Data acquisition supervision platform for operation and maintenance Internet of Things

Abstract

The invention relates to the technical field of Internet of Things data acquisition supervision, in particular to an operation and maintenance Internet of Things-oriented data acquisition supervision platform, which comprises an operation and maintenance management and control platform, an acquisition supervision unit, an analysis feedback unit, a supervision operation unit, an environment management and control analysis unit, an operation data monitoring unit, an early warning unit and a display unit, the method comprises the following steps: acquiring static data before running and dynamic data during running of a server after coding processing, and performing formulation and progressive analysis on internal environment data and external environment data in the static data before running; the corresponding signals and the corresponding server codes are obtained through the modes of symbolized calibration, threshold value substitution comparison and set classification regularization on the dynamic data during operation, so that an operator on duty can be reminded to overhaul the server in time without checking the state of the server regularly, and the working efficiency is improved. The purpose of safe, stable and reliable operation of the network and information system is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things data acquisition and supervision, and particularly to a data acquisition and supervision platform for operation and maintenance Internet of Things. Background Art

[0002] With the continuous influx of artificial intelligence technology into all walks of life, the concept of intelligent operation and maintenance has gradually been put on the agenda. Intelligent operation and maintenance refers to the use of artificial intelligence algorithms to perform multi-level and multi-dimensional analysis on a large amount of operation and maintenance data, so as to guide operation and maintenance personnel to troubleshoot faults faster, perform capacity planning, and even pre-detect potential problems; The traditional IT operation and maintenance mode relies on the operation and maintenance management skills and experience of operation and maintenance personnel. However, due to the limited number of operation and maintenance team members and the large amount of operation and maintenance work, relying on the traditional manual method, it is difficult to improve the operation and maintenance efficiency, the troubleshooting difficulty is large, the security risk is also high, and when the monitoring data or indicators of the system monitoring are abnormal, it can only be discovered through manual inspection. The system has no automatic early warning function and requires manual regular inspection. The entire inspection takes a lot of time, and the existing problems cannot be solved in time. Moreover, when a normal server is abnormal, the existing equipment cannot perform self-inspection and early warning; In view of the above technical defects, a solution is proposed now. Summary of the Invention

[0003] The purpose of the present invention is to provide a data acquisition and supervision platform for operation and maintenance Internet of Things to solve the above-mentioned technical defects. By collecting the static data before the server runs and the dynamic data during the running after encoding processing, and performing formulaic and progressive analysis on the internal environment data and external environment data in the static data before running, and for the dynamic data during the server running, through symbolic calibration, threshold substitution comparison, set classification and regularization, and progressive analysis methods, the corresponding signals and corresponding server codes are obtained, and early warning reminders are made, which helps to remind the duty personnel to repair the server in time, and the duty personnel can receive the repair voice at the first time when an abnormality occurs, instead of regularly checking the status of the server, so as to achieve the purpose of safe, stable and reliable operation of the network and information system. At the same time, it helps to repair the cooling fan in time, improve the heat dissipation effect of the equipment, and timely solve the existing fault problems and the problem that the system has no automatic early warning function.

[0004] The purpose of the present invention can be achieved by the following technical solutions: A data acquisition and supervision platform for operation and maintenance Internet of Things includes an operation and maintenance control platform, a collection and supervision unit, an analysis and feedback unit, a supervision operation unit, an environment control and analysis unit, an operation data monitoring unit, an early warning unit, and a display unit; The acquisition and supervision unit is used to acquire the static data before the server runs after encoding and processing. The static data includes internal environment data and external environment data, and sends the internal environment data and external environment data in the static data to the analysis and feedback unit and the supervision operation unit respectively. Among them, the internal environment data includes the space volume occupied by dust on the cooling fan inside the server and the characteristic images corresponding to all line joints inside the server, and the external environment data includes the damaged area of the external lines of the server and the average network operation speed value; After receiving the internal environment data, the analysis and feedback unit analyzes the internal environment data, obtains the abnormal instruction and normal instruction of the internal environment data, and sends them to the environment control and analysis unit; After receiving the external environment data, the supervision operation unit analyzes the external environment data, obtains the abnormal instruction and normal instruction of the external environment data, and sends them to the environment control and analysis unit; After receiving the abnormal instruction of the internal environment data, the normal instruction of the internal environment data, the abnormal instruction of the external environment data, and the normal instruction of the external environment data, the environment control and analysis unit performs interactive analysis to obtain the normal signal, abnormal signal, and the corresponding server code, and sends the normal signal, abnormal signal, and the corresponding server code to the display unit and the warning unit respectively; After receiving the normal signal and the corresponding server number, the display unit immediately generates the corresponding "normal" text document of the server to which it belongs according to the corresponding server number. After receiving the abnormal signal and the corresponding server number, the warning unit immediately generates the corresponding "repair" voice of the server to which it belongs according to the corresponding server number; The operation data monitoring unit is used to acquire the dynamic data during the server operation. The dynamic data refers to the average internal temperature value of the server and the average rotation speed of the cooling fan, analyzes the dynamic data, obtains the self-check instruction and the display signal, and sends the display signal to the display unit. After receiving the display signal, the display unit immediately generates the corresponding "normal internal temperature" text document of the server to which it belongs according to the corresponding server number. When the self-check instruction is generated, it analyzes the average rotation speed of the cooling fan inside the server, obtains the maintenance signal, and sends it to the warning unit. After receiving the maintenance signal, the warning unit immediately generates the corresponding "fan maintenance" voice of the server to which it belongs according to the corresponding server number.

[0005] Preferably, the specific internal environment data analysis process of the analysis and feedback unit is as follows: Divide the time threshold evenly into \(i\) sub - time periods, where \(i\) is a natural number greater than zero. Obtain the volume of the space occupied by dust on the cooling fan within each sub - time period of the server. The volume of the space occupied by dust on the cooling fan refers to the volume of dust occupying the operating space of the cooling fan, and mark it as the dust volume \(HC_i\). Then construct a set of the dust volume \(HC_i\). According to the set of the dust volume \(HC_i\), obtain the absolute value of the difference between two adjacent subsets and mark it as the fluctuation value. At the same time, construct a set of the fluctuation values, and according to the set, obtain the average fluctuation value \(BH\). Obtain the characteristic images corresponding to all internal wire joints within the time threshold in real - time, perform grayscale processing on the characteristic images corresponding to all internal wire joints, and mark them as analysis images. Obtain the grayscale values corresponding to the analysis images. Divide the analysis images into two parts with a preset grayscale value as the boundary. Mark the part greater than or equal to the preset grayscale value as the abnormal image, and mark the part less than the preset grayscale value as the normal image. Obtain the difference between the grayscale value of the abnormal image and the preset grayscale value and mark it as the difference value. At the same time, construct a set of the difference values, and according to the set, obtain the average value of the set and mark it as the average difference degree \(PC\). Then obtain the internal environment coefficient \(N\) through a formula, and compare and analyze the internal environment coefficient \(N\) with the preset internal environment coefficient stored in it: If the internal environment coefficient \(N\geq\) the preset internal environment coefficient, generate an internal environment data abnormal instruction; if the internal environment coefficient \(N\lt\) the preset internal environment coefficient, generate an internal environment data normal instruction.

[0006] Preferably, the process of analyzing the external environment data of the supervision operation unit is as follows: Obtain the total area of the damage (such as breakage, bulging, or depression) of the external line within the time threshold under the influence of itself or external forces, and mark it as the damage value \(SH\). Obtain the average network operation speed value within each sub - time period and mark it as the network speed value \(Wi\). At the same time, obtain the change value of the network operation speed corresponding to two adjacent sub - time periods and mark it as the fluctuation value. Construct a set of the fluctuation values. Take time as the \(X\) - axis and the network operation speed value as the \(Y\) - axis, and establish a time - fluctuation value bar chart, marked as the analysis bar chart. Draw a preset fluctuation value range curve on the analysis bar chart, mark the fluctuation values outside the preset fluctuation value range curve as "1", obtain the total number of "1"s, and mark the total number as the interference number \(GS\). Then obtain the external environment coefficient \(W\) through a formula, and compare and analyze the external environment coefficient \(W\) with the preset external environment coefficient stored in it: If the external environment coefficient \(W\geq\) the preset external environment coefficient, generate an external environment data abnormal instruction; if the external environment coefficient \(W\lt\) the preset external environment coefficient, generate an external environment data normal instruction.

[0007] Preferably, the interactive analysis process of the environmental control analysis unit is as follows: The abnormal instruction of internal environment data and the abnormal instruction of external environment data result in internal and external abnormalities. The abnormal instruction of internal environment data and the normal instruction of external environment data result in internal abnormality and external normality. The normal instruction of internal environment data and the abnormal instruction of external environment data result in internal normality and external abnormality. The normal instruction of internal environment data and the normal instruction of external environment data result in internal and external normality; If it is internal and external normal, a normal signal is generated. If it is not internal and external normal, an abnormal signal is generated.

[0008] Preferably, the dynamic data analysis process of the operation data monitoring unit is as follows: Collect the duration from the start time to the end time of the operation of the acquisition server, and mark it as the analysis time. Divide the analysis time into g sub-time periods, where g is a natural number greater than zero. Obtain the internal average temperature value of the server within each sub-time period, and mark it as the average temperature value Wg. Construct a set of average temperature values Wg, and establish a rectangular coordinate system with time as the X-axis and the average temperature value Wg as the Y-axis, and draw a time-average temperature value Wg curve graph. Draw the preset internal temperature value threshold curve set on the time-average temperature value Wg curve graph, and mark it as the reference graph. Obtain the total duration corresponding to the average temperature value Wg above the preset internal temperature value threshold curve in the reference graph, and mark it as the interference duration K; And compare and analyze the interference duration K with the preset interference duration stored in it: If the interference duration K ≥ the preset interference duration, a self-check instruction is generated; If the interference duration K < the preset interference duration, a display signal is generated.

[0009] Preferably, the analysis process of the average rotation speed of the cooling fan in the server by the operation data monitoring unit is as follows: Obtain the average rotation speed of the cooling fan in the server within each sub-time period, and mark it as the wind rotation speed. Construct a set of wind rotation speeds, and draw a rectangular coordinate system curve graph of time-wind rotation speed according to the set of wind rotation speeds, and mark it as the rotation speed curve graph. Draw the preset wind rotation speed interval curve on the rotation speed curve graph, and mark it as the analysis change graph. Analyze according to the analysis change graph, re-mark those within or on the preset wind rotation speed interval curve as "0", and obtain the total number of "0". Re-mark those outside the preset wind rotation speed interval curve as "2", and obtain the total number of "2". Divide the total number of "2" by the total number of "0" and mark it as the number ratio GB. Compare and analyze the number ratio GB with the preset number ratio stored in it: If the number ratio GB ≥ the preset number ratio, a maintenance signal is generated; If the number ratio GB < the preset number ratio, no signal is generated.

[0010] The beneficial effects of the present invention are as follows: According to the present invention, static data before the server runs and dynamic data during the operation are collected and encoded. The internal environment data and external environment data in the static data before operation are analyzed in a formulaic and progressive manner, that is, by combining and comparing the hierarchical divisions of the collection objects and processing processes, to obtain abnormal instructions for internal environment data, normal instructions for internal environment data, abnormal instructions for external environment data, and normal instructions for external environment data. Then, the obtained results are compared and analyzed interactively and in-depth to obtain normal signals, abnormal signals, and corresponding server numbers. When the display unit receives a normal signal, it immediately generates a corresponding "normal" text document for the server according to the corresponding server number, which helps to intuitively understand the state of the server before operation, facilitates the operation of the server, and enables the warning unit to generate a corresponding "maintenance" voice for the server according to the corresponding server number immediately after receiving an abnormal signal, which helps to remind the duty personnel to repair the server in time. Moreover, the duty personnel can receive the maintenance voice at the first time when an abnormality occurs, without having to regularly check the status of the server, achieving the purpose of safe, stable, and reliable operation of the network and information system, and solving the problem of the existing system without an automatic warning function; By symbolically calibrating, substituting thresholds for comparison, classifying and regularizing sets, and analyzing progressively the dynamic data during the server operation, corresponding self-check instructions and display signals are obtained. When self-check instructions are generated, through information feedback and in-depth analysis, maintenance signals are obtained. When the warning unit receives a maintenance signal, it immediately generates a corresponding "fan maintenance" voice for the server according to the corresponding server number, which helps to repair the cooling fan in time, improve the heat dissipation effect of the equipment, and solve existing fault problems in time. At the same time, when the display unit receives a display signal, it immediately generates a corresponding "normal internal temperature" text document for the server according to the corresponding server number, which helps to intuitively understand the operating state of the server and facilitate the timely formulation of countermeasures. Brief Description of the Drawings

[0011] The present invention will be further described below in conjunction with the accompanying drawings; Figure 1 is the system block diagram of the present invention; Figure 2 is the coordinate system analysis diagram of the present invention. Detailed Embodiments

[0012] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0013] Embodiment 1 Please refer to Figure 1-2 As shown, the present invention is a data acquisition and supervision platform for operation and maintenance Internet of Things, including an operation and maintenance control platform, a collection and supervision unit, an analysis and feedback unit, a supervision operation unit, an environment control and analysis unit, an operation data monitoring unit, an early warning unit, and a display unit. The collection and supervision unit is unidirectionally communicatively connected to both the analysis and feedback unit and the supervision operation unit. The analysis and feedback unit and the supervision operation unit are unidirectionally communicatively connected to the operation and maintenance control platform. The operation and maintenance control platform is bidirectionally communicatively connected to the operation data monitoring unit. The operation data monitoring unit is unidirectionally communicatively connected to both the early warning unit and the display unit; The collection and supervision unit is used to collect the static data before the server runs after encoding processing. The static data includes internal environment data and external environment data. Among them, the internal environment data includes the space volume occupied by dust on the heat dissipation fan inside the server and the characteristic images corresponding to all the wire joints inside the server. The external environment data includes the damaged area of the external wire of the server and the average network operation speed value, and sends the internal environment data and the external environment data in the static data to the analysis and feedback unit and the supervision operation unit respectively; After receiving the static data, the analysis and feedback unit analyzes the internal environment data in the static data. The specific internal environment data analysis process is as follows: Collect a period of time before the server runs and set it as the time threshold. Divide the time threshold evenly into i sub-time periods, where i is a natural number greater than zero. Obtain the space volume occupied by dust on the heat dissipation fan in each sub-time period of the server. It should be noted that the space volume occupied by dust on the heat dissipation fan refers to the volume of dust occupying the operating space of the heat dissipation fan, and mark it as the dust volume, labeled as HCi, and construct a set {HC1, HC2, HC3,..., HCi} of the dust volume HCi. According to the set of the dust volume HCi, obtain the absolute value of the difference between two adjacent subsets and mark it as the fluctuation value. At the same time, construct a set of the fluctuation values. According to the set, obtain the average fluctuation value, labeled as BH. It should be noted that the larger the value of the average fluctuation value BH, the faster the growth rate of the space volume occupied by dust on the heat dissipation fan, the greater the impact on the server, and the greater the risk of causing the server to malfunction. On the contrary, the smaller the value of the average fluctuation value BH, the slower the growth rate of the space volume occupied by dust on the heat dissipation fan, the smaller the impact on the server, and the smaller the risk of causing the server to malfunction; Obtain the characteristic images corresponding to all the wire joints inside in real time within the time threshold, perform gray-scale processing on the characteristic images corresponding to all the wire joints inside, and mark them as analysis images. Obtain the gray-scale values corresponding to the analysis images. It should be noted that the darker the gray color depth of the rust part, on the contrary, the lighter the gray color of the normal part, The analyzed image is divided into two parts with a preset gray value as the boundary. The part greater than or equal to the preset gray value is marked as an abnormal image, and the part less than the preset gray value is marked as a normal image. The difference between the gray value of the abnormal image and the preset gray value is obtained and marked as the difference value. At the same time, a set of difference values is constructed, and the average value of the set is obtained according to the set and marked as the average difference degree, labeled as PC. It should be noted that the larger the value of the average difference degree PC, the more serious the impact caused by the corrosion of the internal circuit joints of the server, the greater the risk of server failure, and the greater the interference to the operation of the server. On the contrary, the larger the value of the average difference degree PC, the smaller the impact caused by the corrosion of the internal circuit joints of the server, the smaller the risk of server failure, and the smaller the interference to the operation of the server; And through the formula: The internal environment coefficient is obtained, where a and b are the correction coefficients of the average fluctuation value BH and the average difference degree PC respectively, b > a > 0, a + b = 1.245, and N represents the internal environment coefficient; And the internal environment coefficient N is compared and analyzed with the preset internal environment coefficient stored in it: If the internal environment coefficient N ≥ the preset internal environment coefficient, it is determined that the internal environment data of the server is abnormal, an internal environment data abnormality instruction is generated and sent to the environmental control analysis unit. It should be noted that the abnormal internal environment data of the server means that the internal environment data of the server interferes with the server; If the internal environment coefficient N < the preset internal environment coefficient, it is determined that the internal environment data of the server is normal, an internal environment data normality instruction is generated and sent to the environmental control analysis unit. It should be noted that the normal internal environment data of the server means that the internal environment data of the server does not interfere with the server.

[0014] Embodiment 2 The supervision and operation unit analyzes the external environment data in the static data. The specific external environment data analysis process is as follows: Obtain the total area of damage, bulging, or depression of the external line within the time threshold due to its own or external forces, and mark it as the damage value, labeled as SH. Obtain the average network operation speed value within each sub-time period, and mark it as the network speed value Wi. At the same time, obtain the change value of the network operation speed corresponding to two adjacent sub-time periods, and mark it as the fluctuation value. Construct a set of fluctuation values {|W2 - W1|, |W3 - W2|, |W4 - W3|, …, |Wi - W(i - 1)|}. Use time as the X-axis and the network operation speed value as the Y-axis, and establish a time-fluctuation value bar chart, labeled as the analysis bar chart. Draw a preset fluctuation value range curve on the analysis bar chart. Mark the fluctuation values outside the preset fluctuation value range curve as "1". Obtain the total number of "1", and mark the total number as the interference number GS. It should be noted that the larger the value of the damage value SH, the greater the risk of a fault in the external line of the server and the greater the interference to the server. Conversely, the smaller the value of the damage value SH, the smaller the risk of a fault in the external line of the server and the smaller the interference to the server. The larger the value of the interference number GS, the worse the network stability, the more unstable the change, and the greater the impact on the operation of the server and the greater the interference risk to the operation of the server. Conversely, the smaller the value of the interference number GS, the better the network stability, the more stable the change, the smaller the impact on the operation of the server, and the smaller the interference risk to the operation of the server; And through the formula: Obtain the external environment coefficient. Among them, α and β are the correction factors of the interference number GS and the damage value SH respectively, α - β = 1.367, α > β > 0, and W is the external environment coefficient. It should be noted that the larger the value of the external environment coefficient W, the greater the risk of a fault in the server. Conversely, the smaller the value of the external environment coefficient W, the smaller the risk of a fault in the server; And compare and analyze the external environment coefficient W with the preset external environment coefficient stored internally: If the external environment coefficient W ≥ the preset external environment coefficient, it is determined that the external environment data of the server is abnormal, generate an external environment data abnormal instruction, and send it to the environmental control analysis unit; If the external environment coefficient W < the preset external environment coefficient, it is determined that the external environment data of the server is normal, generate an external environment data normal instruction, and send it to the environmental control analysis unit; After receiving the internal environment data anomaly instruction, internal environment data normal instruction, external environment data anomaly instruction, and external environment data normal instruction, the environmental control analysis unit conducts interactive analysis to obtain: internal anomaly and external anomaly (internal environment data anomaly instruction, external environment data anomaly instruction), internal anomaly and external normal (internal environment data anomaly instruction, external environment data normal instruction), internal normal and external anomaly (internal environment data normal instruction, external environment data anomaly instruction), and internal normal and external normal (internal environment data normal instruction, external environment data normal instruction). If it is internal normal and external normal (internal environment data normal instruction, external environment data normal instruction), a normal signal is generated, and the normal signal and the corresponding server number are sent to the display unit through the operation and maintenance control platform. After receiving the normal signal and the corresponding server number, the display unit immediately generates the corresponding "normal" text document of the server according to the corresponding server number, such as "Server No. 9 is normal", which helps to intuitively understand the state of the server before operation and facilitates the operation of the server. If it is not internal normal and external normal (internal environment data normal instruction, external environment data normal instruction), an abnormal signal is generated, and the abnormal signal and the corresponding server number are sent to the warning unit through the operation and maintenance control platform. After receiving the abnormal signal and the corresponding abnormal server number, the warning unit immediately generates the corresponding "maintenance" voice of the server according to the corresponding server number, such as "Server No. 11 needs maintenance", which helps to remind the duty personnel to promptly maintain the server, and the duty personnel can receive the maintenance voice at the first time when the anomaly occurs, instead of regularly checking the status of the server, achieving the purpose of safe, stable and reliable operation of the network and information system.

[0015] Embodiment 3

[0016] The operation data monitoring unit is used to collect the dynamic data during the operation of the server. The dynamic data refers to the internal average temperature value of the server and the average rotation speed of the cooling fan, and analyzes the dynamic data. The specific analysis process is as follows: Obtain the duration from the start time to the end time when the acquisition server is running, and mark it as the analysis time. Divide the analysis time into g sub-time periods, where g is a natural number greater than zero. Obtain the internal average temperature value of the server within each sub-time period, and mark it as the average temperature value Wg. Construct a set of average temperature values Wg, {W1, W2, W3,..., Wg}. Establish a rectangular coordinate system with time as the X-axis and the average temperature value Wg as the Y-axis, and draw a curve graph of time - average temperature value Wg. Draw a preset internal temperature threshold curve on the time - average temperature value Wg curve graph, and mark it as the reference graph. Obtain the total duration corresponding to the average temperature value Wg above the preset internal temperature threshold curve in the reference graph, and mark it as the interference duration K. It should be noted that the larger the value of the interference duration K, the longer the abnormal heating duration inside the server, and the greater the risk of damage to the internal components of the server. On the contrary, the smaller the value of the interference duration K, the shorter the abnormal heating duration inside the server, and the smaller the risk of damage to the internal components of the server. Then, compare and analyze the interference duration K with the preset interference duration stored internally: If the interference duration K ≥ the preset interference duration, generate a self-check instruction. When the self-check instruction is generated, the operation data monitoring unit obtains the average rotation speed of the cooling fan inside the server within each sub-time period, and marks it as the wind rotation speed. Construct a set of wind rotation speeds, and draw a rectangular coordinate system curve graph of time - wind rotation speed according to the set of wind rotation speeds, and mark it as the rotation speed curve graph. Draw a preset wind rotation speed interval curve on the rotation speed curve graph, and mark it as the analysis change graph. Analyze according to the analysis change graph. Re-mark those within or on the preset wind rotation speed interval curve as "0", and obtain the total number of "0". Re-mark those outside the preset wind rotation speed interval curve as "2", and obtain the total number of "2". Mark the total number of "2" divided by the total number of "0" as the number ratio GB. It should be noted that the larger the value of the number ratio GB, the more prominent the abnormal working characteristics of the cooling fan, and the greater the risk of damage. On the contrary, the smaller the value of the number ratio GB, the less prominent the abnormal working characteristics of the cooling fan, and the smaller the risk of damage. Then, compare and analyze the number ratio GB with the preset number ratio stored internally: If the number ratio GB ≥ the preset number ratio, generate a maintenance signal and send it to the warning unit. After receiving the maintenance signal, the warning unit immediately generates the corresponding "fan maintenance" voice for the server according to the corresponding server number, such as "Fan maintenance for server No. 12", which helps to repair the cooling fan in a timely manner, improve the heat dissipation effect of the device, and solve the existing fault problems in a timely manner. If the number ratio GB < the preset number ratio, no signal is generated; If the interference duration K is less than the preset interference duration, a display signal is generated and sent to the display unit. After receiving the display signal, the display unit immediately generates the corresponding "internal temperature normal" text document of the server according to the corresponding server number. For example, "The internal temperature of server No. 3 is normal" helps to intuitively understand the operating status of the server and facilitates the timely formulation of countermeasures. In summary, the present invention collects the static data before the server operation and the dynamic data during the operation after encoding and processing, and performs a formulaic and progressive analysis on the internal environment data and the external environment data in the static data before the operation, that is, combines and compares the hierarchical division of the acquisition object and the processing process to obtain the internal environment data abnormality instruction, the internal environment data normal instruction, the external environment data abnormality instruction, and the external environment data normal instruction, and performs an interactive and in-depth comparison and analysis on the obtained results to obtain the normal signal, the abnormal signal, and the corresponding server number. After the display unit receives the normal signal, it immediately generates the corresponding "normal" text document of the server according to the corresponding server number, which helps to intuitively understand the status before the server operation and facilitates the operation of the server. After the warning unit receives the abnormal signal, it immediately generates the corresponding "repair" voice of the server according to the corresponding server number, which helps to remind the duty personnel to repair the server in time, and the duty personnel can receive the repair voice at the first time when the abnormality appears, without having to regularly check the status of the server, so as to achieve the purpose of safe, stable and reliable operation of the network and information system. For the dynamic data during the server operation, through symbolic calibration, threshold substitution comparison, set classification regularization, and progressive analysis, the corresponding self-check instructions and display signals are obtained. When the self-check instructions are generated, through information feedback and in-depth analysis, the maintenance signal is obtained. After the warning unit receives the maintenance signal, it immediately generates the corresponding "fan maintenance" voice of the server according to the corresponding server number, which helps to repair the cooling fan in time, improve the heat dissipation effect of the equipment, and timely solve the existing fault problems. At the same time, after the display unit receives the display signal, it immediately generates the corresponding "internal temperature normal" text document of the server according to the corresponding server number, which helps to intuitively understand the operating status of the server and facilitates the timely formulation of countermeasures.

[0017] The above formulas are all obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value. The coefficients in the formula are set by those skilled in the art according to the actual situation. The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A data acquisition and supervision platform for operation and maintenance Internet of Things, characterized in that, It includes an operation and maintenance control platform, a collection and supervision unit, an analysis and feedback unit, a supervision and operation unit, an environmental control analysis unit, an operation data monitoring unit, an early warning unit, and a display unit; The collection and supervision unit is used to collect the static data before the server runs after coding processing. The static data includes internal environment data and external environment data, and sends the internal environment data and external environment data in the static data to the analysis and feedback unit and the supervision and operation unit respectively. Among them, the internal environment data includes the space volume occupied by dust on the cooling fan inside the server and the characteristic images corresponding to all the wire joints inside the server, and the external environment data includes the damaged area of the external wires of the server and the average network operation speed value; After receiving the internal environment data, the analysis and feedback unit analyzes the internal environment data, obtains an internal environment data abnormality instruction and an internal environment data normal instruction, and sends them to the environmental control analysis unit; After receiving the external environment data, the supervision and operation unit analyzes the external environment data, obtains an external environment data abnormality instruction and an external environment data normal instruction, and sends them to the environmental control analysis unit; After receiving the internal environment data abnormality instruction, the internal environment data normal instruction, the external environment data abnormality instruction, and the external environment data normal instruction, the environmental control analysis unit performs interactive analysis to obtain a normal signal, an abnormal signal, and the corresponding server code, and sends the normal signal, the abnormal signal, and the corresponding server code to the display unit and the early warning unit respectively; After receiving the normal signal and the corresponding server number, the display unit immediately generates a corresponding "normal" text document of the server to which it belongs according to the corresponding server number. After receiving the abnormal signal and the corresponding server number, the early warning unit immediately generates a corresponding "repair" voice of the server to which it belongs according to the corresponding server number; The operation data monitoring unit is used to collect the dynamic data during the server operation. The dynamic data refers to the average internal temperature value of the server and the average rotation speed of the cooling fan, analyzes the dynamic data, obtains a self-check instruction and a display signal, and sends the display signal to the display unit. After receiving the display signal, the display unit immediately generates a corresponding "normal internal temperature" text document of the server to which it belongs according to the corresponding server number. When the self-check instruction is generated, it analyzes the average rotation speed of the cooling fan inside the server, obtains a maintenance signal, and sends it to the early warning unit. After receiving the maintenance signal, the early warning unit immediately generates a corresponding "fan maintenance" voice of the server to which it belongs according to the corresponding server number.

2. The data acquisition and supervision platform for operation and maintenance Internet of Things according to claim 1, characterized in that, The specific internal environment data analysis process of the analysis and feedback unit is as follows: The time threshold is evenly divided into i sub-time periods, where i is a natural number greater than zero. The space volume occupied by dust on the cooling fan in each sub-time period of the server is obtained. The space volume occupied by dust on the cooling fan refers to the volume of dust occupying the operating space of the cooling fan, and is marked as the dust volume HCi. A set of the dust volume HCi is constructed. According to the set of the dust volume HCi, the absolute value of the difference between two adjacent subsets is obtained and marked as the fluctuation value. At the same time, a set of the fluctuation values is constructed. According to the set, the average fluctuation value BH is obtained; Obtain the characteristic images corresponding to all internal line joints within the time threshold in real time, perform grayscale processing on the characteristic images corresponding to all internal line joints, and mark them as analysis images. Obtain the grayscale values corresponding to the analysis images. Divide the analysis images into two parts with a preset grayscale value as the boundary. Mark the part greater than or equal to the preset grayscale value as abnormal images, and mark the part less than the preset grayscale value as normal images. Obtain the difference between the grayscale value of the abnormal images and the preset grayscale value, and mark it as the difference value. At the same time, construct a set of difference values, obtain the average value of the set according to the set, and mark it as the average difference degree PC. Obtain the internal environment coefficient N through a formula, and compare and analyze the internal environment coefficient N with the preset internal environment coefficient stored in it: If the internal environment coefficient N ≥ the preset internal environment coefficient, generate an internal environment data abnormality instruction; if the internal environment coefficient N < the preset internal environment coefficient, generate an internal environment data normal instruction.

3. The data acquisition and supervision platform for operation and maintenance Internet of Things according to claim 1, characterized in that The process of analyzing the external environment data of the supervision and operation unit is as follows: Obtain the total area of damage such as breakage, bulging, and depression of the external lines within the time threshold due to their own or external forces, and mark it as the damage value SH. Obtain the average network operation speed value within each sub-time period, and mark it as the network speed value Wi. At the same time, obtain the change value of the network operation speed corresponding to two consecutive sub-time periods, and mark it as the fluctuation value. Construct a set of fluctuation values. Take time as the X-axis and the network operation speed value as the Y-axis, and establish a time-fluctuation value bar chart, marked as the analysis bar chart. Draw a preset fluctuation value range curve on the analysis bar chart, mark the fluctuation values outside the preset fluctuation value range curve as "1", obtain the total number of "1", and mark the total number as the interference number GS; Obtain the external environment coefficient W through a formula, and compare and analyze the external environment coefficient W with the preset external environment coefficient stored in it: If the external environment coefficient W ≥ the preset external environment coefficient, generate an external environment data abnormality instruction; if the external environment coefficient W < the preset external environment coefficient, generate an external environment data normal instruction.

4. The data acquisition and supervision platform for operation and maintenance Internet of Things according to claim 1, characterized in that The interactive analysis process of the environmental control analysis unit is as follows: The internal environment data abnormality instruction and the external environment data abnormality instruction result in internal abnormality and external abnormality, the internal environment data abnormality instruction and the external environment data normal instruction result in internal abnormality and external normality, the internal environment data normal instruction and the external environment data abnormality instruction result in internal normality and external abnormality, and the internal environment data normal instruction and the external environment data normal instruction result in internal normality and external normality; If it is internal normality and external normality, generate a normal signal; if it is not internal normality and external normality, generate an abnormal signal.

5. The data acquisition and supervision platform for operation and maintenance Internet of Things according to claim 1, characterized in that The dynamic data analysis process of the operation data monitoring unit is as follows: The duration from the start time to the end time of the collection server is recorded and marked as the analysis time. The analysis time is divided into g sub-time periods, where g is a natural number greater than zero. The internal average temperature value of the server in each sub-time period is obtained and marked as the average temperature value Wg. A set of average temperature values Wg is constructed. A rectangular coordinate system is established with time as the X-axis and the average temperature value Wg as the Y-axis, and a curve graph of time-average temperature value Wg is plotted. A preset internal temperature value threshold curve is plotted on the curve graph of time-average temperature value Wg and marked as the reference graph. The total duration corresponding to the average temperature value Wg above the preset internal temperature value threshold curve in the reference graph is obtained and marked as the interference duration K; The interference duration K is compared and analyzed with the preset interference duration stored internally; If the interference duration K ≥ the preset interference duration, a self-check instruction is generated; If the interference duration K < the preset interference duration, a display signal is generated.

6. The data acquisition and supervision platform for operation and maintenance Internet of Things according to claim 1, characterized in that The process of analyzing the average rotation speed of the cooling fan in the server by the operation data monitoring unit is as follows: The average rotation speed of the cooling fan in the server in each sub-time period is obtained and marked as the wind rotation speed. A set of wind rotation speeds is constructed. A rectangular coordinate system curve graph of time-wind rotation speed is plotted based on the set of wind rotation speeds and marked as the rotation speed curve graph. A preset wind rotation speed interval curve is plotted on the rotation speed curve graph and marked as the analysis change graph. According to the analysis change graph, those within or on the preset wind rotation speed interval curve are re-marked as "0", and the total number of "0" is obtained. Those outside the preset wind rotation speed interval curve are re-marked as "2", and the total number of "2" is obtained. The total number of "2" is divided by the total number of "0" and marked as the number ratio GB. The number ratio GB is compared and analyzed with the preset number ratio stored internally: If the number ratio GB ≥ the preset number ratio, a maintenance signal is generated; If the number ratio GB < the preset number ratio, no signal is generated.

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