A data monitoring method, device and medium based on an intelligent three-color lamp

Abstract

The application discloses a data monitoring method and device based on an intelligent three-color lamp, and a medium, the method comprising: an Internet of Things platform acquiring operation data of a monitoring device and influence factors corresponding to the monitoring device sent by an intelligent three-color lamp; publishing the operation data, environmental data and working data in the Internet of Things platform; receiving an analysis request sent by a user and analyzing the operation data to obtain a first operation state curve corresponding to the monitoring device; determining a specified state curve representing a device waiting state in the first operation state curve; compensating the specified state curve according to the influence factors to obtain a second operation state curve; and showing the second operation state curve to the user. When the intelligent three-color lamp performs data monitoring, the state curve of the monitoring device is compensated by the influence factors, the misjudgment of the monitoring device can be corrected while considering more comprehensively, and therefore the user can obtain more accurate device analysis results.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data monitoring, in particular to a data monitoring method based on intelligent three-color lights, a device and a medium. BACKGROUND

[0002] A three-color light is a commonly used mechanical and electrical equipment operating condition monitoring element, which is generally composed of red, yellow and green three colors, among which, the red light indicates device abnormality, the yellow light indicates device waiting, and the green light indicates device normal operation. Compared with ordinary three-color lights, the intelligent three-color light increases a processing chip, and can realize preliminary data processing, data transmission and other functions.

[0003] The three-color light is usually installed on the top of the equipment, so that the factory operator can view the equipment operating state at close range, and facilitate rapid equipment maintenance, management, etc. For many mechanical equipment, numerical control equipment, machine tool equipment and other monitoring equipment, the three-color light is a very important component. However, in the existing detection scheme based on three-color lights, only the monitoring equipment itself is considered for monitoring data, so that the final monitoring analysis result is not accurate enough. SUMMARY

[0004] In order to solve the above problems, the present application provides a data monitoring method based on intelligent three-color lights, which comprises: an Internet of Things platform acquires running data of a monitoring device sent by an intelligent three-color light, influence factors corresponding to the monitoring device, the influence factors including environmental data in which the monitoring device is located and working data of the intelligent three-color light; the running data of the monitoring device, the environmental data in which the monitoring device is located and the working data of the intelligent three-color light are published in the Internet of Things platform, so that users with authority can check; receiving an analysis request sent by the user, and analyzing the running data to obtain a first running state curve corresponding to the monitoring device; determining a specified state curve in the first running state curve representing a device waiting state; compensating the specified state curve according to the influence factors, so as to obtain a second running state curve according to the specified state curve after compensation; and displaying the second running state curve as an analysis result to the user.

[0005] In one example, the compensation of the specified state curve according to the influence factors specifically comprises: determining, according to the specified state curve, whether there is an abnormal adjacent state of the monitoring device in the device waiting state; if there is the abnormal adjacent state, determining, for each of all the influence factors, an association level between the influence factor and the abnormal adjacent state; taking the influence factor whose association level exceeds a preset threshold as a specified influence factor, and compensating the abnormal adjacent state through the specified influence factor.

[0006] In one example, the determining the correlation level between the influence factor and the abnormal proximity state specifically comprises: determining a starting time of the abnormal proximity state according to historical running data recorded by the intelligent three-color light, and determining a device influence level corresponding to the influence factor according to a preset device influence level table; determining a trace duration corresponding to the starting time according to the device influence level, the device influence level being positively correlated with the trace duration; tracing back the starting time according to the trace duration to obtain an environmental influence time; taking the environmental influence time as a starting point and an end time of the abnormal proximity state as an ending point to obtain an abnormal occurrence time period between the starting point and the ending point; and determining the correlation level between the influence factor and the abnormal proximity state within the abnormal occurrence time period.

[0007] In one example, the determining the correlation level between the influence factor and the abnormal proximity state within the abnormal occurrence time period specifically comprises: dividing the abnormal occurrence time period into a plurality of subintervals; for each of the subintervals, determining a deviation level of the influence factor compared with preset normal data, and determining a fluctuation level of the abnormal proximity state, the fluctuation level being determined by a difference between the abnormal proximity state and an abnormal state, and determining a correlation level between the deviation level and the fluctuation level in the subinterval according to a preset mapping relationship table; and determining the correlation level between the influence factor and the abnormal proximity state within the abnormal occurrence time period by averaging the correlation levels in each of the subintervals.

[0008] In one example, the compensating the abnormal proximity state by the specified influence factor specifically comprises: compensating the abnormal proximity state by environmental data and working data in the specified influence factor respectively, wherein a compensation degree of the working data is higher than a compensation degree of the environmental data.

[0009] In one example, if the abnormal proximity state exists, the method further comprises: extending the first running state curve according to a terminal gradient of the first running state curve to predict a time of a future abnormal state; generating a predicted running state curve according to a prediction result, and displaying the predicted running state curve as an analysis result to the user.

[0010] In one example, the second operating state curve is displayed to the user as an analysis result, specifically including: based on the user's demand, the second operating state curve is uploaded to the IP address specified by the user after being encrypted by a public key as an analysis result; through the industrial internet platform, a private key matching the public key is sent to the user, so that the user obtains the analysis result by decrypting the private key.

[0011] In one example, the working data includes at least one of three-color lamp temperature and three-color lamp pressure; and the environmental data includes at least one of environmental temperature and environmental humidity.

[0012] In another aspect, the present application also provides a data monitoring device based on an intelligent three-color lamp, including: at least one processor; and a memory in communication connection with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform: an internet of things platform acquires running data of a monitoring device sent by an intelligent three-color lamp, influence factors corresponding to the monitoring device, the influence factors including environmental data in which the monitoring device is located and working data of the intelligent three-color lamp; the running data of the monitoring device, the environmental data in which the monitoring device is located and the working data of the intelligent three-color lamp are published in the internet of things platform, so that users with authority can check; receiving an analysis request sent by the user and analyzing the running data to obtain a first operating state curve corresponding to the monitoring device; determining a specified state curve representing a device waiting state in the first operating state curve; compensating the specified state curve according to the influence factors, so as to obtain a second operating state curve according to the specified state curve after compensation; and displaying the second operating state curve to the user as an analysis result.

[0013] In another aspect, the application also provides a non-volatile computer storage medium, which stores computer executable instructions, and the computer executable instructions are configured to: an Internet of Things platform acquires running data of a monitoring device sent by a smart three-color lamp, an influence factor corresponding to the monitoring device, and the influence factor includes environmental data where the monitoring device is located and working data of the smart three-color lamp; the running data of the monitoring device, the environmental data where the monitoring device is located and the working data of the smart three-color lamp are published in the Internet of Things platform, so that a user with authority can check; an analysis request sent by the user is received, and the running data is analyzed to obtain a first running state curve corresponding to the monitoring device; a specified state curve representing a device waiting state in the first running state curve is determined; the specified state curve is compensated according to the influence factor, so that a second running state curve is obtained according to the specified state curve after compensation; and the second running state curve is taken as an analysis result and displayed to the user.

[0014] The data monitoring method based on the smart three-color lamp can bring the following beneficial effects:

[0015] When the smart three-color lamp performs data monitoring, not only the running data of the monitoring device is considered, but also the influence factors such as the environmental data and the working data are considered, the state curve of the monitoring device is compensated through the influence factors, the misjudgment of the monitoring device can be corrected while considering more comprehensively, so that the user can obtain more accurate device analysis results. BRIEF DESCRIPTION OF DRAWINGS

[0016] The accompanying drawings, which are included to provide a further understanding of the application and constitute a part of this application, illustrate certain illustrative embodiments of the application and together with the description serve to explain the application. In the drawings:

[0017] Figure 1 FIG. 1 is a flowchart of the data monitoring method based on the smart three-color lamp in the embodiments of the application;

[0018] Figure 2 FIG. 2 is a schematic diagram of the data monitoring device based on the smart three-color lamp in the embodiments of the application. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions and advantages of the application clearer, the following will combine the embodiments of the application and the corresponding drawings to clearly and completely describe the technical solutions of the application. Obviously, the described embodiments are only a part of the embodiments of the application, but not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0020] The technical scheme provided by the embodiments of the present application is described in detail below with reference to the drawings.

[0021] As Figure 1 shown, the present application provides a data monitoring method based on an intelligent three-color light, comprising:

[0022] S101: An Internet of Things platform acquires running data of a monitored device sent by an intelligent three-color light (hereinafter referred to as a three-color light), and influence factors corresponding to the monitored device, wherein the influence factors include environmental data in which the monitored device is located and working data of the intelligent three-color light.

[0023] The Internet of Things platform is associated with the three-color light in advance, the monitored device refers to a device currently monitored by the three-color light, and the running data can include a boot-up rate and a running duration of the monitored device. The influence factors represent factors that can affect the running data, at this time, the monitored device can not have an abnormality, but the existence of the abnormal factors makes the finally collected running data show an abnormality. The environmental data can include environmental temperature and environmental humidity, and the working data can include temperature and pressure of the three-color light itself.

[0024] S102: The running data of the monitored device, the environmental data in which the monitored device is located, and the working data of the intelligent three-color light are published in the Internet of Things platform, so that a user with authority can check.

[0025] After being published in the Internet of Things platform, the user can check. Whether the user has authority can be identified by the identity of the user, for example, an administrator usually has high authority, and all parties of the monitored device usually have authority over the monitored device.

[0026] S103: An analysis request sent by the user is received, and the running data is analyzed to obtain a first running state curve corresponding to the monitored device.

[0027] When the running data has multiple, a corresponding running state curve (hereinafter referred to as a first running state curve) can be generated for each running data, of course, although the curve can completely represent the running data, its representation form can not be limited to the curve, for example, a line chart, a histogram, and a pie chart can also be displayed and recorded.

[0028] S104: A specified state curve representing a device waiting state in the first running state curve is determined.

[0029] For the three-color light, a red light indicates a device abnormality, a yellow light indicates a device waiting, and a green light indicates a device normal operation, so that the specified state curve corresponding to the device waiting state can be obtained in a time period of the yellow light of the three-color light.

[0030] S105: compensate the specified state curve according to the influence factor, to obtain a second running state curve according to the compensated specified state curve.

[0031] At this time, for the device waiting state, because it does not start work, and no abnormal state occurs, the device is in standby state, and itself is not in work, so the possibility of being affected by the outside is the largest compared with the other two states. At this time, the specified state curve is compensated according to the influence factor, and then the original specified state curve is replaced by the compensated specified state curve, that is, the second running state curve can be obtained.

[0032] S106: display the second running state curve as an analysis result to the user.

[0033] The second running state is sent to the client of the user as an analysis result, which can meet the needs of the user for the analysis request. When the intelligent three-color lamp monitors the data, not only the running data of the monitoring device is considered, but also the environmental data, work data and other influence factors are considered. The state curve of the monitoring device is compensated by the influence factor, which can correct the misjudgment of the monitoring device while considering more comprehensively, so that the user can get more accurate device analysis results.

[0034] In one embodiment, when the specified state curve is compensated, the specified state curve can be used to determine whether there is an abnormal adjacent state of the monitoring device in the device waiting state. Once the data of the monitoring device is abnormal, the three-color lamp will turn red, and the abnormal adjacent state means that it has not reached the abnormal state, and the difference between the abnormal state is lower than the preset threshold. Judging the abnormal adjacent state helps to analyze the cause of the abnormal state. If there is an abnormal adjacent state, it can be determined whether the abnormal adjacent state is caused by the influence factor. At this time, for each influence factor in all influence factors, the association level between the influence factor and the abnormal adjacent state is determined. The association level represents the possibility of the influence factor causing the abnormal adjacent state, and the higher the association level, the higher the possibility. After obtaining the association level, the influence factor with an association level exceeding a preset threshold can be used as a specified influence factor, and the abnormal adjacent state is likely to be caused by the specified influence factor. At this time, the abnormal adjacent state is compensated by the specified influence factor, so as to ensure the accuracy of the second running state. The influence factor can include environmental data and work data, at this time, the environmental data and work data in the specified influence factor are used to compensate the abnormal adjacent state, and because the three-color lamp itself is more closely related to the monitoring device, the compensation degree of the work data is higher than that of the environmental data. The compensation can be to increase or decrease the data value in the abnormal adjacent state.

[0035] Specifically, in determining the correlation level, the starting time of the abnormal proximity state can be determined according to the historical operation data recorded by the intelligent tricolor lamp, and the device influence level corresponding to the influence factor can be determined according to the preset device influence level table. The higher the device influence level, the greater the possibility of negative influence of the influence factor on the monitoring device, and the higher the degree of negative influence. Then, according to the device influence level, the trace duration corresponding to the starting time is determined, and the device influence level is positively correlated with the trace duration. Generally, the device does not suddenly produce an abnormality due to the influence factor, but slowly accumulates until an abnormality occurs. Therefore, it is likely that the device has already had a problem before the starting time of the abnormal proximity state. At this time, according to the trace duration, the starting time is traced back, and the obtained time is called the environmental influence time.

[0036] Taking the environmental influence time as the starting point and the end time of the abnormal proximity state as the ending point, then the abnormal occurrence time period between the starting point and the ending point is taken as the time period for data processing. At this time, the correlation level between the influence factor and the abnormal proximity state within the abnormal occurrence time period is determined.

[0037] Further, in determining the correlation level, the abnormal occurrence time period can be divided into multiple subintervals, and then for each subinterval, the deviation level of the influence factor compared to the preset normal data is determined, and the fluctuation level of the abnormal proximity state is determined. The normal data is preset, the greater the difference between the influence factor and the normal data, the higher the deviation level, and the closer the abnormal proximity state to the abnormal state, the higher the fluctuation level, which can be determined by the difference between the abnormal proximity state and the abnormal state. Then, according to the preset mapping relationship table, the correlation level between the deviation level and the fluctuation level in the subinterval is determined. The closer the trend between the deviation level and the fluctuation level, the higher the correlation level. Finally, the correlation level between the influence factor and the abnormal proximity state within the abnormal occurrence time period can be determined by averaging or summing the correlation levels in each subinterval.

[0038] In addition, when the abnormal proximity state is monitored, it means that it is close to the abnormal state. At this time, the time of the abnormal state in the future can be predicted according to the first running state curve, such as the trend of the first running state curve is obtained according to the gradient of the end of the first running state curve, and then the first running state curve is extended according to the trend to make the prediction. Then, a predicted running state curve is generated according to the prediction result, and the predicted running state curve is taken as an analysis result and displayed to the user to assist the user in making a judgment.

[0039] In one embodiment, when the user is presented, the second running state curve can be uploaded to the IP address specified by the user after being encrypted by the public key based on the user's needs, and then the private key matched with the public key is sent to the user through the industrial internet platform, so that the user can obtain the analysis result by decrypting the private key. The analysis result and the private key are sent separately, which can ensure the user's privacy as much as possible.

[0040] As Figure 2 shown, the embodiment of the application further provides a data monitoring device based on an intelligent three-color lamp, comprising:

[0041] at least one processor; and,

[0042] a memory in communication connection with the at least one processor; wherein,

[0043] the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute:

[0044] The Internet of Things platform acquires the running data of the monitoring device sent by the intelligent three-color lamp, the influence factor corresponding to the monitoring device, the influence factor including the environmental data where the monitoring device is located, and the working data of the intelligent three-color lamp;

[0045] The running data of the monitoring device, the environmental data where the monitoring device is located, and the working data of the intelligent three-color lamp are published in the Internet of Things platform, so that users with authority can check;

[0046] receive the analysis request sent by the user, and analyze the running data to obtain the first running state curve corresponding to the monitoring device;

[0047] determine the specified state curve representing the device waiting state in the first running state curve;

[0048] According to the influence factor, the specified state curve is compensated to obtain the second running state curve according to the specified state curve after compensation;

[0049] The second running state curve is taken as an analysis result and presented to the user.

[0050] The embodiment of the application further provides a non-volatile computer storage medium, which stores computer executable instructions, and the computer executable instructions are set to:

[0051] The Internet of Things platform acquires running data of the monitoring device sent by the smart three-color lamp, an influence factor corresponding to the monitoring device, the influence factor including environment data where the monitoring device is located and working data of the smart three-color lamp;

[0052] The running data of the monitoring device, the environment data where the monitoring device is located and the working data of the smart three-color lamp are published in the Internet of Things platform, so that a user with authority can check them;

[0053] An analysis request sent by the user is received, and the running data is analyzed to obtain a first running state curve corresponding to the monitoring device;

[0054] A specified state curve representing a device waiting state in the first running state curve is determined;

[0055] The specified state curve is compensated according to the influence factor, so that a second running state curve is obtained according to the specified state curve after compensation;

[0056] The second running state curve is displayed to the user as an analysis result.

[0057] Each of the embodiments in the present application is described in a progressive manner, and the same or similar parts of each of the embodiments can be referred to each other. Each of the embodiments mainly describes the difference from other embodiments. In particular, the device and medium embodiments are basically similar to the method embodiments, and thus the description is relatively simple, and the relevant parts can be referred to the part of the method embodiments.

[0058] The device and medium provided by the embodiments of the present application are one-to-one corresponding to the method, and thus the device and medium also have the similar beneficial technical effects as the method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the device and medium will not be described here.

[0059] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can be in the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can be in the form of a computer program product implemented on one or more computer usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0060] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof.

[0061] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof.

[0062] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof.

[0063] In one typical configuration, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0064] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) and / or cache memory, non-volatile memory, such as read-only memory (ROM), EPROM, and / or flash memory. The memory is an example of computer-readable media.

[0065] Computer-readable media includes permanent and non-permanent, movable and non-movable media that can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carriers.

[0066] It should also be noted that the terms "comprising", "containing", or any other variant thereof are intended to cover a non-exclusive inclusion, such that a process, method, article or apparatus that comprises a list of elements does not only include those elements, but can also include other elements not expressly listed or inherent to such process, method, article or apparatus. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0067] The above only describes the embodiments of the present application and is not intended to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the scope of claims of the present application.

Claims

1. A method for data monitoring based on intelligent tricolor light, characterized in that, The method comprises the following steps: An Internet of Things platform acquires running data of a monitoring device sent by a smart three-color lamp, and influence factors corresponding to the monitoring device, the influence factors including environmental data in which the monitoring device is located and working data of the smart three-color lamp; The running data of the monitoring device, the environmental data in which the monitoring device is located and the working data of the smart three-color lamp are published in the Internet of Things platform, so that users with authority can check them; An analysis request sent by the user is received, and the running data is analyzed to obtain a first running state curve corresponding to the monitoring device; A specified state curve representing a device waiting state in the first running state curve is determined, the device waiting state corresponding to no start of work and no abnormal state; The specified state curve is compensated according to the influence factors, so that a second running state curve is obtained according to the specified state curve after compensation; The second running state curve is displayed to the user as an analysis result; The compensation of the specified state curve according to the influence factors specifically comprises the following steps: According to the specified state curve, it is determined whether there is an abnormal adjacent state of the monitoring device in the device waiting state; If the abnormal adjacent state exists, the association level between each influence factor and the abnormal adjacent state is determined for all the influence factors; The influence factor whose association level exceeds a preset threshold is taken as a specified influence factor, and the abnormal adjacent state is compensated through the specified influence factor; The determination of the association level between the influence factor and the abnormal adjacent state specifically comprises the following steps: According to historical running data recorded by the smart three-color lamp, a start time of the abnormal adjacent state is determined, and a device influence level corresponding to the influence factor is determined according to a preset device influence level reference table; According to the device influence level, a trace duration corresponding to the start time is determined, and the device influence level is positively correlated with the trace duration; According to the trace duration, the start time is traced back to obtain an environmental influence time; Taking the environmental influence time as a starting point and an end time of the abnormal adjacent state as an ending point, an abnormal occurrence time period between the starting point and the ending point is obtained; The association level between the influence factor and the abnormal adjacent state in the abnormal occurrence time period is determined.

2. The method of claim 1, wherein, The determination of the association level between the influence factor and the abnormal adjacent state in the abnormal occurrence time period specifically comprises the following steps: The abnormal occurrence time period is divided into a plurality of subintervals; For each subinterval, a deviation level of the influence factor compared with preset normal data is determined, a fluctuation level of the abnormal adjacent state is determined, the fluctuation level is determined by a difference between the abnormal adjacent state and an abnormal state, and an association level between the deviation level and the fluctuation level in the subinterval is determined according to a preset mapping relationship table. Determine the correlation level between the specified influence factor and the abnormal adjacent state in the abnormal occurrence period by averaging the correlation levels in each of the sub-intervals.

3. The method of claim 1, wherein, The compensation of the abnormal adjacent state by the specified influence factor specifically includes: Compensate the abnormal adjacent state by the environmental data and the working data in the specified influence factor, wherein the compensation degree of the working data is higher than that of the environmental data.

4. The method of claim 1, wherein, If the abnormal adjacent state exists, the method further includes: Extend the first running state curve according to the end gradient of the first running state curve to predict the time of future abnormal state; Generate a predicted running state curve according to the prediction result, and display the predicted running state curve as the analysis result to the user.

5. The method of claim 1, wherein, The display of the second running state curve as the analysis result to the user specifically includes: Based on the user's demand, the second running state curve is uploaded to the IP address specified by the user after being encrypted by a public key as the analysis result; Through the industrial internet platform, the private key matched with the public key is sent to the user, so that the user can obtain the analysis result by decrypting the private key.

6. The method of any one of claims 1-5, wherein, The working data includes at least one of three-color lamp temperature and three-color lamp pressure; the environmental data includes at least one of environmental temperature and environmental humidity.

7. A smart tri-color light based data monitoring device, characterized in that, It includes: At least one processor; And The memory is in communication connection with the at least one processor; wherein The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the intelligent three-color lamp-based data monitoring method of claim 1.

8. A non-volatile computer storage medium storing computer-executable instructions, characterized in that, The computer executable instructions are set as the intelligent three-color lamp-based data monitoring method of claim 1.

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