CN117666958A - Method and device for prolonging storage life of identification module, electronic equipment and storage medium - Google Patents
Method and device for prolonging storage life of identification module, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention provides a method for prolonging the storage life of an identification module, which comprises the following steps: receiving a feature matching instruction from the identification module, and obtaining a self-learning judgment result according to the feature matching instruction; according to the self-learning judging result, indicating the existence of a self-learning process, storing the self-learned characteristic data into a storage module of the identification module, and simultaneously recording the storage state of the storage module; and adjusting the self-learning strategy of the identification module according to the storage state of the storage module. And dynamically selecting a proper storage area according to the self-learning result and the abrasion condition of the current Flash storage module, and updating the self-learning strategy at the same time so as to achieve the effect of balancing abrasion and greatly prolong the storage life of the identification module.
Description
Technical Field
The invention relates to the field of data storage, in particular to a method and a device for prolonging the storage life of an identification module.
Background
With the rapid development of deep learning, deep learning technology can process large-scale complex data sets and extract high-level abstract features from the complex data sets by constructing a deep neural network model. The technology breaks through to make machine learning obtain breakthrough results in the fields of image processing, natural language processing and the like, for example, in the application of the deep learning technology in the field of computer vision, objects and scenes in pictures can be identified, and the functions of intelligent image searching, image labeling and the like are realized.
Taking the face recognition field as an example, a general face recognition module lacks a self-learning mechanism, namely the face characteristics after registration are unchanged all the time, so that the face registration only records the current face characteristic state and posture information, and then the face can not be automatically adapted to daily changes of the face, and the risk of being unable to unlock after a period of time exists.
The face recognition module comprising the self-learning mechanism can update and record daily changes of the face and gradually complement information of other uncovered gestures, directions and the like during registration. However, the face recognition module usually uses a simple operating system without file system management, and the face feature storage position and the Flash area adopt a simple one-to-one mapping relationship, and the search of free available Flash algorithm is also a sequential search from beginning to end, which leads to inconsistent Flash abrasion degree of each block: the common and frequently updated face features have more Flash erasing times, and the Flash blocks at the head also have more Flash erasing times due to more frequent addition and deletion. This also results in faster life of these blocks.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides the method and the device for prolonging the storage life of the identification module, which can dynamically select a proper storage area according to a self-learning result and the abrasion condition of the current Flash storage module and update the self-learning strategy at the same time so as to achieve the effect of balancing abrasion and greatly prolong the storage life of the identification module.
The technical scheme adopted for solving the technical problems is as follows: in a method of extending the storage life of an identification module, the improvement comprising: receiving a feature matching instruction from the identification module, and obtaining a self-learning judgment result according to the feature matching instruction; according to the self-learning judging result, indicating the existence of a self-learning process, storing the self-learned characteristic data into a storage module of the identification module, and simultaneously recording the storage state of the storage module; and adjusting the self-learning strategy of the identification module according to the storage state of the storage module.
According to the technical scheme, the method for receiving the feature matching instruction from the identification module, obtaining the self-learning judgment result according to the feature matching instruction comprises the following steps: when the identification module receives an ID registration instruction, saving the current characteristic data carried by the ID to a storage module; if the current characteristic data carried by the ID is different from the characteristic data stored in the storage module, carrying out data fusion on the current characteristic data of the ID and the stored characteristic data; based on the characteristic data after the data fusion, the self-learning judgment result indicates that a self-learning process exists in the identification module.
According to the technical scheme, the method for storing the self-learned characteristic data into the storage module of the identification module and recording the storage state of the storage module simultaneously comprises the following steps: traversing a plurality of storage blocks in the storage module, and searching the empty storage blocks in the storage module if the erasing times of the current storage blocks reach the multiple of the set erasing times; and according to the search result, indicating that an idle storage block with fewer erasing times exists in the storage module, storing the self-learned characteristic data stored in the current storage block into the idle storage block, and recording the erasing times of the storage blocks and the use frequency of each ID.
After the recording of the erasing times of the plurality of storage blocks and the use frequency of each ID in the above technical solution, the method further includes: acquiring the use frequency of each ID, and arranging the IDs from high to low according to the use frequency; judging whether a series of IDs which reach a set difference value with the current ID use frequency exist in the IDs which are lower than the current ID use frequency or not; if so, exchanging the ID with the minimum erasing frequency in the series of IDs with the current ID storage block, adding one to the erasing frequency of the exchanged storage block, adding one to the current ID using frequency, and modifying the corresponding information value.
According to the above technical scheme, adjusting the self-learning strategy of the identification module according to the storage state of the storage module includes: if the number of times of erasing of the whole storage module is smaller than a first threshold value, configuring the self-learning Xi Ce of the identification module to be a first mode; if the number of times of erasing of the whole storage module reaches a second threshold range, configuring the self-learning Xi Ce of the identification module to be a second mode slightly; if the number of times of erasing of the whole storage module reaches a third threshold range, configuring the self-learning Xi Ce of the identification module to be a third mode slightly; if the erasing times of the whole storage module reach more than a fourth threshold value, the characteristic data are not self-learned any more, and the characteristic matching instruction is returned to the upper layer so as to inform the upper layer of making early warning or other processing strategies.
The first mode of the self-learning strategy in the above technical scheme is normal learning; the second mode of the self-learning strategy is: for newly registered IDs, the first 300 maintains the behavior that must learn new features; after 300 times, learning is performed if the difference between the current characteristic data and the stored characteristic data reaches 10%; the third mode of the self-learning strategy is: for newly registered IDs, the first 30 maintains the behavior that must learn new features; after 30 times, learning is performed if the difference between the current characteristic data and the stored characteristic data reaches 15%.
The technical scheme adopted for solving the technical problems is as follows: an apparatus for extending the storage life of an identification module, the apparatus comprising: the self-learning judging module is used for receiving the characteristic matching instruction from the identification module and obtaining a self-learning judging result according to the characteristic matching instruction; the data storage module is used for indicating the existence of a self-learning process according to the self-learning judging result, storing the self-learned characteristic data into the storage module of the identification module, and simultaneously recording the storage state of the storage module; and the strategy updating module is used for adjusting the self-learning strategy of the identification module according to the storage state of the storage module.
The technical scheme adopted for solving the technical problems is as follows: an electronic device, comprising: at least one processor, at least one memory, wherein the memory has program instructions or code stored thereon; the program instructions or code are loaded and executed by the processor to cause the electronic device to implement the method of extending the storage life of the identification module.
The technical scheme adopted for solving the technical problems is as follows: a storage medium having stored thereon program instructions or code that are loaded and executed by a processor to implement the method of extending the storage life of an identification module.
The beneficial effects of the invention are as follows: and dynamically selecting a proper storage area according to the self-learning result and the abrasion condition of the current Flash storage module and updating the self-learning strategy at the same time so as to achieve the effect of balancing abrasion, greatly prolong the storage life of the identification module and avoid resource waste.
Drawings
FIG. 1 is a flow chart of a method for prolonging the storage life of an identification module according to the present invention;
FIG. 2 is a flow chart of a method of step 10 shown in FIG. 1 in an exemplary embodiment;
FIG. 3 is a flow chart of a method of step 20 shown in FIG. 1 in an exemplary embodiment;
FIG. 4 is a schematic diagram illustrating data transfer between memory blocks according to the present invention;
FIG. 5 is a flow chart of a method of step 20 shown in FIG. 1 in another exemplary embodiment;
FIG. 6 is a schematic diagram illustrating a data exchange between memory blocks according to the present invention;
FIG. 7 is a flow chart of a method of step 30 shown in FIG. 1 in an exemplary embodiment;
FIG. 8 is a schematic illustration of an apparatus for extending the storage life of an identification module according to the present invention;
fig. 9 is a block diagram of an electronic device according to the present invention.
Detailed Description
The invention will be further described with reference to the drawings and examples.
The conception, specific structure, and technical effects produced by the present invention will be clearly and completely described below with reference to the embodiments and the drawings to fully understand the objects, features, and effects of the present invention. It is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments, and that other embodiments obtained by those skilled in the art without inventive effort are within the scope of the present invention based on the embodiments of the present invention. In addition, all the coupling/connection relationships referred to in the patent are not direct connection of the single-finger members, but rather, it means that a better coupling structure can be formed by adding or subtracting coupling aids depending on the specific implementation. The technical features in the invention can be interactively combined on the premise of no contradiction and conflict.
As shown in fig. 1, the present invention provides a method for prolonging the storage life of an identification module, which includes the following steps:
and step 10, receiving a feature matching instruction from the identification module, and obtaining a self-learning judgment result according to the feature matching instruction.
Taking a face recognition module including a self-learning mechanism as an example, when the face recognition module performs feature entry for the same ID, with daily changes of the face, the changed face features need to be matched with the stored face features, so as to determine whether a self-learning process exists in the face recognition module.
Specifically, in one possible implementation, as shown in fig. 2, the step 10 may include the following steps:
and 110, when the identification module receives an ID registration instruction, saving the current characteristic data carried by the ID to a storage module.
When an ID registration action aiming at the identification module exists, the identification module receives an ID registration instruction generated by the ID registration action, and the current feature data carried by the ID is stored under the control of the ID registration instruction.
And 120, if the current characteristic data carried by the ID is different from the characteristic data stored in the storage module, performing data fusion on the current characteristic data of the ID and the stored characteristic data.
Specifically, the storage module of the recognition module can preferentially take the feature data stored for the first time as the face feature, when the face changes, the storage module can match the current feature data with the stored feature data aiming at the feature data of the same identity ID, and when the matching result indicates that the difference between the feature data of the same identity ID exceeds a set threshold value, the registered face feature changes, and the stored data is required to be covered and supplemented according to the polarity of the changed feature data so as to obtain the self-learned feature data.
And 130, based on the feature data after the data fusion, the self-learning judgment result indicates that a self-learning process exists in the identification module.
Through the steps, whether the self-learning process exists in the identification module can be determined, and the self-learning strategy of the identification module is further performed after the self-learning process is determined to exist.
And step 20, according to the self-learning judging result, indicating the existence of a self-learning process, storing the self-learned characteristic data into a storage module of the identification module, and simultaneously recording the storage state of the storage module.
It should be noted that the feature data after self-learning may be stored in the inactive area in the memory module of the identification module, or may be stored in the active area in the memory module of the identification module, which is not limited herein. The inactive area refers to a flash block with less erasing times or less ID use times in the memory module; the active area refers to a flash block with a large number of erasures or ID uses in the memory module.
Specifically, in one possible implementation, as shown in fig. 3, the step 20 may include the following steps:
step 210, traversing a plurality of memory blocks in the memory module, and searching the empty memory blocks in the memory module if the erasing times of the current memory block reach the multiple of the set erasing times.
Normally, the erasing times of the flash block is 10 ten thousand times, the set erasing times of the storage block are set by itself, if the set value is 1% of the maximum erasing times of the storage block, when the erasing times of the current storage block reach 1000 times, the idle storage block in the storage module needs to be searched, so that the data in the current storage block is transferred to the idle storage block, and the waste of resources is avoided. Wherein the free memory block refers to the inactive area.
Step 220, according to the search result, indicating that there is a free memory block with fewer erasing times in the memory module, storing the self-learned feature data stored in the current memory block into the free memory block, and recording the erasing times of the memory blocks and the use frequency of each ID.
Specifically, as shown in fig. 4, the block a is the current memory block of the feature data, and after calculation, the number of times of erasing reaches a multiple of the set number of times of erasing, the self-learned feature data in the block is newly built into temporary data X, and after the memory module retrieves the free memory block B, the temporary data X is written into the free memory block B.
It should be noted that when the storage module simultaneously retrieves the plurality of idle storage blocks B1, B2, B3, etc., the temporary data X may be sorted according to the number of times of erasing or the number of times of ID use in the plurality of idle storage blocks, and written from large to small according to the number of times of erasing or the number of times of ID use, so as to ensure that each idle storage block can be used, and meanwhile, balance the wear degree of each storage block, and prolong the service life.
In an exemplary embodiment, as shown in fig. 5, after step 220, the method may further include the steps of:
in step 230, the frequency of use of each ID is obtained and arranged from high to low according to the frequency of use.
Step 240, it is determined whether a series of IDs having a frequency up to a set difference from the current ID use frequency exist in the IDs having a frequency lower than the current ID use frequency.
Specifically, the ID (denoted as X) with the least number of erasing times in the ID using frequency in step 230 is obtained, whether the difference between the current ID erasing times and the X erasing times reaches a set value is determined, and if the difference reaches the set value, the two ID storage blocks are interchanged, so as to achieve the effect of balancing the abrasion degree of the storage blocks, and prolong the service life of the module.
Step 250, if so, exchanging the memory block with the minimum erasing frequency in the series of IDs with the memory block with the current ID, and adding one to the erasing frequency of the exchanged memory block, adding one to the current ID using frequency, and modifying the corresponding information value.
Specifically, as shown in fig. 6, the block a is a current ID storage block, and the block B is a block with the minimum erasing frequency in the series of IDs. Then the characteristic data contained in the current ID of the block A is newly built into temporary data Y, meanwhile, the characteristic data contained in the block B is newly built into temporary data X, then the temporary data Y originally stored in the block A is written into the block B, meanwhile, the temporary data X originally stored in the block B is written into the block A, meanwhile, the erasing frequency of the exchanged storage block is increased by one, the current ID using frequency is increased by one, and the corresponding storage information is modified.
Through the process, data exchange between two blocks can be realized, so that the effect of balancing the abrasion degree of the storage blocks is achieved, and the service life of the module is prolonged.
And step 30, adjusting the self-learning strategy of the identification module according to the storage state of the storage module.
As described above, the application can dynamically select a proper storage area through the self-learning result and the abrasion condition of the current Flash storage module so as to achieve the effect of balancing abrasion.
In an exemplary embodiment, as shown in fig. 7, the above step 30 may include the steps of:
in step 310, if the number of erasing times of the whole memory module is smaller than a first threshold, the self-learning Xi Ce of the identification module is configured to be a first mode.
Optionally, taking the case that the first threshold is configured as that the erasing frequency of the whole memory module is less than 50% of the total life, at this time, the self-learning Xi Ce of the recognition module is slightly normal learning, that is, when the difference between the feature data aiming at the same identity ID is detected to exceed the set threshold, the polarity of the stored data is covered and supplemented, so that the feature data after self-learning is obtained.
In step 320, if the number of erasing times of the whole memory module reaches the second threshold range, the self-learning Xi Ce of the identification module is configured to be a second mode.
Optionally, taking the second threshold value as an example that the erasing frequency of the whole storage module reaches 50% -80% of the total service life, the self-learning Xi Ce of the identification module is slightly more rigorous learning, namely, for the newly registered ID, the first 300 times keep the behavior of having to learn the new feature; after 300 times, learning is performed if the difference between the current characteristic data and the stored characteristic data reaches 10%.
In step 330, if the number of erasing times of the whole memory module reaches the third threshold range, the self-learning Xi Ce of the identification module is configured to be a third mode.
Optionally, taking the case that the third threshold is configured to be that the erasing frequency of the whole storage module reaches 80% -98% of the total service life, the self-learning Xi Ce of the identification module is slightly the most conservative learning, namely, for the newly registered ID, the first 30 times keep the behavior of having to learn the new feature; after 30 times, learning is performed if the difference between the current characteristic data and the stored characteristic data reaches 15%.
And 340, if the number of times of erasing of the whole storage module reaches more than a fourth threshold value, no self-learning is performed on the feature data, and a feature matching instruction is returned to the upper layer so as to inform the upper layer of making early warning or other processing strategies.
When the erasing times of the whole storage module reaches more than 98% of the total service life, the fourth threshold is configured, self-learning is not performed on the feature data, and a feature matching instruction is returned to the upper layer so as to inform the upper layer of making early warning or other processing strategies.
Through the process, the self-learning strategy can be updated according to the abrasion condition of the current storage module, so that the existing more used and better used characteristics of the identification module are ensured, the operation of the storage module is enabled to achieve balanced abrasion, and the service life of a product is prolonged.
It should be understood that, although the steps in the flowcharts described above are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described above may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, and the order of execution of the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternately with at least a part of the sub-steps or stages of other steps or other steps.
The following is an embodiment of the apparatus of the present application, which may be used to execute the method for extending the storage life of the identification module according to the present application. For details not disclosed in the device embodiments of the present application, please refer to an embodiment of a method for prolonging the storage life of an identification module related to the present application.
Referring to fig. 8, in an embodiment of the present application, there is provided an apparatus 40 for prolonging a storage life of an identification module, where the apparatus 40 for prolonging a storage life of an identification module includes, but is not limited to: a self-learning judging module 410, a data storage module 420 and a strategy updating module 430.
The self-learning judging module 410 is configured to receive a feature matching instruction from the identifying module, and obtain a self-learning judging result according to the feature matching instruction.
The data storage module 420 is configured to store the self-learned feature data into the storage module of the identification module according to the self-learning determination result indicating that the self-learning process exists, and record the storage state of the storage module.
And the policy updating module 430 is configured to adjust a self-learning policy of the identification module according to the storage state of the storage module.
It should be noted that, when the device for prolonging the storage life of the identification module provided in the above embodiment is used for prolonging the storage life of the identification module, only the division of the functional modules is used for illustrating, in practical application, the function allocation may be completed by different functional modules according to needs, that is, the internal structure of the device for prolonging the storage life of the identification module is divided into different functional modules so as to complete all or part of the functions described above. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In addition, the apparatus for prolonging the storage life of the identification module and the method for prolonging the storage life of the identification module provided in the above embodiments belong to the same concept, and the specific manner in which each module performs the operation has been described in detail in the method embodiment, which is not repeated here.
Referring to fig. 9, an electronic device 4000 is provided in an embodiment of the present application. Among other things, data interaction between the processor 4001 and the memory 4003 may be achieved by at least one communication bus 4002. The communication bus 4002 may include a path for transferring data between the processor 4001 and the memory 4003. The communication bus 4002 may be a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus or an EISA (Extended Industry Standard Architecture ) bus, or the like. The communication bus 4002 can be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in fig. 9, but not only one bus or one type of bus.
Optionally, the electronic device 4000 may further comprise a transceiver 4004, the transceiver 4004 may be used for data interaction between the electronic device and other electronic devices, such as transmission of data and/or reception of data, etc. It should be noted that, in practical applications, the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 is not limited to the embodiment of the present application.
The processor 4001 may be a CPU (Central Processing Unit ), general purpose processor, DSP (Digital Signal Processor, data signal processor), ASIC (Application Specific Integrated Circuit ), FPGA (Field Programmable Gate Array, field programmable gate array) or other programmable logic device, transistor logic device, hardware components, or any combination thereof. Which may implement or perform the various exemplary logic blocks, modules, and circuits described in connection with this disclosure. The processor 4001 may also be a combination that implements computing functionality, e.g., comprising one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.
Memory 4003 may be, but is not limited to, ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, RAM (Random Access Memory ) or other type of dynamic storage device that can store information and instructions, EEPROM (Electrically Erasable Programmable Read Only Memory ), CD-ROM (Compact Disc Read Only Memory, compact disc Read Only Memory) or other optical disk storage, optical disk storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program instructions or code in the form of instructions or data structures and that can be accessed by electronic device 400.
The memory 4003 has stored thereon program instructions or codes, and the processor 4001 can read the program instructions or codes stored in the memory 4003 through the communication bus 4002.
The program instructions or code, when executed by the processor 4001, implement the method of extending the memory life of the identification module in each of the embodiments described above.
Further, in an embodiment of the present application, there is provided a storage medium having stored thereon program instructions or codes that are loaded and executed by a processor to implement a method of extending the storage life of an identification module as described above.
In an embodiment of the present application, a computer program product is provided, where the computer program product includes program instructions or codes, where the program instructions or codes are stored in a storage medium, and a processor of an electronic device reads the program instructions or codes from the storage medium, loads and executes the program instructions or codes, so that the electronic device implements a method for prolonging a storage life of an identification module as described above.
While the preferred embodiment of the present invention has been described in detail, the present invention is not limited to the embodiments, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and these equivalent modifications or substitutions are included in the scope of the present invention as defined in the appended claims.
Claims (9)
1. A method of extending the storage life of an identification module, the method comprising:
receiving a feature matching instruction from the identification module, and obtaining a self-learning judgment result according to the feature matching instruction;
according to the self-learning judging result, indicating the existence of a self-learning process, storing the self-learned characteristic data into a storage module of the identification module, and simultaneously recording the storage state of the storage module;
and adjusting the self-learning strategy of the identification module according to the storage state of the storage module.
2. The method for prolonging storage life of an identification module according to claim 1, wherein the receiving the feature matching instruction from the identification module, obtaining a self-learning judgment result according to the feature matching instruction, comprises:
when the identification module receives an ID registration instruction, saving the current characteristic data carried by the ID into a storage module;
if the current characteristic data carried by the ID is different from the characteristic data stored in the storage module, carrying out data fusion on the current characteristic data of the ID and the stored characteristic data;
based on the characteristic data after the data fusion, the self-learning judgment result indicates that a self-learning process exists in the identification module.
3. The method for extending the memory life of an identification module according to claim 1, wherein storing the self-learned feature data in a memory module of the identification module while recording a memory state of the memory module comprises:
traversing a plurality of storage blocks in the storage module, and searching the empty storage blocks in the storage module if the erasing times of the current storage blocks reach the multiple of the set erasing times;
and according to the search result, indicating that an idle storage block with fewer erasing times exists in the storage module, storing the self-learned characteristic data stored in the current storage block into the idle storage block, and recording the erasing times of the storage blocks and the use frequency of each ID.
4. The method for extending storage life of an identification module according to claim 3, wherein after said recording and said recording of the number of erasures of said plurality of storage blocks and the frequency of use of each ID, said method further comprises:
acquiring the use frequency of each ID, and arranging the IDs from high to low according to the use frequency;
judging whether a series of IDs which reach a set difference value with the current ID use frequency exist in the IDs which are lower than the current ID use frequency or not;
if the data exists, exchanging the ID with the minimum erasing frequency in the series of IDs with the current ID storage block, adding one to the erasing frequency of the exchanged storage block, adding one to the current ID using frequency, and modifying the corresponding information value when the data is used.
5. The method for extending the storage life of an identification module according to claim 1, wherein the adjusting the self-learning strategy of the identification module according to the storage state of the storage module comprises:
if the number of times of erasing of the whole storage module is smaller than a first threshold value, configuring the self-learning Xi Ce of the identification module to be a first mode;
if the number of times of erasing of the whole storage module reaches a second threshold range, configuring the self-learning Xi Ce of the identification module to be a second mode slightly;
if the number of times of erasing of the whole storage module reaches a third threshold range, configuring the self-learning Xi Ce of the identification module to be a third mode slightly;
if the erasing times of the whole storage module reach more than a fourth threshold value, the characteristic data are not self-learned any more, and the characteristic matching instruction is returned to the upper layer so as to inform the upper layer of making early warning or other processing strategies.
6. The method for extending the memory life of an identification module of claim 5, comprising:
the first mode of the self-learning strategy is normal learning;
the second mode of the self-learning strategy is: for newly registered IDs, the first 300 maintains the behavior that must learn new features; after 300 times, learning is performed if the difference between the current characteristic data and the stored characteristic data reaches 10%;
the third mode of the self-learning strategy is: for newly registered IDs, the first 30 maintains the behavior that must learn new features; after 30 times, learning is performed if the difference between the current characteristic data and the stored characteristic data reaches 15%.
7. An apparatus for extending the storage life of an identification module, the apparatus comprising:
the self-learning judging module is used for receiving the characteristic matching instruction from the identification module and obtaining a self-learning judging result according to the characteristic matching instruction;
the data storage module is used for indicating the existence of a self-learning process according to the self-learning judging result, storing the self-learned characteristic data into the storage module of the identification module, and simultaneously recording the storage state of the storage module;
and the strategy updating module is used for adjusting the self-learning strategy of the identification module according to the storage state of the storage module.
8. An electronic device, comprising: at least one processor, at least one memory, wherein,
program instructions or codes are stored on the memory;
the program instructions or code being loaded and executed by the processor to cause the electronic device to implement a method of extending the memory life of an identification module as claimed in any one of claims 1 to 6.
9. A storage medium having stored thereon program instructions or code which are loaded and executed by a processor to implement a method of extending the storage life of an identification module as claimed in any one of claims 1 to 6.
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