CN108845888A - A kind of method and system based on TensorFlow image recognition diagnosis memory failure - Google Patents

Abstract

The embodiment of the present invention discloses a method and system for diagnosing memory faults based on TensorFlow image recognition. The method includes the steps of: submitting the current memory photo of the server to the Server side; TensorFlow processes the current memory photo into unified standard data; Compare the data of the current memory photo processed by TensorFlow with the stored memory data in a normal state; if the data comparison is consistent, the memory state is normal; if not, the memory state is faulty. The method and system for diagnosing memory faults of the present application judge the state of the memory by comparing the current state data of the memory with the normal state data by the server, so that maintenance personnel do not need to rely too much on daily work experience, and facilitate the diagnosis and maintenance of memory faults , which reduces the working threshold and learning cost of maintenance personnel.

Description

A method and system for diagnosing memory faults based on TensorFlow image recognition

technical field

The invention relates to the field of computer software development, in particular to a method and system for diagnosing memory faults based on TensorFlow image recognition.

Background technique

With the development of information technology and the continuous improvement of informatization, the scale of the server room is increasing, and the number of devices in the room is also increasing, which makes the fault diagnosis of server memory more and more complicated, and the maintenance cost is also increasing. higher.

In the prior art, staff members mainly rely on long-term experience accumulation for fault diagnosis of server memory. There is a memory working indicator light installed on the server. When the memory of the server fails, the state of the working light corresponding to the memory changes. Experienced staff will diagnose the fault of the server memory according to the change of the state of the light, and then take corresponding measures. maintenance measures. The disadvantage of this existing memory fault diagnosis method is that maintenance personnel need to rely heavily on daily work experience. high learning costs.

Based on the above problems, the present invention proposes a method and system for diagnosing memory faults based on TensorFlow image recognition. The server compares the current state data of the memory with the normal state data to judge the state of the memory, which facilitates the diagnosis and maintenance of memory faults.

Contents of the invention

Embodiments of the present invention provide a method and system for diagnosing memory faults based on TensorFlow image recognition, which facilitates diagnosis and maintenance of memory faults and reduces learning costs for maintenance personnel.

In order to solve the above technical problems, the embodiment of the present invention discloses the following technical solutions:

The first aspect of the present invention provides a kind of method based on TensorFlow image recognition diagnosis memory failure, and described method comprises the following steps:

Submit the current memory photo of the server to the server side;

TensorFlow processes the current memory photos into unified standard data;

The server compares the data of the current memory photo processed by TensorFlow with the stored memory data in a normal state;

If the data comparison is consistent, the memory status is normal; if not, the memory status is faulty.

Based on the above scheme, this method is optimized as follows:

As an optimization, submitting the current memory photo of the server to the server side includes the following steps:

Take photos of the current memory of the server through the App client;

The App client submits the current memory photos taken to the Server.

Further, before the server compares the data of the current memory photo processed by TensorFlow with the stored memory data in a normal state, it also includes the following steps: the server side initializes the server memory data in a normal state.

Further, the Server end also stores the fault information data of the internal memory. If the data of the current internal memory photo is inconsistent with the internal memory data in the normal state, the Server end continues to compare the data of the current internal memory photo with the fault information data of the internal memory. Yes, when the comparison between the two is consistent, the server will feed back the fault information data of the memory to the App client and display it in the App.

The second aspect of the present invention provides a system for diagnosing memory faults based on TensorFlow image recognition, said system comprising an App client unit, a Server unit and a TensorFlow image recognition processing unit;

The App client unit is used to take a photo of the current memory of the server, and submits the photo of the current memory taken to the Server unit;

The TensorFlow image recognition processing unit is used to process the current memory photo into unified standard data;

The Server unit is used to compare the data of the current memory photo processed by the TensorFlow image recognition processing unit with the stored memory data in a normal state;

If the data comparison is consistent, the memory status is normal; if not, the memory status is faulty.

Further, before the server unit compares the data of the current memory photo processed by the TensorFlow image recognition processing unit with the stored memory data in the normal state, it will also initialize the server memory data in the normal state.

In the system for diagnosing memory faults based on TensorFlow image recognition as described above, the Server unit also stores the fault information data of the memory. The data of the memory is compared with the fault information data of the memory, and when the two are compared, the server unit feeds the fault information data of the memory to the App client unit and displays it in the App client unit.

The technical solutions provided by the embodiments of the present application include the following beneficial effects:

A method for diagnosing memory faults based on TensorFlow image recognition provided in the embodiment of the present application includes submitting the current memory photo of the server to the server; TensorFlow processes the memory photo into unified standard data; the server processes the data of the memory photo processed by TensorFlow Compare with the stored memory data in the normal state, if they are consistent, the memory is normal; if not, the memory is faulty. The method for diagnosing memory faults based on TensorFlow image recognition in the embodiment of the present application uses the server to compare the current state data of the memory with the normal state data to judge the state of the memory, so that maintenance personnel do not need to rely too much on daily work experience, which is convenient for memory Diagnosis and maintenance of faults reduces the working threshold and learning costs of maintenance personnel.

A system for diagnosing memory faults based on TensorFlow image recognition in the second aspect of the present invention can implement the method in the first aspect and achieve the same effect.

Description of drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description serve to explain the principles of the application.

Fig. 1 is a schematic flow chart of a method for diagnosing a memory fault based on TensorFlow image recognition provided by an embodiment of the present application;

FIG. 2 is a schematic structural diagram of a system for diagnosing memory faults based on TensorFlow image recognition provided by an embodiment of the present application.

Reference signs: 1-App client unit, 2-Server unit, 3-TensorFlow image recognition processing unit.

Detailed ways

In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described The embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

Figure 1 is a method for diagnosing memory faults based on TensorFlow image recognition provided by the embodiment of the present application. As can be seen from Figure 1, the method of this embodiment includes the following steps:

S1. Shoot the current memory photo of the server through the App client;

S2, the App client submits the current memory photo taken to the Server;

S3. The server side sends the acquired current memory photo to TensorFlow, and TensorFlow processes the current memory photo into unified standard data;

S4, the server end initializes the server memory data under normal conditions;

S5. The server compares the data of the current memory photo processed by TensorFlow with the stored memory data in a normal state;

S6. If the data comparison is consistent, the memory state is normal; if not, the memory state is faulty;

S7. The server side feeds back the memory status to the App client and displays it in the App.

Specifically, in the method for diagnosing memory faults based on TensorFlow image recognition as described above, the server side also stores memory fault information data. In said S6, if the data of the current memory photo is inconsistent with the memory data in the normal state, the server side continues to compare the data of the current memory photo with the fault information data of the memory. When the comparison is consistent, the server side Feedback the fault information data of the internal memory to the App client, and display it as a note at the position of the original photo in the App.

FIG. 2 is a system for diagnosing memory faults based on TensorFlow image recognition provided by an embodiment of the present application. It can be seen from FIG. 2 that the system of this embodiment includes an App client unit 1, a server unit 2, and a TensorFlow image recognition processing unit 3.

The App client unit 1 is used to take a photo of the current memory of the server, and submit the photo of the current memory taken to the Server unit;

The TensorFlow image recognition processing unit 3 is used to process the current memory photo into unified standard data;

The Server unit 2 is used to compare the data of the current memory photo processed by the TensorFlow image recognition processing unit with the stored memory data in a normal state;

If the data comparison is consistent, the memory status is normal; if not, the memory status is faulty.

Specifically, in the system for diagnosing memory faults based on TensorFlow image recognition as described above, the Server unit 2 compares the data of the current memory photo processed by the TensorFlow image recognition processing unit with the stored memory data in a normal state Before, the server memory data in the normal state will be initialized first. Server unit 2 also stores the fault information data of the memory, if the data of the current memory photo is inconsistent with the memory data in the normal state, the Server unit 2 continues to compare the data of the current memory photo with the fault information data of the memory. When the comparison between the two is consistent, the Server unit will feed back the fault information data in the memory to the App client unit 1 and display it in the App client unit 1 .

The above descriptions are only specific embodiments of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention will not be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (7)

1. A method based on TensorFlow image recognition diagnosis memory fault, is characterized in that, comprises the following steps: Submit the current memory photo of the server to the server side; TensorFlow processes the current memory photos into unified standard data; The server compares the data of the current memory photo processed by TensorFlow with the stored memory data in a normal state; If the data comparison is consistent, the memory status is normal; if not, the memory status is faulty. 2. a kind of method based on TensorFlow image recognition diagnosis memory failure according to claim 1, is characterized in that, the described current memory photograph of server is submitted to Server end, comprises the steps: Take photos of the current memory of the server through the App client; The App client submits the current memory photos taken to the Server. 3. a kind of method based on TensorFlow image recognition diagnosis memory failure according to claim 2, it is characterized in that, described Server compares the data of the current memory photograph of TensorFlow processing with the memory data under the stored normal state Before, the following steps are also included: the server end initializes the server memory data in a normal state. 4. a kind of method based on TensorFlow image recognition diagnosis memory fault according to claim 1, it is characterized in that, described Server end also stores the fault information data of internal memory, if the data of current internal memory photo and the internal memory under normal state When the data comparison is inconsistent, the server side continues to compare the data of the current internal memory photo with the fault information data of the internal memory. show. 5. A system based on TensorFlow image recognition diagnosis memory fault, is characterized in that, described system comprises App client unit, Server unit and TensorFlow image recognition processing unit; The App client unit is used to take the current memory photo of the server, and submit the taken current memory photo to the Server unit; The TensorFlow image recognition processing unit is used to process the current memory photos into unified standard data; The Server unit is used to compare the data of the current memory photo processed by the TensorFlow image recognition processing unit with the stored memory data in a normal state; If the data comparison is consistent, the memory status is normal; if not, the memory status is faulty. 6. a kind of system based on TensorFlow image recognition diagnosis memory fault according to claim 5, it is characterized in that, described Server unit is under the normal state of the data of the current memory photo that TensorFlow image recognition processing unit is processed and stored Before the memory data of the server is compared, the server memory data in the normal state will also be initialized. 7. A kind of system based on TensorFlow image recognition diagnosis memory fault according to claim 6, it is characterized in that, described Server unit also stores the fault information data of internal memory, if the data of current internal memory photo and the internal memory under normal state When the data comparison is inconsistent, the server unit continues to compare the data of the current internal memory photo with the fault information data of the internal memory. displayed in the end unit.