CN118882746A - A method and device for collecting and analyzing collision data of electronic equipment - Google Patents

Abstract

The present invention relates to a method and device for collecting and analyzing collision data of electronic equipment, and belongs to the field of collision detection and analysis. The method collects multi-dimensional collision data by installing sensors on electronic equipment, and transmits the data to a control device after serializing and packaging the data. The control device forms a time series sequence based on the received data, and determines that the first collision condition is met when the data amplitude and change rate meet the preset collision condition. Furthermore, by fitting the data curve and matching it with a preset set of negligible collision curves, it is determined whether the second collision condition is met. For data that meets the first collision condition but does not meet the second collision condition, it is identified as data that has collided, and a corresponding collision response strategy is formulated. This method improves the timeliness of data processing and the effectiveness of collision identification, reduces unnecessary collision response processing, and is suitable for electronic equipment under complex working conditions.

Description

A method and device for collecting and analyzing collision data of electronic equipment

Technical Field

The invention belongs to the field of collision detection and analysis, and in particular relates to a method and device for collecting and analyzing collision data of electronic equipment.

Background Art

Electronic devices may collide due to environmental factors during use and normal operation, which may cause device damage, data loss, and even safety hazards. In some operation scenarios, such as remote control operation, although operators can make manual adjustments through control devices, the collision often occurs in a very short time and cannot be followed up and responded. Therefore, how to automatically generate collision response strategies is a very meaningful measure.

However, in scenarios such as remote-controlled vehicles and remote-controlled flying devices, electronic equipment will continue to be exposed to harsh working conditions. Many conventional collisions do not require a response due to the offsetting effect of subsequent working conditions. For example, a car does not necessarily fall over when passing through pits and protrusions, and a flying device does not necessarily roll out of control when encountering sudden airflow interference. In other words, although the transient parameters of the electronic device are abnormal, they often do not constitute a collision threat that requires a response. It is very necessary to conduct effective collision assessments to improve the effectiveness of collision protection for electronic equipment.

Summary of the invention

In order to solve the problems of the prior art, the present invention proposes a method and device for collecting and analyzing collision data of electronic devices. Specifically, the present application relates to a method for collecting and analyzing collision data of electronic devices, which is characterized by comprising:

A sensor is mounted on the electronic device, the sensor collects multi-dimensional collision data of the electronic device, and then serializes and packages the multi-dimensional collision data, wherein the package data includes: sensor number, data dimension type, parameter value sequence, and transmits the packaged data to the control device via a wireless connection;

The control device forms time series data of a preset first time length according to the received multi-dimensional collision data, and determines that the first collision condition is met when the data amplitude and change rate of the time series meet the preset collision condition;

Based on the time series data satisfying the first collision condition, a data curve is formed by fitting, and the data curve is matched with a preset set of negligible collision curves, and when the matching result is greater than a preset matching threshold, it is determined that the second collision condition is satisfied;

The multi-dimensional collision data that meets the first collision condition but does not meet the second collision condition is identified as collision data, and the control device formulates a collision response strategy according to the collision data.

Furthermore, a plurality of sensors are installed on the electronic device, and the collected data of different sensors are distinguished by the sensor numbers in the package data.

Furthermore, the data dimension types in the package data include: displacement, acceleration and angular velocity.

Furthermore, the collision data of each dimension is represented by a quaternion, wherein the parameter value sequence in the package data is an array sequence formed by the real parameter and three imaginary parameters of the quaternion.

Furthermore, the data amplitude and change rate of the time series are calculated as follows: the data amplitude is determined according to the modulus of the quaternion, and the change rate is determined according to the change ratio of the data amplitude.

Furthermore, the preset collision condition is satisfied, including one or a combination of the following conditions: the data amplitude of the time series at any time point is greater than a preset amplitude threshold, and the data change rate of the time series at any adjacent time point is greater than a preset change rate threshold.

Furthermore, the back-end computing device performs ignorable collision marking according to the collision sample data of the electronic device, obtains a ignorable collision curve set based on the marking data fitting, and the back-end computing device sends the ignorable collision curve set to the control device.

Further, for each dimension of collision data, time series data of a preset first time length is formed, wherein the matching with the preset negligible collision curve set includes:

Matching the fitting curve of each dimension of collision data with the preset negligible collision curve set of the corresponding dimension to obtain the matching value of the corresponding dimension;

The matching result is obtained by multiplying the preset weight value of each dimension by the matching value of the corresponding dimension.

The present application also relates to a control device, characterized in that it receives multi-dimensional collision data collected by sensors on electronic devices, identifies collision data according to the collision data collection and analysis method of electronic devices as described above, and formulates a collision response strategy.

The present application also relates to a computer-readable storage medium, on which a program code is stored. When the program code is executed by a processor, the steps of the method for collecting and analyzing collision data of an electronic device as described above are executed.

The beneficial technical effects of the patent of this invention include: for the multi-dimensional collision data collected by the electronic device sensor, the bottom layer direct packaging method is used for serialized packaging and transmission, which is more efficient than application layer analysis and improves the timeliness of data processing. Combining collision detection and further negligible collision detection makes the collision identification of electronic devices under complex working conditions more effective and reduces unnecessary collision response processing.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a framework diagram of a method flow according to an embodiment of the present invention.

FIG. 2 is a diagram showing a structure of a collection data package according to an embodiment of the present invention.

FIG. 3 is a diagram showing a method for obtaining a set of negligible collision curves according to an embodiment of the present invention.

FIG. 4 is a diagram showing a structure of device interactions according to an embodiment of the present invention.

DETAILED DESCRIPTION

In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

This embodiment provides a method for collecting and analyzing collision data of an electronic device, aiming to achieve effective evaluation of a collision event through the collection and analysis of multi-dimensional data.

FIG1 shows a method flow framework diagram according to an embodiment of the present invention. First, the sensor collects multi-dimensional collision data of the electronic device. Multiple sensors are arranged on the electronic device. In some embodiments, six-axis or nine-axis sensors can be used. These sensors can monitor parameters such as acceleration and angular velocity of the electronic device in real time to provide basic data for posture detection. In some embodiments, the collected raw data can also be preprocessed and filtered to remove noise and interference signals. Common filtering methods include low-pass filtering, high-pass filtering, band-pass filtering, etc. Appropriate filters can be selected according to specific application scenarios to improve the quality and accuracy of the data.

The sensor is responsible for collecting collision data of the device in different dimensions. The data dimensions include relevant indicator parameters used for evaluation when a collision occurs, including displacement, acceleration, angular velocity, etc., and conventional indicator parameters familiar to those skilled in the art are included in this list.

The data collected by each sensor is transmitted using data packets. In some embodiments, the data is packaged using TLV encapsulation, as shown in FIG2 :

Type: indicates that the business type is collision data collection;

Length: indicates the length of the data packet;

Package body: The elements of the data package include sensor number, data dimension type and parameter value sequence; the sensor number distinguishes the collected data of different sensors, the data dimension types include displacement, acceleration, angular velocity, etc., and the parameter value sequence is an array sequence formed by the real parameter of the quaternion and the three imaginary parameters.

The data is packaged and transmitted to the control device. The data collected by the sensor is transmitted to the control device via a wireless connection. The control device is connected to the electronic device wirelessly and establishes a communication connection with the back-end computing device. Wireless connections include Bluetooth, WiFi, 4/5G networks, etc., which makes the interaction between the control device and the electronic device more flexible. The back-end computing device includes a self-built server, a cloud server or a SaaS service.

In some embodiments, forming time series data of a preset first time length includes: after the control device receives the data, it deserializes it to obtain package data including sensor number, data dimension type and parameter value sequence. The control device generates time series data of a preset first time length based on the received multi-dimensional collision data. Time series data refers to a collection of continuous data points collected by a sensor within a certain time range. The first time length preset here is an empirical value based on the collision stress response of the electronic device under actual working conditions in an artificial manner, and is not specifically limited here.

In some embodiments, the determination of the collision condition includes: when the data amplitude and the change rate of the time series meet the preset collision condition, the control device determines that the first collision condition is met. The preset collision condition may include one of the following situations or a combination thereof:

The data amplitude of the time series at any time point is greater than the preset amplitude threshold;

The data change rate of the time series at any adjacent time points is greater than a preset change rate threshold.

Based on the time series data that meets the first collision condition, the control device fits to form a data curve. The data curve refers to a continuous curve obtained by fitting the time series data, which is used to describe the trend of data changes over time. The control device matches the fitted data curve with a preset set of negligible collision curves. When the matching result is greater than a preset matching threshold, it is determined that the second collision condition is met.

The set of negligible collision curves is a set of negligible collision curves obtained by analyzing and marking a large amount of collision sample data, as shown in FIG3 , which is generated by a back-end computing device and sent to a control device. In some embodiments, the set of negligible collision curves is obtained in the following manner: the back-end computing device performs negligible collision marking according to the collision sample data of the electronic device, and based on the marked data, the back-end computing device fits the set of negligible collision curves and sends it to the control device. In this embodiment, the specific implementation means of performing negligible collision marking according to the collision sample data of the electronic device can be marked manually or combined with machine learning.

In some embodiments, labeling is performed in combination with machine learning. First, a large amount of collision data is collected, including various types of collisions and non-collision events, and then which ones are negligible collisions and which ones require responses are marked. Useful features such as displacement, acceleration, angular velocity, etc. are extracted from the raw data. Model training is performed using the labeled data set, and the model performance is evaluated using a cross-validation method. The trained model is then used to predict the collision data, and a curve for negligible collisions is fitted. The fitted curves are combined to form a set of negligible collision curves. Preferably, new collision data is collected regularly, and the model is retrained and updated to improve the accuracy and adaptability of the model. It should be noted that the present invention does not limit the model selection, and those familiar to those skilled in the art, such as random forests, support vector machines (SVMs), neural networks, etc., are all included in this list.

Figure 4 shows a diagram of the device interaction structure according to an embodiment of the present invention, where the control device analyzes the collision data of the electronic device and makes a collision determination based on the data support of the back-end computing device. In some embodiments, the functions of the back-end computing device can also be integrated into the control device as needed.

In this embodiment, the collision data of each dimension is represented by quaternion. Quaternion is a mathematical tool for describing three-dimensional rotation, with real part and three imaginary part parameters. The parameter value sequence in the package data is composed of an array sequence formed by the real part parameter and three imaginary part parameters of the quaternion. The data amplitude and change rate of the time series are calculated as follows:

Data amplitude: determined by the modulus of the quaternion, that is, the modulus of the quaternion represents the amplitude of the data.

Data change rate: determined according to the change ratio of the data amplitude, that is, the data change rate is obtained by calculating the change ratio of the data amplitude at adjacent time points.

In some embodiments, a time series data of a preset first time length is formed for each dimension of collision data. The control device matches the fitting curve of each dimension of collision data with the preset negligible collision curve set of the corresponding dimension to obtain the matching value of the corresponding dimension. According to the preset weight value of each dimension, the matching value of the corresponding dimension is multiplied to obtain the final matching result.

Here, the matching calculation of the displacement index dimension is taken as an example. The calculated fitting curve is f _d (t). The preset displacement index response can ignore each curve in the collision curve set as g _d,i (t), where i is the i-th curve. The displacement matching calculation formula is:

= max( 1 - )

The matching calculation method of other indicator dimensions such as angular velocity and acceleration is similar to the matching calculation method of displacement, which will not be repeated here.

The comprehensive matching result is calculated based on the preset weight value of each dimension:

M = w _d * M _d + w _a * M _a + w _w * M _w

Among them, w _d , _wa , _wl are the preset weight values of displacement, acceleration, and angular velocity, respectively, and M _d , _Ma , M _w are the matching degrees of displacement, acceleration, and angular velocity, respectively.

For the multi-dimensional collision data that meets the first collision condition but does not meet the second collision condition, the control device identifies it as collision data, and the control device formulates a collision response strategy according to the collision data.

In some embodiments, formulating a response strategy includes: controlling the device to formulate a corresponding collision response strategy based on the identified data. The collision response strategy may include one or a combination of the following measures:

Send out an alarm signal to alert operators;

Automatically adjust the operating parameters of the equipment to reduce the impact of collisions;

Record detailed data of collision events for subsequent analysis and improvement.

The disclosed embodiment also provides a control device, characterized in that the control device receives multi-dimensional collision data collected by sensors on the electronic device, identifies collision data according to the method steps described in the above embodiment, and formulates a collision response strategy.

The embodiments of the present disclosure further provide a non-volatile computer storage medium, wherein the computer-readable storage medium stores program code; the computer storage medium stores computer-executable instructions, and the computer-executable instructions can execute the method steps described in the above embodiments.

It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted by any appropriate medium, including: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above. The above-mentioned computer-readable medium may be contained in the above-mentioned electronic device; or it may exist alone without being assembled into the electronic device. The computer program code for performing the operation of the present disclosure may be written in one or more programming languages or a combination thereof, and the above-mentioned programming languages include object-oriented programming languages—such as Java, Smalltalk, C++, and also conventional procedural programming languages—such as "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network including a Local Area Network (AN) or a Wide Area Network (WAN), or may be connected to an external computer.

The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, which contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions. The units involved in the embodiments described in the present disclosure can be implemented by software or by hardware. Among them, the name of the unit does not constitute a limitation on the unit itself under certain circumstances.

The above introduces the preferred embodiments of the present invention, which is intended to make the spirit of the present invention clearer and easier to understand, but is not intended to limit the present invention. All modifications, substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection outlined by the claims attached to the present invention.

Claims (10)

1. The method for collecting and analyzing the collision data of the electronic equipment is characterized by comprising the following steps of:

The electronic equipment is provided with a sensor, the sensor collects multidimensional collision data of the electronic equipment, and then the multidimensional collision data is subjected to serialization packet, wherein the packet body data comprises: the sensor number, the data dimension type and the parameter value sequence are transmitted to the control equipment through wireless connection;

The control equipment forms time sequence data with preset first time length according to the received multidimensional collision data, and when the data amplitude and the change rate of the time sequence meet preset collision conditions, the control equipment judges that the first collision conditions are met;

Fitting to form a data curve based on the time sequence data meeting the first collision condition, matching the data curve with a preset negligible collision curve set, and judging that the second collision condition is met when the matching degree result is larger than a preset matching degree threshold value;

and identifying the multidimensional collision data which meet the first collision condition but not meet the second collision condition as collision data, and making a collision response strategy according to the collision data by the control equipment.

2. The method for collecting and analyzing collision data of electronic equipment according to claim 1, wherein a plurality of sensors are arranged on the electronic equipment, and collected data of different sensors are distinguished through the sensor numbers in the bag body data.

3. The method of claim 1, wherein the data dimension types in the weld data comprise: displacement, acceleration and angular velocity.

4. The method for collecting and analyzing collision data of electronic equipment according to claim 1, wherein the collision data of each dimension is represented by a quaternion, and the parameter value sequence in the inclusion data is an array sequence formed by a real part parameter and 3 imaginary part parameters of the quaternion.

5. The method for collecting and analyzing collision data of electronic equipment according to claim 4, wherein the calculation mode of the data amplitude and the change rate of the time sequence is as follows: and determining the data amplitude according to the modulus of the quaternion, and determining the change rate according to the change proportion of the data amplitude.

6. The method for collecting and analyzing collision data of electronic equipment according to claim 1, wherein the meeting of the preset collision condition includes one or a combination of the following conditions: the data amplitude of the time sequence at any time point is larger than a preset amplitude threshold value, and the data change rate of the time sequence at any adjacent time point is larger than a preset change rate threshold value.

7. The method for collecting and analyzing collision data of electronic equipment according to claim 1, wherein a back-end computing device performs negligible collision marking according to collision sample data of the electronic equipment, a set of negligible collision curves is obtained by fitting based on marking data, and the back-end computing device sends the set of negligible collision curves to the control device.

8. The method of claim 1, wherein forming time series data of a predetermined first time length for each dimension of collision data, wherein said matching with a predetermined set of negligible collision curves comprises:

Matching the fitting curve of each dimension of collision data with a preset negligible collision curve set of the corresponding dimension to obtain a matching degree value of the corresponding dimension;

And multiplying the preset weight value of each dimension by the matching degree value of the corresponding dimension to obtain the matching degree result.

9. A control device for receiving multidimensional crash data collected by a sensor on an electronic device, and for formulating a crash response strategy by identifying the data of a crash in accordance with a method of collecting and analyzing the crash data of the electronic device as claimed in any one of claims 1 to 8.

10. A computer readable storage medium having stored thereon program code which, when executed by a processor, performs the steps of the method of collecting and analyzing collision data of an electronic device according to any of claims 1-8.