CN113392240A - Biological characteristic storage optimization method and device, electronic equipment and storage medium - Google Patents
Biological characteristic storage optimization method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application provides a biological feature storage optimization method and device, electronic equipment and a storage medium, belongs to the field of information storage, and aims to solve the problems of huge current data volume and slow face retrieval speed. The method comprises the following steps: classifying the biological characteristic data to be stored according to the biological characteristic library field to obtain classified biological characteristic data; partitioning the storage medium according to the biological characteristic library field; and according to a set rule, correspondingly processing the classified biological characteristic data, and writing a processed result into a corresponding partition. The device comprises: the device comprises a classification module, a partition module and a writing module. The optimization method and the device improve the speed of retrieval and query, improve the user experience, reduce the disk storage space, increase the total storage amount and save the hardware resource cost.
Description
Technical Field
The application belongs to the field of information storage, and particularly relates to a biological characteristic storage optimization method and device, electronic equipment and a storage medium.
Background
With the continuous development of face recognition technology, face recognition is widely used in commercialization, and convenience is provided for daily life of people, but with the continuous expansion of user groups, the diversification of use scenes and the accumulation of face feature data, higher requirements are provided for the retrieval performance of face recognition services.
At present, in the prior art, when the data volume in the database is larger and larger, the face retrieval speed is low, the requirements cannot be met, if a large amount of data in the database is required to be accommodated, the storage space of a hardware disk needs to be increased, the application cost of face recognition can be increased by the solution, and the development of the face recognition technology can be further hindered by ultrahigh cost.
The method aims at solving the problems that in the prior art, when the data size is huge, the face retrieval speed is slow, and the requirements cannot be met, an effective technical scheme is not provided.
Disclosure of Invention
Aiming at the problems in the prior art, the application provides a biological feature storage optimization method, a biological feature storage optimization device, electronic equipment and a storage medium, so as to solve the problems that the data volume is huge, the face retrieval speed is slow, and the requirements cannot be met.
In a first aspect, the present application provides a biometric feature storage optimization method, including the following steps:
classifying the biological characteristic data to be stored according to the biological characteristic library field to obtain classified biological characteristic data;
partitioning the storage medium according to the biological characteristic library field;
and according to a set rule, correspondingly processing the classified biological characteristic data, and writing a processed result into a corresponding partition.
The setting rule comprises at least one of the following rules:
a first rule that stores the biometric data after indexing in a retrieval dimension;
a second rule that stores the biometric data after format conversion and compression; and
a third rule that stores the mapping data of the biometric data after the mapping data is established.
The first rule is specifically as follows: n galleries are created under each partition, and each piece of biometric data is stored in each gallery in chronological order and as a random number.
The second rule is specifically: and converting the biological characteristic data corresponding to the biological characteristic field in the characteristic storage table into binary data, compressing the binary data, and storing the compressed result.
The third rule is specifically as follows: and establishing mapping addresses aiming at all the biological characteristic data in the characteristic storage table, only storing the mapping addresses when data storage is carried out, and calling the biological characteristic data to be retrieved through the mapping addresses when retrieval is carried out.
The biometric data has a unique feature ID as a search condition, and when a single feature is used as a search condition, the feature ID or the gallery name and the feature ID are used as constraint conditions for searching.
Under the condition of storing according to the first rule, when searching, placing the gallery corresponding to a period of time under the partition into a cache according to the searching condition, and searching the data required to be searched in the gallery corresponding to the period of time according to the time sequence.
In a second aspect, the present application provides a biometric storage optimization apparatus, which is implemented by the biometric storage optimization method, and includes: the device comprises a classification module, a partition module and a write-in module;
the classification module, the partition module and the write-in module are sequentially connected;
the classification module is used for classifying the biological characteristic data to be stored according to the biological characteristic library field to obtain classified biological characteristic data;
the partition module is used for partitioning the storage medium according to the biological characteristic library field;
and the writing module is used for correspondingly processing the classified biological characteristic data according to a set rule and writing a processed result into a corresponding subarea.
In a third aspect, the present application provides an electronic device, comprising:
one or more processors;
a memory;
one or more applications stored in the memory and configured to be executed by the one or more processors to implement the biometric storage optimization method of the first aspect.
In a fourth aspect, the present application proposes a computer-readable storage medium having stored thereon a computer program that can be loaded and executed by a processor with the biometric storage optimization method of the first aspect.
The beneficial technical effects are as follows:
the application provides a biological characteristic storage optimization method and device, electronic equipment and a storage medium, which improve the speed of retrieval and query, improve the user experience, reduce the disk storage space, increase the total storage amount and save the hardware resource cost.
Drawings
FIG. 1 is a flowchart of a biometric storage optimization method according to an embodiment of the present disclosure;
FIG. 2 is a schematic block diagram of a biometric storage optimization device according to an embodiment of the present application;
FIG. 3 is a schematic view of a partition according to an embodiment of the present application;
FIG. 4 is a schematic diagram of an electronic device according to an embodiment of the present application;
among them, 100-electronic device, 101-processor, 102-bus, 103-memory.
Detailed Description
The application provides a biological characteristic storage optimization method, a biological characteristic storage optimization device, electronic equipment and a storage medium, and aims to solve the problems that when the data volume is large, the face retrieval speed is slow, and the requirements cannot be met.
In a first aspect, the present application provides a biometric storage optimization method, as shown in fig. 1, including the following steps:
step S1: classifying the biological characteristic data to be stored according to the biological characteristic library field to obtain classified biological characteristic data;
step S2: partitioning the storage medium according to the biological characteristic library field;
step S3: and according to a set rule, correspondingly processing the classified biological characteristic data, and writing a processed result into a corresponding partition.
The biological characteristic data to be stored can be a characteristic storage table, the characteristic storage table is partitioned according to the fields of the biological characteristic library, the logical table is still a complete table, and only the data in the table is physically stored in a plurality of table spaces (physical files), so that the data query cannot be performed by scanning the whole table every time, and the data query speed is greatly improved only by searching the required data from the current partition.
The feature storage table includes: a biometric field and biometric data.
The setting rule comprises at least one of the following rules:
a first rule that stores the biometric data after indexing in a retrieval dimension;
a second rule that stores the biometric data after format conversion and compression; and
a third rule that stores the mapping data of the biometric data after the mapping data is established.
The first rule is specifically as follows: n galleries are created under each partition, and each piece of biometric data is stored in each gallery in chronological order and as a random number.
Under the condition of storing according to the first rule, when searching, placing the gallery corresponding to a period of time under the partition into a cache according to the searching condition, and searching the data required to be searched in the gallery corresponding to the period of time according to the time sequence.
From the perspective of time dimension of the biological characteristic library, the biological characteristic database is partitioned, the main key is made according to the graph library, time and random numbers, the biological characteristic data of the graph library under the aggregation index are continuous, hit data can be easily found out from pre-read data according to the time dimension, and the query speed is improved.
The biometric data has a unique feature ID, which is generated when the biometric data is stored, for use as a search condition, and when a single feature is used as the search condition, the feature ID or the gallery name and the feature ID are used as constraints for the search.
And (4) single feature retrieval from the image library, wherein the biological feature library and the feature ID are subjected to unique constraint, when the row data with the same condition are matched, the data can stop being continuously searched, the data meeting the condition is returned, and if the indexing is not carried out, the database can inquire the data of the whole table and search the data meeting the condition.
The second rule is specifically: and converting the biological characteristic data corresponding to the biological characteristic field in the characteristic storage table into binary data, compressing the binary data, and storing the compressed result. The biometric field is converted into binary by base64, and then compressed and stored by the binary according to a compression algorithm (Gzip, LZ4, bzip2 and the like), so that the use space of a database disk is reduced, the total data storage amount is increased, the hardware resource cost is saved, and the operation and maintenance complexity and cost are reduced. The biometric field may be biometric data corresponding to the face feature field: and extracting the face key information in the picture with the face to obtain a binary value sorted according to a fixed format.
The third rule is specifically as follows: and establishing mapping addresses aiming at all the biological characteristic data in the characteristic storage table, only storing the mapping addresses when data storage is carried out, and calling the biological characteristic data to be retrieved through the mapping addresses when retrieval is carried out. When the characteristics are written in, the biological characteristic data are not stored, only the mapping address of the characteristics is stored, and when the characteristic data are used, the characteristic value is taken through the mapping address, so that the use space of a database disk is reduced, the total data storage amount is increased, the hardware resource cost is saved, and meanwhile, the operation and maintenance complexity and cost are reduced.
As shown in fig. 3, the biometric data is extracted from the feature storage to obtain a biometric data set, the biometric data set is classified according to the biometric library field, the classification result is stored in each partition, for example, the biometric library field 1 is correspondingly stored in the partition-1 partition, when the biometric data is retrieved, a piece of content in the corresponding partition meeting the condition is extracted according to the keyword input by the user and stored in the cache, for example, the contents of the gallery-1 to the gallery-n are found in the partition 1 and stored in the cache, and then the biometric data meeting the condition is found in the cache. The partition can adopt the following strategies of Range partition, Hash partition, Key partition, Composite partition and the like, and can be selected by the user according to the actual user scene.
Example 1:
taking data storage of an attendance machine of a certain company as an example, the company has about 1000 employees, the attendance machine is an attendance machine which takes fingerprints as identification bases, firstly, a feature storage table which is already created in an attendance machine system is extracted, and the feature storage table comprises: a biometric field and biometric data, the biometric field comprising: fingerprint picture, job number, department name, attendance record;
classifying the characteristic storage table according to the fingerprint picture, the job number, the department name and the attendance record;
partitioning the storage medium according to the biological characteristic library field, wherein the storage medium partition is also as follows: fingerprint picture, job number, department name, attendance record, the partition method adopts Range partition;
and according to a set rule, correspondingly processing the classified biological characteristic data, and writing a processed result into a corresponding partition. The setting rule of this embodiment includes: a first rule and a second rule.
The first rule is specifically as follows: under each partition, 50 galleries were created, in each of which each piece of biometric data was saved in chronological order and a random number. When the biological characteristic data is collected, if a piece of biological characteristic data is intelligently collected at the same time, the random number is set to be 1.
The second rule is specifically: and converting the biological characteristic data corresponding to the biological characteristic field in the characteristic storage table into binary data, compressing the binary data, and storing the compressed result. In the embodiment, the fingerprint picture field is converted into the binary system from base64, and then the binary system is compressed and stored according to the Gzip compression algorithm, so that the storage space is greatly reduced, and the retrieval speed is improved.
The biological characteristic data corresponding to the fingerprint picture has a unique characteristic ID which is used as a retrieval condition, when the employee performs attendance checking, the characteristic code extracted by the fingerprint is used as the characteristic ID corresponding to the fingerprint picture for retrieval, and due to the uniqueness, the retrieved biological characteristic data is also unique, and the biological characteristic data corresponding to the work number, the department name and the attendance record are extracted from the corresponding fingerprint picture which is retrieved and displayed on a display screen of the attendance checking machine.
Example 2:
in the embodiment, data storage of a medical image query system is taken as an example, a comprehensive hospital has a large amount of medical image data every day, such as CT images, three-dimensional color ultrasonography, X-ray chest pictures and the like, and a large number of pictures may be stored at the same time, and the large amount of picture data is stored, which brings huge pressure to a hardware system.
Firstly, extracting a feature storage table of a medical image query system, and classifying the feature storage table according to biological feature library fields, wherein the feature storage table comprises: a biometric field and biometric data, in this embodiment, the biometric field includes but is not limited to: CT image, three-dimensional color ultrasound, X-ray chest film, department, time of treatment, doctor of visiting, illness state and treatment method.
Partitioning the storage medium according to the biological characteristic library field; the biometric library field includes: CT images, three-dimensional color ultrasound, X-ray chest films, departments, time of seeing a doctor, doctor of visiting a doctor, illness state and treatment method, and a Composite method is adopted for partitioning.
And correspondingly processing the classified biological characteristic data according to a first rule, a second rule and a third rule, and writing the processed result into a corresponding partition.
The first rule is specifically as follows: 366 galleries were created under each partition, each biometric datum being stored in each gallery in chronological order and as a random number. In this embodiment, 366 image libraries represent 366 days, one image library is used for storage every day, the hospital can store medical image data of one year for a patient, and medical data of the day stores each piece of biological characteristic data according to a time sequence and a random number.
The second rule is specifically: storing the generation in the feature storage table: and converting the biological characteristic data corresponding to the CT image, the three-dimensional color ultrasound and the X-ray chest film into binary data, compressing the binary data, and storing the compressed result by adopting Gzip, LZ4 and bzip2 compression algorithms respectively.
The third rule is specifically as follows: and establishing mapping addresses aiming at all the biological characteristic data in the characteristic storage table, only storing the mapping addresses when data storage is carried out, and calling the biological characteristic data to be retrieved through the mapping addresses when retrieval is carried out. If the retrieval is not carried out, only the mapping address is stored in the feature storage table, so that the storage space is greatly reduced, if the retrieval is carried out, data corresponding to the mapping address is called in a targeted manner aiming at a retrieved object, for example, only a CT image is retrieved, only the mapping address of the CT image can find a corresponding CT biological feature data picture, and other biological feature data in other feature storage tables are stored mapping addresses, so that the retrieval speed is greatly increased.
When the CT image is taken as a search condition, the characteristic ID of the CT image or the corresponding gallery name of the CT image and the characteristic ID of the CT image are input as constraint conditions for searching. If the retrieval is performed only by using the feature ID of the CT image, the corresponding CT biological feature data picture can be retrieved, but the retrieval speed is slower than the retrieval speed by using the corresponding gallery name of the CT image and the feature ID of the CT image, so the retrieval is performed by using the corresponding gallery name of the CT image and the feature ID of the CT image.
The above embodiments demonstrate that the data storage of the medical image query system greatly reduces the hardware cost and improves the retrieval speed.
Example 3:
the embodiment takes the data storage of a face recognition access control system as an example, a community has about 1000 people, the owner of the community needs to input faces of the owner and the family into a database of the face recognition access control system, when the owner enters the community, the face recognition access control system can match the current face data of the owner with the face data stored in the database, if the matching is successful, a gate is opened for the owner, the owner is allowed to pass, and if the matching is unsuccessful, an alarm is given to disallow the owner to pass. The method is characterized in that only the face feature data is frequently used data, other data are recorded into the system, but the face feature data does not need to be called under the common condition, and the retrieval speed of the face feature data needs to be considered, so that the real-time requirement can be met.
Firstly, calling a feature storage table from a face recognition access control system, and classifying the feature storage table according to biological feature library fields; the feature storage table includes: the system comprises a biological characteristic field and biological characteristic data, wherein the biological characteristic field comprises a face data picture, a building number, a unit number, a room number, an owner name and family members;
partitioning the storage medium according to the biological characteristic library field; the classification result is as follows: the face data picture, the building number, the unit number, the room number, the owner name and the family member are partitioned by adopting a Key method.
And according to the second and third rules, correspondingly processing the classified biological characteristic data, and writing the processed result into the corresponding partition.
The second rule is specifically: and converting the biological characteristic data corresponding to the face data picture into binary data, compressing the binary data, and storing the compressed result. The face data picture is converted into the binary system by the base64, the face key information in the picture with the face is extracted to obtain the binary value which is sorted according to the fixed format, and then the binary system is compressed and stored according to the Gzip compression algorithm, so that the use space of a database disk is reduced, the total data storage amount is increased, the hardware resource cost is saved, and the operation and maintenance complexity and cost are reduced. .
The third rule is specifically as follows: establishing mapping addresses aiming at all biological characteristic data (including human face data pictures, building numbers, unit numbers, room numbers, owner names and the biological characteristic data of family members) in the characteristic storage table, only storing the mapping addresses when data are stored, and calling the biological characteristic data to be retrieved through the mapping addresses when the data are retrieved. When the characteristics are written in, the biological characteristic data are not stored, only the mapping address of the characteristics is stored, and when the characteristic data are used, the characteristic value is taken through the mapping address, so that the use space of a database disk is reduced, the total data storage amount is increased, the hardware resource cost is saved, and meanwhile, the operation and maintenance complexity and cost are reduced.
In the above embodiment, when the owner performs face detection through the face recognition access control system, the face data corresponding to the mapping address in the database is called into the cache for decompression, the decompressed data is matched with the face data of the owner, and when the matching is successful, the cache data is released, the gate is opened, and the owner is allowed to pass; and if the matching is unsuccessful, calling the face data of the other mapping address into a cache, releasing the previous face data, matching the face data currently in the cache with the face data of the owner, and repeating the operation until the matching is successful, wherein if the face data corresponding to all the mapping data are not successfully matched, the face recognition access control system gives an alarm and does not open the gate.
The three embodiments listed in the present application are methods for implementing the biometric feature storage optimization method described in the present application using different setting rules in different application scenarios, and the scope of protection is not limited to the three embodiments, and any setting rule is applied to implement the biometric feature storage optimization method described in the present application.
In a second aspect, the present application provides a biometric storage optimization apparatus, which is implemented by using the biometric storage optimization method, as shown in fig. 2, including: the device comprises a classification module, a partition module and a write-in module;
the classification module, the partition module and the write-in module are sequentially connected;
the classification module is used for classifying the biological characteristic data to be stored according to the biological characteristic library field to obtain classified biological characteristic data;
the partition module is used for partitioning the storage medium according to the biological characteristic library field;
and the writing module is used for correspondingly processing the classified biological characteristic data according to a set rule and writing a processed result into a corresponding subarea.
In a third aspect, the present application provides an electronic device, comprising:
one or more processors;
a memory;
one or more applications stored in the memory and configured to be executed by the one or more processors to implement the biometric storage optimization method of the first aspect. .
As shown in fig. 4, the electronic apparatus 100 includes: a processor 101 and a memory 103. Wherein the processor 101 is coupled to the memory 103, such as via a bus 102.
The structure of the electronic device 100 is not limited to the embodiment of the present application.
The processor 101 may be a CPU, general purpose processor, DSP, ASIC, FPGA or other programmable logic device, transistor logic device, hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. The processor 101 may also be a combination of computing functions, e.g., comprising one or more microprocessors, DSPs, and microprocessors.
The memory 103 may be, but is not limited to, a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, an EEPROM, a CD-ROM or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
In a fourth aspect, the present application proposes a computer-readable storage medium having stored thereon a computer program, which is loadable and executable by a processor to implement the biometric storage optimization method of the first aspect.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
Claims (10)
1. A biometric storage optimization method, comprising the steps of:
classifying the biological characteristic data to be stored according to the biological characteristic library field to obtain classified biological characteristic data;
partitioning the storage medium according to the biological characteristic library field;
and according to a set rule, correspondingly processing the classified biological characteristic data, and writing a processed result into a corresponding partition.
2. The biometric characteristic storage optimization method according to claim 1, wherein the setting rule comprises at least one of the following rules:
a first rule that stores the biometric data after indexing in a retrieval dimension;
a second rule that stores the biometric data after format conversion and compression; and
a third rule that stores the mapping data of the biometric data after the mapping data is established.
3. The method for optimizing storage of biometric features according to claim 2, wherein the first rule is specifically: n galleries are created under each partition, and each piece of biometric data is stored in each gallery in chronological order and as a random number.
4. The method for optimizing storage of biometric features according to claim 2, wherein the second rule is specifically: and converting the biological characteristic data corresponding to the biological characteristic field in the characteristic storage table into binary data, compressing the binary data, and storing the compressed result.
5. The method for optimizing storage of biometric features according to claim 2, wherein the third rule is specifically: and establishing mapping addresses aiming at all the biological characteristic data in the characteristic storage table, only storing the mapping addresses when data storage is carried out, and calling the biological characteristic data to be retrieved through the mapping addresses when retrieval is carried out.
6. The biometric feature storage optimization method according to claim 3, 4 or 5, wherein the biometric data has a unique feature ID as a search condition, and when a single feature is used as the search condition, the search is performed with the feature ID or the gallery name and the feature ID as constraints.
7. The method according to claim 3, wherein in the case of storing according to the first rule, when performing the search, the gallery corresponding to a period of time under the partition is placed in the cache according to the search condition, and the data to be searched is searched in the gallery corresponding to the period of time in chronological order.
8. A biological characteristic storage optimization device comprises a classification module, a partition module and a writing module;
the classification module, the partition module and the write-in module are sequentially connected;
the classification module is used for classifying the biological characteristic data to be stored according to the biological characteristic library field to obtain classified biological characteristic data;
the partition module is used for partitioning the storage medium according to the biological characteristic library field;
and the writing module is used for correspondingly processing the classified biological characteristic data according to a set rule and writing a processed result into a corresponding subarea.
9. An electronic device, comprising:
one or more processors;
a memory;
one or more applications stored in the memory and configured to be executed by the one or more processors to implement the biometric storage optimization method of any one of claims 1-7.
10. A computer-readable storage medium, having stored thereon a computer program which can be loaded and executed by a processor to implement the method of biometric storage optimization of any one of claims 1 to 7.
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