CN112422958A

CN112422958A - Network image diagnosis method and image processing apparatus

Info

Publication number: CN112422958A
Application number: CN202011221212.4A
Authority: CN
Inventors: 钱秀华
Original assignee: Individual
Current assignee: Individual
Priority date: 2020-02-28
Filing date: 2020-02-28
Publication date: 2021-02-26
Also published as: CN111372075B; CN112422957A; CN111372075A

Abstract

The network image diagnosis method and the image processing equipment provided by the invention can detect whether the equipment description information corresponding to the image diagnosis equipment stored in the equipment information list is complete before the image to be diagnosed is obtained, and generate the grading label corresponding to each image diagnosis equipment on the premise that the equipment description information corresponding to the image diagnosis equipment stored in the equipment information list is complete. A first image diagnostic trace is then generated based on the hierarchical label and a second image diagnostic trace is derived based on the stored device description information. After the image to be diagnosed is obtained, the second image diagnosis track and the image to be diagnosed can be fused, the target image is sent to the image diagnosis device based on the second image diagnosis track, and the image to be diagnosed is diagnosed through cooperation between the image diagnosis devices. The second image diagnosis track is loaded with compatibility processing instructions of the image diagnosis devices, so that the compatibility among the image diagnosis devices can be improved, and the image diagnosis efficiency can be improved.

Description

Network image diagnosis method and image processing apparatus

Technical Field

The invention relates to the technical field of image processing, in particular to a network image diagnosis method and image processing equipment.

Background

Images are widely applied to various fields of modern communication networks as a carrier form for carrying information. In practical application, abundant information can be obtained through images, and image transmission provides a lot of convenience for information transmission. Taking image diagnosis as an example, the image diagnosis technology can perform deep analysis on an image and further perform corresponding business processing, however, it is difficult for the existing image diagnosis technology to improve the incompatibility between different devices, which may result in low efficiency of image diagnosis.

Disclosure of Invention

In order to improve the above-described problems, the present invention provides a network image diagnosis method and an image processing apparatus.

In a first aspect of the embodiments of the present invention, there is provided a network image diagnosis method applied to an image processing apparatus, the image processing apparatus communicating with a plurality of image diagnosis apparatuses, the method including:

detecting whether equipment description information corresponding to each image diagnosis equipment is stored in a preset equipment information list; if the device description information corresponding to at least one image diagnosis device is not stored in the device information list, sending request information to the at least one image diagnosis device, and calling image coding logic, an image diagnosis mode and a transmission interface type from the at least one image diagnosis device after receiving authorization information fed back by the at least one image diagnosis device according to the request information so as to generate and store the device description information of the at least one image diagnosis device; if the device information list stores the device description information corresponding to each image diagnosis device, generating a hierarchical label corresponding to each image diagnosis device according to the image diagnosis mode in each device description information;

generating a first image diagnosis track according to the hierarchical label of each image diagnosis device, wherein the first image diagnosis generated track comprises a plurality of diagnosis nodes, each diagnosis node corresponds to one image diagnosis device, every two diagnosis nodes are connected through a directed line segment, and the directed line segment is used for representing the sequence of the two image diagnosis devices corresponding to every two diagnosis nodes in image diagnosis;

loading a compatibility processing instruction of the image diagnosis equipment corresponding to the diagnosis node in each diagnosis node of the first image diagnosis track according to the equipment description information corresponding to each image diagnosis equipment stored in the equipment information list to obtain a second image diagnosis track;

acquiring an image to be diagnosed, and fusing the second image diagnosis track and the image to be diagnosed to obtain a target image; and sending the target image to first image diagnosis equipment based on the second image diagnosis track so that the first image diagnosis equipment cooperates with at least part of the second image diagnosis equipment according to the second image diagnosis track included in the target image to diagnose the image to be diagnosed.

Preferably, the fusing the second image diagnosis track and the image to be diagnosed to obtain the target image includes:

identifying an operation log when the corresponding compatible processing instruction is loaded to each diagnostic node from each diagnostic node of the second image diagnostic track, wherein the operation log comprises loading thread parameters stored when the corresponding compatible processing instruction is loaded to each diagnostic node, the loading thread parameters are used for building loading threads for loading the corresponding compatible processing instruction to each diagnostic node, and the loading thread parameters corresponding to different diagnostic nodes are different;

determining image coding information corresponding to the image to be diagnosed and an execution function call list of transcoding the image coding information by the image processing device, wherein the image coding information is information corresponding to the image to be diagnosed during transmission, and the execution function call list comprises path information of a target execution function for transcoding the image coding information;

determining the path information from the execution function calling list, identifying a calling path and a non-calling path of the path information, and associating the calling path in the path information with a track curve of the second image diagnosis track; the calling path is a path for calling the target execution function, the non-calling path is a path for establishing a path logic relationship for the calling path, and a track curve of the second image diagnosis track is obtained through a first relative position of each diagnosis node in the second image diagnosis track and a second relative position corresponding to two diagnosis nodes connected with each directed line segment;

calling the target execution function according to the calling path to transcode the image coding information to obtain transcoding information, mapping the track curve to the transcoding information according to the incidence relation between the calling path and the track curve, acquiring redundant information in the transcoding information except for the mapping information corresponding to the track curve when mapping the track curve, and removing the redundant information; and determining the target image according to the transcoding information from which the redundant information is removed.

Preferably, the transmitting the target image to the first image diagnosis apparatus based on the second image diagnosis trajectory includes:

analyzing diagnosis requirement information from the target image, and determining at least partial image diagnosis categories corresponding to the target image according to the diagnosis requirement information;

determining a device identifier of an image diagnosis device corresponding to each image diagnosis category according to each image diagnosis category in at least partial image diagnosis categories;

mapping each determined equipment identifier to the second image diagnosis track, determining a target diagnosis node corresponding to each equipment identifier, determining a current diagnosis node according to the sequence of the target diagnosis nodes in the second image diagnosis track, determining first image diagnosis equipment according to the current diagnosis node, and sending the target image to the first image diagnosis equipment.

Preferably, the transmitting the target image to the first image diagnosis apparatus includes:

analyzing a plurality of communication links established with the first image diagnosis device, listing link protocols of the communication links, and establishing a protocol sequence; the protocol sequence is a multi-stage sequence, each stage of sequence corresponds to a sequence identifier, each sequence identifier is provided with at least one link protocol, and each stage of sequence of the protocol sequence has a high-to-low ordering relation;

reading the parameterized features of the target image in each communication link; extracting a target link protocol in at least one protocol sequence contained in the parameterized features of the target image;

establishing a conversion relation between a target communication link corresponding to the target link protocol and the protocol sequence, and generating a transmission path topology according to the conversion relation; wherein, generating the transmission path topology according to the conversion relation comprises: converting each target communication link into a path parameter format; respectively generating at least one parameter array of each path parameter format; acquiring a parameter array which is not repeated by the target communication link to form an array set; mapping each parameter array in the array set to the protocol sequence to form a transmission path topology;

comparing the link protocols contained in the parameterized features of the target image with each link protocol in the transmission path topology one by one; in the comparison process one by one, if all target link protocols corresponding to one parameter array are contained in the parameterized features of the target image, the parameter array is recorded as an initial transmission path of the target image, a final transmission path which is in accordance with the target image is determined according to each initial transmission path of the target image, and the target image is sent to the first image diagnosis device based on the final transmission path.

Preferably, the causing the first image diagnosis apparatus to cooperate with at least a part of the second image diagnosis apparatus according to the second image diagnosis trajectory included in the target image to diagnose the image to be diagnosed includes:

enabling the first image diagnosis device to execute corresponding image diagnosis on the image to be processed included in the target image to obtain a current diagnosis result, and then overlapping the current diagnosis result to the target image to obtain a current diagnosis image;

and enabling the first image diagnosis device to send the current diagnosis image to a second image diagnosis device behind the diagnosis sequence of the first image diagnosis device according to a second image diagnosis track included in the current diagnosis image, enabling the second image diagnosis device behind the diagnosis sequence of the first image diagnosis device to execute steps similar to those of the first image diagnosis device until the diagnosis of the image to be diagnosed is completed and the current diagnosis result sent by the image diagnosis device which completes the image diagnosis at last is obtained, and superposing a plurality of diagnosis results in the current diagnosis result sent by the image diagnosis device which completes the image diagnosis at last.

Preferably, the superimposing the current diagnosis result to the target image to obtain a current diagnosis image includes:

determining a result distribution sequence in the current diagnosis result, wherein the result distribution sequence is used for indicating that the coding mode of the current diagnosis result is changed;

determining a coding difference coefficient between a first result of the modified coding mode and a second result of the unmodified coding mode in the current diagnosis result according to the result distribution sequence;

based on the coding difference coefficient, splicing a first result and a second result in the current diagnosis result and determining a current coding mode of the spliced first result and the spliced second result;

determining a historical encoding mode of the superposed third result in the target image; when the historical coding mode is the same as the current coding mode, splicing the current diagnosis result and the third result to obtain a current diagnosis image; and when the historical coding mode is different from the current coding mode, transcoding the current diagnosis result according to the historical coding mode, and splicing the current diagnosis result with the third result to obtain the current diagnosis image.

Preferably, the method further comprises:

determining a communication state parameter set acquired on a per image diagnostic apparatus basis;

determining, for a current state parameter in a communication state parameter set corresponding to each image diagnostic apparatus, an update frequency of the current state parameter in a set time period based on a first cumulative value in which the current state parameter is updated in the set time period and a second cumulative value in which each of the communication state parameter sets is updated in the set time period;

determining the updating frequency of the current state parameter updated between two adjacent set time periods according to the updating frequency of the current state parameter in the two adjacent set time periods;

determining whether the current state parameter is an abnormal state parameter based on the updating frequency; when the current state parameter is an abnormal state parameter, determining the fluctuation trend of the updated second accumulated value of each communication state parameter set in two adjacent set time periods according to the updating frequency of the current state parameter in the two adjacent set time periods and the updated second accumulated value of each communication state parameter set in each set time period;

and determining whether the image diagnosis equipment corresponding to the current state parameter is in a stable communication state or not based on the fluctuation trend of the second accumulated value, and performing parameter optimization on the network environment where the image diagnosis equipment corresponding to the current state parameter is located when determining that the image diagnosis equipment corresponding to the current state parameter is not in the stable communication state.

In a second aspect of the embodiments of the present invention, there is provided a network image diagnosis apparatus including:

the detection module is used for detecting whether equipment description information corresponding to each image diagnosis equipment is stored in a preset equipment information list; if the device description information corresponding to at least one image diagnosis device is not stored in the device information list, sending request information to the at least one image diagnosis device, and calling image coding logic, an image diagnosis mode and a transmission interface type from the at least one image diagnosis device after receiving authorization information fed back by the at least one image diagnosis device according to the request information so as to generate and store the device description information of the at least one image diagnosis device; if the device information list stores the device description information corresponding to each image diagnosis device, generating a hierarchical label corresponding to each image diagnosis device according to the image diagnosis mode in each device description information;

the generating module is used for generating a first image diagnosis track according to the hierarchical label of each image diagnosis device, the first image diagnosis track comprises a plurality of diagnosis nodes, each diagnosis node corresponds to one image diagnosis device, every two diagnosis nodes are connected through a directed line segment, and the directed line segment is used for representing the sequence of the two image diagnosis devices corresponding to every two diagnosis nodes in image diagnosis;

a loading module, configured to load, according to device description information corresponding to each image diagnostic device stored in the device information list, a compatibility processing instruction of the image diagnostic device corresponding to the diagnostic node in each diagnostic node of the first image diagnostic track to obtain a second image diagnostic track;

the diagnosis module is used for acquiring an image to be diagnosed and fusing the second image diagnosis track and the image to be diagnosed to obtain a target image; and sending the target image to first image diagnosis equipment based on the second image diagnosis track so that the first image diagnosis equipment cooperates with at least part of the second image diagnosis equipment according to the second image diagnosis track included in the target image to diagnose the image to be diagnosed.

In a third aspect of embodiments of the present invention, there is provided an image processing apparatus including: a processor and a memory and bus connected to the processor; the processor and the memory are communicated with each other through the bus; the processor is used for calling the computer program in the memory to execute the network image diagnosis method.

In a fourth aspect of the embodiments of the present invention, there is provided a readable storage medium on which a program is stored, the program implementing the network image diagnosis method described above when executed by a processor.

The network image diagnosis method and the image processing device provided by the embodiment of the invention can detect whether the device description information corresponding to the image diagnosis device stored in the device information list is complete before the image to be diagnosed is obtained, and generate the hierarchical label corresponding to each image diagnosis device on the premise that the device description information corresponding to the image diagnosis device stored in the device information list is complete. A first image diagnostic trace is then generated based on the hierarchical label and a second image diagnostic trace is derived based on the stored device description information. After the image to be diagnosed is obtained, the second image diagnosis track and the image to be diagnosed can be fused, the target image is sent to the image diagnosis device based on the second image diagnosis track, and the image to be diagnosed is diagnosed through cooperation between the image diagnosis devices. The second image diagnosis track is loaded with compatibility processing instructions of the image diagnosis devices, so that the compatibility among the image diagnosis devices can be improved, and the image diagnosis efficiency can be improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.

Fig. 1 is a schematic communication connection diagram of a network image diagnosis system according to an embodiment of the present invention.

Fig. 2 is a flowchart of a network image diagnosis method according to an embodiment of the present invention.

Fig. 3 is a functional block diagram of a network image diagnosis apparatus according to an embodiment of the present invention.

Fig. 4 is a schematic diagram of product modules of an image processing apparatus according to an embodiment of the present invention.

Icon:

100-network image diagnosis system;

200-an image processing apparatus; 201-network image diagnosis device; 2011-detection module; 2012-a generation module; 2013-loading module; 2014-a diagnostic module; 211-a processor; 212-a memory; 213-a bus;

300-image diagnosis device.

Detailed Description

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

In order to better understand the technical solutions of the present invention, the following detailed descriptions of the technical solutions of the present invention are provided with the accompanying drawings and the specific embodiments, and it should be understood that the specific features in the embodiments and the examples of the present invention are the detailed descriptions of the technical solutions of the present invention, and are not limitations of the technical solutions of the present invention, and the technical features in the embodiments and the examples of the present invention may be combined with each other without conflict.

In order to improve the incompatibility of devices generated by common image diagnosis technologies, embodiments of the present invention provide a network image diagnosis method and an image processing device, which can improve the compatibility between different devices, enable image transmission and collaborative diagnosis between different devices to be performed without obstacles, and improve the efficiency of image diagnosis.

Referring to fig. 1, a schematic structural diagram of a network image diagnosis system 100 according to an embodiment of the present invention is shown, where the network image diagnosis system 100 includes an image processing apparatus 200 and a plurality of image diagnosis apparatuses 300 communicatively connected to each other. In the present embodiment, the image processing apparatus 200 and the image diagnosis apparatus 300 may be a handheld terminal, a laptop computer, a notebook computer, a microcomputer, and the like, and are not limited thereto.

In fig. 1, the image processing apparatus 200 is configured to send an acquired image to be detected to one of the image diagnosis apparatuses 300, and the image diagnosis apparatus 300 performs image diagnosis on the image to be detected, and then continuously sends a diagnosis result and the image to be diagnosed to the other image diagnosis apparatus 300, so as to perform iterative diagnosis on the image to be diagnosed.

In fig. 1, each image diagnosis apparatus 300 can perform diagnosis of a corresponding dimension on an image to be diagnosed, and compatibility improvement is performed between the image processing apparatus 200 and each image diagnosis apparatus 300 to ensure barrier-free transmission of the image to be diagnosed, so that coordinated diagnosis between the image processing apparatus 200 and each image diagnosis apparatus 300 can be achieved, thereby improving the efficiency of image diagnosis.

On the basis of the above, please refer to fig. 2, which is a flowchart of a network image diagnosis method according to an embodiment of the present invention, the method can be applied to the image processing apparatus 200 in fig. 1, and specifically includes the following steps.

Step S21, detecting whether or not device description information corresponding to each image diagnostic device is stored in a preset device information list; if the device description information corresponding to at least one image diagnosis device is not stored in the device information list, sending request information to the at least one image diagnosis device, and calling image coding logic, an image diagnosis mode and a transmission interface type from the at least one image diagnosis device after receiving authorization information fed back by the at least one image diagnosis device according to the request information so as to generate and store the device description information of the at least one image diagnosis device; and if the equipment description information corresponding to each image diagnosis equipment is stored in the equipment information list, generating a hierarchical label corresponding to each image diagnosis equipment according to the image diagnosis mode in each equipment description information.

In the present embodiment, the device description information is used to characterize the image coding logic, the image diagnosis mode, and the transmission interface type of each image diagnosis device 300, and the image processing device 200 may determine the compatibility adjustment policy with each image diagnosis device 300 through the image coding logic, the image diagnosis mode, and the transmission interface type of each image diagnosis device 300, and then perform the compatibility adjustment of each image diagnosis device 300 before performing image transmission interaction with each image diagnosis device 300, so as to avoid distortion or loss during image transmission.

In this embodiment, the hierarchical label is used to represent the sequence of the image diagnosis apparatus 300 during image diagnosis, the hierarchical label can be represented by a numerical value between 1 and 10, the higher the numerical value of the hierarchical label, the earlier the sequence of the corresponding image diagnosis apparatus 300 during image diagnosis, and by setting the hierarchical label for the image diagnosis apparatus 300, the image diagnosis modes corresponding to the image diagnosis apparatus 300 can be sorted to reduce the diagnosis interference of the image diagnosis modes of adjacent sequence to the image diagnosis image.

Step S22, a first image diagnosis track is generated according to the hierarchical label of each image diagnosis device, where the first image diagnosis track includes a plurality of diagnosis nodes, each diagnosis node corresponds to one image diagnosis device, and every two diagnosis nodes are connected by a directed line segment, where the directed line segment is used to represent the sequence of two image diagnosis devices corresponding to every two diagnosis nodes when performing image diagnosis.

Step S23, according to the device description information corresponding to each image diagnostic device stored in the device information list, loading a compatibility processing instruction of the image diagnostic device corresponding to the diagnostic node in each diagnostic node of the first image diagnostic trajectory to obtain a second image diagnostic trajectory.

In this embodiment, the compatibility processing instruction corresponding to each diagnostic node includes a compatibility adjustment parameter corresponding to a previous diagnostic node of the diagnostic node and a compatibility adjustment parameter corresponding to a next diagnostic node of the diagnostic node. For the sake of understanding, the first image diagnosis track includes a diagnosis node a, a diagnosis node B, and a diagnosis node C.

Further, the image diagnosis order corresponding to the first image diagnosis track may be a diagnosis node a, a diagnosis node B and a diagnosis node C. Correspondingly, in the second image diagnosis track, the compatibility processing instruction loaded in the diagnosis node B includes a compatibility adjustment parameter corresponding to the diagnosis node a and a compatibility adjustment parameter corresponding to the diagnosis node C.

Therefore, the diagnostic node B can adjust the equipment parameters thereof according to the upstream and downstream compatibility adjustment parameters, so as to ensure the compatibility of the diagnostic node B in image transmission with the diagnostic node A and the diagnostic node C and avoid the distortion or loss of images in the image diagnostic equipment corresponding to different diagnostic nodes.

Step S24, acquiring an image to be diagnosed, and fusing the second image diagnosis track and the image to be diagnosed to obtain a target image; and sending the target image to first image diagnosis equipment based on the second image diagnosis track so that the first image diagnosis equipment cooperates with at least part of the second image diagnosis equipment according to the second image diagnosis track included in the target image to diagnose the image to be diagnosed.

In this embodiment, the first image diagnosis device may be a corresponding image diagnosis device in the second image diagnosis trajectory for performing the first round of image diagnosis on the image to be diagnosed. For example, the image processing apparatus 200 and five image diagnosis apparatuses M1 to M5 communicate with each other.

When the image processing device 200 acquires the image to be diagnosed, the image diagnosis device to be used for performing image diagnosis on the image to be diagnosed can be determined according to the diagnosis requirement corresponding to the image to be diagnosed, the diagnosis requirement can be acquired when the image to be diagnosed is acquired, and the image to be diagnosed and the diagnosis requirement can be sent to the image processing device 200 through the user terminal.

For example, the image processing apparatus 200 determines that the image diagnosis apparatuses to be subjected to image diagnosis on the image to be diagnosed are M2, M3, and M5 according to the diagnosis requirement corresponding to the image to be diagnosed, and then the image processing apparatus 200 sends the target image to the image diagnosis apparatus M2, so that the image diagnosis apparatus M2 cooperates with the image diagnosis apparatus M3 and the image diagnosis apparatus M5 to diagnose the image to be diagnosed according to the second image diagnosis trajectory in the target image.

For another example, the image diagnosis apparatus M2 may adjust its own apparatus parameters according to the compatibility processing instruction loaded in the second image diagnosis trajectory, so as to ensure compatibility when the image diagnosis apparatus M2 and the image diagnosis apparatus M3 perform image transmission to be diagnosed, and avoid distortion or loss when the image to be diagnosed is transmitted between different image diagnosis apparatuses.

It is understood that, by cooperation of the image diagnosis apparatus M2, the image diagnosis apparatus M3, and the image diagnosis apparatus M5, it is possible to perform a multi-aspect diagnosis on an image to be diagnosed, determine a diagnosis result corresponding to a diagnosis requirement, and return all the diagnosis results to the image processing apparatus 200 by the last image diagnosis apparatus that completes the diagnosis of the image to be diagnosed. In the above example, all the diagnosis results may be returned to the image processing apparatus 200 by the image diagnosis apparatus M5.

In this embodiment, through steps S21 to S25, it is possible to detect whether the device description information corresponding to the image diagnostic device stored in the device information list is complete before the image to be diagnosed is acquired, and generate the hierarchical label corresponding to each image diagnostic device on the premise that the device description information corresponding to the image diagnostic device stored in the device information list is complete. A first image diagnostic trace is then generated based on the hierarchical label and a second image diagnostic trace is derived based on the stored device description information. After the image to be diagnosed is obtained, the second image diagnosis track and the image to be diagnosed can be fused, the target image is sent to the image diagnosis device based on the second image diagnosis track, and the image to be diagnosed is diagnosed through cooperation between the image diagnosis devices. The second image diagnosis track is loaded with compatibility processing instructions of the image diagnosis devices, so that the compatibility among the image diagnosis devices can be improved, and the image diagnosis efficiency can be improved.

In an alternative embodiment, in order to ensure that the plurality of image diagnosis apparatuses 300 do not interfere with each other when diagnosing images in advance and then ensure the accuracy of image diagnosis, in step S21, the step of generating the hierarchical label corresponding to each image diagnosis apparatus according to the image diagnosis manner in each apparatus description information may specifically include the following.

Step S211, determining a diagnosis logic sequence corresponding to the image diagnosis mode in each piece of device description information, and a plurality of diagnosis influence factors, where the diagnosis logic sequence is used to characterize the characteristic processing behavior of the image diagnosis mode on the image, and the diagnosis influence factors are used to characterize the influence of the image diagnosis mode on the image quality of the image.

In this embodiment, the image quality includes, but is not limited to, gray scale quality, boundary quality, smooth quality, etc., the influence of different image diagnosis manners on the image quality of the image is different, and the diagnosis accuracy of some image diagnosis manners may be limited by the image quality, for this reason, it is necessary to accurately determine the hierarchical label corresponding to each image diagnosis apparatus according to the diagnosis logic sequence and the diagnosis influence factor corresponding to the image diagnosis manner in the description information of each apparatus, and further perform image diagnosis sorting for each image diagnosis apparatus, so as to ensure the diagnosis accuracy of the image after continuous diagnosis by each image diagnosis apparatus.

Step S212, when it is determined that the device description information includes the first weight list according to the diagnosis logic sequence, determining a first matching coefficient between each diagnosis influence factor of the device description information in the second weight list and each diagnosis influence factor of the device description information in the first weight list according to the diagnosis influence factor and the position information of the device description information in the first weight list.

In this embodiment, the first weight list may be a multiple influence weight list, and the second weight list may be a single influence weight list. Wherein the diagnostic impact factors in the multiple list of impact weights and the diagnostic impact factors in the single list of impact weights are adjustable with respect to each other.

Step S213, transferring the diagnosis influence factor of which the first matching coefficient between the diagnosis influence factors of the device description information under the second weight list and under the first weight list reaches a preset value to the first weight list.

In this embodiment, the preset value may be set according to actual conditions, for example, the preset value may be set according to the number of the device description information, and the larger the number of the device description information is, the smaller the preset value may be.

Step S214, when the device description information includes a plurality of diagnosis influence factors in the second weight list, determining a second matching coefficient between the diagnosis influence factors of the device description information in the second weight list according to the diagnosis influence factors of the device description information in the first weight list and the position information thereof, and screening the diagnosis influence factors in the second weight list according to the second matching coefficient between the diagnosis influence factors; and setting a list position grade for the screened target diagnosis influence factor according to the diagnosis influence factor and the position information of each piece of equipment description information in the first weight list, and transferring the target diagnosis influence factor to a list interval corresponding to the list position grade in the first weight list.

Step S215, determining the hierarchical label of the image diagnosis device corresponding to each device description information according to all the diagnosis influence factors located in the first weight list.

In this embodiment, the hierarchical label corresponding to each image diagnosis device can be accurately determined according to the diagnosis logic sequence corresponding to the image diagnosis manner and the diagnosis influence factor in the device description information, so as to perform image diagnosis sequencing for each image diagnosis device, and ensure the diagnosis accuracy of the images after continuous diagnosis through each image diagnosis device.

In specific implementation, in order to avoid the data capacity of the target image being too large, when the second image diagnosis track is fused with the image to be diagnosed, the redundant data between the second image diagnosis track and the image to be diagnosed needs to be accurately extracted, so that the redundant data is deleted during fusion, so as to reduce the data capacity corresponding to the target image. For this purpose, in step S24, the second image diagnosis track and the image to be diagnosed are fused to obtain the target image, which may specifically include the following.

Step S2411, identifying, from each diagnostic node in the second image diagnostic trajectory, an operation log when a corresponding compatible processing instruction is loaded to each diagnostic node, where the operation log includes a loading thread parameter stored when a corresponding compatible processing instruction is loaded to each diagnostic node, the loading thread parameter is used to build a loading thread that loads a corresponding compatible processing instruction to each diagnostic node, and the loading thread parameters corresponding to different diagnostic nodes are different.

Step S2412, determining image coding information corresponding to the image to be diagnosed and an execution function call list for transcoding the image coding information by the image processing device, where the image coding information is information corresponding to the image to be diagnosed when the image is transmitted, and the execution function call list includes path information of a target execution function for transcoding the image coding information.

Step S2413, determining the path information from the execution function calling list, identifying a calling path and a non-calling path of the path information, and associating the calling path in the path information with a trajectory curve of the second image diagnosis trajectory; the calling path is a path for calling the target execution function, the non-calling path is a path for establishing a path logic relationship for the calling path, and the trajectory curve of the second image diagnosis trajectory is obtained through a first relative position of each diagnosis node in the second image diagnosis trajectory and a second relative position corresponding to two diagnosis nodes connected by each directed line segment.

Step S2414, calling the target execution function according to the calling path to transcode the image coding information to obtain transcoding information, mapping the track curve to the transcoding information according to the incidence relation between the calling path and the track curve, acquiring redundant information in the transcoding information except for the mapping information corresponding to the track curve when mapping the track curve, and removing the redundant information; and determining the target image according to the transcoding information from which the redundant information is removed.

Based on the steps S2411 to S2414, the redundant data between the second image diagnosis track and the image to be diagnosed can be accurately extracted when the second image diagnosis track and the image to be diagnosed are fused, so that the redundant data is deleted during the fusion, and the data capacity corresponding to the target image is reduced.

In particular, in order to reduce the number of times of transmission of target images in a plurality of image diagnosis apparatuses to improve the transmission loss of the target images, it is necessary to accurately determine an initial image diagnosis apparatus corresponding to the target images according to the diagnosis requirement information. For this purpose, in step S24, the sending the target image to the first image diagnostic apparatus based on the second image diagnostic track may specifically include the following.

Step S2421, analyzing diagnosis requirement information from the target image, and determining at least partial image diagnosis categories corresponding to the target image according to the diagnosis requirement information.

In the present embodiment, different diagnostic categories correspond to different image diagnostic apparatuses 300, wherein the total number of image diagnostic categories is equal to or less than the total number of image diagnostic apparatuses 300 in the network image diagnostic system 100 shown in fig. 1, and therefore, it is necessary to determine the image diagnostic apparatus 300 that performs image diagnosis first according to the number of image diagnostic categories.

Step S2422 determines a device identification of the image diagnostic device corresponding to each image diagnostic category from each of the at least partial image diagnostic categories.

In the present embodiment, the device identification is used to distinguish between different image diagnostic devices 300.

Step S2423, mapping each determined device identifier to the second image diagnosis track, determining a target diagnosis node corresponding to each device identifier, determining a current diagnosis node according to the sequence of the target diagnosis nodes in the second image diagnosis track, determining a first image diagnosis device according to the current diagnosis node, and sending the target image to the first image diagnosis device.

It is understood that based on the steps S2421 to S2423, at least a partial image diagnosis category corresponding to the target image can be determined based on the diagnosis requirement information, so as to determine the image diagnosis device 300 actually participating in the image diagnosis in the network image diagnosis system 100 according to the at least partial image diagnosis category, and thus, the image diagnosis device 300 not participating in the diagnosis of the target image does not need to be enabled, and the number of times of transmission of the target image in the plurality of image diagnosis devices 300 is reduced, thereby improving the transmission loss of the target image.

In a specific process of executing the above method, in order to ensure the rate of transferring the target image from the data processing apparatus 200 to the first image diagnosis apparatus, in step S2423, the sending of the target image to the first image diagnosis apparatus may specifically include the following.

(1) Analyzing a plurality of communication links established with the first image diagnosis device, listing link protocols of the communication links, and establishing a protocol sequence; the protocol sequence is a multi-stage sequence, each stage of sequence corresponds to a sequence identifier, each sequence identifier is provided with at least one link protocol, and each stage of sequence of the protocol sequence has a high-to-low ordering relation.

(2) Reading the parameterized features of the target image in each communication link; and extracting a target link protocol in at least one protocol sequence contained in the parameterized features of the target image.

(3) Establishing a conversion relation between a target communication link corresponding to the target link protocol and the protocol sequence, and generating a transmission path topology according to the conversion relation; wherein, generating the transmission path topology according to the conversion relation comprises: converting each target communication link into a path parameter format; respectively generating at least one parameter array of each path parameter format; acquiring a parameter array which is not repeated by the target communication link to form an array set; and mapping each parameter array in the array set to the protocol sequence to form a transmission path topology.

(4) Comparing the link protocols contained in the parameterized features of the target image with each link protocol in the transmission path topology one by one; in the comparison process one by one, if all target link protocols corresponding to one parameter array are contained in the parameterized features of the target image, the parameter array is recorded as an initial transmission path of the target image, a final transmission path which is in accordance with the target image is determined according to each initial transmission path of the target image, and the target image is sent to the first image diagnosis device based on the final transmission path.

It can be understood that, by the above, a final transmission path to which the target image corresponds can be determined, and based on the final transmission path, the rate at which the target image is transmitted from the data processing apparatus 200 to the first image diagnosis apparatus can be ensured, thereby improving the timeliness of the subsequent image diagnosis.

In order to ensure the continuity of the diagnosis result and reduce the error caused by the transmission of the diagnosis result between the plurality of image diagnosis apparatuses 300, in step S24, the making of the first image diagnosis apparatus cooperate with at least a part of the second image diagnosis apparatus according to the second image diagnosis trajectory included in the target image to diagnose the image to be diagnosed may specifically include the following.

Step S2431 is to enable the first image diagnosis device to perform corresponding image diagnosis on the image to be processed included in the target image to obtain a current diagnosis result, and then superimpose the current diagnosis result on the target image to obtain a current diagnosis image.

Step S2432 is to make the first image diagnostic apparatus send the current diagnostic image to a second image diagnostic apparatus located after the diagnostic sequence of the first image diagnostic apparatus according to a second image diagnostic track included in the current diagnostic image and make the second image diagnostic apparatus located after the diagnostic sequence of the first image diagnostic apparatus execute a step similar to the first image diagnostic apparatus until the diagnosis of the image to be diagnosed is completed and the current diagnostic result sent by the image diagnostic apparatus which has finally completed the image diagnosis is obtained, and a plurality of diagnostic results are superimposed in the current diagnostic result sent by the image diagnostic apparatus which has finally completed the image diagnosis.

It is understood that based on steps S2431 to S2432, the diagnostic results diagnosed by each image diagnostic apparatus 300 can be superimposed one by one and transmitted to the image processing apparatus 200 by the image diagnostic apparatus that has completed the image diagnosis last, and thus, the continuity of the diagnostic results can be ensured and errors caused by the transmission of the diagnostic results among the plurality of image diagnostic apparatuses 300 can be reduced.

In a specific implementation, in order to further reduce errors caused by transmission of diagnosis results among the plurality of image diagnosis apparatuses 300, in step S2431, the current diagnosis result is superimposed on the target image to obtain a current diagnosis image, and the following may be specifically included.

(1) Determining a result distribution sequence in the current diagnosis result, wherein the result distribution sequence is used for indicating that the coding mode of the current diagnosis result is changed.

(2) And determining a coding difference coefficient between a first result of the modified coding mode and a second result of the unmodified coding mode in the current diagnosis result according to the result distribution sequence.

(3) And splicing the first result and the second result in the current diagnosis result based on the coding difference coefficient, and determining the current coding mode of the spliced first result and the spliced second result.

(4) Determining a historical encoding mode of the superposed third result in the target image; when the historical coding mode is the same as the current coding mode, splicing the current diagnosis result and the third result to obtain a current diagnosis image; and when the historical coding mode is different from the current coding mode, transcoding the current diagnosis result according to the historical coding mode, and splicing the current diagnosis result with the third result to obtain the current diagnosis image.

Based on the above, the encoding method of different diagnostic results can be taken into account when the diagnostic results are superimposed, so that the different diagnostic results can be spliced based on the same encoding method when the diagnostic results are superimposed, and errors caused by the transmission of the diagnostic results among the plurality of image diagnostic apparatuses 300 can be reduced.

On the basis of the above, in order to ensure the stability of communication between the image processing apparatus 200 and the image diagnostic apparatus 300 and thus the reliability of transmission of the image to be processed and the diagnostic result between the image processing apparatus 200 and the image diagnostic apparatus 300, the image processing apparatus 200 may further perform the following steps before step S21.

In step S31, the communication-state parameter set acquired on a per image diagnostic apparatus basis is determined.

In step S32, for a current state parameter in a communication state parameter set corresponding to each image diagnostic apparatus, an update frequency of the current state parameter in a set time period is determined based on a first integrated value in which the current state parameter is updated in the set time period and a second integrated value in which each of the communication state parameter sets is updated in the set time period.

Step S33, determining the update frequency of the current state parameter updated between two adjacent set time periods according to the update frequency of the current state parameter in two adjacent set time periods.

Step S34, determining whether the current state parameter is an abnormal state parameter based on the updating frequency; when the current state parameter is an abnormal state parameter, determining the fluctuation trend of the updated second accumulated value of each communication state parameter set in two adjacent set time periods according to the updating frequency of the current state parameter in two adjacent set time periods and the updated second accumulated value of each communication state parameter set in each set time period.

Step S35, determining whether the image diagnostic apparatus corresponding to the current state parameter is in a stable communication state based on the fluctuation trend of the second accumulated value, and performing parameter optimization on the network environment where the image diagnostic apparatus corresponding to the current state parameter is located when it is determined that the image diagnostic apparatus corresponding to the current state parameter is not in the stable communication state.

In the present embodiment, based on the above, it is possible to detect and analyze the communication network state of each image diagnostic apparatus 300, and further perform parameter optimization on the network environment when the image diagnostic apparatus 300 is not in the communication stable state, thereby ensuring the communication stability between the image processing apparatus 200 and the image diagnostic apparatus 300, and further ensuring the reliability of the transmission of the image to be processed and the diagnostic result between the image processing apparatus 200 and the image diagnostic apparatus 300.

On the basis of the above, please refer to fig. 3, which is a block diagram of a network image diagnosis apparatus 201 according to an embodiment of the present invention, wherein the network image diagnosis apparatus 201 may include the following modules.

A detecting module 2011, configured to detect whether device description information corresponding to each image diagnostic device is stored in a preset device information list; if the device description information corresponding to at least one image diagnosis device is not stored in the device information list, sending request information to the at least one image diagnosis device, and calling image coding logic, an image diagnosis mode and a transmission interface type from the at least one image diagnosis device after receiving authorization information fed back by the at least one image diagnosis device according to the request information so as to generate and store the device description information of the at least one image diagnosis device; and if the equipment description information corresponding to each image diagnosis equipment is stored in the equipment information list, generating a hierarchical label corresponding to each image diagnosis equipment according to the image diagnosis mode in each equipment description information.

A generating module 2012, configured to generate a first image diagnosis track according to the hierarchical label of each image diagnosis device, where the first image diagnosis track includes a plurality of diagnosis nodes, each diagnosis node corresponds to one image diagnosis device, and every two diagnosis nodes are connected by a directed line segment, where the directed line segment is used to represent a sequence of two image diagnosis devices corresponding to every two diagnosis nodes when performing image diagnosis.

A loading module 2013, configured to load, according to the device description information corresponding to each image diagnostic device stored in the device information list, a compatibility processing instruction of the image diagnostic device corresponding to the diagnostic node in each diagnostic node of the first image diagnostic track to obtain a second image diagnostic track.

The diagnosis module 2014 is used for acquiring an image to be diagnosed, and fusing the second image diagnosis track and the image to be diagnosed to obtain a target image; and sending the target image to first image diagnosis equipment based on the second image diagnosis track so that the first image diagnosis equipment cooperates with at least part of the second image diagnosis equipment according to the second image diagnosis track included in the target image to diagnose the image to be diagnosed.

An embodiment of the present invention further provides a readable storage medium, on which a program is stored, which when executed by a processor implements the network image diagnosis method described above.

The embodiment of the invention also provides a processor, which is used for running the program, wherein the network image diagnosis method is executed when the program runs.

In the present embodiment, as shown in fig. 4, the image processing apparatus 200 includes at least one processor 211, and at least one memory 212 and a bus 213 connected to the processor 211. The processor 211 and the memory 212 are in communication with each other via a bus 213. The processor 211 is used for calling the program instructions in the memory 212 to execute the network image diagnosis method described above.

To sum up, the network image diagnosis method and the image processing device provided by the embodiments of the present invention can detect whether the device description information corresponding to the image diagnosis device stored in the device information list is complete before the image to be diagnosed is acquired, and generate the hierarchical label corresponding to each image diagnosis device on the premise that the device description information corresponding to the image diagnosis device stored in the device information list is complete. A first image diagnostic trace is then generated based on the hierarchical label and a second image diagnostic trace is derived based on the stored device description information. After the image to be diagnosed is obtained, the second image diagnosis track and the image to be diagnosed can be fused, the target image is sent to the image diagnosis device based on the second image diagnosis track, and the image to be diagnosed is diagnosed through cooperation between the image diagnosis devices. The second image diagnosis track is loaded with compatibility processing instructions of the image diagnosis devices, so that the compatibility among the image diagnosis devices can be improved, and the image diagnosis efficiency can be improved.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, cloud image processing devices (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing cloud image processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing cloud image processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a cloud image processing device includes one or more processors (CPUs), memory, and a bus. The cloud image processing device may also include an input/output interface, a network interface, and the like.

The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip. The memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), random access memory with other feature weights (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Disc (DVD) or other optical storage, magnetic tape cassettes, magnetic tape disk storage or other magnetic storage cloud image processing devices, or any other non-transmission medium that can be used to store information that can be matched by a computing cloud image processing device. As defined herein, computer readable media does not include transitory computer readable media such as modulated data signals and carrier waves.

It is also to be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or cloud image processing apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or cloud image processing apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or cloud image processing apparatus that comprises the element.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A network image diagnosis method applied to an image processing apparatus which communicates with a plurality of image diagnosis apparatuses, the method comprising:

acquiring an image to be diagnosed, and fusing the second image diagnosis track and the image to be diagnosed to obtain a target image; sending the target image to first image diagnosis equipment based on the second image diagnosis track so that the first image diagnosis equipment cooperates with at least part of the second image diagnosis equipment according to the second image diagnosis track included in the target image to diagnose the image to be diagnosed;

the method for generating the hierarchical label corresponding to each image diagnosis device according to the image diagnosis mode in each device description information comprises the following steps:

determining a diagnosis logic sequence corresponding to an image diagnosis mode in each piece of equipment description information and a plurality of diagnosis influence factors, wherein the diagnosis logic sequence is used for representing the characteristic processing behavior of the image diagnosis mode on the image, and the diagnosis influence factors are used for representing the influence of the image diagnosis mode on the image quality of the image;

when determining that the equipment description information contains a first weight list according to the diagnosis logic sequence, determining a first matching coefficient between each diagnosis influence factor of the equipment description information under a second weight list and each diagnosis influence factor of the equipment description information under the first weight list according to the diagnosis influence factor and the position information of the equipment description information under the first weight list;

transferring the diagnosis influence factor of which the first matching coefficient between the diagnosis influence factors of the equipment description information under the second weight list and the diagnosis influence factor under the first weight list reaches a preset value to the first weight list;

when each piece of equipment description information contains a plurality of diagnosis influence factors in the second weight list, determining second matching coefficients among the diagnosis influence factors of each piece of equipment description information in the second weight list according to the diagnosis influence factors and position information of each piece of equipment description information in the first weight list, and screening each diagnosis influence factor in the second weight list according to the second matching coefficients among the diagnosis influence factors; setting list position grades for the screened target diagnosis influence factors according to the diagnosis influence factors and the position information of the equipment description information in the first weight list, and transferring the target diagnosis influence factors to a list interval corresponding to the list position grades in the first weight list;

and determining the hierarchical label of the image diagnosis equipment corresponding to the equipment description information according to all the diagnosis influence factors in the first weight list.

2. The method according to claim 1, wherein the fusing the second image diagnosis track and the image to be diagnosed to obtain a target image comprises:

3. The method of claim 1, wherein sending the target image to a first image diagnostic device based on the second image diagnostic trajectory comprises:

4. The method according to claim 3, wherein the sending the target image to the first image diagnosis apparatus includes:

5. The method according to claim 1, wherein the causing the first image diagnosis apparatus to cooperate with at least a part of a second image diagnosis apparatus to diagnose the image to be diagnosed according to a second image diagnosis trajectory included in the target image includes:

6. The method of claim 5, wherein said superimposing a current diagnostic result into said target image resulting in a current diagnostic image comprises:

7. The method according to any one of claims 1-6, further comprising:

8. An image processing apparatus characterized by comprising: a processor and a memory and bus connected to the processor; the processor and the memory are communicated with each other through the bus; the processor is used for calling the computer program in the memory to execute the network image diagnosis method of any one of the above claims 1-7.