Monitoring data processing method and device, equipment and storage medium

Abstract

The application relates to the technical field of equipment monitoring, and in particular to a monitoring data processing method and device, equipment and a storage medium, wherein the method comprises the following steps: obtaining monitoring data, processing the monitoring data to obtain a data change rate based on a preset change rate calculation model; dynamically adjusting a preset first change rate threshold to obtain a second change rate threshold based on the data change rate, and taking the second change rate threshold as a new first change rate threshold; judging whether the data change rate exceeds the new first change rate threshold; if yes, adjusting equipment corresponding to the monitoring data to make the monitoring data return to a stable state; wherein the stable state represents that the monitoring data is limitedly floated up and down based on a preset reference value; and the application facilitates improving the running stability of the equipment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of device monitoring, and in particular to a monitoring data processing method and device, equipment and a storage medium. BACKGROUND

[0002] A device in a factory will generate corresponding running data, such as temperature data, during operation. During device operation, monitoring data is obtained by monitoring the running data, so as to subsequently monitor the device based on the monitoring data.

[0003] At present, one way of processing monitoring data to achieve device monitoring is to first preset upper and lower limits of the monitoring data, then in the running process of the device, real-time monitoring data is obtained, and then it is determined whether the monitoring data exceeds the preset upper and lower limits. If so, it indicates that the device is currently running abnormally, and a data abnormality alarm information is issued so as to enable the staff to adjust the device, so that the monitoring data returns to the upper and lower limits, that is, the device is re-operated normally.

[0004] However, the above-mentioned way of processing monitoring data can achieve adjustment of the device, but cannot avoid the situation that the device still runs abnormally during operation. It can be seen that processing monitoring data by the prior art will result in poor running stability of the device. SUMMARY

[0005] In order to improve the running stability of the device, the embodiments of the present application provide a monitoring data processing method, device, equipment and storage medium.

[0006] In a first aspect, the embodiments of the present application provide a monitoring data processing method, comprising:

[0007] obtaining monitoring data, and processing the monitoring data based on a preset change rate calculation model to obtain a data change rate;

[0008] dynamically adjusting a preset first change rate threshold based on the data change rate to obtain a second change rate threshold, and taking the second change rate threshold as a new first change rate threshold;

[0009] determining whether the data change rate exceeds the new first change rate threshold;

[0010] if so, adjusting the device corresponding to the monitoring data to make the monitoring data return to a stable state; wherein the stable state represents that the monitoring data is limited to fluctuate based on a preset reference value.

[0011] In a second aspect, the embodiments of the present application provide a monitoring data processing device, comprising:

[0012] The change rate calculation module is configured to obtain monitoring data, and process the monitoring data based on a preset change rate calculation model to obtain a data change rate.

[0013] The threshold dynamic adjustment module is configured to dynamically adjust a preset first change rate threshold based on the data change rate to obtain a second change rate threshold, and use the second change rate threshold as a new first change rate threshold.

[0014] The threshold judgment module is configured to judge whether the data change rate exceeds the new first change rate threshold.

[0015] The device adjustment module is configured to, if yes, adjust a device corresponding to the monitoring data, so as to make the monitoring data return to a stable state; wherein the stable state represents that the monitoring data is limitedly floated up and down based on a preset reference value.

[0016] In a third aspect, an embodiment of the present application provides a computer device, which comprises a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method when executing the computer program.

[0017] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the steps in the above method.

[0018] In a fifth aspect, an embodiment of the present application further provides a computer program product. The computer program product comprises a computer program. The computer program is executed by a processor to implement the steps in any of the above method embodiments.

[0019] The above embodiments of the monitoring data processing method, device, equipment and storage medium obtain monitoring data, process the monitoring data based on a preset change rate calculation model to obtain a data change rate, dynamically adjust a preset first change rate threshold based on the data change rate to obtain a second change rate threshold, use the second change rate threshold as a new first change rate threshold, judge whether the data change rate exceeds the new first change rate threshold, and if yes, adjust a device corresponding to the monitoring data, so as to make the monitoring data return to a stable state. The stable state represents that the monitoring data is limitedly floated up and down based on a preset reference value. In this way, it is convenient to judge in advance whether the monitoring data has a risk of exceeding the data upper and lower limits, and when it is judged that the monitoring data has the risk of exceeding the data upper and lower limits, the device is immediately adjusted, so that the monitoring data returns to the stable state, thereby facilitating to improve the running stability of the device.

[0020] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is an application environment diagram of a monitoring data processing method in one embodiment of this application;

[0023] Figure 2 This is a flowchart of a monitoring data processing method provided in Embodiment 1 of this application;

[0024] Figure 3 This is a flowchart of a monitoring data processing method provided in Embodiment 2 of this application;

[0025] Figure 4 This is a flowchart of a monitoring data processing method provided in Embodiment 3 of this application;

[0026] Figure 5 This is a flowchart of a monitoring data processing method provided in Embodiment 4 of this application;

[0027] Figure 6 This is a schematic diagram of the structure of a monitoring data processing device provided in Embodiment 5 of this application;

[0028] Figure 7 This is a schematic diagram of the structure of a computer device provided in one embodiment of this application;

[0029] Figure 8 This is an internal structural diagram of a computer-readable storage medium provided in one embodiment of this application. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this disclosure clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this disclosure.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of this document and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, device, product or equipment including a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or equipment.

[0032] In this paper, the term "and / or" is only a description of the association relationship between the associated objects, which means that there can be three relationships. For example, A and / or B can represent three cases: A exists alone, A and B exist together, and B exists alone. In addition, the character " / " in this paper generally represents that the front and rear associated objects are in an "or" relationship.

[0033] To solve the above problems, the embodiments of the present disclosure provide a monitoring data processing method, which can be applied to an application environment as shown in Figure 1 The terminal 102 communicates with the server 104 through the network. The data storage system can store the data required by the server 104 to process. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers. The terminal 102 can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things device can be a smart speaker, a smart television, a smart air conditioner, a smart vehicle device, etc. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0034] Embodiment one

[0035] Figure 2 A monitoring data processing method flowchart is provided for the first embodiment of the present application, as shown in Figure 2 The method can be executed by a device executing the method, which can be implemented by software and / or hardware. The method comprises the following steps:

[0036] S110, obtaining monitoring data, processing the monitoring data based on a preset change rate calculation model to obtain a data change rate.

[0037] It should be noted that, in order to facilitate monitoring of the equipment in the factory, in the implementation, a device monitoring system is preset, the device monitoring system is used to monitor the equipment, obtain the monitoring data of the equipment at a certain frequency, and obtain the state of the equipment by analyzing the monitoring data, and based on the state of the equipment, the corresponding adjustment control is performed on the equipment, thereby improving the stability of the equipment operation; for example, the monitoring data can be temperature data of the equipment.

[0038] A change rate calculation model is preset in the device monitoring system, and the change rate calculation model is used to calculate the gradient corresponding to each monitoring data in a group of monitoring data, so as to reflect the data change rate corresponding to each monitoring data through the gradient.

[0039] In the implementation, the device monitoring system obtains the monitoring data of the equipment in real time at a preset frequency, and then inputs the monitoring data into the preset change rate calculation model for calculation, the change rate calculation model outputs the gradient corresponding to each monitoring data, and then the gradient is taken as the data change rate corresponding to the monitoring data; for example, the device monitoring system collects temperature data of 10 devices per minute, and then sends the collected temperature data to the change rate calculation model for calculation, and then the change rate calculation model inputs the gradient corresponding to each temperature data, so that each gradient is taken as the data change rate of the corresponding temperature data.

[0040] S120, based on the data change rate, dynamically adjusting the preset first change rate threshold to obtain a second change rate threshold, and taking the second change rate threshold as a new first change rate threshold.

[0041] It should be noted that the data size of the monitoring data is changing in real time, generally, in order to facilitate the judgment of whether the monitoring data is in a normal state, in the implementation, the monitoring data is provided with a data upper and lower limit, if the monitoring data is between the data upper and lower limit, the monitoring data is in a normal state, that is, the equipment is running normally; and generally, the monitoring data is closer to the lower limit of the data upper and lower limit. However, even if the monitoring data is between the data upper and lower limit, if the data change rate of the monitoring data is continuously in an increasing state, that is, the data size of the monitoring data is continuously rising, and the rising amplitude is getting larger and larger, it means that the next monitoring data has a great possibility of exceeding the upper limit of the upper and lower limit, if the monitoring data exceeds the upper limit, it means that the equipment is running abnormally.

[0042] In order to facilitate judging whether the monitoring data has the risk of exceeding the upper and lower limits through the data change rate, in the implementation, a first change rate threshold is preset, which is a change rate threshold initially set based on historical experience. The calculated data change rate is compared with the first change rate threshold. If the data change rate exceeds the first change rate threshold, it indicates that the data size of the monitoring data continues to rise, and the rising amplitude becomes larger and larger, that is, it indicates that the subsequent monitoring data has a greater possibility of exceeding the data upper and lower limits, that is, it indicates that the subsequent device has the risk of running abnormally. However, the initially set first change rate threshold may not be appropriate. For example, assuming that a group of temperature data corresponds to data change rates of 0.1, 0.1, 0.3, 0.6, and 1.1, and the preset first change rate threshold is 1, when the data change rate is 1.1, the data change rate exceeds the first change rate threshold, and at this time, it is judged that the subsequent monitoring data has a greater possibility of exceeding the upper and lower limits. However, in fact, when the data change rate is 0.3, the data change rate has begun to appear a state of continuous increase. However, in the case where the first change rate threshold is set to 1, it can be judged that the data change rate begins to appear a state of continuous increase only when the data change rate becomes 1.1. Obviously, based on the initially set first change rate threshold, the state of the data change rate beginning to appear a state of continuous increase cannot be judged in time and accurately. In order to facilitate judging the state of the data change rate beginning to appear a state of continuous increase in time and accurately, the first change rate threshold needs to be adjusted, the first change rate threshold is reduced to obtain a second change rate threshold, and the second change rate threshold is used as a new first change rate threshold for subsequent comparison and judgment with the data change rate. For example, the first change rate threshold is reduced to 0.4, and 0.4 is used as the second change rate threshold, that is, the new first change rate threshold.

[0043] It should be noted that since the monitoring data is real-time changing, the second change rate threshold generated may still have the problem of the initially set first change rate threshold. Therefore, the new first change rate threshold needs to be continuously adjusted, so as to realize dynamic adjustment of the first change rate threshold. Obviously, the first change rate threshold is not fixed, but is dynamically adjusted with the implementation of the monitoring data, so as to facilitate judging the state of the data change rate beginning to appear a state of continuous increase in time and accurately.

[0044] S130, judging whether the data change rate exceeds the new first change rate threshold.

[0045] It should be noted that if the first change rate threshold is dynamically adjusted, the dynamically adjusted first change rate threshold is used as the second change rate threshold.

[0046] In implementation, after the data change rate corresponding to each monitoring data is calculated by the change rate calculation model, it is further judged whether each data change rate exceeds the current corresponding second change rate threshold, that is, it is further judged whether each data change rate exceeds the current corresponding new first change rate threshold.

[0047] In S140, if yes, the device corresponding to the monitoring data is adjusted to make the monitoring data return to a stable state; wherein the stable state represents that the monitoring data is limitedly floated up and down based on the preset reference value.

[0048] It should be noted that if it is judged by the above steps that one data change rate exceeds the current corresponding second change rate threshold, it means that the subsequent monitoring data has a high risk of exceeding the data upper and lower limits, so that the risk of possible operation abnormality of the device can be predicted in advance, so that the device monitoring system can take countermeasures in advance, thereby avoiding the situation that the device subsequently appears operation abnormality. In the case where it is judged that one data change rate exceeds the current corresponding second change rate threshold, at this time the monitoring data has not exceeded the data upper and lower limits, the device monitoring system adjusts the corresponding device, so that the data size of the monitoring data is reduced to return to the original stable state; wherein the stable state represents that the monitoring data is limitedly floated up and down based on the preset reference value; in this embodiment, that is, the monitoring data no longer continues to approach the upper limit of the data upper and lower limits but approaches the lower limit of the data upper and lower limits, and gradually becomes parallel to the lower limit of the data upper and lower limits.

[0049] For example, it is judged by the above steps that the data change rate corresponding to the temperature data exceeds the current corresponding second change rate threshold, then the device monitoring system considers that the subsequent device has the risk of over-temperature; then the device monitoring system controls the device to take corresponding cooling operation, so that the temperature data continuously decreases until it returns to a stable state.

[0050] It should be noted that in the above embodiment, by the above monitoring data processing method, device, equipment and storage medium, the monitoring data is obtained, the data change rate is obtained by processing the monitoring data based on the preset change rate calculation model; the second change rate threshold is obtained by dynamically adjusting the preset first change rate threshold based on the data change rate, and the second change rate threshold is used as the new first change rate threshold; it is judged whether the data change rate exceeds the new first change rate threshold; if yes, the device corresponding to the monitoring data is adjusted to make the monitoring data return to a stable state; wherein the stable state represents that the monitoring data is limitedly floated up and down based on the preset reference value; in this way, it is convenient to judge in advance whether the monitoring data has the risk of exceeding the data upper and lower limits, and when it is judged that the monitoring data has the risk of exceeding the data upper and lower limits, the device is immediately adjusted, so that the monitoring data returns to a stable state, thereby improving the operation stability of the device.

[0051] Figure 2 Figure 1 is a flowchart illustrating a method for monitoring data processing according to an embodiment. It should be understood that although the steps within the flowchart of Figure 1 are depicted in a particular order, this is not meant to be limiting. In other embodiments, the steps can be performed in a different order, or some steps can be performed in parallel. In some embodiments, some steps can be performed by different components, or some steps can be performed by the same component. Figure 2 It should be understood that although the steps in the flowchart of Figure 1 are shown in a particular order, these steps are not necessarily performed in the order shown by the arrows; unless otherwise specified herein, the steps are not necessarily performed in a strict sequence; these steps can be performed in other sequences; and Figure 2 At least some of the steps in Figure 1 can include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but can be performed at different times, and the order of the sub-steps or stages is not necessarily sequential, but can be performed in rotation or alternation with at least some of the other steps or sub-steps or stages of other steps.

[0052] In a specific embodiment, the second change rate threshold is obtained by dynamically adjusting the first change rate threshold based on the data change rate, including:

[0053] S121, determining whether the data change rate of the monitoring data not in the stable state exceeds the first change rate threshold.

[0054] It should be noted that if the monitoring data does not float within the upper and lower limits based on the preset reference value, that is, the monitoring data exceeds the upper and lower limits of the reference value, it is considered that the monitoring data is not in a stable state; if it is determined that the monitoring data is not in a stable state, it is considered that the monitoring data has a trend of exceeding the upper and lower limits of the monitoring data, at which time it is determined whether the data change rate of the monitoring data not in the stable state exceeds the first change rate threshold, if yes, it is considered that the first change rate threshold is initially set too high.

[0055] In implementation, in response to determining that the monitoring data is not in a stable state, it is further determined whether the change rate of the monitoring data not in the stable state exceeds the first change rate threshold.

[0056] S122, if yes, the first change rate threshold is adjusted to the second change rate threshold.

[0057] In implementation, if it is determined that the change rate of the monitoring data not in the stable state does not exceed the first change rate threshold, it is considered that the first change rate threshold is initially set too high, at which time the first change rate threshold needs to be adjusted to obtain the second change rate threshold; for example, if it is determined that the change rate of the monitoring data not in the stable state does not exceed the first change rate threshold, the first change rate threshold is reduced to obtain the second change rate threshold.

[0058] Embodiment two

[0059] Figure 3 A monitoring data processing method flow chart is provided for Embodiment Two of the present application, referring to Figure 3 The method can be executed by a device for executing the method, which can be realized by software and / or hardware. The method comprises the following steps:

[0060] S210, obtaining monitoring data, and processing the monitoring data based on a preset change rate calculation model to obtain a data change rate.

[0061] S220, dynamically adjusting a preset first change rate threshold based on the data change rate to obtain a second change rate threshold, and taking the second change rate threshold as a new first change rate threshold.

[0062] S230, judging whether the data change rate exceeds the new first change rate threshold.

[0063] S240, if yes, adjusting a device corresponding to the monitoring data to make the monitoring data return to a stable state; wherein the stable state represents that the monitoring data has an upper limit and a lower limit of floating based on a preset reference value.

[0064] S250, judging whether the monitoring data starts to return to the stable state.

[0065] It should be noted that, in the case where it is judged that the data change rate exceeds the new first change rate threshold, it means that the monitoring data has a risk of exceeding the upper and lower limits in the future, that is, it means that the device has a risk of losing control in the future. In order to eliminate this risk, the device monitoring system adjusts the corresponding device, so that the monitoring data returns to the stable state. However, when adjusting the device, there may be a case of adjustment failure. If it is found that the device does not start to return to the stable state after adjusting the device, it means that the adjustment of the device fails, and the subsequent monitoring data will exceed the upper and lower limits of the monitoring data with the original trend. Wherein, the monitoring data starting to return to the stable state represents that the data which originally increases continuously now starts to decrease and develops towards the stable state.

[0066] In the implementation, after adjusting the device corresponding to the monitoring data, it is further judged whether the monitoring data starts to return to the stable state.

[0067] S260, if no, issuing a data anomaly alarm information.

[0068] It should be noted that, if it is judged that the monitoring data does not start to return to the stable state, it means that the adjustment of the device fails, and there is a high possibility that the device itself is abnormal. At this time, the device monitoring system issues a data anomaly alarm information so as to enable the staff to find the abnormal condition of the device in time.

[0069] In implementation, when it is judged that the monitoring data does not start to return to the stationary state, the device monitoring system sends data abnormality alarm information to the staff, wherein the data abnormality alarm information is generated in a manner including but not limited to sound and light alarm, voice broadcast alarm, short message alarm, and APP notification alarm.

[0070] It should be noted that through implementation of each step described above in the embodiment, corresponding data abnormality alarm information can be sent in time when the device is out of control, so as to alert the staff to react to the device abnormality in time, thereby improving the stability and safety of device operation.

[0071] Figure 3 The flowchart of the monitoring data processing method in one embodiment is shown in FIG. 3. It should be understood that although each step in the flowchart is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow; unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences; and Figure 3 at least part of the steps in the flowchart can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these sub-steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or sub-steps or stages of other steps. Figure 3

[0072] Embodiment Three

[0073] Figure 4 The flowchart of the monitoring data processing method provided in Embodiment Three of the present application is shown in FIG. 3, which is described above. Figure 4 The method can be executed by a device for executing the method, which can be realized in software and / or hardware, and the method includes the following steps.

[0074] S310, acquiring monitoring data.

[0075] S320, in response to the monitoring data exceeding the preset data upper and lower limits for the first time, sending data abnormality alarm information, and taking the monitoring data as initial out-of-control data.

[0076] ​It should be noted that the monitoring data may exceed the corresponding data upper and lower limits, wherein the data upper and lower limits include the preset data upper limit and the data lower limit for the monitoring data; if the monitoring data is not in the data upper and lower limits, it is considered that the device is out of control, in which case the prior art generally performs an alarm each time the monitoring data exceeds the data upper and lower limits and lasts for a predetermined time period; however, if multiple monitoring data exceed the data upper and lower limits due to the same reason and lasts for a predetermined time period, the corresponding alarm will be triggered each time, so that multiple alarms may be caused due to the same reason in a short period of time, thereby affecting the judgment of the running state of the device by the staff.

[0077] In order to solve the problem of multiple alarms due to the same reason in the prior art, in the implementation, when the monitoring data exceeds the data upper and lower limits for the first time, the device monitoring system sends a data anomaly alarm information to the staff, and records the monitoring data which exceeds the data upper and lower limits for the first time due to a certain reason as initial out-of-control data.

[0078] S330, determining the out-of-control type and the out-of-control reason of the initial out-of-control data.

[0079] It should be noted that the monitoring data exceeding the data upper and lower limits is generally caused by device out-of-control, and the device out-of-control has different out-of-control types and out-of-control reasons; if the monitoring data exceeds the data upper and lower limits due to the same out-of-control type and out-of-control reason, only one data anomaly alarm information needs to be sent, so as to prevent the same out-of-control type and out-of-control reason from causing multiple data anomaly alarm information to be sent.

[0080] In the implementation, after obtaining the initial out-of-control data, the out-of-control type and the out-of-control reason corresponding to the initial out-of-control data are further determined.

[0081] In one specific embodiment, the out-of-control type includes device automatic out-of-control and human operation out-of-control; and the out-of-control reason includes device part failure, device part aging, and poor connection of device parts.

[0082] It should be noted that the device automatic out-of-control means that the device is out of control due to the reason of the device itself; the human operation out-of-control means that the device is out of control due to the reason of human operation; the device part failure means that the device part has a fault, for example, a circuit fault of the device part; the device part aging means that the device part is rusted; and the poor connection of device parts means that the connection between the associated device parts is unstable.

[0083] S340, in response to the monitoring data exceeding the data upper and lower limits again, judging whether the out-of-control type and the out-of-control reason of the monitoring data are consistent with the out-of-control type and the out-of-control reason of the out-of-control data.

[0084] It should be noted that after the initial data of loss of control is obtained, there is still a possibility that the monitoring data may exceed the upper and lower limits again.

[0085] During implementation, after determining the type and cause of the initial loss of control data, it is further determined whether there is any monitoring data that exceeds the upper and lower limits again. If so, the type and cause of the loss of control of the monitoring data that exceeds the upper and lower limits this time are determined, and then it is determined whether the type and cause of the loss of control of the current monitoring data are consistent with the type and cause of the loss of control of the previous data.

[0086] S350: If yes, do not issue a data anomaly alarm; otherwise, issue a data anomaly alarm.

[0087] In implementation, if the type and cause of the out-of-control data are consistent with those of the previous out-of-control data, it indicates that the same reason caused the data to exceed the upper and lower limits. In this case, only one data anomaly alarm needs to be issued to the staff to inform them of the out-of-control type and cause, without issuing another alarm when the data exceeds the limits again. Therefore, if the type and cause of the out-of-control data are consistent, no further alarms will be issued, and so on. However, if the type and cause of the out-of-control data are inconsistent, alarms will continue to be issued to help staff understand why the data exceeds the limits.

[0088] In this embodiment, the implementation of the above steps can help prevent the equipment monitoring system from frequently issuing data anomaly alarms due to the same type and cause of loss of control, thus making it easier for staff to quickly determine the type and cause of loss of control.

[0089] Figure 4 This is a flowchart illustrating a monitoring data processing method in one embodiment. It should be understood that, although... Figure 4 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows; unless explicitly stated otherwise, there is no strict order requirement for the execution of these steps, and they can be executed in other orders; and Figure 4 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0090] Embodiment Four

[0091] Figure 5 A flow chart of a monitoring data processing method is provided for Embodiment Four of the present application, referring to Figure 5 The method can be executed by a device for executing the method, which can be implemented in software and / or hardware. The method comprises the following steps:

[0092] S410, acquiring monitoring data.

[0093] S420, determining the reason for the shutdown of the device in response to the shutdown of the device.

[0094] It should be noted that while the device monitoring system is acquiring monitoring data in real time, the device may be shutdown due to some reasons. One reason for the shutdown of the device is that the monitoring data exceeds the preset upper and lower limits of data, thereby causing the shutdown of the device. For example, the temperature data exceeds the preset upper and lower limits of data, that is, the device is in an unacceptably high temperature state at this time, and the device automatically shuts down for self-protection. Another reason for the shutdown of the device is human shutdown, for example, the shutdown button of the device is operated by a person to cause the shutdown of the device.

[0095] In implementation, the device monitoring system further determines the specific reason for the shutdown of the device when detecting the shutdown of the device.

[0096] S430, if the reason for the shutdown of the device is that the monitoring data exceeds the preset upper and lower limits of data, thereby causing the shutdown of the device, an abnormal shutdown alarm information is sent, and all alarm functions for sending data abnormal alarm information are turned off.

[0097] In implementation, if it is determined that the reason for the shutdown of the device is that the monitoring data exceeds the preset upper and lower limits of data, thereby causing the shutdown of the device, the device monitoring system further sends only the abnormal shutdown alarm information, so as to inform the staff to timely understand the shutdown condition of the device, and turns off all alarm functions for sending data abnormal alarm information, so as to prevent the alarm functions for sending data abnormal alarm information from being affected by the device abnormality before the device is shutdown, thereby incorrectly sending data abnormal alarm information.

[0098] S440, if the reason for the shutdown of the device is human shutdown, all alarm functions are turned off.

[0099] In implementation, if it is determined that the reason for the shutdown of the device is human shutdown, the device monitoring system further turns off all alarm functions, so as to prevent some alarm information from being incorrectly sent during the shutdown of the device.

[0100] It should be noted that, in the present embodiment, the implementation of the above-mentioned embodiments can facilitate preventing the device from erroneously issuing a certain type of alarm information after shutdown due to certain reasons.

[0101] Figure 5 A flowchart of a monitoring data processing method in one embodiment is shown. It should be understood that, although the steps in the flowchart are shown in a certain order according to the arrows, the steps are not necessarily executed in the order according to the arrows; unless otherwise specified herein, the execution of the steps is not strictly limited in order, and the steps can be executed in other orders; and Figure 5 at least part of the steps in the flowchart can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of the sub-steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or sub-steps or stages of other steps. Figure 5

[0102] Embodiment Five

[0103] Based on the same inventive concept, the present disclosure embodiment five also provides a monitoring data processing device for implementing the above-mentioned monitoring data processing method. The implementation scheme of the device for solving the problem is similar to the implementation scheme described in the above method, so the specific limitations in one or more monitoring data processing device embodiments provided below can refer to the limitations of the monitoring data processing method in the above text, which will not be repeated here.

[0104] In the present embodiment, as shown in Figure 6 , a monitoring data processing device is provided, comprising:

[0105] a change rate calculation module 501 configured to obtain monitoring data, and process the monitoring data based on a preset change rate calculation model to obtain a data change rate;

[0106] a threshold dynamic adjustment module 502 configured to dynamically adjust a preset first change rate threshold based on the data change rate to obtain a second change rate threshold, and take the second change rate threshold as a new first change rate threshold;

[0107] a threshold judgment module 503 configured to judge whether the data change rate exceeds the new first change rate threshold;

[0108] a device adjustment module 504 configured to, if yes, adjust a device corresponding to the monitoring data to make the monitoring data return to a stable state; wherein the stable state represents that the monitoring data is based on a preset reference value and has an upper and lower limit of floating.

[0109] ​The modules in the monitoring data processing apparatus can be implemented by software, hardware, or a combination thereof. The modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a memory in a computer device in software form, so as to be invoked and executed by a processor to perform operations corresponding to the modules.

[0110] In an embodiment, a computer device is provided, which can be a server, and an internal structure diagram of the computer device can be as shown in Figure 7 The computer device includes a processor, a memory, and a network interface connected by a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store data. The network interface of the computer device is configured to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement a monitoring data processing method.

[0111] Those skilled in the art can understand that Figure 7 The structure shown in the above embodiment is only a block diagram of part of the structure related to the present disclosure, and does not limit the computer device to which the present disclosure is applied. Specifically, the computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0112] In an embodiment, a computer device is provided, which includes a memory and a processor. The memory stores a computer program. The processor executes the computer program to implement the steps in the above method embodiments.

[0113] In an embodiment, a computer readable storage medium is provided, as shown in Figure 8 The computer readable storage medium stores a computer program. The computer program is executed by a processor to implement the steps in the above method embodiments.

[0114] In an embodiment, a computer program product is provided, which includes a computer program. The computer program is executed by a processor to implement the steps in the above method embodiments.

[0115] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present disclosure are all information and data authorized by the user or authorized by all parties.

[0116] 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. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in each embodiment provided by the present disclosure can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in each embodiment provided by the present disclosure can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in each embodiment provided by the present disclosure can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0117] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of each technical feature in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present disclosure.

[0118] The above embodiments only express several implementation manners of the present disclosure, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present disclosure. It should be noted that for those skilled in the art, without departing from the concept of the present disclosure, a number of modifications and improvements can be made, which are within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be subject to the appended claims.

Claims

1. A method of monitoring data processing, characterized by, The method comprises the following steps: acquiring monitoring data, and processing the monitoring data to obtain a data change rate based on a preset change rate calculation model; dynamically adjusting a preset first change rate threshold based on the data change rate to obtain a second change rate threshold, and taking the second change rate threshold as a new first change rate threshold; determining whether the data change rate exceeds the new first change rate threshold; if yes, adjusting a device corresponding to the monitoring data to make the monitoring data return to a stable state; wherein the stable state represents that the monitoring data is limitedly floated up and down based on a preset reference value; wherein the step of dynamically adjusting the preset first change rate threshold based on the data change rate to obtain the second change rate threshold comprises the following steps: determining whether the data change rate of the monitoring data not in the stable state exceeds the first change rate threshold; if yes, adjusting the first change rate threshold to the second change rate threshold; wherein the step of acquiring the monitoring data comprises the following steps: in response to the monitoring data exceeding a preset data upper and lower limit for the first time, issuing a data abnormality alarm information, and taking the monitoring data as initial out-of-control data; determining an out-of-control type and an out-of-control reason of the initial out-of-control data; in response to the monitoring data exceeding the data upper and lower limit again, determining whether the out-of-control type and the out-of-control reason of the monitoring data are consistent with the out-of-control type and the out-of-control reason of the out-of-control data; if yes, not issuing the data abnormality alarm information; otherwise, issuing the data abnormality alarm information.

2. The method of claim 1, wherein, The step of adjusting the device corresponding to the monitoring data to make the monitoring data return to the stable state comprises the following steps: determining whether the monitoring data starts to return to the stable state; if no, issuing the data abnormality alarm information.

3. The method of claim 1, wherein, The out-of-control type comprises automatic out-of-control of the device and human operation out-of-control; and the out-of-control reason comprises device part failure, device part aging, and poor connection of device parts.

4. The method of claim 1, wherein, The step of acquiring the monitoring data comprises the following steps: in response to device shutdown, determining a reason of the device shutdown; if the reason of the device shutdown is that the monitoring data exceeds a preset data upper and lower limit to cause the device shutdown, issuing an abnormal shutdown alarm information, and closing all alarm functions for issuing the data abnormality alarm information; if the reason of the device shutdown is human shutdown, closing all alarm functions.

5. A monitoring data processing device, characterized by The device comprises: a change rate calculation module, configured to acquire monitoring data, and process the monitoring data to obtain a data change rate based on a preset change rate calculation model; a threshold dynamic adjustment module, configured to dynamically adjust a preset first change rate threshold based on the data change rate to obtain a second change rate threshold, and take the second change rate threshold as a new first change rate threshold; a threshold determination module, configured to determine whether the data change rate exceeds the new first change rate threshold; a device adjustment module, configured to if yes, adjust a device corresponding to the monitoring data to make the monitoring data return to a stable state; wherein the stable state represents that the monitoring data is limitedly floated up and down based on a preset reference value; wherein the step of dynamically adjusting the preset first change rate threshold based on the data change rate to obtain the second change rate threshold comprises the following steps: determining whether the data change rate of the monitoring data not in the stable state exceeds the first change rate threshold; if yes, adjusting the first change rate threshold to the second change rate threshold; determining whether the data change rate of the monitoring data not in the stable state exceeds the first change rate threshold value; if yes, adjusting the first change rate threshold value to the second change rate threshold value; wherein the step of obtaining monitoring data comprises: in response to the monitoring data exceeding the preset data upper and lower limits for the first time, issuing a data abnormality alarm information, and taking the monitoring data as initial out-of-control data; determining the out-of-control type and reason of the initial out-of-control data; in response to the monitoring data exceeding the data upper and lower limits again, determining whether the out-of-control type and reason of the monitoring data are consistent with the out-of-control type and reason of the out-of-control data; if yes, not issuing a data abnormality alarm information; otherwise, issuing a data abnormality alarm information. 6.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-5 when the computer program is executed by the processor. The processor executes the computer program to realize the steps of the method in any one of claims 1 to 4.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the method in any one of claims 1 to 4.

8. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the method in any one of claims 1 to 4.

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