Method for starting mobile phone function based on mobile phone shell with function key

Abstract

The invention relates to the technical field of mobile equipment peripherals, and discloses a method for starting a mobile phone function based on a mobile phone shell with a function key, and the method comprises the steps: S1, detecting a pressure value borne by the function key of the mobile phone shell, when the detected pressure value is greater than or equal to a preset pressure value and the duration time exceeds a preset first time, generating a trigger signal; S2, executing the trigger signal, and reading multi-dimensional data of a built-in sensor of the mobile phone; according to the method, misoperation is filtered through a dual pressure detection mechanism, a scene feature model is constructed based on multi-sensor data fusion, precise triggering of functions is realized in combination with a hierarchical decision algorithm, and compared with a traditional single-key triggering scheme, the method forms an anti-mistaken-touch security policy through four-dimensional triggering conditions, so that the accuracy of triggering is improved. And meanwhile, the operation convenience is improved by utilizing scenarized intelligent matching, so that the method has the remarkable advantages of reducing the false triggering rate, enhancing the function execution adaptability and optimizing the user interaction experience.

Description

Technical Field

[0001] This application relates to the technical field of mobile device peripherals, and in particular to a method for starting mobile phone functions based on a mobile phone case with function keys. Background Art

[0002] With the popularization of smart devices, mobile phone cases have gradually evolved from simple protective accessories to intelligent peripherals with interactive functions. The solutions for realizing basic operations such as taking pictures and adjusting the volume through physical buttons or simple sensors have gradually become the mainstream.

[0003] In the prior art, although the mobile phone case can trigger preset functions through buttons, it generally does not have the ability of multi-dimensional environment perception, and cannot dynamically match the user scenario in combination with data such as light intensity and device posture, which may lead to inefficient interaction problems such as the inability to automatically start the pedometer during exercise and the need to manually switch to mute during a meeting; at the same time, for sensitive functions such as payment and privacy permissions, the mobile phone case lacks a biometric authentication mechanism and only relies on a single pressure button to trigger, which may cause the risk of misoperation or malicious intrusion and is difficult to meet the usage requirements of high-security scenarios. Summary of the Invention

[0004] In order to improve the scenario adaptability of mobile phone case function triggering and the security of sensitive operations, this application provides a method for starting mobile phone functions based on a mobile phone case with function keys.

[0005] In a first aspect, this application provides a method for starting mobile phone functions based on a mobile phone case with function keys, adopting the following technical solutions: A method for starting mobile phone functions based on a mobile phone case with function keys, the method comprising: S1. Detect the pressure value received by the function key of the mobile phone case, and generate a trigger signal when the detected pressure value is greater than or equal to a preset pressure value and the duration exceeds a preset first time: S2. Execute the trigger signal and read the multi-dimensional data of the built-in sensors of the mobile phone; S3. Perform scene matching degree analysis and calculation on the multi-dimensional data, and generate the corresponding instruction with the largest scene response characteristic value; S4. Transmit the generated instruction to the mobile phone, and the mobile phone executes the corresponding instruction.

[0006] By adopting the above technical solutions, the deep integration of physical button operations and multi-dimensional environment data is realized, the accuracy and intelligence of mobile phone function triggering are significantly improved, and the misoperation caused by single pressure detection is effectively avoided.

[0007] Optionally, the multi-dimensional data includes: Light intensity, which is collected by an ambient light sensor and used to detect the ambient light around the mobile phone; Acceleration, acquired through an acceleration sensor, is used to detect the linear acceleration of the mobile phone in three-dimensional space; The gyroscope angular velocity is acquired through the gyroscope sensor and is used to detect the angular velocity of the mobile phone around three axes.

[0008] By adopting the above technical solutions, the fusion analysis of multi-sensor data can fully reflect the actual status of the user's usage scenario, such as distinguishing indoor / outdoor lighting environment, motion / stationary state, etc., thereby providing richer decision-making basis for scene matching.

[0009] Optionally, in step S3, the process of performing scene matching analysis and calculation on the multi-dimensional data includes: S31, define an independent condition combination for each preset function, set the weight coefficient corresponding to each dimension data and the corresponding basic response threshold; S32, calculating the independent matching degree corresponding to each preset function according to the collected multi-dimensional data; S33, analyzing and calculating the independent matching degrees, the response thresholds of the preset functions and the priority weights of the preset functions, obtaining the response characteristic values ​​of the preset functions, and generating the preset function execution instruction with the largest response characteristic value.

[0010] By adopting the above technical solution, the dynamic matching mechanism based on weight coefficient and priority ensures the strict triggering conditions of high-security level functions while taking into account the convenience of daily functions, thus achieving a balance between security and user experience.

[0011] Optionally, in step S32, the process of calculating the independent matching degree corresponding to each preset function includes: Constructing a sliding window , read the multidimensional data in the sliding window, and calculate and extract the statistical features of the multidimensional data; ; ; ; The above formula is used to analyze and calculate the average illumination value in the sliding window , acceleration variance , mean angular velocity ; ; ; ; ; The independent matching degree corresponding to the i-th preset function is obtained by the above formula combined analysis and calculation ; Among them, there are several preset functions, where \(i\) is the sequence number of the preset function, is the current time, is the sliding window time length, and there are \(N\) light intensity data points collected by the ambient light sensor within the sliding window, , is the light intensity value of the \(j\)th light intensity data point, and there are \(M\) acceleration data points collected by the acceleration sensor, , is the acceleration value of the \(l\)th acceleration data point, is the average value of all acceleration data within the sliding window, is a function of the angular velocity changing with time, is the preset light intensity reference value, is the preset acceleration variance control value, is the preset angular velocity control value, is to take the minimum value between the two, is the light intensity similarity, is the preset light intensity weight coefficient corresponding to the \(i\)th preset function, is the acceleration variance similarity, is the preset acceleration variance weight coefficient corresponding to the \(i\)th preset function, is the angular velocity similarity, is the preset angular velocity weight coefficient corresponding to the \(i\)th preset function.

[0012] By adopting the above technical solutions, the sliding window statistical feature analysis can effectively filter out the instantaneous noise in the sensor data, improve the data stability, and at the same time achieve the quantitative evaluation of the scene features through multi-dimensional similarity calculation.

[0013] Optionally, in step S33, the process of generating the preset function execution instruction with the largest response feature value includes: Screen the function set \(F\) that satisfies ; If the function set \(F\) is a non-empty set, calculate the response feature value for the functions in the function set \(F\): ; Generate the preset function execution instruction with the largest response feature value in the function set \(F\); If the function set \(F\) is an empty set, no preset function execution instruction is generated; Among them, is the basic execution threshold of the \(i\)th preset function, is the priority weight coefficient of the \(i\)th preset function.

[0014] By adopting the above technical solution, the comprehensive calculation method of the response characteristic value ensures the selection of the optimal solution among multiple candidate functions, and the priority weight mechanism further strengthens the execution priority of high-security level functions to avoid interference from low-priority functions.

[0015] Optionally, the assignment rule of the basic execution threshold and the priority weight coefficient is: Classify the preset functions according to the security level, which are divided into high security level, medium security level and low security level; Basic execution thresholds for high-security preset functions , the basic execution threshold of the preset function of the medium security level , the basic execution threshold of the low security level preset function ,in, ; Priority weight coefficient of high security level preset function , the priority weight coefficient of the preset function of the medium security level , priority weight coefficient of low security level preset function ,in, .

[0016] By adopting the above technical solution and setting differentiated thresholds based on security levels, we can ensure the security of high-risk operations (such as payment and unlocking) and lower the triggering threshold of daily functions (such as taking photos and muting), which meets the actual needs of users.

[0017] Optionally, the method further comprises: S5, when it is detected that the finger continues to touch the key for more than a preset second time, the fingerprint recognition module is started to collect fingerprint data; S6. In the process of generating the corresponding instruction with the largest scene response characteristic value, if the preset function with the largest response characteristic value is a high security level, it is necessary to simultaneously satisfy that the fingerprint feature similarity is greater than or equal to the preset benchmark fingerprint feature similarity. Otherwise, a preset function execution instruction with the largest response characteristic value of a non-high security level is generated.

[0018] By adopting the above technical solutions, the introduction of the fingerprint recognition module provides a dual verification mechanism for high-security level functions. The composite authentication of physical buttons + biometrics significantly improves the system's anti-attack capabilities and prevents malicious operations.

[0019] In a second aspect, the present application provides a mobile phone case, which adopts the following technical solution: A mobile phone case, the mobile phone case being used to implement a method for starting a mobile phone function based on a mobile phone case with a function key, comprising: Function key module, with built-in pressure sensor, used to collect pressure data; The fingerprint recognition module is integrated on the surface of the button and includes a capacitive sensor array for collecting fingerprint data. The instruction generation module is used to perform scene matching degree analysis and calculation on multi-dimensional data and generate a preset function execution instruction.

[0020] By adopting the above technical solution, the mobile phone case, as an independent intelligent terminal, integrates functions of pressure detection, fingerprint recognition and scene analysis, forming a distributed intelligent system with the mobile phone, which not only reduces the computing burden of the mobile phone but also expands the peripheral interaction mode.

[0021] Optionally, it further includes: The wireless communication module is used for wireless signal transmission between the mobile phone case and the mobile phone. The power supply module is built-in with a button battery or reverse wireless charging of the mobile phone.

[0022] By adopting the above technical solution, the wireless communication module realizes a low-power connection between the mobile phone case and the host, and the power supply module supports long-term battery life, ensuring that users can continuously enjoy the intelligent interaction experience without frequent charging.

[0023] In a third aspect, the present application provides a storage medium, adopting the following technical solution: A storage medium stores a program of the method for starting the mobile phone function based on the mobile phone case with a function button described in any one of the above.

[0024] In summary, the present application includes at least one of the following beneficial technical effects: (1) The present invention filters misoperations through a dual pressure detection mechanism (pressure value + pressing duration), constructs a scene feature model based on multi-sensor data fusion (light / acceleration / angular velocity), and combines a hierarchical decision algorithm (matching degree calculation + priority scheduling) to achieve precise triggering of functions. Compared with the traditional single-button triggering scheme, this method forms an anti-mis-touch safety strategy through four-dimensional triggering conditions (pressure threshold, time threshold, environmental data, action data), and at the same time improves the operation convenience by using scene-based intelligent matching. Thus, it has the remarkable advantages of reducing the mis-triggering rate, enhancing the adaptability of function execution, and optimizing the user interaction experience. Description of the Drawings

[0025] Figure 1 is the step flow chart of the method for starting the mobile phone function based on the mobile phone case with a function button proposed by the present invention. Detailed Embodiments

[0026] The following details the embodiments of the present application, and the examples of the embodiments are shown in the drawings.

[0027] In the description of this specification, the descriptions referring to terms such as "certain embodiments", "one embodiment", "some embodiments", "schematic embodiments", "examples", "specific examples", or "some examples" mean that the specific features, structures, materials, or characteristics described in connection with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiments or examples. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0028] An embodiment of the present application discloses a method for starting a mobile phone function based on a mobile phone case with a function button. Referring to Figure 1 , the method includes: S1. Detect the pressure value received by the function button of the mobile phone case through a piezoresistive or capacitive pressure sensor. When the detected pressure value is greater than or equal to a preset pressure value and the duration exceeds a preset first time, a trigger signal is generated. The preset pressure value can be dynamically calibrated according to the user's finger pressing habit, generally set between 3N and 5N. The first time can set a threshold according to experience, generally set between 200 ms and 500 ms: S2. Execute the trigger signal and synchronously read multi-dimensional data of the built-in sensors of the mobile phone. The multi-dimensional data includes light intensity, acceleration, and gyroscope angular velocity; S3. Perform feature extraction and matching degree calculation on the multi-dimensional data through a preset scene matching algorithm, and generate an optimal execution instruction based on the weight coefficient, response threshold, and priority strategy; S4. Transmit the instruction to the mobile phone system through a wireless communication module to drive the execution of corresponding functions (such as taking pictures, making payments, emergency calls, etc.).

[0029] Through the above technical solution, this embodiment provides a method for starting a mobile phone function based on a mobile phone case with a function button. The method filters out misoperations through a dual pressure detection mechanism (pressure value + pressing duration), constructs a scene feature model based on multi-sensor data fusion (light / acceleration / angular velocity), and combines a hierarchical decision-making algorithm (matching degree calculation + priority scheduling) to achieve precise triggering of functions. Compared with the traditional single-button triggering scheme, this method forms an anti-mis-touch safety strategy through four-dimensional triggering conditions (pressure threshold, time threshold, environmental data, action data), and at the same time improves the operation convenience by using scene-based intelligent matching. Thus, it has the significant advantages of reducing the mis-triggering rate, enhancing the adaptability of function execution, and optimizing the user interaction experience.

[0030] In one embodiment, the multi-dimensional data includes: The light intensity, which is obtained by collecting through a photosensitive diode or an ambient light sensor, is used to detect the ambient light around the mobile phone (scenes such as strong light, weak light, and night can be recognized). The acceleration, which is obtained by collecting through an acceleration sensor, is used to detect the linear acceleration of the mobile phone in three-dimensional space (the holding state of the mobile phone, the shaking amplitude, etc. can be recognized). The gyroscope angular velocity, which is obtained by collecting through a three-axis gyroscope sensor, is used to detect the angular velocity of the mobile phone around three axes (actions such as rotation and flipping can be recognized).

[0031] Through the above technical solution, this embodiment provides an environment and action detection method based on multi-sensor fusion. The method collects three core data of light intensity, acceleration, and gyroscope angular velocity, constructs a multi-dimensional scene feature vector, and provides rich input dimensions for subsequent scene matching. In this way, it has the advantages of avoiding misjudgment of a single sensor and improving the robustness of scene recognition.

[0032] In one embodiment, in step S3, the process of analyzing and calculating the scene matching degree of multi-dimensional data includes: S31. Define independent condition combinations for each preset function (such as taking pictures, flashlight, quick payment, etc.), set the weight coefficients corresponding to each dimension data (supporting user-defined or system adaptive learning) and the corresponding basic response thresholds (for example, the basic threshold for taking pictures is 0.5, and the basic threshold for the flashlight is 0.6, etc.); S32. Calculate the independent matching degrees corresponding to each preset function according to the collected multi-dimensional data; S33. Analyze and calculate by integrating each independent matching degree, each preset function response threshold, and the priority weight of the preset function (for example, the priority of the quick payment function is higher than that of the taking pictures function), obtain the response characteristic values of each preset function, and generate an execution instruction for the preset function with the largest response characteristic value.

[0033] Through the above technical solution, this embodiment provides a hierarchical scene matching analysis method. The method defines an exclusive condition combination and weight system for each function, and makes a comprehensive decision in combination with the priority strategy to avoid multi-scene conflicts. In this way, it can achieve the effects of improving the pertinence of function execution and the efficiency of system resource allocation.

[0034] In one embodiment, in step S32, the process of calculating the independent matching degrees corresponding to each preset function includes: Construct a sliding window , read the multi-dimensional data in the sliding window, and calculate and extract the statistical features of the multi-dimensional data; Through the formula Analyze and calculate to obtain the average light intensity in the sliding window ; Through the formula Analyze and calculate the acceleration variance within the sliding window ; By formula Analyze and calculate the mean angular velocity within the sliding window ; By formula Analyze and calculate the similarity of light intensity ; By formula Analyze and calculate the acceleration variance similarity ; By formula Analyze and calculate to obtain angular velocity similarity ; ; The independent matching degree corresponding to the i-th preset function is obtained by the above formula combined analysis and calculation ; There are several preset functions, i is the serial number of the preset function, is the current time, is the length of the sliding window. There are N light intensity data points collected by the ambient light sensor in the sliding window. , is the light intensity value of the jth light intensity data point. There are M acceleration data points collected by the acceleration sensor. , is the acceleration value of the lth acceleration data point, is the mean of all acceleration data in the sliding window, is the function of angular velocity changing with time, is the preset light intensity reference value, is the preset acceleration variance control value, is the preset angular velocity control value, To obtain the minimum value between the two, is the light intensity similarity, is the preset light intensity weight coefficient corresponding to the i-th preset function, is the acceleration variance similarity, is the acceleration variance weight coefficient corresponding to the i-th preset function, is the angular velocity similarity, The preset angular velocity weight coefficient corresponding to the i-th preset function.

[0035] Through the above technical solution, this embodiment provides a multi-dimensional data matching method based on a sliding window and statistical features. The method adapts the window duration dynamically to different operation types (for example, a small window is used to capture mutation features for a quick action triggered by a short press, and a large window is used to analyze trend features for a continuous action triggered by a long press). The extraction of statistical features (such as mean, variance, etc.) can effectively filter high-frequency noise and retain the core trend information (for example, the mean illumination reflects the stable state of the ambient brightness, and the variance of acceleration characterizes the amplitude of movement). The similarity calculation model eliminates the dimension difference through normalization processing, making the data of different dimensions comparable. Thus, it has the technical advantages of adapting to dynamic action time series, improving the accuracy of real-time data processing, and enhancing the feature expression ability in complex scenarios.

[0036] In one embodiment, in step S33, the process of generating the preset function execution instruction with the largest response feature value includes: Screen the function set F that satisfies ; If the function set F is a non-empty set, calculate the response feature value for the functions in the function set F: ; Generate the preset function execution instruction with the largest response feature value in the function set F; If the function set F is an empty set, do not generate any preset function execution instructions; wherein, is the basic execution threshold of the i-th preset function, is the priority weight coefficient of the i-th preset function.

[0037] Through the above technical solution, this embodiment provides a decision-making method based on threshold screening and priority weighting. The method filters low-matching requests through the basic execution threshold, avoiding false triggers caused by sensor noise or environmental interference; secondly, priority weighting calculation is performed on the function set passed through the screening to ensure that even if the matching degree of a key function is slightly lower, it can still be preferentially executed due to the high priority weight. If the matching degree of all functions is lower than the threshold, the system remains silent to avoid invalid instructions. Thus, it can achieve the technical effects of balancing the function response sensitivity and security, optimizing the system resource allocation strategy, and ensuring the priority execution of key tasks.

[0038] In one embodiment, the assignment rules for the basic execution threshold and the priority weight coefficient are: Classify the preset functions according to the security level, including high security level (such as mobile payment, password unlocking), medium security level (such as taking pictures, recording, video recording), and low security level (such as flashlight, calculator); The basic execution threshold (Generally taken as 0.8), the basic execution threshold of the preset function for medium security level (Generally taken as 0.5), the basic execution threshold of the preset function for low security level (Generally taken as 0.3), where ; The priority weight coefficient of the preset function for high security level (Generally taken as 2), the priority weight coefficient of the preset function for medium security level (Generally taken as 1.2), the priority weight coefficient of the preset function for low security level (Generally taken as 0.8), where .

[0039] Through the above technical solution, this embodiment provides a method for configuring differential thresholds and weights based on security levels. By setting higher basic execution thresholds and priority weights for high-security functions (such as operations involving funds or privacy), and requiring more stringent scenario matching conditions (such as simultaneously meeting the lighting environment, acceleration, and angular velocity), unauthorized access is prevented; for low-security functions (such as flashlights), lower thresholds are used to ensure quick response to daily needs. Through the security classification mechanism, the system achieves a balance between convenience and security. Especially in high-risk scenarios, the risk of being cracked is reduced through multiple condition restrictions. Thus, it has the technical advantages of strengthening the security protection of sensitive operations, optimizing the user classification management experience, and improving the overall reliability of the system.

[0040] In one embodiment, the method further includes: S5. When it is detected that the finger continuously touches the button for a time exceeding the preset second time, start the fingerprint recognition module to collect fingerprint data. The preset second time is generally set between 1S and 3S; S6. During the process of generating the instruction corresponding to the maximum scene response feature value, if the preset function with the maximum response feature value is of high security level, it is necessary to simultaneously meet that the fingerprint feature similarity is greater than or equal to the preset reference fingerprint feature similarity. Conversely, generate the execution instruction of the preset function with the maximum non-high security level response feature value. The preset reference fingerprint feature similarity is obtained by preset according to experience and is generally set as a matching threshold between 80% and 90%.

[0041] Through the above technical scheme, this embodiment provides a dual verification method that integrates multi-dimensional data and fingerprint recognition. The method automatically activates the fingerprint module when a long press action (preset second time trigger) is detected, forming a dual verification process of "scene matching + biometrics". For high-security functions, the command is executed only when the scene matching degree meets the standard and the fingerprint similarity also meets the standard, effectively preventing others from imitating the pressing action or using prosthetic fingerprints to crack. For non-high-security functions (such as taking pictures), even if the fingerprint verification fails, it can still be triggered according to the scene matching degree to ensure daily operation efficiency. This scheme upgrades the "single-factor authentication" of traditional physical buttons to a two-factor authentication of "scene matching + biometrics", which can achieve the technical effect of improving the security level of sensitive functions, preventing brute force cracking and theft, and taking into account both security and operational convenience.

[0042] The embodiment of the present application further discloses a mobile phone case, which is used to implement a method for starting a mobile phone function based on a mobile phone case with a function key, comprising: Function key module, with built-in pressure sensor, used to collect pressure data in real time; The fingerprint recognition module is integrated into the button surface and contains an 8×8 capacitive sensor array for high-sensitivity fingerprint data collection; The instruction generation module is used to perform scene matching analysis and calculation on multi-dimensional data and generate preset function execution instructions.

[0043] Also includes: Wireless communication module, supporting Bluetooth 5.2 or NFC protocol, for low-latency wireless signal transmission between the phone case and the phone; The power supply module has a built-in button battery or supports reverse wireless charging of mobile phones to ensure continuous power supply.

[0044] The embodiment of the present application further discloses a storage medium storing a program of any one of the above-mentioned methods for activating a mobile phone function based on a mobile phone case with function keys.

[0045] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for starting a mobile phone function based on a mobile phone case with function keys, characterized in that, The method comprises: S1. Detect the pressure value of the function button of the mobile phone case, and generate a trigger signal when the detected pressure value is greater than or equal to the preset pressure value and the duration exceeds the preset first time: S2, execute the trigger signal to read the multi-dimensional data of the built-in sensor of the mobile phone; S3, performing scene matching analysis and calculation on the multi-dimensional data, and generating a corresponding instruction with the largest scene response characteristic value; S4. The generated instructions are transmitted to the mobile phone, and the mobile phone executes the corresponding instructions.

2. The method for starting a mobile phone function based on a mobile phone case with function buttons according to claim 1, characterized in that The multi-dimensional data includes: Light intensity, acquired through the ambient light sensor, is used to detect the ambient light around the phone; Acceleration, acquired through an acceleration sensor, is used to detect the linear acceleration of the mobile phone in three-dimensional space; The gyroscope angular velocity is acquired through the gyroscope sensor and is used to detect the angular velocity of the mobile phone around three axes.

3. The method for starting a mobile phone function based on a mobile phone case with function buttons according to claim 2, wherein, In step S3, the process of analyzing and calculating the scene matching degree of the multi-dimensional data includes: S31, define an independent condition combination for each preset function, set the weight coefficient corresponding to each dimension data and the corresponding basic response threshold; S32, calculating the independent matching degree corresponding to each preset function respectively according to the collected multi-dimensional data; S33, analyzing and calculating the independent matching degrees, the response thresholds of the preset functions and the priority weights of the preset functions, obtaining the response characteristic values ​​of the preset functions, and generating the preset function execution instruction with the largest response characteristic value.

4. The method for starting a mobile phone function based on a mobile phone case with function keys according to claim 3, characterized in that, In step S32, the process of calculating the independent matching degree corresponding to each preset function includes: Construct a sliding window , read the multi-dimensional data within the sliding window, and calculate and extract the statistical features of the multi-dimensional data; ； ; ； The average illumination within the sliding window is obtained through the above formula analysis and calculation , the acceleration variance , the average angular velocity ; ； ； ； ； The independent matching degree corresponding to the i-th preset function is obtained through the combined analysis and calculation of the above formulas ; Among them, there are several preset functions, where i is the sequence number of the preset function, is the current time, is the sliding window time length, and there are N light intensity data points collected by the ambient light sensor within the sliding window, , is the light intensity value of the j-th light intensity data point, and there are M acceleration data points collected by the acceleration sensor, , is the acceleration value of the l-th acceleration data point, is the average value of all acceleration data within the sliding window, is the function of the angular velocity changing with time, is the preset light intensity reference value, is the preset acceleration variance control value, is the preset angular velocity control value, is to take the minimum value between the two, is the light intensity similarity, is the preset light intensity weight coefficient corresponding to the i-th preset function, is the acceleration variance similarity, is the preset acceleration variance weight coefficient corresponding to the i-th preset function, is the angular velocity similarity, is the preset angular velocity weight coefficient corresponding to the i-th preset function.

5. The method for starting a mobile phone function based on a mobile phone case with function keys according to claim 4, wherein In step S33, the process of generating a preset function execution instruction with the largest response characteristic value includes: Screen for the function set F that satisfies ; If the function set F is a non-empty set, the response eigenvalues ​​are calculated for the functions in the function set F: ； Generate the response eigenvalue in the function set F The largest preset function execution instruction; If the function set F is an empty set, no preset function execution instruction is generated; Among them, is the basic execution threshold of the i-th preset function, is the priority weight coefficient of the i-th preset function.

6. The method for starting a mobile phone function based on a mobile phone case with function keys according to claim 5, wherein The assignment rules of the basic execution threshold and priority weight coefficient are as follows: Classify the preset functions according to the security level, which are divided into high security level, medium security level and low security level; Basic execution thresholds for preset functions with high security levels , basic execution thresholds for preset functions with medium security levels , basic execution thresholds for preset functions with low security levels , where ; Priority weight coefficient of preset functions with high security level , priority weight coefficient of preset functions with medium security level , priority weight coefficient of preset functions with low security level , where .

7. The method for starting a mobile phone function based on a mobile phone case with a function button according to claim 6, characterized in that, The method further comprises: S5, when it is detected that the finger continues to touch the key for more than a preset second time, the fingerprint recognition module is started to collect fingerprint data; S6. In the process of generating the corresponding instruction with the largest scene response characteristic value, if the preset function with the largest response characteristic value is a high security level, it is necessary to simultaneously meet the fingerprint feature similarity greater than or equal to the preset benchmark fingerprint feature similarity. Otherwise, generate the preset function execution instruction with the largest response characteristic value in a non-high security level.

8. A mobile phone case, characterized in that, The mobile phone case is used to implement the method of activating mobile phone functions based on a mobile phone case with function keys as described in claim 7, comprising: Function key module, with built-in pressure sensor, used to collect pressure data; The fingerprint recognition module is integrated into the button surface and includes a capacitive sensor array for collecting fingerprint data; The instruction generation module is used to perform scene matching analysis and calculation on multi-dimensional data and generate preset function execution instructions.

9. A mobile phone case according to claim 8, characterized in that, Also includes: Wireless communication module, used for wireless signal transmission between the mobile phone case and the mobile phone; Power supply module, built-in button battery or mobile phone reverse wireless charging.

10. A storage medium, characterized in that, Store a program for implementing the method of activating a mobile phone function based on a mobile phone case with function buttons as described in any one of claims 1-7.

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