A method and system for functional testing of smart wearable devices

Abstract

The present invention discloses a method and system for testing the functions of smart wearable devices, which relate to the field of device testing and include: obtaining the usage patterns of application functions; obtaining the upper limit of the total call time of application functions; using the application function with the least upper limit of the total call time as the characteristic application function; forming characteristic evaluation standards for the characteristic application functions and forming evaluation standards for the application functions; forming at least one parallel detection scheme for the application functions; judging whether the response time of the application function meets the evaluation standards; and detecting the stability of the current signal of the application function to judge whether the stability of the current signal of the application function meets the evaluation standards. By setting a time acquisition module, a standard formation module, a response detection module, and a stability detection module, the number of different test situations can be effectively controlled, thereby limiting the test time and ensuring that the performance stability of the smart wearable device that passes the test can be guaranteed within its service life.

Description

Technical Field

[0001] The present invention relates to the field of device testing, and in particular to a method and system for testing the functions of an intelligent wearable device. Background Art

[0002] Smart wearable devices are cutting-edge products that have rapidly developed in recent years with technological advancements. As an integral part of smart homes, they play a vital role in our daily lives. However, due to the complexity and innovative nature of these technologies, testing them has become a challenging task.

[0003] Existing tests for smart wearable devices are usually instant measurements, which cannot predict the usage of smart wearable devices during their service life, resulting in insufficient test reliability. Smart wearable devices that pass the test still have a high risk of failing during their service life. In addition, due to the large number of test functions and the large number of combinations, the test time will be greatly extended. Summary of the Invention

[0004] In order to solve the above technical problems, a method and system for functional testing of smart wearable devices are provided. This technical solution solves the problems raised in the above background technology.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0006] A method for testing the functionality of a smart wearable device, comprising:

[0007] Obtain at least one application function of the smart wearable device, obtain the memory used by the application function, and obtain the usage pattern of the application function;

[0008] Obtain the service life of the smart wearable device and, based on usage patterns and service life, obtain the upper limit of the total call time of the application function;

[0009] The application function with the shortest total call time limit is selected as the characteristic application function;

[0010] Forming feature evaluation criteria for feature application functions, and forming evaluation criteria for application functions based on the feature evaluation criteria and the upper limit of the total call time of the application functions;

[0011] Forming at least one parallel testing scheme for application functions;

[0012] Use a parallel testing solution to test the response time of application functions and determine whether the response time of application functions meets the evaluation criteria;

[0013] Use a parallel detection solution to detect the stability of the current signal of the application function and determine whether the stability of the current signal of the application function meets the evaluation criteria.

[0014] Preferably, obtaining the usage pattern of the application function includes the following steps:

[0015] Obtain at least one sample device of the smart wearable device, where the sample device is a qualified device and is used by different users within its service life;

[0016] During the service life of the sample device, obtain the function running time of each call of the application function of the sample device and the total number of calls of the application function of the sample device;

[0017] Aggregate all function running times of application functions of all sample devices into a running set;

[0018] Using the maximum and minimum values ​​of the function running time in the running set as endpoints to form a classification interval, and evenly dividing the classification interval into at least one local interval;

[0019] Update the data in the running set. When the function running time belongs to the local interval, the value of the function running time in the running set is updated to the value of the midpoint of the local interval;

[0020] In the running set after the data is updated, the proportion of repeated function running time is counted as the occurrence probability. The occurrence probability is paired with the function running time to form the usage pattern of the application function.

[0021] Preferably, obtaining the upper limit of the total calling time of the application function based on the usage pattern and service life includes the following steps:

[0022] The maximum value of the total number of calls to the application function of the sample device is taken as the characteristic value, and the characteristic value is recorded as n;

[0023] The running time of the n functions with the highest probability of occurrence corresponding to the application function is added together to obtain the upper limit of the total calling time of the application function.

[0024] Preferably, forming a feature evaluation criterion by applying a function to the features comprises the following steps:

[0025] During its service life, obtain the response time and current signal strength of the characteristic application function of the sample device each time it is used;

[0026] The response time is averaged to obtain the average response time, and the current signal strength is averaged to obtain the average current signal strength;

[0027] The current signal strength is subtracted from the average current signal strength and the absolute value is taken to obtain the current fluctuation value, and the maximum value of the current fluctuation value is taken as the current allowable error;

[0028] The average response time, average current signal strength and current tolerance are used as characteristic evaluation criteria.

[0029] Preferably, forming the evaluation criteria for the application function based on the feature evaluation criteria and the upper limit of the total call time of the application function comprises the following steps:

[0030] Compare the memory used by the application function with the memory used by the characteristic application function to obtain a first ratio;

[0031] Compare the total call time limit of the application function with the total call time limit of the characteristic application function to obtain a second ratio;

[0032] Multiplying the average response time by the first ratio to obtain a target response time, multiplying the average current signal strength by the first ratio to obtain a target current signal strength, and multiplying the current allowable error by the second ratio to obtain a target allowable error;

[0033] The target response time, target current signal strength and target allowable error are used as evaluation criteria for application functions.

[0034] Preferably, forming at least one parallel detection scheme for the application function comprises the following steps:

[0035] During a test of the sample device, obtaining at least one parallel calling situation in which more than one application function is called simultaneously;

[0036] Construct an estimation function, which is equal to xb / c, where x is the estimation variable, b is the total number of calls of the application function of the sample device within its service life, and c is its service life;

[0037] The maximum value of the estimated variable that satisfies the estimated function less than 1 is used as the preset value;

[0038] Counting the proportion of parallel calls during the test of the sample device, and treating the parallel calls with a proportion greater than a preset value as a pre-set call;

[0039] Calculate the total memory usage of the application functions in the prepared call situation to obtain a comprehensive memory value;

[0040] Sort the prepared calls from large to small according to the memory comprehensive value to obtain a feature sequence;

[0041] According to the order of the feature sequence, the preparatory call conditions are sequentially acquired as a parallel detection scheme. When the application functions in the parallel detection scheme include all application functions, the acquisition of the preparatory call conditions is stopped.

[0042] Preferably, the detecting of the response time of the application function and determining whether the response time of the application function meets the evaluation criteria comprises the following steps:

[0043] At the same time, the application function in the parallel detection solution is called to obtain the first actual response time of the application function;

[0044] After the application function runs for a preset time, obtaining a second actual response time of the application function, the preset time being based on a time limit set for the test;

[0045] The difference between the second actual response time and the first actual response time is divided by the preset time to obtain a change reference value;

[0046] The response delay value is obtained by multiplying the variation benchmark value by the upper limit of the total call time of the application function. The response delay value is added to the first actual response time to obtain the predicted response time.

[0047] Add the target response times of the application functions in the parallel detection scheme to obtain the total response time;

[0048] When the response prediction time is less than the total response time, the evaluation criteria are met; otherwise, the evaluation criteria are not met.

[0049] Preferably, the detecting of the stability of the current signal of the application function and determining whether the stability of the current signal of the application function meets the evaluation criteria comprises the following steps:

[0050] At the same time, an application function in the parallel detection scheme is called to obtain a first actual current intensity of the application function;

[0051] obtaining a second actual current intensity of the application function after the application function runs for a preset time, the preset time being set based on a time limit of the test;

[0052] The difference between the second actual current intensity and the first actual current intensity is divided by the preset time to obtain a current reference value;

[0053] The current reference value is multiplied by the upper limit of the total call time of the application function to obtain a current variation value, and the current variation value is superimposed on the first actual current intensity to obtain a current prediction value;

[0054] The target current signal intensities of the application functions in the parallel detection scheme are superimposed to obtain the total current intensity;

[0055] The target permissible errors of the application functions in the parallel detection scheme are superimposed to obtain the total permissible error;

[0056] When the difference between the current prediction value and the total current intensity is less than the total allowable error, the evaluation criteria are met; otherwise, the evaluation criteria are not met.

[0057] A smart wearable device function testing system, used to implement the above-mentioned smart wearable device function testing method, comprising:

[0058] a data acquisition module, the data acquisition module acquiring at least one application function of the smart wearable device, acquiring the memory used by the application function, and acquiring a usage pattern of the application function;

[0059] A time acquisition module, which acquires the service life of the smart wearable device and obtains the upper limit of the total call time of the application function based on the usage pattern and the service life;

[0060] A feature screening module, wherein the feature screening module selects the application function with the least total call time upper limit as the feature application function;

[0061] a standard forming module, the standard forming module forming a feature evaluation standard for the feature application function, and forming an evaluation standard for the application function based on the feature evaluation standard and the upper limit of the total call time of the application function;

[0062] A solution forming module, wherein the solution forming module forms at least one parallel detection solution for the application function;

[0063] A response detection module, which uses a parallel detection scheme to detect the response time of the application function and determine whether the response time of the application function meets the evaluation criteria;

[0064] The stability detection module uses a parallel detection scheme to detect the stability of the current signal of the application function and determines whether the stability of the current signal of the application function meets the evaluation standard.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] By setting up a time acquisition module, a standard formation module, a response detection module, and a stability detection module, the situations that may occur during the use of smart wearable devices are screened and corresponding parallel detection schemes are formed, thereby effectively controlling the number of different test situations and thus limiting the test time. At the same time, through detection, the response time and current signal stability of the application function within the service life are predicted, so that the qualification is judged based on the prediction results, ensuring that the smart wearable devices that pass the test can maintain the stability of their performance within the service life. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 Schematic diagram of the process of the intelligent wearable device function testing method of the present invention;

[0068] Figure 2 A schematic diagram of a process for obtaining usage rules of application functions according to the present invention;

[0069] Figure 3 This is a flow chart of obtaining the upper limit of the total calling time of an application function based on usage patterns and service life according to the present invention;

[0070] Figure 4 A schematic diagram of a process for forming a feature evaluation standard for a feature application function of the present invention;

[0071] Figure 5 A flow chart of forming an evaluation standard for an application function based on a feature evaluation standard and an upper limit on the total call time of the application function according to the present invention;

[0072] Figure 6 A schematic flow chart of forming at least one parallel detection scheme for application functions of the present invention;

[0073] Figure 7 This is a flow chart of detecting the response time of an application function and determining whether the response time of the application function meets the evaluation criteria according to the present invention;

[0074] Figure 8 This is a flow chart of detecting the stability of the current signal of an application function and determining whether the stability of the current signal of the application function meets the evaluation standard according to the present invention. DETAILED DESCRIPTION

[0075] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0076] Reference Figure 1 As shown, a function testing method for a smart wearable device includes:

[0077] Obtain at least one application function of the smart wearable device, obtain the memory used by the application function, and obtain the usage pattern of the application function;

[0078] Obtain the service life of the smart wearable device and, based on usage patterns and service life, obtain the upper limit of the total call time of the application function;

[0079] The application function with the shortest total call time limit is selected as the characteristic application function;

[0080] Forming feature evaluation criteria for feature application functions, and forming evaluation criteria for application functions based on the feature evaluation criteria and the upper limit of the total call time of the application functions;

[0081] Forming at least one parallel testing scheme for application functions;

[0082] Use a parallel testing solution to test the response time of application functions and determine whether the response time of application functions meets the evaluation criteria;

[0083] Use a parallel detection solution to detect the stability of the current signal of the application function and determine whether the stability of the current signal of the application function meets the evaluation criteria.

[0084] When a smart wearable device is in use, many applications may be running in parallel. Therefore, during testing, it is necessary to predict the limit state of this situation. Otherwise, the test results cannot meet the requirements. When making predictions, it is not possible to use the smart wearable device beyond its service life because this will take too much time. Therefore, in this solution, during testing, the test is completed within the preset time, and based on the test results, the limit state of the smart wearable device when it reaches its service life is predicted, so that its qualification is judged based on the prediction results of the limit state.

[0085] Reference Figure 2 As shown, obtaining the usage pattern of application functions includes the following steps:

[0086] Obtain at least one sample device of the smart wearable device, where the sample device is a qualified device and is used by different users within its service life;

[0087] During the service life of the sample device, obtain the function running time of each call of the application function of the sample device and the total number of calls of the application function of the sample device;

[0088] Aggregate all function running times of application functions of all sample devices into a running set;

[0089] Using the maximum and minimum values ​​of the function running time in the running set as endpoints to form a classification interval, and evenly dividing the classification interval into at least one local interval;

[0090] Update the data in the running set. When the function running time belongs to the local interval, the value of the function running time in the running set is updated to the value of the midpoint of the local interval;

[0091] In the running set after the data is updated, the proportion of repeated function running time is counted as the occurrence probability. The occurrence probability is paired with the function running time to form the usage pattern of the application function.

[0092] When obtaining the usage patterns of application functions, similar function running times are numerically replaced and updated, thereby reducing the number of different function running times, but it will not have much impact on the detection, so that the occurrence of function running times can be counted and the pattern of their occurrence can be formed.

[0093] Reference Figure 3 As shown, based on usage patterns and service life, obtaining the upper limit of the total call time of the application function includes the following steps:

[0094] The maximum value of the total number of calls to the application function of the sample device is taken as the characteristic value, and the characteristic value is recorded as n;

[0095] The running time of the n functions with the highest probability of occurrence corresponding to the application function is added together to obtain the upper limit of the total calling time of the application function.

[0096] Due to various possibilities in actual use, the average value cannot be used to obtain the upper limit of the total call time. The maximum value needs to be used to obtain the upper limit of the total call time. The upper limit of the total call time obtained in this way can exceed the actual call time of the application function within its service life. Therefore, subsequent operations based on the upper limit of the total call time are more reasonable. In order to calculate the upper limit of the total call time, first, the upper limit of the number of calls, that is, the characteristic value, needs to be determined. Secondly, the upper limit of the call time of these call times needs to be estimated. Here, the running times of the n functions with the largest probability of occurrence corresponding to the application function are used for estimation, because they are the running times of the functions with the largest possibility of occurrence. Therefore, the actual call time is almost certainly less than their sum.

[0097] Reference Figure 4 As shown, applying functions to features to form feature evaluation criteria includes the following steps:

[0098] During its service life, obtain the response time and current signal strength of the characteristic application function of the sample device each time it is used;

[0099] The response time is averaged to obtain the average response time, and the current signal strength is averaged to obtain the average current signal strength;

[0100] The current signal strength is subtracted from the average current signal strength and the absolute value is taken to obtain the current fluctuation value, and the maximum value of the current fluctuation value is taken as the current allowable error;

[0101] The average response time, average current signal strength and current tolerance are used as characteristic evaluation criteria.

[0102] The formation of characteristic evaluation standards is to simplify the time-consuming process of standard formation, because an evaluation standard is required for each different application function;

[0103] Since there is a certain proportional relationship between application functions and characteristic application functions, their response time is proportional to the memory required, and the current required is also proportional to the memory required. In addition, the allowable error needs to change accordingly with the change of usage time. Because the current fluctuation increases with the increase of usage time, different error settings need to be made according to the different upper limits of the total call time of the application function.

[0104] Therefore, once the feature evaluation criteria are formed, the evaluation criteria for all application functions can be determined based on the proportional relationship, thereby saving time.

[0105] Reference Figure 5 As shown, based on the feature evaluation criteria and the upper limit of the total call time of the application function, forming the evaluation criteria of the application function includes the following steps:

[0106] Compare the memory used by the application function with the memory used by the characteristic application function to obtain a first ratio;

[0107] Compare the total call time limit of the application function with the total call time limit of the characteristic application function to obtain a second ratio;

[0108] Multiplying the average response time by the first ratio to obtain a target response time, multiplying the average current signal strength by the first ratio to obtain a target current signal strength, and multiplying the current allowable error by the second ratio to obtain a target allowable error;

[0109] The target response time, target current signal strength and target allowable error are used as evaluation criteria for application functions.

[0110] Reference Figure 6 As shown, forming at least one parallel detection scheme for the application function includes the following steps:

[0111] During a test of the sample device, obtaining at least one parallel calling situation in which more than one application function is called simultaneously;

[0112] Construct an estimation function, which is equal to xb / c, where x is the estimation variable, b is the total number of calls of the application function of the sample device within its service life, and c is its service life;

[0113] The maximum value of the estimated variable that satisfies the estimated function less than 1 is used as the preset value;

[0114] Counting the proportion of parallel calls during the test of the sample device, and treating the parallel calls with a proportion greater than a preset value as a pre-set call;

[0115] Calculate the total memory usage of the application functions in the prepared call situation to obtain a comprehensive memory value;

[0116] Sort the prepared calls from large to small according to the memory comprehensive value to obtain a feature sequence;

[0117] According to the order of the feature sequence, the preparatory call conditions are sequentially acquired as a parallel detection scheme. When the application functions in the parallel detection scheme include all application functions, the acquisition of the preparatory call conditions is stopped.

[0118] Here, the unit of service life is day, and the prediction function calculates the number of occurrences per day. If the number of occurrences per day is less than 1, it can be approximately considered impossible to occur. Therefore, the maximum value of the prediction variable that satisfies the prediction function less than 1 is used as the preset value.

[0119] The parallel detection scheme is set up to simplify various parallel situations in actual use, because there may be an infinite number of situations in reality, but it is not necessary to detect all situations. Only the necessary situations need to be detected. It should be noted that the parallel detection scheme must detect all application functions. Therefore, according to the order of the feature sequence, the preparatory call situation is obtained in sequence as the parallel detection scheme. When the application functions in the parallel detection scheme include all application functions, the acquisition of the preparatory call situation is stopped.

[0120] During the test, the preparation call case with greater memory consumption is selected, because when the case with greater memory consumption is qualified, the case with less memory consumption is also qualified.

[0121] Reference Figure 7 As shown, testing the response time of the application function and determining whether the response time of the application function meets the evaluation criteria includes the following steps:

[0122] At the same time, the application function in the parallel detection solution is called to obtain the first actual response time of the application function;

[0123] After the application function runs for a preset time, obtaining a second actual response time of the application function, the preset time being based on a time limit set for the test;

[0124] The difference between the second actual response time and the first actual response time is divided by the preset time to obtain a change reference value;

[0125] The response delay value is obtained by multiplying the variation benchmark value by the upper limit of the total call time of the application function. The response delay value is added to the first actual response time to obtain the predicted response time.

[0126] Add the target response times of the application functions in the parallel detection scheme to obtain the total response time;

[0127] When the response prediction time is less than the total response time, the evaluation criteria are met; otherwise, the evaluation criteria are not met.

[0128] Here, the predicted response time of the application function under extreme conditions within its service life is predicted, and then the qualification or failure can be judged based on this. However, since this is a parallel test, its standards need to be superimposed, and the subsequent current test is similar.

[0129] Reference Figure 8 As shown, testing the stability of the current signal of the application function and determining whether the stability of the current signal of the application function meets the evaluation criteria includes the following steps:

[0130] At the same time, an application function in the parallel detection scheme is called to obtain a first actual current intensity of the application function;

[0131] obtaining a second actual current intensity of the application function after the application function runs for a preset time, the preset time being set based on a time limit of the test;

[0132] The difference between the second actual current intensity and the first actual current intensity is divided by the preset time to obtain a current reference value;

[0133] The current reference value is multiplied by the upper limit of the total call time of the application function to obtain a current variation value, and the current variation value is superimposed on the first actual current intensity to obtain a current prediction value;

[0134] The target current signal intensities of the application functions in the parallel detection scheme are superimposed to obtain the total current intensity;

[0135] The target permissible errors of the application functions in the parallel detection scheme are superimposed to obtain the total permissible error;

[0136] When the difference between the current prediction value and the total current intensity is less than the total allowable error, the evaluation criteria are met; otherwise, the evaluation criteria are not met.

[0137] A smart wearable device function testing system, used to implement the above-mentioned smart wearable device function testing method, comprising:

[0138] a data acquisition module, the data acquisition module acquiring at least one application function of the smart wearable device, acquiring the memory used by the application function, and acquiring a usage pattern of the application function;

[0139] A time acquisition module, which acquires the service life of the smart wearable device and obtains the upper limit of the total call time of the application function based on the usage pattern and the service life;

[0140] A feature screening module, wherein the feature screening module selects the application function with the least total call time upper limit as the feature application function;

[0141] a standard forming module, the standard forming module forming a feature evaluation standard for the feature application function, and forming an evaluation standard for the application function based on the feature evaluation standard and the upper limit of the total call time of the application function;

[0142] A solution forming module, wherein the solution forming module forms at least one parallel detection solution for the application function;

[0143] A response detection module, which uses a parallel detection scheme to detect the response time of the application function and determine whether the response time of the application function meets the evaluation criteria;

[0144] The stability detection module uses a parallel detection scheme to detect the stability of the current signal of the application function and determines whether the stability of the current signal of the application function meets the evaluation standard.

[0145] Furthermore, the present solution also proposes a storage medium on which a computer-readable program is stored. When the computer-readable program is called, the above-mentioned smart wearable device function testing method is executed.

[0146] It is understandable that the storage medium may be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid state disk (SSD).

[0147] To sum up, the advantages of the present invention are: by setting a time acquisition module, a standard formation module, a response detection module and a stability detection module, the situations that may occur during the use of the smart wearable device are screened, and corresponding parallel detection schemes are formed, so as to effectively control the number of different test situations, thereby limiting the test time. At the same time, through detection, the response time of the application function and the stability of the current signal within the service life are predicted, so that the qualification is judged according to the prediction results, ensuring that the smart wearable device that passes the test can ensure the stability of its performance within the service life.

[0148] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for testing the function of a smart wearable device, characterized in that: include: Obtain at least one application function of the smart wearable device, obtain the memory used by the application function, and obtain the usage pattern of the application function; Obtain the service life of the smart wearable device and, based on usage patterns and service life, obtain the upper limit of the total call time of the application function; The application function with the shortest total call time limit is selected as the characteristic application function; Forming feature evaluation criteria for feature application functions, and forming evaluation criteria for application functions based on the feature evaluation criteria and the upper limit of the total call time of the application functions; Forming at least one parallel testing scheme for application functions; Use a parallel testing solution to test the response time of application functions and determine whether the response time of application functions meets the evaluation criteria; Use a parallel detection solution to test the current signal stability of the application function to determine whether the current signal stability of the application function meets the evaluation criteria; The forming of at least one parallel detection scheme for the application function comprises the following steps: During a test of the sample device, obtaining at least one parallel calling situation in which more than one application function is called simultaneously; Construct an estimation function, which is equal to xb / c, where x is the estimation variable, b is the total number of calls of the application function of the sample device within its service life, and c is its service life; The maximum value of the estimated variable that satisfies the estimated function less than 1 is used as the preset value; Counting the proportion of parallel calls during the test of the sample device, and treating the parallel calls with a proportion greater than a preset value as a pre-set call; Calculate the total memory usage of the application functions in the prepared call situation to obtain a comprehensive memory value; Sort the prepared calls from large to small according to the memory comprehensive value to obtain a feature sequence; According to the order of the feature sequence, the preparatory call conditions are sequentially obtained as a parallel detection plan. When the application functions in the parallel detection plan include all application functions, the acquisition of the preparatory call conditions is stopped. The detection of the response time of the application function and the determination of whether the response time of the application function meets the evaluation criteria include the following steps: At the same time, the application function in the parallel detection solution is called to obtain the first actual response time of the application function; After the application function runs for a preset time, obtaining a second actual response time of the application function, the preset time being based on a time limit set for the test; The difference between the second actual response time and the first actual response time is divided by the preset time to obtain a change reference value; The response delay value is obtained by multiplying the variation benchmark value by the upper limit of the total call time of the application function. The response delay value is added to the first actual response time to obtain the predicted response time. Add the target response times of the application functions in the parallel detection scheme to obtain the total response time; When the response prediction time is less than the total response time, it meets the evaluation criteria; otherwise, it does not meet the evaluation criteria; The detection of the current signal stability of the application function and the determination of whether the current signal stability of the application function meets the evaluation criteria include the following steps: At the same time, calling the application function in the parallel detection solution to obtain the first actual current intensity of the application function; obtaining a second actual current intensity of the application function after the application function runs for a preset time, the preset time being set based on a time limit of the test; The difference between the second actual current intensity and the first actual current intensity is divided by the preset time to obtain a current reference value; The current reference value is multiplied by the upper limit of the total call time of the application function to obtain a current variation value, and the current variation value is superimposed on the first actual current intensity to obtain a current prediction value; The target current signal intensities of the application functions in the parallel detection scheme are superimposed to obtain the total current intensity; The target permissible errors of the application functions in the parallel detection scheme are superimposed to obtain the total permissible error; When the difference between the current prediction value and the total current intensity is less than the total allowable error, the evaluation criteria are met; otherwise, the evaluation criteria are not met.

2. A method for testing the function of a smart wearable device according to claim 1, characterized in that: The method of obtaining the usage pattern of the application function includes the following steps: Obtain at least one sample device of the smart wearable device, where the sample device is a qualified device and is used by different users within its service life; During the service life of the sample device, obtain the function running time of each call of the application function of the sample device and the total number of calls of the application function of the sample device; Aggregate all function running times of application functions of all sample devices into a running set; Using the maximum and minimum values ​​of the function running time in the running set as endpoints to form a classification interval, and evenly dividing the classification interval into at least one local interval; Update the data in the running set. When the function running time belongs to the local interval, the value of the function running time in the running set is updated to the value of the midpoint of the local interval; In the running set after the data is updated, the proportion of repeated function running time is counted as the occurrence probability. The occurrence probability is paired with the function running time to form the usage pattern of the application function.

3. A method for testing the function of a smart wearable device according to claim 2, characterized in that: Obtaining the upper limit of the total call time of the application function based on the usage pattern and service life includes the following steps: The maximum value of the total number of calls to the application function of the sample device is taken as the characteristic value, and the characteristic value is recorded as n; The running time of the n functions with the highest probability of occurrence corresponding to the application function is added together to obtain the upper limit of the total calling time of the application function.

4. A method for testing the function of a smart wearable device according to claim 3, characterized in that: The forming of feature evaluation criteria by applying a function to the features comprises the following steps: During its service life, obtain the response time and current signal strength of the characteristic application function of the sample device each time it is used; The response time is averaged to obtain the average response time, and the current signal strength is averaged to obtain the average current signal strength; The current signal strength is subtracted from the average current signal strength and the absolute value is taken to obtain the current fluctuation value, and the maximum value of the current fluctuation value is taken as the current allowable error; The average response time, average current signal strength and current tolerance are used as characteristic evaluation criteria.

5. A method for testing the function of a smart wearable device according to claim 4, characterized in that: The step of forming the evaluation criteria for the application function based on the feature evaluation criteria and the upper limit of the total call time of the application function includes the following steps: Compare the memory used by the application function with the memory used by the characteristic application function to obtain a first ratio; Compare the total call time limit of the application function with the total call time limit of the characteristic application function to obtain a second ratio; Multiplying the average response time by the first ratio to obtain a target response time, multiplying the average current signal strength by the first ratio to obtain a target current signal strength, and multiplying the current allowable error by the second ratio to obtain a target allowable error; The target response time, target current signal strength and target allowable error are used as evaluation criteria for application functions.

6. A smart wearable device function testing system, used to implement the smart wearable device function testing method according to any one of claims 1 to 5, characterized in that: include: a data acquisition module, the data acquisition module acquiring at least one application function of the smart wearable device, acquiring the memory used by the application function, and acquiring a usage pattern of the application function; A time acquisition module, which acquires the service life of the smart wearable device and obtains the upper limit of the total call time of the application function based on the usage pattern and the service life; A feature screening module, wherein the feature screening module selects the application function with the least total call time upper limit as the feature application function; a standard forming module, the standard forming module forming a feature evaluation standard for the feature application function, and forming an evaluation standard for the application function based on the feature evaluation standard and the upper limit of the total call time of the application function; A solution forming module, wherein the solution forming module forms at least one parallel detection solution for the application function; A response detection module, which uses a parallel detection scheme to detect the response time of the application function and determine whether the response time of the application function meets the evaluation criteria; The stability detection module uses a parallel detection scheme to detect the stability of the current signal of the application function and determines whether the stability of the current signal of the application function meets the evaluation standard.

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