Image recognition processing system and method based on data analysis

Abstract

The invention discloses an image recognition processing system and method based on data analysis, belonging to the field of image processing technology. The image files collected by the maritime radar are classified and compiled, and storage space is allocated to each classification and compilation; the acquisition frequency nodes are unified to form node segments, and the image files collected by the maritime radar in each node segment are uploaded to each image file layer in the service memory; the weight of the memory size of the service memory occupied by each image file layer is counted in real time, and the authority security of each image file layer is evaluated; each image pixel point is identified, and the image bit depth of each image pixel point is recorded; by means of image compression iterative analysis, it is evaluated whether the storage space size of the service memory meets the storage requirements of the image file of the next node segment at the end of the current node segment; after the iteration stops, the storage space size of the service memory is updated, and the storage space size of each classification and compilation is updated.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to an image recognition processing system and method based on data analysis. Background Art

[0002] The pixels, pixel values, pixel bit depth, and image resolution of an image are crucial considerations in image processing and display. Pixels are the basic units that make up a digital image. They are the smallest controllable light points on the screen and the smallest units in an image. In monitors, digital cameras, camcorders, and other devices, the more pixels there are, the finer the details that can be represented. Pixel value refers to the color or brightness information represented by each pixel, generally expressed in numerical form. Pixel bit depth refers to the number of binary bits used to record the color or brightness information of each pixel. Image resolution refers to the amount of information stored in an image, that is, how many pixels are there per inch of the image, usually in PPI (Pixels Per Inch).

[0003] In view of this, the two key factors affecting the image memory size are the number of pixels and the color or brightness information at each pixel, namely, the bit depth. In the prior art, the image size is generally changed by compressing the number of pixels. However, changing the number of pixels will inevitably lead to a reduction in the display screen, which directly affects the size integrity of the displayed content and causes the display content to be lost. Although changing the bit depth at the pixel can make the displayed content blurred, an effective bit depth adjustment strategy can maximize the clarity of the image data to meet the storage space requirements. Summary of the invention

[0004] The purpose of the present invention is to provide an image recognition processing system and method based on data analysis to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] An image recognition processing system based on data analysis, the system comprises: a service memory module, a storage authority analysis module, an image compression processing analysis module and a service memory update module;

[0007] The service memory module classifies and organizes the image files collected by the maritime radar through the service memory, and allocates storage space for each classification and organization; unifies the acquisition frequency nodes to form node segments, and at the end of each node segment, uploads the image files collected by the maritime radar in each node segment to each image file layer in the service memory;

[0008] The storage permission analysis module counts the weight of the memory size of the service memory occupied by each image file layer in real time at the end of each node segment, and evaluates the permission security of each image file layer to make a prior judgment on the compression of the image file layer;

[0009] The image compression processing and analysis module identifies each image pixel of each image file and records the image bit depth of each image pixel; through image compression iterative analysis, it is evaluated whether the storage space size of the service memory meets the storage requirements of the image file of the next node segment at the end of the current node segment; if so, the iteration stops; if not, the next iteration is performed until the iteration stops when the requirements are met;

[0010] The service memory update module updates the storage space size of the service memory and the storage space size of each classification book after the iteration stops, which is used for image compression iterative analysis of each image file before the start of the next node segment to meet the storage requirements of each image file.

[0011] Further, the service memory module includes a storage space allocation unit and a collection behavior configuration unit;

[0012] The storage space allocation unit is used to classify and compile image files collected by the maritime radar and record them in the service memory, wherein one maritime radar corresponds to one classification, and one classification corresponds to a storage space in the service memory; the classification formed by any i-th storage space in the service memory is recorded as CB i ;

[0013] The acquisition behavior configuration unit is used to unify the acquisition frequency nodes, form a node segment between each adjacent two consecutive acquisition frequency nodes, and record any a-th node segment as NS a ; Obtain the image files collected by the maritime radar in each node segment and upload them to the classification book. The server backend automatically allocates an image file layer in each storage space through software instructions. The image file layer has the unique instruction verification attribute of the node segment. a The corresponding allocated image file layer is denoted as CB i |NS a .

[0014] Further, the storage permission analysis module includes a permission security assessment unit and a prior verification judgment unit;

[0015] The authority security evaluation unit is used to count the weight of the memory size of the service memory occupied by each image file layer in real time at the end of each node segment, denoted as MS(CB i |NS a), analyze and evaluate the security of permissions of each image file layer, the formula is as follows:

[0016]

[0017] In the formula, PE(CB i |NS a ) represents the image file layer CB i |NS a The permission security degree, μ represents the real-time average value of the weight of the memory size of the service memory occupied by each image file layer, and σ 2 represents the real-time variance of the weight of the memory size of the service memory occupied by each image file layer, and A represents the real-time total number of node segments, and II represents the fixed total number of storage spaces;

[0018] The pre-verification judgment unit is used to preset the permission security threshold. If the image file layer CB i |NS a Permission security PE(CB i |NS a ) is less than or equal to the permission security threshold, then the image file layer CB is extracted i |NS a Otherwise, the image file layer CB is not extracted. i |NS a .

[0019] Further, the image compression processing and analysis module includes a bit depth data acquisition unit and an image compression iteration analysis unit;

[0020] The bit depth data acquisition unit is used to identify the extracted image file layer CB i |NS a For each image pixel of the x-th image file, any r-th image pixel is recorded as (CB i |NS a , xr), and obtain the image pixel point PP rx (CB i |NS a ) image bit depth, denoted as BD(CB i |NS a , xr);

[0021] The image compression iterative analysis unit is used to establish an image compression iterative analysis model:

[0022] Get all the extracted image file layers and construct the image file layer sample set to be compressed, denoted as SS = {CB i |NS a,i∈[1,I],a∈[1,A]};

[0023] The image file layer sample set of the tth iteration is denoted as SS t , in the image file layer sample set SS t Select any image file layer CB i |NS a , in the image file layer CB i |NS a Select any x-th image file from the image file, and select any image pixel point PP from the x-th image file rx (CB i |NS a ), adjust the image pixel PP rx (CB i |NS a ) bit depth value, set the initial adjustment ratio q, then the image bit depth BD (CB i |NS a , xr) The adjusted image bit depth is recorded as qBD(CB i |NS a , xr);

[0024] After the image bit depth is adjusted, the process returns to step S2 and updates the image file layer CB. i |NS a Permission security PE(CB i |NS a ), if the updated permission security is less than or equal to the permission security threshold, then the image file layer sample set SS t Eliminate the image file layer CB i |NS a , and output the image file layer sample set after iteration, and record the image file layer sample set after the tth iteration as SS t+1 ;

[0025] Determine whether the current storage space meets the requirements of the a+1th node segment NS a+1 If the storage requirements of the image files in , the iteration stops; if not, the t+1th iteration is performed, and the image file layer sample set of the t+1th iteration is SS t+1 ;

[0026] When performing the t+1th iteration, the initial adjustment ratio q is increased, and the increase in the adjustment ratio at each iteration is set, recorded as Q. Then, when performing the t+1th iteration, the adjustment ratio set is q+(t+1)×Q;

[0027] Until it is determined whether the current storage space satisfies the a+1th node segment NS a+1The iteration stops when the storage requirement for the image file is reached.

[0028] An image recognition processing method based on data analysis, the method comprises the following steps:

[0029] Step S1: classify and compile image files collected by the maritime radar through the service memory, and allocate storage space for each classification and compilation; unify the acquisition frequency nodes to form node segments, and at the end of each node segment, upload the image files collected by the maritime radar in each node segment to each image file layer in the service memory;

[0030] Step S2: At the end of each node segment, the weight of the memory size of the service memory occupied by each image file layer is counted in real time, and the authority security of each image file layer is evaluated to make a prior judgment on the compression of the image file layer;

[0031] Step S3: Identify each image pixel of each image file and record the image bit depth of each image pixel; evaluate whether the storage space size of the service memory meets the storage requirements of the image file of the next node segment at the end of the current node segment through image compression iterative analysis. If so, the iteration stops; if not, proceed to the next iteration until the iteration stops when the requirements are met.

[0032] Step S4: After the iteration stops, the storage space size of the service memory is updated, and the storage space size of each classification book is updated, which is used for image compression iterative analysis of each image file before the start of the next node segment to meet the storage requirements of each image file.

[0033] Furthermore, the specific implementation process of step S1 includes:

[0034] Step S11: classify and compile the image files collected by the maritime radar and record them in the service memory, wherein one maritime radar corresponds to one classification, and one classification corresponds to a storage space in the service memory; the classification formed by any i-th storage space in the service memory is recorded as CB i ;

[0035] Step S12: Unify the acquisition frequency nodes, form a node segment between every two adjacent acquisition frequency nodes, and record any a-th node segment as NS a ; Obtain the image files collected by the maritime radar in each node segment and upload them to the classification book. The server backend automatically allocates an image file layer in each storage space through software instructions. The image file layer has the unique instruction verification attribute of the node segment. a The corresponding allocated image file layer is denoted as CB i |NS a .

[0036] Furthermore, the specific implementation process of step S2 includes:

[0037] Step S21: At the end of each node segment, the weight of the memory size of the service memory occupied by each image file layer is counted in real time, recorded as MS (CB i |NS a ), analyze and evaluate the security of permissions of each image file layer, the formula is as follows:

[0038]

[0039] In the formula, PE(CB i |NS a ) represents the image file layer CB i |NS a The permission security degree, μ represents the real-time average value of the weight of the memory size of the service memory occupied by each image file layer, and σ 2 represents the real-time variance of the weight of the memory size of the service memory occupied by each image file layer, and A represents the real-time total number of node segments, and I represents the fixed total number of storage spaces;

[0040] Step S22: Preset the permission security threshold. If the image file layer CB i |NS a Permission security PE(CB i |NS a ) is less than or equal to the permission security threshold, then the image file layer CB is extracted i |NS a Otherwise, the image file layer CB is not extracted. i |NS a .

[0041] Furthermore, the specific implementation process of step S3 includes:

[0042] Step S31: Identify the extracted image file layer CB i |NS a For each image pixel of the x-th image file, any r-th image pixel is recorded as (CB i |NS a , xr), and obtain the image pixel point PP rx (CB i |NS a ) image bit depth, denoted as BD(CB i |NS a , xr);

[0043] Step S32: Establishing an image compression iterative analysis model:

[0044] Get all the extracted image file layers and construct the image file layer sample set to be compressed, denoted as SS = {CB i |NS a ,i∈[1,I],a∈[1,A]};

[0045] The image file layer sample set of the tth iteration is denoted as SS t , in the image file layer sample set SS t Select any image file layer CB i |NS a , in the image file layer CB i |NS a Select any x-th image file from the image file, and select any image pixel point PP from the x-th image file rx (CB i |NS a ), adjust the image pixel PP rx (CB i |NS a ) bit depth value, set the initial adjustment ratio q, then the image bit depth BD (CB i |NS a , xr) The adjusted image bit depth is recorded as qBD(CB i |NS a , xr);

[0046] After the image bit depth is adjusted, the process returns to step S2 and updates the image file layer CB. i |NS a Permission security PE(CB i |NS a ), if the updated permission security is less than or equal to the permission security threshold, then the image file layer sample set SS t Eliminate the image file layer CB i |NS a , and output the image file layer sample set after iteration, and record the image file layer sample set after the tth iteration as SS t+1 ;

[0047] Determine whether the current storage space meets the requirements of the a+1th node segment NS a+1 If the storage requirements of the image files in , the iteration stops; if not, the t+1th iteration is performed, and the image file layer sample set of the t+1th iteration is SS t+1 ;

[0048] When performing the t+1th iteration, the initial adjustment ratio q is increased, and the increase in the adjustment ratio at each iteration is set, recorded as Q. Then, when performing the t+1th iteration, the adjustment ratio set is q+(t+1)×Q;

[0049] Until it is determined whether the current storage space satisfies the a+1th node segment NS a+1 The iteration stops when the storage requirement for the image file is reached.

[0050] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned image recognition processing method based on data analysis when executing the program.

[0051] A storage medium containing computer executable instructions, characterized in that the computer executable instructions are used to execute the above-mentioned image recognition processing method based on data analysis when executed by a computer processor.

[0052] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: in an image recognition processing system and method based on data analysis provided by the present invention, image files collected by maritime radar are classified and compiled, and storage space is allocated to each classification and compilation; the acquisition frequency nodes are unified to form node segments, and the image files collected by the maritime radar in each node segment are uploaded to each image file layer in the service memory; the weight of the memory size of the service memory occupied by each image file layer is counted in real time, and the authority security of each image file layer is evaluated; each image pixel point is identified, and the image bit depth of each image pixel point is recorded; by means of image compression iterative analysis, it is evaluated whether the storage space size of the service memory meets the storage requirements of the image file of the next node segment at the end of the current node segment; after the iteration stops, the storage space size of the service memory is updated, and the storage space size of each classification and compilation is updated; the present invention can save the storage space of the service memory while maximizing the clarity of the image data. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0054] Figure 1 It is a structural schematic diagram of an image recognition processing system based on data analysis of the present invention;

[0055] Figure 2 It is a schematic diagram of the steps of an image recognition processing method based on data analysis of the present invention. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] See also Figure 1 , in the first embodiment: a data analysis-based image recognition processing system is provided, the system comprising: a service memory module, a storage authority analysis module, an image compression processing analysis module and a service memory update module;

[0058] The service memory module classifies and organizes the image files collected by the maritime radar through the service memory, and allocates storage space for each classification and organization; unifies the acquisition frequency nodes to form node segments, and at the end of each node segment, uploads the image files collected by the maritime radar in each node segment to each image file layer in the service memory;

[0059] Preferably, the service memory module includes a storage space allocation unit and a collection behavior configuration unit;

[0060] The storage space allocation unit is used to classify and compile image files collected by the maritime radar and record them in the service memory, wherein one maritime radar corresponds to one classification, and one classification corresponds to a storage space in the service memory; the classification formed by any i-th storage space in the service memory is recorded as CB i ;

[0061] The acquisition behavior configuration unit is used to unify the acquisition frequency nodes, form a node segment between each adjacent two consecutive acquisition frequency nodes, and record any a-th node segment as NS a ; Obtain the image files collected by the maritime radar in each node segment and upload them to the classification book. The server backend automatically allocates an image file layer in each storage space through software instructions. The image file layer has the unique instruction verification attribute of the node segment. a The corresponding allocated image file layer is denoted as CB i |NS a ;

[0062] The storage permission analysis module counts the weight of the memory size of the service memory occupied by each image file layer in real time at the end of each node segment, and evaluates the permission security of each image file layer to make a prior judgment on the compression of the image file layer;

[0063] Preferably, the storage permission analysis module includes a permission security assessment unit and a prior verification and judgment unit;

[0064] The authority security evaluation unit is used to count the weight of the memory size of the service memory occupied by each image file layer in real time at the end of each node segment, denoted as MS(CB i |NS a ), analyze and evaluate the security of permissions of each image file layer, the formula is as follows:

[0065]

[0066] In the formula, PE(CB i |NS a ) represents the image file layer CB i |NS a The permission security degree, μ represents the real-time average value of the weight of the memory size of the service memory occupied by each image file layer, and σ 2 represents the real-time variance of the weight of the memory size of the service memory occupied by each image file layer, and A represents the real-time total number of node segments, and II represents the fixed total number of storage spaces;

[0067] The pre-verification judgment unit is used to preset the permission security threshold. If the image file layer CB i |NS a Permission security PE(CB i |NS a ) is less than or equal to the permission security threshold, then the image file layer CB is extracted i |NS a Otherwise, the image file layer CB is not extracted. i |NS a ;

[0068] The image compression processing and analysis module identifies each image pixel of each image file and records the image bit depth of each image pixel; through image compression iterative analysis, it is evaluated whether the storage space size of the service memory meets the storage requirements of the image file of the next node segment at the end of the current node segment; if so, the iteration stops; if not, the next iteration is performed until the iteration stops when the requirements are met;

[0069] Preferably, the image compression processing and analysis module includes a bit depth data acquisition unit and an image compression iteration analysis unit;

[0070] The bit depth data acquisition unit is used to identify the extracted image file layer CB i |NS aFor each image pixel of the x-th image file, any r-th image pixel is recorded as (CB i |NS a , xr), and obtain the image pixel point PP rx (CB i |NS a ) image bit depth, denoted as BD(CB i |NS a , xr);

[0071] The image compression iterative analysis unit is used to establish an image compression iterative analysis model:

[0072] Get all the extracted image file layers and construct the image file layer sample set to be compressed, denoted as SS = {CB i |NS a ,i∈[1,I],a∈[1,A]};

[0073] The image file layer sample set of the tth iteration is denoted as SS t , in the image file layer sample set SS t Select any image file layer CB i |NS a , in the image file layer CB i |NS a Select any x-th image file from the image file, and select any image pixel point PP from the x-th image file rx (CB i |NS a ), adjust the image pixel PP rx (CB i |NS a ) bit depth value, set the initial adjustment ratio q, then the image bit depth BD (CB i |NS a , xr) The adjusted image bit depth is recorded as qBD(CB i |NS a , xr);

[0074] After the image bit depth is adjusted, the process returns to step S2 and updates the image file layer CB. i |NS a Permission security PE(CB i |NS a ), if the updated permission security is less than or equal to the permission security threshold, then the image file layer sample set SS t Eliminate the image file layer CB i |NS a , and output the image file layer sample set after iteration, and record the image file layer sample set after the tth iteration as SSt+1 ;

[0075] Determine whether the current storage space meets the requirements of the a+1th node segment NS a+1 If the storage requirements of the image files in , the iteration stops; if not, the t+1th iteration is performed, and the image file layer sample set of the t+1th iteration is SS t+1 ;

[0076] When performing the t+1th iteration, the initial adjustment ratio q is increased, and the increase in the adjustment ratio at each iteration is set, recorded as Q. Then, when performing the t+1th iteration, the adjustment ratio set is q+(t+1)×Q;

[0077] Until it is determined whether the current storage space satisfies the a+1th node segment NS a+1 The iteration stops when the storage requirement of the image file is within;

[0078] The service memory update module updates the storage space size of the service memory and the storage space size of each classification book after the iteration stops, which is used for image compression iterative analysis of each image file before the start of the next node segment to meet the storage requirements of each image file.

[0079] See also Figure 2 In the second embodiment, a method for image recognition based on data analysis is provided, the method comprising the following steps:

[0080] Step S1: classify and compile image files collected by the maritime radar through the service memory, and allocate storage space for each classification and compilation; unify the acquisition frequency nodes to form node segments, and at the end of each node segment, upload the image files collected by the maritime radar in each node segment to each image file layer in the service memory;

[0081] Exemplarily, the image files collected by the maritime radar are classified and recorded in the service memory, wherein one maritime radar corresponds to one classification, and one classification corresponds to a storage space in the service memory; the classification formed by any i-th storage space in the service memory is recorded as CB i ;

[0082] Unify the acquisition frequency nodes, form a node segment between every two adjacent acquisition frequency nodes, and record any a-th node segment as NS a ; Obtain the image files collected by the maritime radar in each node segment and upload them to the classification book. The server backend automatically allocates an image file layer in each storage space through software instructions. The image file layer has the unique instruction verification attribute of the node segment. aThe corresponding allocated image file layer is denoted as CB i |NS a ;

[0083] Step S2: At the end of each node segment, the weight of the memory size of the service memory occupied by each image file layer is counted in real time, and the authority security of each image file layer is evaluated to make a prior judgment on the compression of the image file layer;

[0084] For example, at the end of each node segment, the weight of the memory size of the service memory occupied by each image file layer is counted in real time, which is recorded as MS(CB i |NS a ), analyze and evaluate the security of permissions of each image file layer, the formula is as follows:

[0085]

[0086] In the formula, PE(CB i |NS a ) represents the image file layer CB i |NS a The permission security degree, μ represents the real-time average value of the weight of the memory size of the service memory occupied by each image file layer, and σ 2 represents the real-time variance of the weight of the memory size of the service memory occupied by each image file layer, and A represents the real-time total number of node segments, and I represents the fixed total number of storage spaces;

[0087] Preset permission security threshold, if the image file layer CB i |NS a Permission security PE(CB i |NS a ) is less than or equal to the permission security threshold, then the image file layer CB is extracted i |NS a Otherwise, the image file layer CB is not extracted. i |NS a ;

[0088] Step S3: Identify each image pixel of each image file and record the image bit depth of each image pixel; evaluate whether the storage space size of the service memory meets the storage requirements of the image file of the next node segment at the end of the current node segment through image compression iterative analysis. If so, the iteration stops; if not, proceed to the next iteration until the iteration stops when the requirements are met.

[0089] Exemplarily, identifying the extracted image file layer CB i |NS aFor each image pixel of the x-th image file, any r-th image pixel is recorded as (CB i |NS a , xr), and obtain the image pixel point PP rx (CB i |NS a ) image bit depth, denoted as BD(CB i |NS a , xr);

[0090] Establish an iterative analysis model for image compression:

[0091] Get all the extracted image file layers and construct the image file layer sample set to be compressed, denoted as SS = {CB i |NS a ,i∈[1,I],a∈[1,A]};

[0092] The image file layer sample set of the tth iteration is denoted as SS t , in the image file layer sample set SS t Select any image file layer CB i |NS a , in the image file layer CB i |NS a Select any x-th image file from the image file, and select any image pixel point PP from the x-th image file rx (CB i |NS a ), adjust the image pixel PP rx (CB i |NS a ) bit depth value, set the initial adjustment ratio q, then the image bit depth BD (CB i |NS a , xr) The adjusted image bit depth is recorded as qBD(CB i |NS a , xr);

[0093] After the image bit depth is adjusted, the process returns to step S2 and updates the image file layer CB. i |NS a Permission security PE(CB i |NS a ), if the updated permission security is less than or equal to the permission security threshold, then the image file layer sample set SS t Eliminate the image file layer CB i |NS a , and output the image file layer sample set after iteration, and record the image file layer sample set after the tth iteration as SS t+1 ;

[0094] Determine whether the current storage space meets the requirements of the a+1th node segment NS a+1 If the storage requirements of the image files in , the iteration stops; if not, the t+1th iteration is performed, and the image file layer sample set of the t+1th iteration is SS t+1 ;

[0095] When performing the t+1th iteration, the initial adjustment ratio q is increased, and the increase in the adjustment ratio at each iteration is set, recorded as Q. Then, when performing the t+1th iteration, the adjustment ratio set is q+(t+1)×Q;

[0096] Until it is determined whether the current storage space satisfies the a+1th node segment NS a+1 The iteration stops when the storage requirement of the image file is within;

[0097] Step S4: After the iteration stops, the storage space size of the service memory is updated, and the storage space size of each classification book is updated, which is used for image compression iterative analysis of each image file before the start of the next node segment to meet the storage requirements of each image file.

[0098] An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned image recognition processing method based on data analysis when executing the program;

[0099] A storage medium containing computer executable instructions, characterized in that the computer executable instructions are used to perform the above-mentioned image recognition processing method based on data analysis when executed by a computer processor;

[0100] Exemplarily, computer storage media can use any combination of one or more computer-readable media. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.

[0101] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0102] The program code included in the computer-readable medium can be transmitted with any appropriate medium, including but not limited to wireless, electric wire, optical cable, etc., or any suitable combination of the above. The computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or their combinations, and the programming language includes object-oriented programming languages-such as Java, Smalltalk, C++, and also includes conventional procedural programming languages-such as "C" language or similar programming languages. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0103] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0104] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An image recognition processing method based on data analysis, characterized in that: The method comprises the following steps: Step S1: classify and compile image files collected by the maritime radar through the service memory, and allocate storage space for each classification and compilation; unify the acquisition frequency nodes to form node segments, and at the end of each node segment, upload the image files collected by the maritime radar in each node segment to each image file layer in the service memory; Step S2: At the end of each node segment, the weight of the memory size of the service memory occupied by each image file layer is counted in real time, and the authority security of each image file layer is evaluated to make a prior judgment on the compression of the image file layer; Step S3: Identify each image pixel of each image file and record the image bit depth of each image pixel; evaluate whether the storage space size of the service memory meets the storage requirements of the image file of the next node segment at the end of the current node segment through image compression iterative analysis. If so, the iteration stops; if not, proceed to the next iteration until the iteration stops when the requirements are met. Step S4: after the iteration stops, the storage space size of the service memory is updated, and the storage space size of each classification book is updated, which is used for image compression iterative analysis of each image file before the next node segment starts, so as to meet the storage requirements of each image file; The specific implementation process of step S1 includes: Step S11: classify and compile the image files collected by the maritime radar and record them in the service memory, wherein one maritime radar corresponds to one classification, and one classification corresponds to a storage space in the service memory; the classification formed by any i-th storage space in the service memory is recorded as CB i ; Step S12: Unify the acquisition frequency nodes, form a node segment between every two adjacent acquisition frequency nodes, and record any a-th node segment as NS a ; Obtain the image files collected by the maritime radar in each node segment and upload them to the classification book. The server backend automatically allocates an image file layer in each storage space through software instructions. The image file layer has the unique instruction verification attribute of the node segment. a The corresponding allocated image file layer is denoted as CB i |NS a ; The specific implementation process of step S2 includes: Step S21: At the end of each node segment, the weight of the memory size of the service memory occupied by each image file layer is counted in real time, recorded as MS (CB i |NS a ), analyze and evaluate the security of permissions of each image file layer, the formula is as follows: In the formula, PE(CB i |NS a ) represents the image file layer CB i |NS a The permission security degree, μ represents the real-time average value of the weight of the memory size of the service memory occupied by each image file layer, and σ 2 represents the real-time variance of the weight of the memory size of the service memory occupied by each image file layer, and A represents the real-time total number of node segments, and I represents the fixed total number of storage spaces; Step S22: Preset the permission security threshold. If the image file layer CB i |NS a Permission security PE(CB i |NS a ) is less than or equal to the permission security threshold, then the image file layer CB is extracted i |NS a Otherwise, the image file layer CB is not extracted. i |NS a .

2. The image recognition processing method based on data analysis according to claim 1, characterized in that: The specific implementation process of step S3 includes: Step S31: Identify the extracted image file layer CB i |NS a For each image pixel of the x-th image file, any r-th image pixel is recorded as (CB i |NS a , xr), and obtain the image pixel point PP rx (CB i |NS a ) image bit depth, denoted as BD(CB i |NS a , xr); Step S32: Establishing an image compression iterative analysis model: Get all the extracted image file layers and construct the image file layer sample set to be compressed, denoted as SS = {CB i |NS a ,i∈[1,I],a∈[1,A]}; The image file layer sample set of the tth iteration is denoted as SS t , in the image file layer sample set SS t Select any image file layer CB i |NS a , in the image file layer CB i |NS a Select any x-th image file from the image file, and select any image pixel point PP from the x-th image file rx (CB i |NS a ), adjust the image pixel PP rx (CB i |NS a ) bit depth value, set the initial adjustment ratio q, then the image bit depth BD (CB i |NS a , xr) The adjusted image bit depth is recorded as qBD(CB i |NS a , xr); After the image bit depth is adjusted, the process returns to step S2 and updates the image file layer CB. i |NS a Permission security PE(CB i |NS a ), if the updated permission security is less than or equal to the permission security threshold, then the image file layer sample set SS t Eliminate the image file layer CB i |NS a , and output the image file layer sample set after iteration, and record the image file layer sample set after the tth iteration as SS t+1 ; Determine whether the current storage space meets the requirements of the a+1th node segment NS a+1 If the storage requirements of the image files in , the iteration stops; if not, the t+1th iteration is performed, and the image file layer sample set of the t+1th iteration is SS t+1 ; When performing the t+1th iteration, the initial adjustment ratio q is increased, and the increase in the adjustment ratio at each iteration is set, recorded as Q. Then, when performing the t+1th iteration, the adjustment ratio set is q+(t+1)×Q; Until it is determined whether the current storage space satisfies the a+1th node segment NS a+1 The iteration stops when the storage requirement for the image file is reached.

3. An image recognition processing system based on data analysis, characterized in that: The system comprises: a service memory module, a storage authority analysis module, an image compression processing analysis module and a service memory update module; The service memory module classifies and organizes the image files collected by the maritime radar through the service memory, and allocates storage space for each classification and organization; unifies the acquisition frequency nodes to form node segments, and at the end of each node segment, uploads the image files collected by the maritime radar in each node segment to each image file layer in the service memory; The storage permission analysis module counts the weight of the memory size of the service memory occupied by each image file layer in real time at the end of each node segment, and evaluates the permission security of each image file layer to make a prior judgment on the compression of the image file layer; The image compression processing and analysis module identifies each image pixel of each image file and records the image bit depth of each image pixel; through image compression iterative analysis, it is evaluated whether the storage space size of the service memory meets the storage requirements of the image file of the next node segment at the end of the current node segment; if so, the iteration stops; if not, the next iteration is performed until the iteration stops when the requirements are met; The service memory update module, after the iteration stops, updates the storage space size of the service memory and the storage space size of each classification book, which is used for image compression iterative analysis of each image file before the next node segment starts, so as to meet the storage requirements of each image file; The service memory module includes a storage space allocation unit and a collection behavior configuration unit; The storage space allocation unit is used to classify and compile image files collected by the maritime radar and record them in the service memory, wherein one maritime radar corresponds to one classification, and one classification corresponds to a storage space in the service memory; the classification formed by any i-th storage space in the service memory is recorded as CB i ; The acquisition behavior configuration unit is used to unify the acquisition frequency nodes, form a node segment between each adjacent two consecutive acquisition frequency nodes, and record any a-th node segment as NS a ; Obtain the image files collected by the maritime radar in each node segment and upload them to the classification book. The server backend automatically allocates an image file layer in each storage space through software instructions. The image file layer has the unique instruction verification attribute of the node segment. a The corresponding allocated image file layer is denoted as CB i |NS a ; The storage permission analysis module includes a permission security assessment unit and a prior verification judgment unit; The authority security evaluation unit is used to count the weight of the memory size of the service memory occupied by each image file layer in real time at the end of each node segment, denoted as MS(CB i |NS a ), analyze and evaluate the security of permissions of each image file layer, the formula is as follows: In the formula, PE(CB i |NS a ) represents the image file layer CB i |NS a The permission security degree, μ represents the real-time average value of the weight of the memory size of the service memory occupied by each image file layer, and σ 2 represents the real-time variance of the weight of the memory size of the service memory occupied by each image file layer, and A represents the real-time total number of node segments, and I represents the fixed total number of storage spaces; The pre-verification judgment unit is used to preset the permission security threshold. If the image file layer CB i |NS a Permission security PE(CB i |NS a ) is less than or equal to the permission security threshold, then the image file layer CB is extracted i |NS a Otherwise, the image file layer CB is not extracted. i |NS a .

4. The image recognition processing system based on data analysis according to claim 3, characterized in that: The image compression processing and analysis module includes a bit depth data acquisition unit and an image compression iteration analysis unit; The bit depth data acquisition unit is used to identify the extracted image file layer CB i |NS a For each image pixel of the x-th image file, any r-th image pixel is recorded as (CB i |NS a , xr), and obtain the image pixel point PP rx (CB i |NS a ) image bit depth, denoted as BD(CB i |NS a , xr); The image compression iterative analysis unit is used to establish an image compression iterative analysis model: Get all the extracted image file layers and construct the image file layer sample set to be compressed, denoted as SS = {CB i |NS a ,i∈[1,I],a∈[1,A]}; The image file layer sample set of the tth iteration is denoted as SS t , in the image file layer sample set SS t Select any image file layer CB i |NS a , in the image file layer CB i |NS a Select any x-th image file from the image file, and select any image pixel point PP from the x-th image file rx (CB i |NS a ), adjust the image pixel PP rx (CB i |NS a ) bit depth value, set the initial adjustment ratio q, then the image bit depth BD (CB i |NS a , xr) The adjusted image bit depth is recorded as qBD(CB i |NS a , xr); After the image bit depth is adjusted, the process returns to step S2 and updates the image file layer CB. i |NS a Permission security PE(CB i |NS a ), if the updated permission security is less than or equal to the permission security threshold, then the image file layer sample set SS t Eliminate the image file layer CB i |NS a , and output the image file layer sample set after iteration, and record the image file layer sample set after the tth iteration as SS t+1 ; Determine whether the current storage space meets the requirements of the a+1th node segment NS a+1 If the storage requirements of the image files in , the iteration stops; if not, the t+1th iteration is performed, and the image file layer sample set of the t+1th iteration is SS t+1 ; When performing the t+1th iteration, the initial adjustment ratio q is increased, and the increase in the adjustment ratio at each iteration is set, recorded as Q. Then, when performing the t+1th iteration, the adjustment ratio set is q+(t+1)×Q; Until it is determined whether the current storage space satisfies the a+1th node segment NS a+1 The iteration stops when the storage requirement for the image file is reached.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, an image recognition processing method based on data analysis as described in any one of claims 1-2 is implemented.

6. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions are used to execute the image recognition processing method based on data analysis as described in any one of claims 1-2 when executed by a computer processor.

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