Monitoring data processing method and system, computer and storage medium

Abstract

The invention provides a monitoring data processing method and system, a computer and a storage medium, and the method comprises the following steps: obtaining a monitoring data set, so as to judge whether the data output of target monitoring equipment is normal or not; and carrying out time sequence linear regression segmentation fitting on the data to judge whether the monitoring point has a data exception risk or not. By analyzing the data characteristics of the monitoring data set in the collection period of the single monitoring device, the state of the monitoring device is alarmed and prompted. By setting the monitoring time window and performing time sequence linear regression on the index of each piece of monitoring data, the trend of the monitoring data can be automatically monitored and early warned, and the data processing efficiency can be improved; by calculating the online rate and the continuity rate of the equipment, in the project operation and maintenance process, the system can monitor data changes in real time and automatically generate a statistical report, instant decision support is provided for data analysts, and the data analysis efficiency is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering safety monitoring, and particularly relates to a method and system for processing monitoring data, a computer, and a storage medium. Background Art

[0002] With the increase in the business volume of enterprises and the accumulation of time, the amount of data generated by equipment in enterprises is also increasing, and the method of simple manual review is very inefficient. In the monitoring industry, the lack of clarity in the data quality review standards mainly relies on the experience judgment of structural analysts, which leads to a high dependence on manual review. Or there is a review method for a single monitoring item, and various review means are adopted to review the monitoring data obtained from a certain monitoring device.

[0003] In the prior art, during the internal acceptance stage before data goes online, the continuity and stability of monitoring data are reviewed item by item. This process not only consumes a large amount of time but also has low efficiency. Similarly, during the project operation and maintenance stage, in order to meet the needs of data display and analysis, data analysts have to review and count the overall data situation one by one, which not only increases the labor cost but also may cause errors due to human factors. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the purpose of the present invention is to provide a method and system for processing monitoring data, a computer, and a storage medium, aiming to solve the technical problem of low data processing efficiency in the prior art.

[0005] To achieve the above purpose, in the first aspect, the present invention provides: A method for processing monitoring data, including the following steps: Obtain a monitoring data set of a target monitoring device within a collection period, and calculate the range, standard deviation, and median corresponding to each monitoring data in the monitoring data set, so as to judge whether the data output of the target monitoring device is normal based on the range, standard deviation, and median; If the data output of the target monitoring device is abnormal, mark the target monitoring device as an abnormal device and give an alarm prompt; Set a monitoring time window, and read each monitoring data of the monitoring points within the monitoring time window based on the current collection time to form an initial window monitoring data set; Replace the monitoring data corresponding to the abnormal device in the window monitoring data set with standard data to obtain a corrected monitoring data set; Perform time series linear regression piecewise fitting on the monitoring indicators of each monitoring data in the corrected monitoring data set, and calculate the determination coefficient corresponding to the corrected monitoring data set; Monitor and update the coefficient of determination corresponding to the monitoring data in real time within the monitoring time window. When the coefficient of determination is greater than the third preset value, it is determined that there is a risk of abnormal data at the monitoring point and an alarm prompt is given.

[0006] According to one aspect of the above technical solution, the steps of judging whether the data output of the target monitoring device is normal based on the range, standard deviation and median specifically include: Judge whether the range is less than the first preset value and whether the standard deviation is less than the second preset value; If the range is less than the first preset value and the standard deviation is less than the second preset value, it is determined that the output data stability of the monitoring device corresponding to the monitoring data set is normal; Calculate the data median corresponding to each monitoring data in the monitoring data set. If the median meets the preset range, it is determined that the output data accuracy of the monitoring device corresponding to the monitoring data set is normal; If the output data stability and accuracy of the monitoring device are both normal, it is determined that the data output of the target monitoring device is normal.

[0007] According to one aspect of the above technical solution, after the steps of judging whether the range is less than the first preset value and whether the standard deviation is less than the second preset value, the method further includes: If the range is greater than the first preset value or the standard deviation is greater than the second preset value, it is determined that the data output of the target monitoring device is abnormal.

[0008] According to one aspect of the above technical solution, the method further includes: Based on the time difference between the acquisition time of the most recently acquired monitoring data of each monitoring device and the current time, if the time difference is greater than the fourth preset value, it is determined that the monitoring device is an offline device; Calculate the device online rate in the monitoring project according to the ratio of the offline device to the total device.

[0009] According to one aspect of the above technical solution, the method further includes: Calculate the ratio of the number of acquisitions in the monitoring data set to the preset number of acquisitions to obtain the continuity rate corresponding to the target monitoring device. If the continuity rate is less than the fifth preset value, it is determined that the target monitoring device is a non-conforming device.

[0010] In a second aspect, the present invention provides a monitoring data processing system, including: A data monitoring module, configured to obtain a monitoring data set of a target monitoring device within an acquisition period, and calculate the range, standard deviation and median corresponding to each monitoring data in the monitoring data set, so as to judge whether the data output of the target monitoring device is normal based on the range, standard deviation and median; The first alarm module is used to mark the target monitoring device as an abnormal device and give an alarm prompt if there is an abnormality in the data output of the target monitoring device; The monitoring window module is used to set a monitoring time window, read each monitoring data of the monitoring points within the monitoring time window based on the current acquisition time, and form an initial window monitoring data set; The correction module is used to replace the monitoring data corresponding to the abnormal device in the window monitoring data set with standard data to obtain a corrected monitoring data set; The trend module is used to perform piecewise fitting of time series linear regression on the monitoring indicators of each monitoring data in the corrected monitoring data set, and calculate the determination coefficient corresponding to the corrected monitoring data set; The second alarm module is used to monitor and update the determination coefficient corresponding to the monitoring data in real time within the monitoring time window. When the determination coefficient is greater than the third preset value, it is determined that there is a risk of data abnormality at the monitoring point and an alarm prompt is given.

[0011] According to one aspect of the above technical solution, the data monitoring module is specifically used for: Judge whether the range is less than the first preset value and whether the standard deviation is less than the second preset value; If the range is less than the first preset value and the standard deviation is less than the second preset value, it is determined that the output data stability of the monitoring device corresponding to the monitoring data set is normal; Calculate the data median corresponding to each monitoring data in the monitoring data set. If the median meets the preset range, it is determined that the output data accuracy of the monitoring device corresponding to the monitoring data set is normal; If the output data stability and accuracy of the monitoring device are both normal, it is determined that the data output of the target monitoring device is normal.

[0012] According to one aspect of the above technical solution, the data monitoring module is further used for: If the range is greater than the first preset value or the standard deviation is greater than the second preset value, it is determined that the data output of the target monitoring device is abnormal.

[0013] According to one aspect of the above technical solution, the system further includes: The online rate module is used to, based on the time difference between the acquisition time of the most recent monitoring data of each monitoring device and the current time, determine that the monitoring device is an offline device if the time difference is greater than the fourth preset value; Calculate the device online rate in the monitoring project according to the ratio of the offline device to the total number of devices.

[0014] According to one aspect of the above technical solution, the system further includes: A continuous rate module is used to calculate the ratio of the number of acquisitions in the monitoring dataset to the preset number of acquisitions, so as to obtain the continuous rate corresponding to the target monitoring device. If the continuous rate is less than the fifth preset value, it is determined that the target monitoring device is a non-conforming device.

[0015] In a third aspect, the present invention further provides a computer, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the monitoring data processing method described in the above technical solution is implemented.

[0016] In a fourth aspect, the present invention further provides a storage medium, on which a computer program is stored. When the program is executed by a processor, the monitoring data processing method described in the above technical solution is implemented.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: By analyzing the data characteristics of the monitoring dataset within the acquisition period of a single monitoring device, it is determined whether the target monitoring data corresponding to the monitoring data is normal, so as to give an alarm prompt for the status of the monitoring device, which is convenient for tracing and maintenance of abnormal monitoring devices in the project; When monitoring the data of the monitoring point, by setting the monitoring time window and performing time series linear regression on the indicators of each monitoring data, the trend of the monitoring data can be automatically monitored and warned, which can improve the data processing efficiency. In addition, by replacing the data of the abnormal monitoring device with standard data, false alarms caused by the abnormal status of a single monitoring device in project monitoring can be avoided; By calculating the online rate and continuous rate of the device, during the project operation and maintenance process, the system can monitor data changes in real time, automatically generate statistical reports, and provide instant decision support for data analysts, greatly improving the efficiency of data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic flowchart of the monitoring data processing method in the first embodiment of the present invention; Figure 2 It is a structural block diagram of the monitoring data processing system in the second embodiment of the present invention; Figure 3 It is a schematic hardware structure diagram of the computer in the third embodiment of the present invention; The following specific embodiments will further illustrate the present invention in conjunction with the above drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] To facilitate the understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided to make the disclosure of the present invention more thorough and comprehensive.

[0020] It should be noted that when an element is referred to as being "fixed to" another element, it can be directly on the other element or there can also be an intermediate element. When an element is considered to be "connected to" another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this invention belongs. The terms used herein in the description of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0022] Embodiment 1 Please refer to Figure 1 , which shows a flowchart of the monitoring data processing method in the first embodiment of the present invention. As shown in the figure, the method includes the following steps: Step S100, obtain a monitoring data set of a target monitoring device within a collection period, and calculate the range, standard deviation and median corresponding to each monitoring data in the monitoring data set, so as to judge whether the data output of the target monitoring device is normal based on the range, standard deviation and median.

[0023] It can be understood that the length of the above collection period can be adaptively adjusted according to the data change accuracy and the project period. In this embodiment, it is preferably one week.

[0024] Specifically, in this embodiment, the step of judging whether the data output of the target monitoring device is normal based on the range, standard deviation and median specifically includes: Step S110, judge whether the range is less than a first preset value and whether the standard deviation is less than a second preset value.

[0025] Step S120, if the range is less than the first preset value and the standard deviation is less than the second preset value, then determine that the output data stability of the monitoring device corresponding to the monitoring data set is normal.

[0026] Step S130, calculate the data median corresponding to each monitoring data in the monitoring data set. If the median meets the preset range, then determine that the output data accuracy of the monitoring device corresponding to the monitoring data set is normal. The above preset range can be determined according to the on-site observation range or the empirical value in the actual detection process.

[0027] Step S140: If the output data stability and accuracy of the monitoring device are both normal, it is determined that the data output of the target monitoring device is normal.

[0028] Preferably, in this embodiment, after the steps of determining whether the range is less than the first preset value and whether the standard deviation is less than the second preset value, the method further includes: Step S150: If the range is greater than the first preset value or the standard deviation is greater than the second preset value, it is determined that the data output of the target monitoring device is abnormal.

[0029] Step S200: If the data output of the target monitoring device is abnormal, mark the target monitoring device as an abnormal device and give an alarm prompt. It can be understood that if the range is greater than the first preset value or the standard deviation is greater than the second preset value, it means that the data does not meet the stability requirements, and it is determined that the data is abnormal, and alarm troubleshooting is required; if the data stability meets the requirements, but the median is not within the preset range interval, it does not meet the data accuracy requirements, and alarm troubleshooting is required. At the same time, summarize the devices at normal / abnormal points into a table for operation and maintenance personnel to view.

[0030] Step S300: Set a monitoring time window, and read each monitoring data of the monitoring points within the monitoring time window based on the current acquisition time to form an initial window monitoring data set. In this step, the monitoring time window is preferably one week.

[0031] Step S400: Replace the monitoring data corresponding to the abnormal device in the window monitoring data set with standard data to obtain a corrected monitoring data set. In this embodiment, the purpose of the standard data is to eliminate the influence of the abnormal data output by the abnormal device on the overall data monitoring. The above standard data can be the monitoring data of other adjacent monitoring points in the monitoring project until the target monitoring device returns to normal.

[0032] Step S500: Perform time series linear regression piecewise fitting on the monitoring indicators of each monitoring data in the corrected monitoring data set, and calculate the determination coefficient corresponding to the corrected monitoring data set.

[0033] Step S600: Real-time monitor and update the determination coefficient corresponding to the monitoring data within the monitoring time window. When the determination coefficient is greater than the third preset value, it is determined that there is a data abnormality risk at this monitoring point and an alarm prompt is given. By performing time series linear regression piecewise fitting on the monitoring data such as displacement in each sliding window, if the calculated determination coefficient R 2 is greater than the third preset value R k, it indicates that the data has a significant trend. By giving an alarm prompt, it is convenient for the operation and maintenance personnel to monitor the progress of data changes in real time. In this embodiment, the above third preset value can be taken as 0.5 - 0.99, and in this embodiment, it is preferably 0.6.

[0034] Further, in this embodiment, the method further includes: Based on the time difference between the acquisition time of the most recently acquired monitoring data of each monitoring device and the current time, if the time difference is greater than a fourth preset value, it is determined that the monitoring device is an offline device; According to the ratio of the offline devices to the total devices, the device online rate in the monitoring project is calculated. Specifically, the above fourth preset value is preferably 12 hours. If the time difference between the acquisition time of the most recent monitoring data of the device and the current time exceeds 12 hours, it is determined to be offline. The calculation formula for the above online rate w1 is: w1 = m1 / (m1 + m2) * 100%, where m1 is the number of online devices and m2 is the number of offline devices. Among them, for the sub - item online rate w 1i The expression is: w 1i = m 1i / (m 1i + m 2i ) * 100%, where m 1i is the number of online devices in the sub - item, and m 2i is the number of offline devices.

[0035] Preferably, in this embodiment, the method further includes: Calculate the ratio of the number of acquisitions in the monitoring data set to the preset number of acquisitions to obtain the continuity rate corresponding to the target monitoring device. If the continuity rate is less than a fifth preset value, it is determined that the target monitoring device is a non - qualified device.

[0036] In this embodiment, according to the preset number of acquisitions ni and the actual number of acquisitions nj in the monitoring data set, with a one - week time window and a collection granularity of 1 time / h for the temperature measurement points, the preset number of acquisitions is 1 * 24h * 7 days = 168 times. If the actual number of acquisitions is 120 times, the continuity rate = 71.4%. Calculate the continuity rate p1 = n2 / n1 * 100% for each device. The preset value p0 = 90%. When p1 is greater than or equal to p0 = 90%, it is determined that the continuity rate is qualified. When p1 is less than p0 = 90%, it is determined to be unqualified. Calculate and summarize the device continuity rate and the names of non - qualified devices for the operation and maintenance personnel to view.

[0037] In summary, in the monitoring data processing method of the above embodiments of the present invention, by analyzing the data characteristics of the monitoring data set within the collection period of a single monitoring device, it is determined whether the target monitoring data corresponding to the monitoring data is normal, so as to give an alarm prompt for the status of the monitoring device, which is convenient for tracing and maintaining the abnormality of the monitoring device in the project; when monitoring the data of the monitoring point, by setting the monitoring time window and performing time series linear regression on the indicators of each monitoring data, the trend of the monitoring data can be automatically monitored and warned, which can improve the data processing efficiency. In addition, by replacing the data of the abnormal monitoring device with the standard data, false alarms caused by the abnormal status of a single monitoring device in project monitoring can be avoided; by calculating the online rate and continuity rate of the device, during the project operation and maintenance process, the system can monitor the data changes in real time, automatically generate statistical reports, and provide immediate decision support for data analysts, greatly improving the efficiency of data analysis.

[0038] Embodiment 2 The second embodiment of the present application further provides a monitoring data processing system, which is used to implement the above embodiments and preferred implementation manners, and those that have been described will not be repeated. As used below, terms such as "module", "unit", "sub-unit", etc. can be a combination of software and / or hardware that can achieve a predetermined function. Although the systems described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0039] As Figure 2 shown, the system includes: a data monitoring module 100, a first alarm module 200, a monitoring window module 300, a correction module 400, a trend module 500, and a second alarm module 600.

[0040] The data monitoring module 100 is used to obtain the monitoring data set of the target monitoring device within the collection period, and calculate the range, standard deviation and median corresponding to each monitoring data in the monitoring data set, so as to judge whether the data output of the target monitoring device is normal based on the range, standard deviation and median; The first alarm module 200 is used to mark the target monitoring device as an abnormal device and give an alarm prompt if the data output of the target monitoring device is abnormal; The monitoring window module 300 is used to set a monitoring time window, and read each monitoring data of the monitoring point within the monitoring time window based on the current collection time to form an initial window monitoring data set; The correction module 400 is used to replace the monitoring data corresponding to the abnormal device in the window monitoring data set with standard data to obtain a corrected monitoring data set; The trend module 500 is used to perform piecewise fitting of time series linear regression on the monitoring indicators of each monitoring data in the corrected monitoring data set, and calculate the determination coefficient corresponding to the corrected monitoring data set; The second alarm module 600 is used to monitor and update the determination coefficient corresponding to the monitoring data in the monitoring time window in real time. When the determination coefficient is greater than the third preset value, it is determined that there is a risk of abnormal data at the monitoring point and an alarm prompt is given.

[0041] Preferably, in this embodiment, the data monitoring module 100 is specifically used for: Judging whether the range is less than the first preset value and whether the standard deviation is less than the second preset value; If the range is less than the first preset value and the standard deviation is less than the second preset value, it is determined that the output data of the monitoring device corresponding to the monitoring data set is stable; Calculate the data median corresponding to each monitoring data in the monitoring data set. If the median meets the preset range, it is determined that the output data of the monitoring device corresponding to the monitoring data set is accurate; If the stability and accuracy of the output data of the monitoring device are both normal, it is determined that the data output of the target monitoring device is normal.

[0042] Preferably, in this embodiment, the data monitoring module 100 is further used for: If the range is greater than the first preset value or the standard deviation is greater than the second preset value, it is determined that the data output of the target monitoring device is abnormal.

[0043] Preferably, in this embodiment, the system further includes: An online rate module, which is used to calculate, based on the time difference between the acquisition time of the most recently acquired monitoring data of each monitoring device and the current time, that if the time difference is greater than the fourth preset value, it is determined that the monitoring device is an offline device; Calculate the device online rate in the monitoring project according to the ratio of the offline device to the total number of devices.

[0044] Preferably, in this embodiment, the system further includes: A continuity rate module, which is used to calculate the ratio of the number of acquisitions in the monitoring data set to the preset number of acquisitions to obtain the continuity rate corresponding to the target monitoring device. If the continuity rate is less than the fifth preset value, it is determined that the target monitoring device is a non-conforming device.

[0045] It should be noted that the above-mentioned modules can be functional modules or program modules, and can be implemented either by software or by hardware. For the modules implemented by hardware, the above-mentioned modules can be located in the same processor; or the above-mentioned modules can also be located in different processors respectively in any combined form.

[0046] Embodiment III The third embodiment of the present application provides a computer, which may include a processor 81 and a memory 82 storing computer program instructions.

[0047] Specifically, the above-mentioned processor 81 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured as one or more integrated circuits implementing the embodiments of the present application.

[0048] Among them, the memory 82 may include a mass storage for data or commands. By way of example and not limitation, the memory 82 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disc, a magneto-optical disc, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 82 may include removable or non-removable (or fixed) media. Where appropriate, the memory 82 may be internal or external to the data processing device. In a particular embodiment, the memory 82 is non-volatile memory. In a particular embodiment, the memory 82 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable read-only memory (EAROM), or a flash memory, or a combination of two or more of these. Where appropriate, the RAM may be a static random access memory (SRAM) or a dynamic random access memory (DRAM), where the DRAM may be a fast page mode dynamic random access memory (FPMDRAM), an extended date out dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0049] The memory 82 can be used to store or cache various data files required for processing and / or communication, as well as possible computer program commands executed by the processor 81.

[0050] The processor 81 reads and executes the computer program commands stored in the memory 82 to implement any one of the monitoring data processing methods in the above embodiments.

[0051] In some of the embodiments, the computer may further include a communication interface 83 and a bus 80. Among them, as Figure 3 shown, the processor 81, the memory 82, and the communication interface 83 are connected through the bus 80 and complete communication with each other.

[0052] The communication interface 83 is used to implement communication between the modules, devices, units, and / or devices in the embodiments of the present application. The communication interface 83 can also implement data communication with other components, such as external devices, image / data acquisition devices, databases, external storage, and image / data processing workstations, etc.

[0053] Bus 80 includes hardware, software, or both, and couples components of a computer together. Bus 80 includes, but is not limited to, at least one of the following: Data Bus, Address Bus, Control Bus, Expansion Bus, Local Bus. By way of example and not limitation, Bus 80 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable bus or a combination of two or more of these. Where appropriate, Bus 80 may include one or more buses. Although the embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0054] Embodiment Four The fourth embodiment of the present application provides a readable storage medium. Computer program commands are stored on the readable storage medium; when the computer program commands are executed by a processor, any one of the monitoring data processing methods in the above embodiments is implemented.

[0055] The technical features of the above-described embodiments may be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0056] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A monitoring data processing method, characterized in that: The following steps are involved: Obtain a monitoring data set of the target monitoring device within a collection period, and calculate the range, standard deviation and median corresponding to each monitoring data in the monitoring data set, so as to determine whether the data output of the target monitoring device is normal based on the range, standard deviation and median; If the data output of the target monitoring device is abnormal, the target monitoring device is marked as an abnormal device and an alarm is issued; Setting a monitoring time window, reading each monitoring data of the monitoring points within the monitoring time window based on the current acquisition time, and forming an initial window monitoring data set; The monitoring data corresponding to the abnormal device in the window monitoring data set is replaced with standard data to obtain a corrected monitoring data set; Performing time series linear regression piecewise fitting on the monitoring indicators of each monitoring data in the revised monitoring data set, and calculating the determination coefficient corresponding to the revised monitoring data set; The determination coefficient corresponding to the monitoring data within the monitoring time window is monitored and updated in real time. When the determination coefficient is greater than a third preset value, it is determined that there is a risk of data abnormality at the monitoring point and an alarm is issued.

2. The monitoring data processing method according to claim 1, characterized in that: Based on the range, standard deviation and median, the step of judging whether the data output of the target monitoring device is normal specifically includes: Determine whether the range is less than a first preset value, and whether the standard deviation is less than a second preset value; If the range is smaller than a first preset value, and the standard deviation is smaller than a second preset value, it is determined that the stability of the output data of the monitoring device corresponding to the monitoring data set is normal; Calculate the data median corresponding to each monitoring data in the monitoring data set, and if the median meets the preset range, determine that the accuracy of the monitoring device output data corresponding to the monitoring data set is normal; If the stability and accuracy of the output data of the monitoring device are normal, it is determined that the data output of the target monitoring device is normal.

3. The monitoring data processing method according to claim 2, characterized in that: After the step of determining whether the range is less than a first preset value and whether the standard deviation is less than a second preset value, the method further includes: If the range is greater than a first preset value, or the standard deviation is greater than a second preset value, it is determined that the data output of the target monitoring device is abnormal.

4. The monitoring data processing method according to claim 1, characterized in that: The method further comprises: Based on the time difference between the acquisition time of the most recent monitoring data collected by each monitoring device and the current time, if the time difference is greater than a fourth preset value, determining that the monitoring device is an offline device; Based on the ratio of offline devices to total devices, the online rate of devices in the monitored project is calculated.

5. The monitoring data processing method according to claim 1, characterized in that: The method further comprises: The ratio of the acquisition number in the monitoring data set to the preset acquisition number is calculated to obtain the continuity rate corresponding to the target monitoring device. If the continuity rate is less than the fifth preset value, the target monitoring device is determined to be an unqualified device.

6. A monitoring data processing system, characterized in that: include: A data monitoring module is used to obtain a monitoring data set of a target monitoring device within a collection period, and calculate the range, standard deviation and median corresponding to each monitoring data in the monitoring data set, so as to determine whether the data output of the target monitoring device is normal based on the range, standard deviation and median; A first alarm module, used for marking the target monitoring device as an abnormal device and giving an alarm prompt if the data output of the target monitoring device is abnormal; A monitoring window module is used to set a monitoring time window, read the monitoring data of each monitoring point within the monitoring time window based on the current acquisition time, and form an initial window monitoring data set; A correction module, used to replace the monitoring data corresponding to the abnormal device in the window monitoring data set with standard data to obtain a corrected monitoring data set; A trend module, used for performing time series linear regression piecewise fitting on the monitoring indicators of each monitoring data in the revised monitoring data set, and calculating the determination coefficient corresponding to the revised monitoring data set; The second alarm module is used to monitor and update the determination coefficient corresponding to the monitoring data in the monitoring time window in real time. When the determination coefficient is greater than a third preset value, it is determined that there is a risk of data abnormality at the monitoring point and an alarm is issued.

7. The monitoring data processing system according to claim 6, characterized in that: The data monitoring module is specifically used for: Determine whether the range is less than a first preset value, and whether the standard deviation is less than a second preset value; If the range is smaller than a first preset value, and the standard deviation is smaller than a second preset value, it is determined that the stability of the output data of the monitoring device corresponding to the monitoring data set is normal; Calculate the data median corresponding to each monitoring data in the monitoring data set, and if the median meets the preset range, determine that the accuracy of the monitoring device output data corresponding to the monitoring data set is normal; If the stability and accuracy of the output data of the monitoring device are normal, it is determined that the data output of the target monitoring device is normal.

8. The monitoring data processing system according to claim 7, characterized in that: The data monitoring module is also used for: If the range is greater than a first preset value, or the standard deviation is greater than a second preset value, it is determined that the data output of the target monitoring device is abnormal.

9. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the monitoring data processing method according to any one of claims 1 to 5 is implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the monitoring data processing method as described in any one of claims 1 to 5 is implemented.

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