Data acquisition method, system and device based on mobile terminal and storage medium

Abstract

The invention relates to the technical field of mobile terminals, and discloses a data acquisition method, system and device based on a mobile terminal and a storage medium, the method comprises the following steps: acquiring initial data, and classifying the initial data to obtain first data; comparing the first data based on a data demand condition input by a user, and deleting data which do not accord with the data demand condition to obtain second data; performing error analysis on the second data to obtain third data; performing precision detection on the third data, and performing storage operation on the data according to a precision detection result; wherein when the data after precision detection accords with a preset precision range, data storage operation is carried out according to the data after precision detection; and when the data after precision detection does not accord with a preset precision range, carrying out data correction operation to obtain corrected data, and carrying out storage operation on the corrected data. The method can improve the data acquisition efficiency of the mobile terminal and improve the utilization rate of the data storage space.

Description

Technical Field

[0001] The present invention relates to the field of mobile terminal technology, and in particular to a data collection method, system, device and storage medium based on a mobile terminal. Background Art

[0002] At present, with the continuous development of mobile terminal technology, mobile devices are increasingly used in daily life and work. Mobile terminals can collect various types of data such as users' vital signs, environmental information, and motion data through built-in sensors. The collection of these data provides important support for medical, sports, psychological and other fields. However, with the rapid increase in the amount of data, the storage space of mobile terminals is facing tremendous pressure. How to achieve efficient data storage has become one of the important directions of current research.

[0003] In one prior art, mobile terminal devices usually use built-in storage to directly store the collected data in the terminal device without screening or deletion. This means that all data, whether valid or not, is stored for a long time, which quickly occupies the limited storage space of the mobile terminal. In addition, the stored data also needs to be transmitted to other devices via wireless transmission (such as Bluetooth), and wireless transmission technology has the problem of insufficient security, increasing the risk of data leakage.

[0004] The existing technology has the problem of low storage space utilization efficiency. Since the data is not screened and cleaned after collection, a large amount of redundant data occupies the limited storage space of the mobile terminal, which ultimately affects the operation efficiency of the device and user experience. Therefore, how to intelligently screen and optimize the storage of data through specific algorithms or rules to improve the utilization of storage space has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] The present invention provides a data collection method, system, device and storage medium based on a mobile terminal, which can improve the data collection efficiency of the mobile terminal and enhance the utilization rate of the data storage space.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a data collection method based on a mobile terminal, comprising: Collecting initial data, and classifying the initial data to obtain first data; Comparing the first data based on the data requirement condition input by the user, deleting the data that does not meet the data requirement condition, and obtaining the second data; performing error analysis on the second data to obtain third data; Performing precision detection on the third data, and performing a storage operation on the data according to the precision detection result; Wherein, when the data after the precision detection meets the preset precision range, the data storage operation is performed according to the data after the precision detection; When the data after the accuracy detection does not meet the preset accuracy range, a data correction operation is performed to obtain the corrected data, and a storage operation is performed on the corrected data. Preferably, the classifying the initial data to obtain the first data includes: The initial data is classified according to the data type, data stamp, valid data bit length or data flag bit to obtain the first data.

[0007] Preferably, the step of comparing the first data based on the data requirement condition input by the user, deleting the data that does not meet the data requirement condition, and obtaining the second data includes: Based on the data requirements entered by the user, standard data features are extracted; Extracting features from the first data respectively to obtain first data features; The first data feature is compared with the standard data feature. If the comparison is consistent, the first data corresponding to the first data feature is determined as the second data; if the comparison is inconsistent, the first data is deleted.

[0008] Preferably, performing error analysis on the second data to obtain third data includes: Performing error analysis on the second data to determine whether the second data is within a preset error range; When the error of the second data is less than a preset error range, determining the second data as third data; When the error of the second data exceeds a preset error range, the second data is deleted. Preferably, the performing accuracy detection on the third data includes: The third data is input into a data precision detection function to obtain a precision output value, wherein the data precision detection function is: in, is the data accuracy detection function; is the third data; is the preset precision value; represents an absolute error between the third data and a preset accuracy value; When the precision output value is less than or equal to the lower precision value, a warning that the data setting value is too small is issued, and a data correction operation is performed based on the preset precision value; When the precision output value is greater than or equal to the upper precision value, a warning that the data setting value is too large is issued, and a data correction operation is performed based on the preset precision value; When the precision output value is greater than the lower precision limit value and less than the upper precision limit value, a data storage operation is performed on the detected data.

[0009] Preferably, the data correction operation includes: When the precision output value is less than or equal to the lower precision value, based on the preset precision value, the data whose precision output value is less than or equal to the lower precision value is input into the first correction formula to obtain the corrected data; Wherein, the first correction formula is: When the precision output value is greater than or equal to the upper limit precision value, based on the preset precision value, the data whose precision output value is greater than or equal to the upper limit precision value is input into the second correction formula to obtain the corrected data; Wherein, the second correction formula is: In the formula, is the corrected data; is the preset precision value; The data whose precision output value is less than or equal to the lower precision value; Output data with a value greater than or equal to the upper precision limit for the precision; is the lower limit precision value; is the upper limit precision value; is the calibration parameter.

[0010] Preferably, the storing operation on the data value according to the accuracy detection result includes: When the accuracy output value is greater than the lower limit accuracy value and less than the upper limit accuracy value, the detected data is directly classified and stored; When the precision output value is less than or equal to the lower precision value, or greater than or equal to the upper precision value, the corrected data is subjected to a data encryption judgment operation; When the corrected data does not meet the data encryption judgment condition, the corrected data is directly classified and stored; When the corrected data meets the data encryption judgment condition, a data encryption operation is performed, and the encrypted data is classified and stored.

[0011] In a second aspect, the present invention provides a data collection system based on a mobile terminal, comprising: A data collection and classification module, used for collecting initial data and classifying the initial data to obtain first data; A data comparison module, used to compare the first data based on the data requirement condition input by the user, delete the data that does not meet the data requirement condition, and obtain the second data; an error analysis module, used for performing error analysis on the second data to obtain third data; A data detection and storage module, used for performing precision detection on the third data, and performing a storage operation on the data according to the precision detection result; Wherein, when the data after the precision detection meets the preset precision range, the data storage operation is performed according to the data after the precision detection; When the data after the accuracy detection does not meet the preset accuracy range, a data correction operation is performed to obtain the corrected data, and a storage operation is performed on the corrected data. In a third aspect, the present invention further provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements any one of the above-mentioned data collection methods based on a mobile terminal.

[0012] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned data collection methods based on a mobile terminal.

[0013] Compared with the prior art, the present invention has the following beneficial effects: the present invention discloses a data collection method based on a mobile terminal, comprising collecting initial data, and classifying the initial data to obtain first data; comparing the first data based on data requirement conditions input by a user, deleting data that does not meet the data requirement conditions, and obtaining second data; performing error analysis on the second data to obtain third data; performing accuracy detection on the third data, and storing the data according to the accuracy detection result; wherein, when the data after the accuracy detection meets a preset accuracy range, data storage operations are performed according to the data after the accuracy detection; when the data after the accuracy detection does not meet the preset accuracy range, data correction operations are performed to obtain corrected data, and the corrected data are stored. The present invention realizes effective screening and optimization of collected data through the steps of classification of initial data, screening based on user needs, error analysis and accuracy detection; by deleting data that does not meet the needs, correcting data with large errors, and ensuring the accuracy and effectiveness of stored data, the storage of redundant and invalid data is reduced, thereby significantly improving the storage space utilization rate of mobile terminals; in addition, the accuracy detection and data correction mechanism further ensures the reliability and quality of stored data, improves data collection efficiency, optimizes user experience, can improve mobile terminal data collection efficiency, and improves the utilization rate of data storage space. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a flowchart of a data collection method based on a mobile terminal provided by a first embodiment of the present invention; Figure 2 It is a structural diagram of a data acquisition system based on a mobile terminal provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of 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.

[0016] Reference Figure 1 The first embodiment of the present invention provides a data collection method based on a mobile terminal, comprising the following steps: S11, collecting initial data, and classifying the initial data to obtain first data; S12, comparing the first data based on the data requirement condition input by the user, deleting the data that does not meet the data requirement condition, and obtaining the second data; S13, performing error analysis on the second data to obtain third data; S14, performing precision detection on the third data, and performing a storage operation on the data according to the precision detection result; Wherein, when the data after the precision detection meets the preset precision range, the data storage operation is performed according to the data after the precision detection; When the data after the accuracy detection does not meet the preset accuracy range, a data correction operation is performed to obtain the corrected data, and a storage operation is performed on the corrected data.

[0017] In step S11, initial data is collected and classified to obtain first data.

[0018] First, the initial data is collected through the sensors of the mobile terminal. These data may include the user's vital signs (such as heart rate, body temperature, breathing rate, etc.), environmental information (such as temperature, humidity, light intensity, etc.) and the user's motion data (such as steps, acceleration, displacement, etc.). Due to the variety of data collected by the mobile terminal and the huge amount of data, directly storing the unprocessed initial data will take up a lot of storage space, so these data need to be classified and processed to improve the efficiency of subsequent data processing and storage.

[0019] Secondly, data classification is based on multiple dimensions. First, the data is initially classified according to its type. For example, vital signs data is distinguished from environmental data and motion data. Different types of data often have different processing and storage requirements. The classified data types will help to optimize more targetedly in the subsequent screening and processing process.

[0020] Specifically, in order to ensure the timeliness and traceability of data, each piece of data will be accompanied by a timestamp to record the specific time of data collection. According to the different timestamps, the data can also be classified by time period, which is convenient for analyzing time series data and detecting the continuity and integrity of the data collection process. In addition, the initial data is also classified according to the effective data bit length, because different types of data may require different bit lengths when stored. By distinguishing the data bit length, the storage structure can be optimized to avoid unnecessary waste of space.

[0021] At the same time, the system will use data flags to further identify the data, such as marking the validity, source or verification status of the data. In this way, valid data can be distinguished from invalid or redundant data during the classification process, ensuring that subsequent processing and storage only operate on valid data.

[0022] In step S11 of the present invention, the initial data is organized into a structured first data set. This process significantly improves the data management efficiency, not only reduces the waste of storage space, but also provides a basis for subsequent data screening, error analysis and accuracy detection, thereby improving the data processing performance of the mobile terminal and the utilization rate of storage space.

[0023] In step S12, the first data needs to be compared based on the data requirement condition input by the user, and the data that does not meet the data requirement condition is deleted to obtain the second data.

[0024] First, in the implementation process of the present invention, in order to optimize the data storage efficiency, the collected initial data is screened and cleaned based on the data requirements entered by the user to ensure that only data that meets the requirements is stored. After the user enters the data requirements, the system will analyze according to these conditions to extract standard data features. The standard data features include but are not limited to information such as data type, timestamp, valid bit length, data flag bit, etc. The purpose of this step is to establish a unified standard for the subsequent data screening to ensure the accuracy and effectiveness of the screening process.

[0025] Secondly, after the initial data collection and classification, each set of data contains multiple features. The system will extract features from these data separately, and the extracted information is consistent with the previously set standard data features. For example, the system will extract the type, format, timestamp, etc. of each piece of data, and these features will serve as the basis for comparison with the standard data features.

[0026] Next, the system will compare each data feature in the first data with the standard data feature one by one. If the feature of a piece of data is consistent with the standard data feature, the system will regard the data as meeting the user's needs and retain it, which is called the second data; if the feature of a piece of data does not meet the standard data feature, the system will delete the data. Through this comparison and screening process, invalid or redundant data that does not meet user needs can be removed, thereby reducing the waste of storage space.

[0027] In step S12 of the present invention, the system can effectively screen the initial data, delete the data that does not meet the requirements, and ensure that the final stored data has high efficiency, accuracy and relevance. This not only optimizes the storage space utilization of the mobile terminal, but also improves the efficiency of data processing and transmission, and improves the user experience. First, the system will first extract the data information range expected by the user according to the data requirement conditions entered by the user on the mobile terminal. For example, the user may set certain data types, value ranges or time periods as key data collection objects.

[0028] In step S13, it is necessary to perform error analysis on the second data to obtain third data.

[0029] It should be noted that when processing the second data, the system will perform an error analysis to determine whether the data is within a preset error range. The error range can be adjusted according to user needs or the system default standard to ensure that the analysis of the data is targeted. By judging the error range, outliers or data with large errors in the data can be preliminarily screened out, thereby preventing these data from affecting subsequent data processing and storage operations.

[0030] Specifically, when the system detects that the error of a certain second data is less than the preset error range, the system determines the data as valid data that meets the accuracy requirements. At this time, the data is confirmed as the third data and can participate in subsequent accuracy detection and storage operations. Through this process, the accuracy and reliability of the data are guaranteed, and the storage of invalid data is avoided.

[0031] Accordingly, when the error of the second data exceeds the preset error range, the system will determine that the accuracy of the data is insufficient and cannot meet the data storage requirements. In order to avoid invalid data occupying storage space, the system will automatically delete the data that exceeds the error range. This processing method can not only reduce the storage of invalid data and release limited storage space, but also improve the data storage efficiency of the mobile terminal.

[0032] In step S13 of the present invention, the accuracy and precision of the stored data are ensured during the data collection and storage process, thereby greatly improving the utilization rate of the storage space and ensuring that the data entering the next step of precision testing has higher accuracy.

[0033] In step S14, the third data is subjected to precision detection, and the data is stored according to the precision detection result; Wherein, when the data after the precision detection meets the preset precision range, the data storage operation is performed according to the data after the precision detection; When the data after the accuracy detection does not meet the preset accuracy range, a data correction operation is performed to obtain the corrected data, and a storage operation is performed on the corrected data.

[0034] In a first specific embodiment, the third data is subjected to accuracy detection.

[0035] Optionally, the collected third data is subjected to precision detection, and the precision output value is calculated by inputting the third data into a data precision detection function. The precision detection function is defined as: in, is the data accuracy detection function; is the third data; is the preset precision value; represents an absolute error between the third data and a preset accuracy value; It is worth noting that when comparing the precision output value with the set precision range, when the precision output value is less than or equal to the lower precision value, the system will issue a warning of "data setting value is too small", prompting the user that the data error is too low and may lead to insufficient data accuracy. At this time, the system corrects the data according to the preset precision value to ensure that the final stored data meets the established precision requirements. Similarly, when the precision output value is greater than or equal to the upper precision value, the system will issue a warning of "data setting value is too large", indicating that the data has too large an error. At this time, data correction will also be performed to ensure that the data is not distorted due to excessive precision.

[0036] If the accuracy output value is between the lower limit and the upper limit, indicating that the data accuracy is within the acceptable range, the system will directly perform data storage operations without any correction processing. This mechanism can ensure that only data that meets the accuracy requirements is saved, thereby reducing the storage of invalid or inaccurate data. At the same time, through accuracy correction, it can avoid the impact of errors in the collection process on subsequent data analysis.

[0037] In the first specific embodiment of step S14 of the present invention, the accuracy detection step not only improves the accuracy and precision of data storage, but also ensures the efficient use of limited storage space of the mobile terminal, thereby improving the data collection and management capabilities of the entire system.

[0038] In the second specific embodiment, the key step of the data correction method is to perform accuracy correction on the collected data based on a preset accuracy value.

[0039] First, before data collection, the system will preset a precision value ,This accuracy value represents the target standard for data correction, which is usually determined based on the allowable error range or ,accuracy requirements in actual application scenarios. This accuracy value is the basis of the entire data correction ,process and is used to compare and adjust the collected raw data.

[0040] Next, the collected data will be compared with the preset accuracy value based on its accuracy output value. In order to ensure that the data is within a reasonable accuracy range, it is also necessary to set the upper and lower accuracy limits, define the minimum and maximum allowable accuracy deviations, and define the minimum allowable accuracy value as the lower accuracy limit value. If the accuracy of the collected data is lower than this value, it means that the data quality is poor and needs to be corrected; the maximum value allowed by the definition of accuracy is the upper limit accuracy value If the data accuracy is higher than this value, it may be too accurate and exceed the needs of practical applications, and correction is also required.

[0041] According to different precision output values, different correction formulas are used to adjust the data. When the precision output value is less than or equal to the lower precision value, based on the preset precision value, the data whose precision output value is less than or equal to the lower precision value is input into the first correction formula to obtain the corrected data; Wherein, the first correction formula is: When the precision output value is greater than or equal to the upper limit precision value, based on the preset precision value, the data whose precision output value is greater than or equal to the upper limit precision value is input into the second correction formula to obtain the corrected data; Wherein, the second correction formula is: In the formula, is the corrected data; is the preset precision value; The data whose precision output value is less than or equal to the lower precision limit value; Output data with a value greater than or equal to the upper precision limit for the precision; is the lower limit precision value; is the upper limit precision value; is the calibration parameter.

[0042] According to different correction formulas, the accuracy of the result system is corrected, and finally the data that meets the angle requirements preset by the system is obtained. In the second specific embodiment of step S14 of the present invention, the data collection accuracy of the mobile terminal can be effectively improved, and the errors caused by inconsistent data accuracy can be reduced, thereby improving the effectiveness of data storage and space utilization.

[0043] In a third specific embodiment, a storage operation is performed on the data according to the accuracy detection result.

[0044] First, based on the accuracy test results, if the detected accuracy output value is between the lower limit accuracy value and the upper limit accuracy value (that is, greater than the lower limit and less than the upper limit), the accuracy of the data is considered to meet the requirements and can be directly classified and stored. In this case, there is no need to further process the data, and it can be directly classified and stored to improve the efficiency of data storage.

[0045] Specifically, when the output value of the precision detection is less than or equal to the lower precision value, or greater than or equal to the upper precision value, the system will first correct the data. The corrected data needs to pass the encryption judgment operation. If the data meets the encryption judgment conditions, it will enter the data encryption process, otherwise it will skip encryption and directly store it in categories. Through this judgment mechanism, it can ensure that only necessary data is encrypted, reducing unnecessary computing overhead.

[0046] It is worth noting that when the corrected data meets the encryption judgment conditions, the system will encrypt the data. The data encryption operation involved uses the SM4 block encryption algorithm, which is a symmetric encryption algorithm recommended by the China National Cryptography Administration.

[0047] Specifically, SM4 is a symmetric encryption algorithm, which means that the same key is used for encryption and decryption. During the data encryption process, the mobile terminal and the receiving device need to use the same key to decrypt the encrypted data; the SM4 algorithm has high encryption efficiency on resource-constrained mobile terminals and is suitable for use in the process of storing and transmitting data, ensuring that no excessive computing overhead is added, while being able to quickly complete data encryption operations; SM4 is a block encryption algorithm that divides data into several fixed-size blocks (generally 128 bits) and encrypts each block. This method helps to process large amounts of data and ensures the security of data during transmission and storage.

[0048] For example, when the corrected data meets the encryption judgment conditions, the system will perform SM4 group encryption on the data. The SM4 algorithm encrypts the data after grouping, ensuring that the data in transmission and storage is not easily cracked or tampered with. In the entire data encryption process, the security management of keys is crucial. Usually, the keys are preset by the system or generated by the user and stored in a secure environment.

[0049] By using the SM4 encryption algorithm, important data in mobile terminals is ensured to have high security during storage or transmission, which can effectively prevent data leakage and illegal access.

[0050] The encrypted data will be stored in a certain classification storage method to ensure the security of the data. In this case, encryption can effectively prevent the data from being attacked or leaked during transmission or storage, thereby improving the security of the data.

[0051] For example, if the corrected data does not meet the encryption conditions after encryption judgment, the system will not encrypt the data and directly store it according to the classification. This operation simplifies the data processing process, helps to improve storage efficiency, and avoids unnecessary encryption operations that waste resources.

[0052] In the third specific embodiment of step S14 of the present invention, data is screened by precision detection to improve storage efficiency and avoid storage of invalid data. At the same time, a flexible encryption judgment mechanism is adopted to perform SM4 encryption only on qualified data, thereby enhancing data security and reducing unnecessary computing overhead. The SM4 encryption algorithm is suitable for mobile terminals with limited resources to ensure efficient encryption and decryption operations. Finally, the classified storage strategy optimizes the storage and security of data, making the entire process more flexible, efficient and secure.

[0053] In summary, in step S14 of the present invention, through multi-layer processing of precision detection, encryption judgment and data classification storage, the data storage efficiency of the mobile terminal can be effectively improved, and while ensuring data security, the occupation of system resources by redundant operations can be avoided.

[0054] The following describes the working process of the present invention using a common health monitoring scenario as an example. Figure 2 , which is Figure 1 Schematic diagram of the working scenario of the method.

[0055] The user wears a smart bracelet with multiple sensors, which can monitor the user's vital signs and surrounding environment data in real time, and collect, process and store data through a mobile terminal. In daily life, the user hopes to use the device to understand his or her health status such as heart rate, body temperature, number of steps, and monitor the temperature and humidity of the environment.

[0056] Step S11: collecting initial data and classifying the initial data to obtain first data; The smart bracelet first collects the user's initial data through the built-in sensor. These data include the user's vital signs such as heart rate, body temperature, breathing rate, sports data such as steps, displacement, acceleration, and environmental data such as ambient temperature, humidity, light intensity, etc. Since these initial data are of various types and collected at a high frequency, directly storing these unprocessed initial data will quickly exhaust the storage space of the mobile terminal, so the system first classifies these data.

[0057] During the classification process, the system divides different types of data into three categories: vital signs data, motion data, and environmental data. Each piece of data is accompanied by a timestamp to record the specific time of data collection to ensure the timeliness and traceability of the data. For example, the system will classify heart rate data of different time periods into one data set based on the timestamp, and classify different ambient temperature data into another data set. In addition, according to the storage requirements of different data, the system will also classify the data according to the effective data bit length to avoid unnecessary waste of storage space. At the same time, the system uses data flag bits to identify the validity and source of the data, so that invalid data can be distinguished and subsequent operations can be performed only on valid data.

[0058] After this classification process, the system generates a structured first data set, which lays the foundation for subsequent data screening and processing.

[0059] Step S12: comparing the first data based on the data requirement condition input by the user, deleting the data that does not meet the data requirement condition, and obtaining the second data; The user enters the health indicators and data requirements that he or she is concerned about through the application program interface of the mobile terminal. For example, the user may only want to view the heart rate data and daily step count statistics for the past 24 hours, or want to focus on monitoring whether the ambient temperature exceeds a specific range.

[0060] After the system receives the requirements entered by the user, it will filter the data in the first data set based on these conditions. First, the system analyzes the conditions entered by the user, such as the specified heart rate, number of steps, ambient temperature range, or time period. Then, based on these conditions, the system extracts standard data features that meet the user's requirements. Data features can include information such as data type (such as heart rate or number of steps), timestamp (such as the past 24 hours), and effective data bit length. Then, the system compares each piece of data in the first data set with these standard data features one by one. If the characteristics of a piece of data meet the user's requirements, it will be retained and called the second data; conversely, if the data does not meet the requirements, it will be deleted by the system. This process ensures that only data related to user needs is saved, invalid data is removed, and excessive storage space is avoided.

[0061] For example, if the user only cares about the heart rate data within the past 24 hours, the system will delete the data beyond this time period. Finally, the system generates a second data set that meets the user's needs.

[0062] Step S13: performing error analysis on the second data to obtain third data; Next, the system will perform an error analysis on the second data. The purpose of this step is to ensure that the stored data is accurate and reliable, and to avoid abnormal data interfering with subsequent data analysis or decision-making. For example, the system may detect that the error of some heart rate data exceeds the preset normal range, indicating that the data may be inaccurate due to sensor failure or external interference.

[0063] During the error analysis process, the system will compare each piece of second data with the preset error range. If the error of a piece of data is less than the preset range, the system will consider the data accurate and reliable, mark it as third data, and retain it for subsequent accuracy detection and storage operations; if the error of a piece of data exceeds the preset range, the system will delete the data to avoid unnecessary storage and processing. For example, when the system detects that the heart rate value is too low or too high beyond the normal range, these abnormal data will be automatically deleted to avoid affecting the final health report.

[0064] Through error analysis, the system can screen out data with higher precision, ensuring that subsequent data storage and processing are more accurate and effective.

[0065] Step S14: performing precision detection on the third data, and performing a storage operation on the data according to the precision detection result; Wherein, when the data after the precision detection meets the preset precision range, the data storage operation is performed according to the data after the precision detection; When the data after the accuracy test does not meet the preset accuracy range, a data correction operation is performed to obtain the corrected data, and the corrected data is stored. In this step, the system performs an accuracy test on the third data to ensure the accuracy of the data. During the accuracy test, the system inputs the third data into the accuracy test function and calculates the difference between the data and the preset standard. If the test result shows that the accuracy output value of the data is between the preset upper and lower accuracy limits, the system considers that the data accuracy meets the requirements and does not require further processing, and directly performs classified storage.

[0066] For example, if the user sets the heart rate monitoring accuracy range to 60-100 beats / minute, and the system detects that the data is within this range, it will directly store the heart rate data.

[0067] If the accuracy test result shows that the data accuracy exceeds the preset range, the system will first correct the data. The corrected data needs to go through the encryption judgment operation again. If it meets the encryption judgment conditions, it will enter the data encryption process; otherwise, the system skips the encryption step and directly stores it in categories.

[0068] During the data encryption process, the system uses the SM4 block encryption algorithm to encrypt qualified data. The SM4 algorithm is a symmetric encryption algorithm, which means that the same key is used for encryption and decryption. During the encryption process, the data is divided into blocks of fixed size (usually 128 bits), and each block is encrypted. This method not only ensures the security of the data, but also effectively prevents the data from being cracked or tampered with during transmission. The key for data encryption is usually preset by the system or generated by the user and stored in a secure environment.

[0069] For example, if the user sets heart rate data as sensitive data that needs to be encrypted for storage, the system will encrypt the data after correction and store the encrypted data in the device. On the contrary, if some environmental data does not need to be encrypted, the system will directly store the data according to the category.

[0070] Finally, after data accuracy detection and encryption processing, the system completes the efficient and secure storage of data. The whole process not only ensures the efficient use of mobile terminal storage space, but also ensures the security and accuracy of data through multiple screening, error correction and encryption measures.

[0071] In summary, the present invention discloses a data collection method based on a mobile terminal, including collecting initial data, and classifying the initial data to obtain first data; comparing the first data based on data requirement conditions input by a user, deleting data that does not meet the data requirement conditions, and obtaining second data; performing error analysis on the second data to obtain third data; performing accuracy detection on the third data, and storing the data according to the accuracy detection result; wherein, when the data after the accuracy detection meets a preset accuracy range, data storage operations are performed according to the data after the accuracy detection; when the data after the accuracy detection does not meet the preset accuracy range, data correction operations are performed to obtain corrected data, and the corrected data are stored. The present invention realizes effective screening and optimization of collected data through the steps of classification of initial data, screening based on user needs, error analysis and accuracy detection; by deleting data that does not meet the needs, correcting data with large errors, and ensuring the accuracy and effectiveness of stored data, the storage of redundant and invalid data is reduced, thereby significantly improving the storage space utilization rate of mobile terminals; in addition, the accuracy detection and data correction mechanism further ensures the reliability and quality of stored data, improves data collection efficiency, optimizes user experience, can improve mobile terminal data collection efficiency, and improves the utilization rate of data storage space.

[0072] Reference Figure 2 The second embodiment of the present invention provides a data acquisition system based on a mobile terminal, comprising: A data collection and classification module, used for collecting initial data and classifying the initial data to obtain first data; A data comparison module, used to compare the first data based on the data requirement condition input by the user, delete the data that does not meet the data requirement condition, and obtain the second data; an error analysis module, used for performing error analysis on the second data to obtain third data; A data detection and storage module, used for performing precision detection on the third data, and performing a storage operation on the data according to the precision detection result; Wherein, when the data after the precision detection meets the preset precision range, the data storage operation is performed according to the data after the precision detection; When the data after the accuracy test does not meet the preset accuracy range, a data correction operation is performed to obtain the corrected data, and the corrected data is stored. Preferably, the data acquisition module is specifically used to collect initial data; Preferably, the data collection and classification module is specifically used to collect initial data and classify the initial data to obtain first data, including: The initial data is classified according to the data type, data stamp, valid data bit length or data flag bit to obtain the first data.

[0073] Preferably, the method for comparing the first data based on the data requirement condition input by the user, deleting the data that does not meet the data requirement condition, and obtaining the second data includes: Based on the data requirements entered by the user, standard data features are extracted; Extracting features from the first data respectively to obtain first data features; The first data feature is compared with the standard data feature. If the comparison is consistent, the first data corresponding to the first data feature is determined as the second data; if the comparison is inconsistent, the first data is deleted.

[0074] Preferably, the error analysis module is specifically used to perform error analysis on the second data to obtain third data, including: Performing error analysis on the second data to determine whether the second data is within a preset error range; When the error of the second data is less than a preset range, determining the second data as third data; When the error of the second data exceeds a preset range, the second data is deleted.

[0075] Preferably, the data detection and storage module is specifically used to perform precision detection on the third data, and perform storage operation on the data according to the precision detection result; Wherein, when the data after the precision detection meets the preset precision range, the data storage operation is performed according to the data after the precision detection; When the data after the accuracy detection does not meet the preset accuracy range, a data correction operation is performed to obtain the corrected data, and a storage operation is performed on the corrected data, including: Performing accuracy detection on the third data includes: The third data is input into a data precision detection function to obtain a precision output value, wherein the data precision detection function is: in, is the data accuracy detection function; is the third data; is the preset precision value; represents an absolute error between the third data and a preset accuracy value; When the precision output value is less than or equal to the lower limit precision value, a warning of too small data setting value is issued, and a data correction operation is performed based on the pre-set precision value; When the precision output value is greater than or equal to the upper precision value, a warning of excessive data setting value is issued, and a data correction operation is performed based on compliance with the preset precision value; When the precision output value is greater than the lower precision limit value and less than the upper precision limit value, a data storage operation is performed on the detected data.

[0076] Preferably, the data detection and storage module is specifically used to perform precision detection on the third data, and perform storage operation on the data according to the precision detection result; Wherein, when the data after the precision detection meets the preset precision range, the data storage operation is performed according to the data after the precision detection; When the data after the accuracy detection does not meet the preset accuracy range, a data correction operation is performed to obtain the corrected data, and a storage operation is performed on the corrected data, including: The data correction operation includes: When the precision output value is less than or equal to the lower precision value, based on the preset precision value, the data whose precision output value is less than or equal to the lower precision value is input into the first correction formula to obtain the corrected data; Wherein, the first correction formula is: When the precision output value is greater than or equal to the upper limit precision value, based on the preset precision value, the data whose precision output value is greater than or equal to the upper limit precision value is input into the second correction formula to obtain the corrected data; Wherein, the second correction formula is: In the formula, is the corrected data; is the preset precision value; The data whose precision output value is less than or equal to the lower precision limit value; Output data with a value greater than or equal to the upper precision limit for the precision; is the lower limit precision value; is the upper limit precision value; is the calibration parameter.

[0077] Preferably, the data detection and storage module is specifically used to perform precision detection on the third data, and perform storage operation on the data according to the precision detection result; Wherein, when the data after the precision detection meets the preset precision range, the data storage operation is performed according to the data after the precision detection; When the data after the accuracy detection does not meet the preset accuracy range, a data correction operation is performed to obtain the corrected data, and the corrected data is stored, further comprising: The storing operation on the data value according to the accuracy detection result includes: When the accuracy output value is greater than the lower limit accuracy value and less than the upper limit accuracy value, the detected data is directly classified and stored; When the precision output value is less than or equal to the lower precision value, or greater than or equal to the upper precision value, the corrected data is subjected to a data encryption judgment operation; When the corrected data does not meet the data encryption judgment condition, the corrected data is directly classified and stored; When the corrected data meets the data encryption judgment condition, a data encryption operation is performed, and the encrypted data is classified and stored.

[0078] It should be noted that a data acquisition system based on a mobile terminal provided in an embodiment of the present invention is used to execute all process steps of a data acquisition method based on a mobile terminal in the above embodiment, and the working principles and beneficial effects of the two correspond one to one, so they will not be repeated.

[0079] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a data set detection program. When the processor executes the computer program, the steps in the above-mentioned data collection method embodiments based on a mobile terminal are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the data acquisition module.

[0080] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0081] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0082] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0083] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0084] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0085] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0086] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A data collection method based on a mobile terminal, characterized in that: include: Collecting initial data and classifying the initial data to obtain first data; Comparing the first data based on the data requirement condition input by the user, deleting the data that does not meet the data requirement condition, and obtaining the second data; performing error analysis on the second data to obtain third data; Performing precision detection on the third data, and performing a storage operation on the data according to the precision detection result; Wherein, when the data after the precision detection meets the preset precision range, the data storage operation is performed according to the data after the precision detection; When the data after the accuracy detection does not meet the preset accuracy range, a data correction operation is performed to obtain the corrected data, and a storage operation is performed on the corrected data.

2. A data collection method based on a mobile terminal according to claim 1, characterized in that: The classifying the initial data to obtain first data includes: The initial data is classified according to the data type, data stamp, valid data bit length or data flag bit to obtain the first data.

3. The data collection method based on a mobile terminal according to claim 1, characterized in that: The step of comparing the first data based on the data requirement condition input by the user, deleting the data that does not meet the data requirement condition, and obtaining the second data includes: Based on the data requirements entered by the user, standard data features are extracted; Extracting features from the first data respectively to obtain first data features; The first data feature is compared with the standard data feature. If the comparison is consistent, the first data corresponding to the first data feature is determined as the second data; if the comparison is inconsistent, the first data is deleted.

4. The data collection method based on a mobile terminal according to claim 1, characterized in that: The performing error analysis on the second data to obtain third data comprises: Performing error analysis on the second data to determine whether the second data is within a preset error range; When the error of the second data is less than a preset error range, determining the second data as third data; When the error of the second data exceeds a preset error range, the second data is deleted.

5. A data collection method based on a mobile terminal according to claim 4, characterized in that: The performing accuracy detection on the third data includes: The third data is input into a data precision detection function to obtain a precision output value, wherein the data precision detection function is: in, is the data accuracy detection function; is the third data; is the preset precision value; represents an absolute error between the third data and a preset accuracy value; When the precision output value is less than or equal to the lower precision value, a warning that the data setting value is too small is issued, and a data correction operation is performed based on the preset precision value; When the precision output value is greater than or equal to the upper precision value, a warning that the data setting value is too large is issued, and a data correction operation is performed based on the preset precision value; When the precision output value is greater than the lower precision limit value and less than the upper precision limit value, a data storage operation is performed on the detected data.

6. A data collection method based on a mobile terminal according to claim 5, characterized in that: The data correction operation includes: When the precision output value is less than or equal to the lower precision value, based on the preset precision value, the data whose precision output value is less than or equal to the lower precision value is input into the first correction formula to obtain the corrected data; Wherein, the first correction formula is: When the precision output value is greater than or equal to the upper limit precision value, based on the preset precision value, the data whose precision output value is greater than or equal to the upper limit precision value is input into the second correction formula to obtain the corrected data; Wherein, the second correction formula is: In the formula, is the corrected data; is the preset precision value; The data whose precision output value is less than or equal to the lower precision value; Output data with a value greater than or equal to the upper precision limit for the precision; is the lower limit precision value; is the upper limit precision value; is the calibration parameter.

7. The data collection method based on a mobile terminal according to claim 5, characterized in that: The storing operation on the data value according to the accuracy detection result includes: When the accuracy output value is greater than the lower limit accuracy value and less than the upper limit accuracy value, the detected data is directly classified and stored; When the precision output value is less than or equal to the lower precision value, or greater than or equal to the upper precision value, the corrected data is subjected to a data encryption judgment operation; When the corrected data does not meet the data encryption judgment condition, the corrected data is directly classified and stored; When the corrected data meets the data encryption judgment condition, a data encryption operation is performed, and the encrypted data is classified and stored.

8. A data acquisition system based on a mobile terminal, characterized in that: include: A data collection and classification module, used for collecting initial data and classifying the initial data to obtain first data; A data comparison module, used to compare the first data based on the data requirement condition input by the user, delete the data that does not meet the data requirement condition, and obtain the second data; an error analysis module, used for performing error analysis on the second data to obtain third data; A data detection and storage module, used for performing precision detection on the third data, and performing a storage operation on the data according to the precision detection result; Wherein, when the data after the precision detection meets the preset precision range, the data storage operation is performed according to the data after the precision detection; When the data after the accuracy detection does not meet the preset accuracy range, a data correction operation is performed to obtain the corrected data, and a storage operation is performed on the corrected data.

9. An electronic device, characterized in that: It comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements a data collection method based on a mobile terminal as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute a data collection method based on a mobile terminal as described in any one of claims 1 to 7.

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