A method and system for visualizing optical module data based on the Internet of Things

Abstract

The present invention is applicable to the field of data visualization technology, and provides an optical module data visualization method and system based on the Internet of Things, including the following steps: receiving optical module data; retrieving the corresponding optical module number according to the user account; drawing the overall trend graph of each optical module number according to the optical module data; receiving the number selection instruction, splitting the corresponding overall trend graph into three-dimensional category trend graphs according to the three categories of transmission data volume, transmission quality and environmental parameters; calculating the level of transmission data volume, transmission quality and environmental parameters based on parameter items and corresponding numerical values, and marking the level on the category trend graph; determining the real-time change trend of each parameter item, and displaying the change reason according to all real-time change trends. In this way, it is convenient for users to make detailed observations of different dimensions and categories; and the level will be marked on the category trend graph to display the change reason, so that managers can understand the meaning behind the data.

Description

Technical Field

[0001] The present invention relates to the technical field of data visualization, and in particular to an optical module data visualization method and system based on the Internet of Things. Background Art

[0002] In a complex network environment, the operation status monitoring, data analysis and fault warning of optical modules become particularly important. At present, the optical module data is collected through the Internet of Things technology and then visualized. The existing optical module data visualization means are relatively simple, unable to display in a targeted manner according to the needs of different users, without a good classification display, and it is difficult for managers to quickly understand the meaning behind the data; in addition, when the optical module data is abnormal, it is impossible to give intuitive reference opinions based on the visualized data. Therefore, it is necessary to provide an optical module data visualization method and system based on the Internet of Things to solve the above problems. Summary of the invention

[0003] In view of the deficiencies in the prior art, the purpose of the present invention is to provide an optical module data visualization method and system based on the Internet of Things to solve the problems existing in the above-mentioned background technology.

[0004] The present invention is implemented as follows: a method for visualizing optical module data based on the Internet of Things, the method comprising the following steps:

[0005] Receive the uploaded optical module data and store the optical module data. The optical module data is accompanied by a timestamp and an optical module number. The optical module data includes several parameter items, and each parameter item corresponds to a value.

[0006] Receive the visual query information input by the user, and retrieve the corresponding optical module number according to the user account;

[0007] Draw an overall trend graph of each optical module number according to the optical module data, and visualize the overall trend graph;

[0008] A number selection instruction is received, and a corresponding overall trend graph is split into category trend graphs of three dimensions according to three categories: transmission data volume, transmission quality, and environmental parameters;

[0009] Calculate the level of transmission data volume, transmission quality and environmental parameters based on parameter items and corresponding values, and mark the level on the category trend chart;

[0010] Determine the real-time change trend of each parameter item and display the cause of the change based on all real-time change trends;

[0011] When the cause of the change is an abnormality, the historical similar trend chart is called up to display the cause of the abnormality and the solution on the historical similar trend chart.

[0012] As a further solution of the present invention: the step of calculating the transmission data volume, transmission quality and level of the environmental parameters based on the parameter items and the corresponding values ​​specifically includes:

[0013] Determine the level of the amount of data transmitted based on the transmission rate in the parameter item;

[0014] Determine the level of transmission quality based on the transmitted optical power, received optical power and extinction ratio in the parameter items;

[0015] Determine the level of environmental parameters based on the operating temperature and operating voltage in the parameter items.

[0016] As a further solution of the present invention: the step of determining the real-time change trend of each parameter item and displaying the change reason according to all the real-time change trends specifically includes:

[0017] Determine the real-time change trend of each parameter item, wherein the real-time change trend includes rising, falling and horizontal;

[0018] Input the real-time change trend of each parameter item into a trend reason library for matching, wherein the trend reason library includes a plurality of change reasons, each of which corresponds to a change trend of each parameter item;

[0019] When the match succeeds, the corresponding change reason is directly output; when the match fails, the change reason is determined to be an abnormality.

[0020] As a further solution of the present invention: the step of retrieving the historical similar trend graph and displaying the abnormal cause and solution on the historical similar trend graph specifically includes:

[0021] Inputting the overall trend graph with abnormalities into an abnormal trend graph library for similarity matching, wherein the abnormal trend graph library includes a plurality of abnormal trend graphs, each of which corresponds to an abnormal cause and a solution;

[0022] An abnormal trend graph with the highest similarity value is output, where the abnormal trend graph is a historical similar trend graph, and corresponding abnormal causes and solutions are displayed on the historical similar trend graph.

[0023] As a further solution of the present invention: the step of inputting the general trend graph with abnormalities into the abnormal trend graph library for similarity matching specifically includes:

[0024] Slice each abnormal trend graph in the abnormal trend graph library to obtain a number of slice trend graphs, and the time length of each slice trend graph is the same as the time length of the overall trend graph;

[0025] The similarity between each slice trend graph and the overall trend graph is calculated based on the Euclidean distance or Pearson correlation coefficient.

[0026] As a further solution of the present invention: the method further comprises:

[0027] receiving a number comparison instruction, wherein the number comparison instruction includes at least two optical module numbers;

[0028] Draw a comparative trend chart of each parameter item of the two optical module numbers and perform a visual display;

[0029] Receive parameter item selection instructions, process the corresponding comparison trend graph, and display the comparison result on the comparison trend graph.

[0030] Another object of the present invention is to provide an optical module data visualization system based on the Internet of Things, the system comprising:

[0031] An optical module data storage module is used to receive uploaded optical module data and store the optical module data. The optical module data is accompanied by a timestamp and an optical module number. The optical module data includes several parameter items, and each parameter item corresponds to a value.

[0032] A visual query module is used to receive the visual query information input by the user and retrieve the corresponding optical module number according to the user account;

[0033] An overall trend graph display module is used to draw an overall trend graph of each optical module number according to the optical module data, and to visually display the overall trend graph;

[0034] The category trend graph display module is used to receive the number selection instruction and split the corresponding overall trend graph into category trend graphs of three dimensions according to the three categories of transmission data volume, transmission quality and environmental parameters;

[0035] A level calculation and marking module is used to calculate the level of transmission data volume, transmission quality and environmental parameters based on parameter items and corresponding values, and mark the level on the category trend chart;

[0036] The change reason display module is used to determine the real-time change trend of each parameter item and display the change reason based on all real-time change trends;

[0037] The similar trend chart display module is used to call up the historical similar trend chart when the cause of the change is an abnormality, and display the abnormal cause and solution on the historical similar trend chart.

[0038] As a further solution of the present invention: the grade calculation marking module includes:

[0039] A transmission data volume level unit, used to determine the level of the transmission data volume according to the transmission rate in the parameter item;

[0040] A transmission quality level unit, used to determine the level of transmission quality according to the transmitted optical power, received optical power and extinction ratio in the parameter items;

[0041] The environmental parameter level unit is used to determine the level of the environmental parameter according to the operating temperature and operating voltage in the parameter items.

[0042] As a further solution of the present invention: the change reason display module includes:

[0043] A real-time change trend unit, used to determine the real-time change trend of each parameter item, wherein the real-time change trend includes rising, falling and horizontal;

[0044] A change trend matching unit, used for inputting the real-time change trend of each parameter item into a trend cause library for matching, wherein the trend cause library includes a plurality of change causes, each change cause corresponding to a change trend of each parameter item;

[0045] The change reason determination unit is used to directly output the corresponding change reason when the match is successful; when the match fails, determine the change reason as an abnormality.

[0046] As a further solution of the present invention: the similarity trend graph display module includes:

[0047] A similarity matching unit is used to input the general trend graph with abnormalities into an abnormal trend graph library for similarity matching, wherein the abnormal trend graph library includes a plurality of abnormal trend graphs, each of which corresponds to an abnormal cause and a solution;

[0048] The historical similarity trend unit is used to output an abnormal trend graph with the highest similarity value, wherein the abnormal trend graph is a historical similarity trend graph, and the corresponding abnormal cause and solution are displayed on the historical similarity trend graph.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] The present invention automatically retrieves the corresponding optical module data according to the user account, so that targeted display can be performed according to different users. After the user enters the number selection instruction, the corresponding overall trend graph will be split into three-dimensional category trend graphs according to the three categories of transmission data volume, transmission quality and environmental parameters, so that it is convenient for users to make detailed observations of different dimensions and categories; the level of transmission data volume, transmission quality and environmental parameters will also be calculated according to the parameter items and the corresponding numerical values, and the level will be marked on the category trend graph. The level reflects more in-depth information, and the real-time change trend of each parameter item will be automatically determined. The change reason is displayed according to all real-time change trends, so that managers can understand the meaning behind the data. When the change reason is an abnormality, a historical similar trend graph is retrieved, so that users can make an intuitive comparison of the two trend graphs and quickly solve the problem with reference to the previous abnormal causes and solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 The figure is a flow chart of an optical module data visualization method based on the Internet of Things.

[0052] Figure 2 The flowchart of calculating the level based on parameter items in an optical module data visualization method based on the Internet of Things.

[0053] Figure 3 A flowchart showing the causes of changes in an optical module data visualization method based on the Internet of Things.

[0054] Figure 4 This is a flowchart for retrieving historical similarity trend charts in an optical module data visualization method based on the Internet of Things.

[0055] Figure 5 This is a flowchart for similarity matching in an optical module data visualization method based on the Internet of Things.

[0056] Figure 6 Flowchart for drawing comparative trend charts in an IoT-based optical module data visualization method.

[0057] Figure 7 The figure is a structural diagram of an optical module data visualization system based on the Internet of Things. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0059] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0060] like Figure 1 As shown, an embodiment of the present invention provides an optical module data visualization method based on the Internet of Things, and the method includes the following steps:

[0061] S100, receiving uploaded optical module data, and storing the optical module data, wherein the optical module data is accompanied by a timestamp and an optical module number, and the optical module data includes a plurality of parameter items, and each parameter item corresponds to a value;

[0062] S200, receiving visual reference information input by a user, and retrieving a corresponding optical module number according to the user account;

[0063] S300, drawing a general trend graph of each optical module number according to the optical module data, and visually displaying the general trend graph;

[0064] S400, receiving a number selection instruction, and splitting the corresponding overall trend graph into category trend graphs of three dimensions according to three categories: transmission data volume, transmission quality, and environmental parameters;

[0065] S500, calculating the level of the transmission data volume, transmission quality and environmental parameters based on the parameter items and corresponding values, and marking the level on the category trend graph;

[0066] S600, determining the real-time change trend of each parameter item, and displaying the change reason according to all the real-time change trends;

[0067] S700, when the cause of the change is an abnormality, a historical similar trend chart is retrieved, and the cause of the abnormality and a solution are displayed on the historical similar trend chart.

[0068] In the embodiment of the present invention, the collected optical module data will first be uploaded to the server, and the optical module data will be stored. The optical module data is accompanied by a timestamp and an optical module number. The optical module number reflects which optical module it is and the installation area of ​​the optical module. The optical module data includes several parameter items, such as parameter items including transmission rate, transmitted optical power, received optical power, extinction ratio, operating temperature, operating voltage, etc., and each parameter item corresponds to a specific value. When the user needs to obtain visual information, it is necessary to input the visual reference information, and the visual reference information includes the user account. The embodiment of the present invention will automatically retrieve the corresponding optical module number according to the user account, and specifically determine the responsible area according to the user account, and retrieve the optical module number corresponding to the responsible area, so that it can be displayed in a targeted manner according to different users. Then, the overall trend chart of each optical module number will be drawn according to the optical module data. Each overall trend chart contains the curve trend of all parameter items, the horizontal axis is time, and the vertical axis is each parameter item, and the overall trend chart is visualized. When the user needs to take a closer look at the data of a certain optical module, clicking on the corresponding overall trend graph is equivalent to entering a number selection instruction. At this time, the corresponding overall trend graph will be split into three-dimensional category trend graphs according to the three categories of transmission data volume, transmission quality and environmental parameters. In this way, it is convenient for users to make detailed observations of different dimensions and categories; the embodiment of the present invention will also calculate the level of transmission data volume, transmission quality and environmental parameters according to the parameter items and the corresponding numerical values, and mark the level on the category trend graph. The level reflects more in-depth information, and will automatically determine the real-time change trend of each parameter item, and display the cause of the change according to all real-time change trends, so that managers can understand the meaning behind the data. When the cause of the change is an abnormality, the historical similar trend graph is retrieved, and the abnormal cause and solution are displayed on the historical similar trend graph. In this way, it is convenient for users to make an intuitive comparison between the two trend graphs and quickly solve the problem with reference to the previous abnormal causes and solutions.

[0069] like Figure 2 As shown, as a preferred embodiment of the present invention, the step of calculating the transmission data volume, transmission quality and level of environmental parameters based on parameter items and corresponding values ​​specifically includes:

[0070] S501, determining the level of the amount of transmitted data according to the transmission rate in the parameter item;

[0071] S502, determining the level of transmission quality according to the transmitted optical power, received optical power and extinction ratio in the parameter items;

[0072] S503: Determine the level of the environmental parameter according to the operating temperature and the operating voltage in the parameter items.

[0073] In the embodiment of the present invention, the level of the amount of transmitted data is determined according to the transmission rate in the parameter item, for example, when it is less than 155Mbps, it is level one, when it is 25Gbps-10Gbps, it is level two, and when it is greater than 25Gbps, it is level three; and the level of the transmission quality is determined according to the transmitted optical power, received optical power and extinction ratio in the parameter item. Specifically, it is necessary to calculate the transmission quality base, which is transmission quality base = k1*transmitted optical power + k2*received optical power + k3*extinction ratio, where k1, k2 and k3 are constant coefficients, and the level is determined according to the transmission quality base. Finally, the level of the environmental parameters is determined according to the operating temperature and operating voltage in the parameter item. When the operating temperature and the operating voltage are both within the qualified range, the level is level one; when one of the operating temperature and the operating voltage is within the qualified range, the level is level two; when both the operating temperature and the operating voltage are not within the qualified range, the level is level three.

[0074] like Figure 3 As shown, as a preferred embodiment of the present invention, the step of determining the real-time change trend of each parameter item and displaying the change reason according to all the real-time change trends specifically includes:

[0075] S601, determining a real-time change trend of each parameter item, wherein the real-time change trend includes rising, falling and horizontal;

[0076] S602, inputting the real-time change trend of each parameter item into a trend reason library for matching, wherein the trend reason library includes a plurality of change reasons, each of which corresponds to a change trend of each parameter item;

[0077] S603, when the match is successful, directly output the corresponding change reason; when the match fails, determine that the change reason is an abnormality.

[0078] In an embodiment of the present invention, the real-time change trend of each parameter item is automatically determined at set time intervals, and the real-time change trend includes rising, falling and horizontal. Then, the real-time change trend of each parameter item is input into the trend cause library for matching. The trend cause library is established in advance, and the trend cause library covers the change causes of all normal operating conditions. Each change cause corresponds to the change trend of each parameter item. In this way, matching can be performed based on the change trend. When the match is successful, it means that it belongs to one of the normal operating conditions, and the corresponding change cause is directly output; when the match fails, the change cause is determined to be an abnormality.

[0079] like Figure 4 As shown, as a preferred embodiment of the present invention, the step of retrieving the historical similar trend graph and displaying the abnormal cause and solution on the historical similar trend graph specifically includes:

[0080] S701, inputting the overall trend graph with abnormalities into an abnormal trend graph library for similarity matching, wherein the abnormal trend graph library includes a plurality of abnormal trend graphs, each of which corresponds to an abnormal cause and a solution;

[0081] S702, outputting an abnormal trend graph with the highest similarity value, wherein the abnormal trend graph is a historical similar trend graph, and displaying corresponding abnormal causes and solutions on the historical similar trend graph.

[0082] In the embodiment of the present invention, an abnormal trend graph library is also established in advance, and the abnormal trend graph library includes several abnormal trend graphs, each of which corresponds to an abnormal cause and a solution, and the abnormal trend graphs are constructed based on abnormal problems that have occurred before. The overall trend graph with abnormalities is input into the abnormal trend graph library for similarity matching, and the abnormal trend graph with the highest similarity value is output, and the two trend graphs are compared and displayed to facilitate the user's intuitive experience, and the corresponding abnormal causes and solutions are displayed.

[0083] like Figure 5 As shown, as a preferred embodiment of the present invention, the step of inputting the general trend graph with abnormalities into the abnormal trend graph library for similarity matching specifically includes:

[0084] S7011, slicing each abnormal trend graph in the abnormal trend graph library to obtain a plurality of slice trend graphs, wherein the time length of each slice trend graph is the same as the time length of the overall trend graph;

[0085] S7012, calculating the similarity between each slice trend graph and the overall trend graph based on the Euclidean distance or the Pearson correlation coefficient.

[0086] In the embodiment of the present invention, in order to calculate the similarity of the curve graph, it is first necessary to slice each abnormal trend graph in the abnormal trend graph library to obtain a plurality of slice trend graphs, the time length of each slice trend graph is the same as the time length of the overall trend graph, so that the horizontal lengths of all curves are the same. Then, the similarity between each slice trend graph and the overall trend graph is calculated based on the Euclidean distance or the Pearson correlation coefficient, so that the slice trend graph with the highest similarity can be obtained, and the abnormal trend graph corresponding to the slice trend graph is a historical similarity trend graph.

[0087] like Figure 6 As shown, as a preferred embodiment of the present invention, the method further includes:

[0088] S801, receiving a number comparison instruction, where the number comparison instruction includes at least two optical module numbers;

[0089] S802, drawing a comparative trend chart of each parameter item of the two optical module numbers and performing a visual display;

[0090] S803, receiving a parameter item selection instruction, processing a corresponding comparison trend graph, and displaying a comparison result on the comparison trend graph.

[0091] In an embodiment of the present invention, the user is also supported to compare two sets of optical module data. Specifically, the user needs to input a number comparison instruction, which includes at least two optical module numbers, and then automatically draws a comparison trend chart of each parameter item of the two optical module numbers and displays it visually. The comparison trend chart puts the curves of the same parameter items together. When the user wants to view the comparison of a certain parameter item in detail, the parameter item selection instruction is input, the corresponding comparison trend chart is processed, and the comparison result is displayed on the comparison trend chart. The comparison result includes the trend change difference and the value difference of each stage.

[0092] like Figure 7 As shown, an embodiment of the present invention further provides an optical module data visualization system based on the Internet of Things, the system comprising:

[0093] The optical module data storage module 100 is used to receive the uploaded optical module data and store the optical module data. The optical module data is accompanied by a timestamp and an optical module number. The optical module data includes several parameter items, and each parameter item corresponds to a value.

[0094] The visual reference module 200 is used to receive the visual reference information input by the user and retrieve the corresponding optical module number according to the user account;

[0095] The overall trend graph display module 300 is used to draw an overall trend graph of each optical module number according to the optical module data, and to visually display the overall trend graph;

[0096] The category trend graph display module 400 is used to receive a number selection instruction and split the corresponding overall trend graph into category trend graphs of three dimensions according to the three categories of transmission data volume, transmission quality and environmental parameters;

[0097] A level calculation and marking module 500 is used to calculate the level of the transmission data volume, transmission quality and environmental parameters based on the parameter items and the corresponding values, and mark the level on the category trend graph;

[0098] The change reason display module 600 is used to determine the real-time change trend of each parameter item and display the change reason according to all the real-time change trends;

[0099] The similar trend graph display module 700 is used to retrieve the historical similar trend graph when the cause of the change is an abnormality, and display the abnormality cause and solution on the historical similar trend graph.

[0100] As a preferred embodiment of the present invention, the level calculation marking module 500 includes:

[0101] A transmission data volume level unit, used to determine the level of the transmission data volume according to the transmission rate in the parameter item;

[0102] A transmission quality level unit, used to determine the level of transmission quality according to the transmitted optical power, received optical power and extinction ratio in the parameter items;

[0103] The environmental parameter level unit is used to determine the level of the environmental parameter according to the operating temperature and operating voltage in the parameter items.

[0104] As a preferred embodiment of the present invention, the change reason display module 600 includes:

[0105] A real-time change trend unit, used to determine the real-time change trend of each parameter item, wherein the real-time change trend includes rising, falling and horizontal;

[0106] A change trend matching unit, used for inputting the real-time change trend of each parameter item into a trend cause library for matching, wherein the trend cause library includes a plurality of change causes, each change cause corresponding to a change trend of each parameter item;

[0107] The change reason determination unit is used to directly output the corresponding change reason when the match is successful; when the match fails, determine the change reason as an abnormality.

[0108] As a preferred embodiment of the present invention, the similarity trend graph display module 700 includes:

[0109] A similarity matching unit is used to input the general trend graph with abnormalities into an abnormal trend graph library for similarity matching, wherein the abnormal trend graph library includes a plurality of abnormal trend graphs, each of which corresponds to an abnormal cause and a solution;

[0110] The historical similarity trend unit is used to output an abnormal trend graph with the highest similarity value, wherein the abnormal trend graph is a historical similarity trend graph, and the corresponding abnormal cause and solution are displayed on the historical similarity trend graph.

[0111] The above only describes in detail the preferred embodiments of the present invention, which is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0112] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0113] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0114] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the disclosure in the specification and examples. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

Claims

1. A method for visualizing optical module data based on the Internet of Things, characterized in that: The method comprises the following steps: Receive the uploaded optical module data and store the optical module data. The optical module data is accompanied by a timestamp and an optical module number. The optical module data includes several parameter items, and each parameter item corresponds to a value. Receive the visual query information input by the user, and retrieve the corresponding optical module number according to the user account; Draw an overall trend graph of each optical module number according to the optical module data, and visualize the overall trend graph; A number selection instruction is received, and a corresponding overall trend graph is split into category trend graphs of three dimensions according to three categories: transmission data volume, transmission quality, and environmental parameters; Calculate the level of transmission data volume, transmission quality and environmental parameters based on parameter items and corresponding values, and mark the level on the category trend chart; Determine the real-time change trend of each parameter item and display the cause of the change based on all real-time change trends; When the cause of the change is an abnormality, the historical similar trend chart is called up to display the cause of the abnormality and the solution on the historical similar trend chart.

2. The optical module data visualization method based on the Internet of Things according to claim 1 is characterized in that: The step of calculating the transmission data volume, transmission quality and level of the environmental parameters based on the parameter items and the corresponding values ​​specifically includes: Determine the level of the amount of data transmitted based on the transmission rate in the parameter item; Determine the level of transmission quality based on the transmitted optical power, received optical power and extinction ratio in the parameter items; Determine the level of environmental parameters based on the operating temperature and operating voltage in the parameter items.

3. The optical module data visualization method based on the Internet of Things according to claim 1 is characterized in that: The step of determining the real-time change trend of each parameter item and displaying the change reason according to all the real-time change trends specifically includes: Determine the real-time change trend of each parameter item, wherein the real-time change trend includes rising, falling and horizontal; Input the real-time change trend of each parameter item into a trend reason library for matching, wherein the trend reason library includes a plurality of change reasons, each of which corresponds to a change trend of each parameter item; When the match succeeds, the corresponding change reason is directly output; when the match fails, the change reason is determined to be an abnormality.

4. The optical module data visualization method based on the Internet of Things according to claim 1 is characterized in that: The step of retrieving the historical similar trend graph and displaying the abnormal cause and solution on the historical similar trend graph specifically includes: Inputting the overall trend graph with abnormalities into an abnormal trend graph library for similarity matching, wherein the abnormal trend graph library includes a plurality of abnormal trend graphs, each of which corresponds to an abnormal cause and a solution; An abnormal trend graph with the highest similarity value is output, where the abnormal trend graph is a historical similar trend graph, and corresponding abnormal causes and solutions are displayed on the historical similar trend graph.

5. The method for visualizing optical module data based on the Internet of Things according to claim 4, characterized in that: The step of inputting the overall trend graph with abnormalities into the abnormal trend graph library for similarity matching specifically includes: Slice each abnormal trend graph in the abnormal trend graph library to obtain a number of slice trend graphs, and the time length of each slice trend graph is the same as the time length of the overall trend graph; The similarity between each slice trend graph and the overall trend graph is calculated based on the Euclidean distance or Pearson correlation coefficient.

6. The optical module data visualization method based on the Internet of Things according to claim 1 is characterized in that: The method further comprises: receiving a number comparison instruction, wherein the number comparison instruction includes at least two optical module numbers; Draw a comparative trend chart of each parameter item of the two optical module numbers and perform a visual display; Receive parameter item selection instructions, process the corresponding comparison trend graph, and display the comparison result on the comparison trend graph.

7. An optical module data visualization system based on the Internet of Things, characterized in that: The system comprises: An optical module data storage module is used to receive uploaded optical module data and store the optical module data. The optical module data is accompanied by a timestamp and an optical module number. The optical module data includes several parameter items, and each parameter item corresponds to a value. A visual query module is used to receive the visual query information input by the user and retrieve the corresponding optical module number according to the user account; An overall trend graph display module is used to draw an overall trend graph of each optical module number according to the optical module data, and to visually display the overall trend graph; The category trend graph display module is used to receive the number selection instruction and split the corresponding overall trend graph into category trend graphs of three dimensions according to the three categories of transmission data volume, transmission quality and environmental parameters; A level calculation and marking module is used to calculate the level of transmission data volume, transmission quality and environmental parameters based on parameter items and corresponding values, and mark the level on the category trend chart; The change reason display module is used to determine the real-time change trend of each parameter item and display the change reason based on all real-time change trends; The similar trend chart display module is used to call up the historical similar trend chart when the cause of the change is an abnormality, and display the abnormal cause and solution on the historical similar trend chart.

8. The optical module data visualization system based on the Internet of Things according to claim 7, characterized in that: The grade calculation marking module includes: A transmission data volume level unit, used to determine the level of the transmission data volume according to the transmission rate in the parameter item; A transmission quality level unit, used to determine the level of transmission quality according to the transmitted optical power, received optical power and extinction ratio in the parameter items; The environmental parameter level unit is used to determine the level of the environmental parameter according to the operating temperature and operating voltage in the parameter items.

9. The optical module data visualization system based on the Internet of Things according to claim 7, characterized in that: The change reason display module includes: A real-time change trend unit, used to determine the real-time change trend of each parameter item, wherein the real-time change trend includes rising, falling and horizontal; A change trend matching unit, used for inputting the real-time change trend of each parameter item into a trend cause library for matching, wherein the trend cause library includes a plurality of change causes, each change cause corresponding to a change trend of each parameter item; The change reason determination unit is used to directly output the corresponding change reason when the match is successful; when the match fails, determine the change reason as an abnormality.

10. The optical module data visualization system based on the Internet of Things according to claim 7, characterized in that: The similarity trend graph display module comprises: A similarity matching unit is used to input the general trend graph with abnormalities into an abnormal trend graph library for similarity matching, wherein the abnormal trend graph library includes a plurality of abnormal trend graphs, each of which corresponds to an abnormal cause and a solution; The historical similarity trend unit is used to output an abnormal trend graph with the highest similarity value, wherein the abnormal trend graph is a historical similarity trend graph, and the corresponding abnormal cause and solution are displayed on the historical similarity trend graph.

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