquired high-density angle encoder detection parameter data, the angle change rate between continuous sampling points in the high-density angle encoder detection parameter data is identified and analyzed. The number and duration of the decrease in multiple angle change rates within a set sampling time window are identified, and the lag index is calculated accordingly.

[0113] In the high-density angle encoder detection parameter data, data is extracted from the continuous sampling points in the high-density angle encoder detection parameter data to obtain the sampling angle data sequence. The number of angle direction changes in the sampling angle data sequence is identified and used as the shaking frequency count.

[0114] From the high-density pressure sensor detection parameter data, the rising rate of pressure, falling rate of pressure, maximum pressure peak value and average pressure value of the handle of the lever lock within the set sampling window are identified and obtained, and the grip strength is calculated accordingly.

[0115] The calculated lag index, shaking frequency count, and grip strength are stored in a structured manner and used as a chain of evidence for abnormal operation.

[0116] The hysteresis index is calculated to quantify potential pauses or unevenness in the rotation of a handle lock. Specifically, by analyzing the rate of change of angle between consecutive sampling points in the high-density angle encoder detection parameter data, the number of times the rate of change of angle decreases within a specific sampling time window and its duration can be identified. For example, by analyzing the rate of change of angle between consecutive sampling points, the number of pauses and the total duration of the decrease in the rate of change of angle within a short period can be identified. The hysteresis index can be defined as: Hysteresis Index = (Number of pauses) + (Total duration of pauses / Total duration of window), where the number of pauses is the number of detected pauses, the total duration of pauses is the sum of the durations of all pauses, and the total duration of the window is the duration of the high-resolution sampling window.

[0117] The shake frequency count is used to assess the degree of handle shaking when a user operates the handle lock. Specifically, continuous sampling points are extracted from the high-density angle encoder detection parameter data to form a sampling angle data sequence. The shake frequency count is obtained by identifying the number of angle direction changes within this sequence. For example, if the sampling angle data sequence is [0, 0.5, 0.2, 0.8, 0.4], where 0.5 to 0.2 and 0.8 to 0.4 are opposite fluctuations, then the shake frequency count is 2.

[0118] The calculation of grip strength aims to reflect the force with which a user grips the handle lock. Specifically, by analyzing parameter data detected by a high-density pressure sensor, the rate of pressure rise, rate of pressure fall, peak pressure, and average pressure value applied to the handle within a set sampling window can be identified. These parameters comprehensively reflect the magnitude, speed, and duration of the user's grip on the handle. For example, abnormal grip strength may manifest as excessively high peak pressure (aggressive grip), excessively rapid rate of pressure rise (sudden application of force), or unstable average pressure value (repeated gripping), all of which may be associated with abnormal operation.

[0119] Finally, the calculated hysteresis index, shaking frequency count, and grip strength are structured and stored to form an evidence chain for abnormal operation. This evidence chain provides quantitative and multi-dimensional basis for subsequent abnormal operation diagnosis and analysis.

[0120] In this embodiment, the user operation data processing method for the handle lock further includes:

[0121] Step S1': Identify invalid micro-motion events during the operation of the handle lock using a baseline judgment method, and record the timestamp of the invalid micro-motion event. The invalid micro-motion event indicates a noise event caused by mechanical wear of the handle lock or a micro-motion event caused by unintentional user operation.

[0122] Step S2': Based on the identified invalid micro-motion events, record the number of occurrences or duration of the invalid micro-motion events within a set time period, and use this as a summary of the invalid micro-motion events.

[0123] Step S3': Store the timestamp of the invalid micro-motion event and its corresponding record summary.

[0124] Specifically, the "baseline judgment method" refers to a discrimination mechanism used to distinguish between normal operation, critical events, and non-critical micro-motion events. This method learns the baseline of sensor data of the handle lock in normal standby mode in advance, and when the signal detected by the sensor deviates from the normal baseline but does not reach the threshold for triggering a critical event, it can be identified as an invalid micro-motion event.

[0125] Invalid micro-motion events can be understood as those events that are insufficient to constitute a complete or intentional user action, but do reflect that the handle lock has been subjected to some external force. For example, a user's unintentional touch of the handle causing it to wobble slightly, or the slight loosening or friction noise of internal components of the handle lock due to long-term use. These events themselves do not represent the user's intention to unlock or lock, but their frequency and pattern may contain important information about the device's status or user habits.

[0126] When invalid micro-motion events are identified, their timestamps are accurately recorded for subsequent time series analysis.

[0127] The generation of summary records involves statistically analyzing the "number of occurrences" or "duration" of identified invalid micro-motion events within a pre-defined time period. For example, it might count how many times a particular invalid micro-motion event occurred within an hour or day, or how long each occurrence lasted. This summary data provides a macro-level view of invalid micro-motion events.

[0128] Ultimately, the timestamp of the invalid micro-motion event and its corresponding record summary will be stored in a structured manner to facilitate subsequent data querying, analysis, and utilization.

[0129] This application's solution effectively overcomes the limitations of focusing solely on critical events by introducing the identification and processing of invalid micro-motion events. When the handle lock control system detects minor fluctuations in sensor data, but these fluctuations are insufficient to trigger a preset critical event threshold, a baseline judgment method is activated to assess whether these fluctuations constitute invalid micro-motion events. Once confirmed as invalid micro-motion events, their timestamps and related summary information (such as the number of occurrences or duration) are independently recorded and stored. This mechanism allows the system to separate these non-critical but meaningful events from the main user operation data, avoiding data contamination and enabling subsequent refined analysis.

[0130] Furthermore, the step of recording the timestamp of an invalid micro-motion event when it is detected specifically includes:

[0131] Continuously monitor the operating parameters of the handle lock;

[0132] When the handle of the lever lock is operated and an action occurs, causing the angle encoder to read an angle, the current reading of the angle encoder and the duration are identified;

[0133] Determine whether the current angle encoder reading and duration are less than the preset angle judgment benchmark and time judgment benchmark, and determine whether the current angle encoder reading and duration are within the signal distribution range pre-learned by the handle lock operating system;

[0134] If so, it is determined to be an invalid micro-motion event, and the timestamp of the invalid micro-motion event is recorded.

[0135] Specifically, continuous monitoring of the handle lock's operating parameters refers to the handle lock control system continuously acquiring real-time operational data from various sensors within the handle lock, such as angle encoders and pressure sensors. When the handle lock's handle is operated and an action occurs, causing the angle encoder to register an angle reading, the handle lock control system identifies the current angle encoder reading and the corresponding duration. The angle encoder is used to accurately measure the handle's rotation angle; its reading and duration are key indicators for determining whether the handle has been effectively operated.

[0136] Furthermore, the system compares the current angle encoder reading and duration with preset angle and time judgment benchmarks. The preset angle judgment benchmark can be a very small angle value, such as 1-5 degrees, used to distinguish between intentional unlocking / locking operations and unintentional slight touches or mechanical noise. The preset time judgment benchmark can be a very short time period, such as 50-200 milliseconds, used to distinguish between continuous operation and momentary jitter. Simultaneously, the system also determines whether the current angle encoder reading and duration are within the signal distribution range pre-learned by the handle lock operating system. This signal distribution range is established through long-term learning and statistical analysis of sensor data from the handle lock in normal standby or no-operation states, and is used to characterize minor signal fluctuations caused by normal background noise or mechanical wear.

[0137] Therefore, if the current angle encoder reading and duration are both less than the preset angle judgment benchmark and time judgment benchmark, and these readings and durations fall within the pre-learned signal distribution range, the operation can be accurately determined as an invalid micro-motion event. Once determined to be an invalid micro-motion event, the system will immediately record the timestamp of the event for subsequent summarization and analysis.

[0138] This solution achieves accurate identification and timestamping of invalid micro-motion events by continuously monitoring the operating parameters of the handle lock and combining multiple judgment criteria. Specifically, by continuously monitoring data from sensors such as the angle encoder, the system can capture any minute movements of the handle in real time. When the handle moves and generates an angle encoder reading, the system not only focuses on the magnitude and duration of the reading but also compares it with preset judgment criteria and makes a comprehensive judgment based on the pre-learned signal distribution range. This judgment mechanism can effectively distinguish between intentional unlocking / locking operations by the user and minute, unintentional actions caused by mechanical wear, environmental vibration, or unintentional touch. It is precisely because of this judgment logic that the system can accurately identify invalid micro-motion events and record their timestamps in a timely manner, providing a reliable data foundation for subsequent recording and summarizing of invalid micro-motion events.

[0139] Secondly, in this embodiment, a handle lock user operation data processing system 100 is proposed, including:

[0140] Event recognition module 101 is used to identify key events during the operation of the handle lock, including user operation-related events and system state transition events;

[0141] The timestamp recording module 102 is used to record the timestamp of the key event when the key event is identified.

[0142] Sensor detection parameter extraction module 103 is used to extract sensor detection parameters associated with the key event from the key event.

[0143] The context summary generation module 104 is used to collect sensor detection parameter data within a set time period before and after the timestamp of the key event based on the sensor detection parameters associated with the key event, and to statistically summarize the obtained sensor detection parameter data as a context summary containing the sensor detection parameter data of the key event.

[0144] Data storage module 105 is used to structure and store the timestamp and context summary of the key event.

[0145] This solution provides a user operation data processing system for a handle lock, which applies the aforementioned user operation data processing method for handle locks. It identifies key events during the operation process within the handle lock control system, records their timestamps, and extracts associated sensor detection parameters. Based on this, the method collects sensor detection parameter data within a set time period before and after the key event timestamps, generates a context summary containing this data, and finally stores the timestamps and context summaries in a structured manner. This application innovates the traditional, passive logging mechanism that records raw data streams into a recording strategy centered on key events and accompanied by context summaries. It no longer records all the details of the handle lock operation logs, but instead precisely defines and identifies key turning points in user operations and system states. Around these turning points, it rapidly extracts limited sensor data to generate condensed and information-rich summaries. This strategy ensures the temporal continuity of the operation logs and the integrity of key information even in complex environments where data is sparse due to mechanical wear and power consumption is limited, resulting in reduced sampling frequency. This provides a foundation for accurately determining user intent and efficiently diagnosing faults.

[0146] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit them. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features within the scope of the technology disclosed in this disclosure. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure.

Claims

1. A method for processing user operation data of a handle lock, applied in a handle lock control system, characterized in that, include: The handle lock control system identifies key events during the handle lock operation process, including user operation-related events and system state transition events. When the key event is identified, the timestamp of the key event is recorded; Extract the sensor detection parameters associated with the key event from the key event; Based on the sensor detection parameters associated with the key event, collect sensor detection parameter data within a set time period before and after the timestamp of the key event, and generate a context summary containing the sensor detection parameter data; store the timestamp and context summary of the key event in a structured manner. The step of collecting sensor detection parameter data within a set time period before and after the timestamp of the key event based on sensor detection parameters associated with the key event, and generating a context summary containing the sensor detection parameter data, includes: When the handle lock control system is in low power mode, it collects angle encoder detection parameter data and pressure sensor detection parameter data, and performs preliminary analysis on the collected angle encoder detection parameter data and pressure sensor detection parameter data to identify potential signs of abnormal operation. When potential signs of abnormal operation are identified, spatial interpolation is used to interpolate the parameter sequence composed of the collected angle encoder detection parameter data and the parameter sequence composed of the pressure sensor detection parameter data to obtain high-density angle encoder detection parameter data and high-density pressure sensor detection parameter data. An abnormal operation evidence chain is constructed based on high-density angle encoder detection parameter data and high-density pressure sensor detection parameter data. The abnormal operation evidence chain includes the jamming index, grip strength, and shaking frequency count calculated based on the high-density angle encoder detection parameter data and high-density pressure sensor detection parameter data. Based on the high-density angle encoder detection parameter data and the high-density pressure sensor detection parameter data, the context summary information corresponding to the key event is obtained, and diagnostic labels are added to the context summary in combination with the abnormal operation evidence chain.

2. The user operation data processing method for handle locks according to claim 1, characterized in that: The user operation-related events include successful unlocking and successful locking. The successful unlocking operation completion indication is determined when the angle encoder in the handle lock detects that the handle rotation angle exceeds the preset unlocking threshold and remains there for a set time, triggering an unlocking command in the handle lock control system, and the unlocking operation is considered complete when the bolt of the handle lock pops out; the successful locking operation completion indication is determined when the angle encoder in the handle lock detects that the handle rotation angle has not changed and the signal of the bolt position sensor used to detect whether the bolt of the handle lock is in position changes, indicating that the locking operation is complete.

3. The user operation data processing method for handle locks according to claim 1, characterized in that: In the handle lock control system, a state machine is used to store the standby state, pre-activation state, unlocking state, and locked state of the handle lock; The system state transition events include the handle lock standby state to handle lock pre-activation state transition event, the handle lock pre-activation state to handle lock unlock state transition event, and the handle lock unlock state to handle lock locked state transition event.

4. The user operation data processing method for handle locks according to claim 1, characterized in that: The sensor detection parameters include angle encoder detection parameters, pressure sensor detection parameters, and timing sensor detection parameters.

5. The user operation data processing method for handle locks according to claim 1, characterized in that, The step of collecting angle encoder detection parameter data and pressure sensor detection parameter data when the handle lock control system is in low power mode, and performing preliminary analysis on the collected angle encoder detection parameter data and pressure sensor detection parameter data to identify potential signs of abnormal operation, specifically includes: Periodically record the detection parameter data of the angle encoder and the detection parameter data of the pressure sensor; Based on periodically recorded angle encoder detection parameter data, if there is an angle change in the continuous angle encoder sampling values ​​within a cycle, it is judged as a sign of discontinuous movement in the handle lock; and based on periodically recorded pressure sensor detection parameter data, if there is a change in the pressure difference between the continuous pressure sensor sampling values ​​within a cycle, it is judged as a sign of pulsating grip in the handle lock. Both discontinuous movement and pulsating grip in the handle lock are potential signs of abnormal operation.

6. The user operation data processing method for handle locks according to claim 1, characterized in that, The steps for constructing an abnormal operation evidence chain based on high-density angle encoder detection parameter data and high-density pressure sensor detection parameter data specifically include: In the acquired high-density angle encoder detection parameter data, the angle change rate between continuous sampling points in the high-density angle encoder detection parameter data is identified and analyzed. The number and duration of the decrease in multiple angle change rates within a set sampling time window are identified, and the lag index is calculated accordingly. In the high-density angle encoder detection parameter data, data is extracted from the continuous sampling points in the high-density angle encoder detection parameter data to obtain the sampling angle data sequence. The number of angle direction changes in the sampling angle data sequence is identified and used as the shaking frequency count. From the high-density pressure sensor detection parameter data, the rising rate of pressure, falling rate of pressure, maximum pressure peak value and average pressure value of the handle of the lever lock within the set sampling window are identified and obtained, and the grip strength is calculated accordingly. The calculated lag index, shaking frequency count, and grip strength are stored in a structured manner and used as a chain of evidence for abnormal operation.

7. The user operation data processing method for handle locks according to claim 1, characterized in that, Also includes: A baseline judgment method is used to identify invalid micro-motion events during the operation of the handle lock, and the timestamp of the invalid micro-motion event is recorded. The invalid micro-motion event indicates a noise event caused by mechanical wear of the handle lock or a micro-motion event caused by unintentional user operation. Based on the identified invalid micro-motion events, the number of occurrences or duration of each invalid micro-motion event is recorded within a set time period, and this is used as a summary of the records of invalid micro-motion events. Store the timestamp of the invalid micro-motion event and its corresponding record summary.

8. The user operation data processing method for handle locks according to claim 7, characterized in that, The step of identifying invalid micro-motion events during the operation of the handle lock using the baseline judgment method and recording the timestamp of the invalid micro-motion event specifically includes: Continuously monitor the operating parameters of the handle lock; When the handle of the lever lock is operated and an action occurs, causing the angle encoder to read an angle, the current reading of the angle encoder and the duration are identified; Determine whether the current angle encoder reading and duration are less than the preset angle judgment benchmark and time judgment benchmark, and determine whether the current angle encoder reading and duration are within the signal distribution range pre-learned by the handle lock operating system; If so, it is determined to be an invalid micro-motion event, and the timestamp of the invalid micro-motion event is recorded.

9. A user operation data processing system for a handle lock, characterized in that, include: The event recognition module is used to identify key events during the operation of the handle lock, including user operation-related events and system state transition events. The timestamp recording module is used to record the timestamp of the key event when it is identified. A sensor detection parameter extraction module is used to extract sensor detection parameters associated with the key event from the key event. The context summary generation module is used to collect sensor detection parameter data within a set time period before and after the timestamp of the key event based on the sensor detection parameters associated with the key event, and to statistically summarize the obtained sensor detection parameter data as a context summary containing the sensor detection parameter data of the key event. A data storage module is used to structure and store the timestamp and context summary of the key event. The step involves collecting sensor detection parameter data within a set time period before and after the timestamp of the key event, based on sensor detection parameters associated with the key event, and generating a context summary containing the sensor detection parameter data, including: When the handle lock control system is in low power mode, it collects angle encoder detection parameter data and pressure sensor detection parameter data, and performs preliminary analysis on the collected angle encoder detection parameter data and pressure sensor detection parameter data to identify potential signs of abnormal operation. When potential signs of abnormal operation are identified, spatial interpolation is used to interpolate the parameter sequence composed of the collected angle encoder detection parameter data and the parameter sequence composed of the pressure sensor detection parameter data to obtain high-density angle encoder detection parameter data and high-density pressure sensor detection parameter data. An abnormal operation evidence chain is constructed based on high-density angle encoder detection parameter data and high-density pressure sensor detection parameter data. The abnormal operation evidence chain includes the jamming index, grip strength, and shaking frequency count calculated based on the high-density angle encoder detection parameter data and high-density pressure sensor detection parameter data. Based on the high-density angle encoder detection parameter data and the high-density pressure sensor detection parameter data, the context summary information corresponding to the key event is obtained, and diagnostic labels are added to the context summary in combination with the abnormal operation evidence chain.

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