CN117807414A

CN117807414A - Risk early warning method, device, equipment and storage medium based on Internet of things data

Info

Publication number: CN117807414A
Application number: CN202311821008.XA
Authority: CN
Inventors: 宋晨旭; 刘敏; 钱晨伟
Original assignee: Zhejiang Aerospace Hengjia Data Technology Co ltd
Current assignee: Zhejiang Aerospace Hengjia Data Technology Co ltd
Priority date: 2023-12-27
Filing date: 2023-12-27
Publication date: 2024-04-02

Abstract

The application provides a risk early warning method, device, equipment and storage medium based on internet of things data, and relates to the technical field of data processing, wherein the method comprises the following steps: acquiring detection data of at least one sensor in the internet of things data of a preset industrial place, determining whether each sensor meets corresponding triggering early warning conditions according to the detection data of each sensor, determining whether a target sensor is a pre-configured false alarm prevention sensor, if the target sensor is the pre-configured false alarm prevention sensor, processing the detection data of the target sensor by adopting a preset risk early warning model to obtain a risk early warning detection result, determining whether the target sensor has false alarm risks according to the feature quantity in an early warning feature set, and generating corresponding alarm information based on the detection data if the target sensor does not have false alarm risks. And the detection data of the target sensor is processed by adopting a preset risk early warning model, so that whether the target sensor has false alarm risk or not can be accurately determined.

Description

Risk early warning method, device, equipment and storage medium based on Internet of things data

Technical Field

The invention relates to the technical field of data processing, in particular to a risk early warning method, device and equipment based on internet of things data and a storage medium.

Background

Along with the continuous development of the technology of the internet of things, the application of various safety and environmental protection indexes in the online monitoring and chemical industry of the sensors of the internet of things in the industrial area of the industrial area is wider and wider, the application becomes one of important means of the chemical industry on the safety and environmental protection management, the number of the arranged sensor points is huge, however, the sensors are influenced by the factors of self technology, quality, environment and the like, the situation of false alarm frequently occurs, so that the disposal work of the enterprise after the early warning of the sensors becomes complicated, the personnel investment is large, the final confirmation result is the abnormal early warning which does not need to be processed, and how to reduce the false alarm of the sensors becomes a problem to be solved urgently.

At present, an internet of things sensor applied to the field of safety and environmental protection in the chemical industry generally adopts a mode of low reporting and high reporting based on numerical comparison, and early warning and alarm are triggered when a detection numerical value is in a certain area or higher/lower than a certain numerical value, and as the detection sensitivity of the sensor gradually declines with time, the sensor needs to be corrected regularly, the accuracy of a monitoring numerical value is reduced before the decline is uncorrected, false triggering similar to an alarm phenomenon in the environment is caused, or sudden numerical value abnormality caused by the abnormal state of parts of the sensor or detection technical defects can cause false alarm of the sensor.

Disclosure of Invention

The invention aims to provide a risk early warning method, device, equipment and storage medium based on internet of things data so as to process detection data of a target sensor by adopting a preset risk early warning model and accurately determine whether the target sensor has false alarm risk or not.

In order to achieve the above purpose, the technical solution adopted in the embodiment of the present application is as follows:

in a first aspect, an embodiment of the present application provides a risk early warning method based on internet of things data, which is applied to a server of an internet of things platform; the method comprises the following steps:

acquiring detection data of at least one sensor in Internet of things data of a preset industrial place, wherein the at least one sensor is respectively arranged at least one detection point of the preset industrial place;

determining whether each sensor meets a corresponding trigger early warning condition according to detection data of each sensor;

if the target sensor meets the corresponding triggering early warning condition, determining whether the target sensor is a preconfigured false alarm preventing sensor or not;

if the target sensor is a pre-configured false alarm prevention sensor, processing detection data of the target sensor by adopting a pre-set risk early warning model to obtain a risk early warning detection result, wherein the risk early warning detection result comprises: early warning feature sets;

Determining whether the target sensor has false alarm risk according to the feature quantity in the early warning feature set;

and if the false alarm risk does not exist, generating corresponding alarm information based on the detection data of the target sensor.

In an alternative embodiment, the preset risk early warning model includes: a plurality of data processing rules and risk judgment rules; the processing the detection data of the target sensor by adopting a preset risk early-warning model to obtain a risk early-warning detection result comprises the following steps:

determining whether the target sensor meets the processing trigger conditions corresponding to the plurality of data processing rules;

if the target sensor meets the processing trigger condition of the target data processing rule in the plurality of data processing rules, processing the detection data of the target sensor by adopting the target data processing rule;

and determining whether the processed detection data is in an early warning state or not by adopting the risk judging rule, and if the processed detection data is in the early warning state, adding the processed detection data into the early warning feature set.

In an optional embodiment, the risk early warning detection result further includes: a non-early warning feature set; the method for processing the detection data of the target sensor by adopting the preset risk early-warning model to obtain a risk early-warning detection result further comprises the following steps:

And if the processed detection data is not in the early warning state, adding the risk judgment rule into the non-early warning feature set.

In an alternative embodiment, the processing the detection data of the target sensor using the target data processing rule includes:

if the target data processing rule is a first data processing rule, correcting the detection data of the target sensor by adopting a correction template of the first data processing rule to obtain corrected data; the processed detection data comprises: the corrected data.

if the target data processing rule is a second data processing rule, adopting a continuity verification algorithm of the second data processing rule to perform continuity verification on the detection data of the target sensor to obtain continuous verification data; the processed detection data further comprises: the continuous authentication data.

If the target data processing rule is a third data processing rule, acquiring the associated data of the target sensor by adopting a relativity verification algorithm of the third data processing rule, and carrying out relativity verification on the detection data of the target sensor and the associated data to obtain associated verification data; the processed detection data further comprises: the association verifies data.

In an optional embodiment, the determining, according to the feature number in the early warning feature set, whether the target sensor has a false alarm risk includes:

if the feature quantity in the early warning feature set is not 0, determining that the target sensor does not have false alarm risk;

and if the feature quantity in the early warning feature set is 0, determining that the target sensor has false alarm risk.

In a second aspect, an embodiment of the present application further provides a risk early warning device based on internet of things data, which is applied to a server of an internet of things platform, and the device includes:

the system comprises an acquisition module, a detection module and a control module, wherein the acquisition module is used for acquiring detection data of at least one sensor in Internet of things data of a preset industrial place, and the at least one sensor is respectively arranged at least one detection point of the preset industrial place;

The determining module is used for determining whether each sensor meets the corresponding triggering early warning condition according to the detection data of each sensor;

the determining module is further configured to determine whether the target sensor is a preconfigured false alarm preventing sensor if the target sensor meets a corresponding trigger pre-warning condition;

the processing module is used for processing the detection data of the target sensor by adopting a preset risk early warning model if the target sensor is a preconfigured false alarm prevention sensor to obtain a risk early warning detection result, wherein the risk early warning detection result comprises: early warning feature sets;

the determining module is further configured to determine whether a false alarm risk exists in the target sensor according to the feature quantity in the early warning feature set;

and the generation module is used for generating corresponding alarm information based on the detection data of the target sensor if the false alarm risk does not exist.

In a third aspect, embodiments of the present application further provide a computer device, including: a processor, a storage medium and a bus, the storage medium storing program instructions executable by the processor, the processor and the storage medium communicating over the bus when the computer device is running, the processor executing the program instructions to perform the steps of the risk early warning method according to any one of the first aspects.

In a fourth aspect, embodiments of the present application further provide a computer-readable storage medium, on which a computer program is stored, which when executed by a processor performs the steps of the risk early warning method according to any one of the first aspects.

The beneficial effects of this application are:

the embodiment of the application provides a risk early warning method, device, equipment and storage medium based on internet of things data, and a server of an application and internet of things platform, wherein the method comprises the following steps: acquiring detection data of at least one sensor in internet of things data of a preset industrial place, wherein the at least one sensor is respectively arranged at least one detection point of the preset industrial place, determining whether each sensor meets corresponding triggering early warning conditions according to the detection data of each sensor, determining whether the target sensor is a preconfigured false alarm preventing sensor if the target sensor meets the corresponding triggering early warning conditions, and processing the detection data of the target sensor by adopting a preset risk early warning model if the target sensor is the preconfigured false alarm preventing sensor to obtain a risk early warning detection result, wherein the risk early warning detection result comprises: and finally, determining whether the target sensor has false alarm risks according to the feature quantity in the early-warning feature set, and generating corresponding alarm information based on the detection data of the target sensor if the target sensor does not have false alarm risks.

According to the method, whether the target sensor has false alarm risk or not can be accurately determined according to the feature quantity of the early warning feature set, false alarm prevention data are effectively avoided due to the fact that the target sensor generates false alarm prevention data, and therefore false alarm occurs to the target sensor.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is one of flow diagrams of a risk early warning method based on internet of things data according to an embodiment of the present application;

Fig. 2 is a second flow chart of a risk early warning method based on internet of things data according to an embodiment of the present application;

fig. 3 is a third flow chart of a risk early warning method based on internet of things data according to an embodiment of the present application;

fig. 4 is a schematic functional module diagram of a risk early warning device based on internet of things data according to an embodiment of the present application;

fig. 5 is a schematic diagram of a computer device according to an embodiment of the present application.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention.

Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.

In the description of the present application, it should be noted that, if the terms "upper", "lower", and the like indicate an azimuth or a positional relationship based on the azimuth or the positional relationship shown in the drawings, or an azimuth or the positional relationship that is commonly put when the product of the application is used, it is merely for convenience of description and simplification of the description, and does not indicate or imply that the apparatus or element to be referred to must have a specific azimuth, be configured and operated in a specific azimuth, and therefore should not be construed as limiting the present application.

Furthermore, the terms first, second and the like in the description and in the claims and in the above-described figures, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

It should be noted that, without conflict, features in embodiments of the present application may be combined with each other.

In order to reduce the situation of false alarm of sensors in a preset industrial place, so that alarm information of the sensors is more accurate, the embodiment of the application provides a risk early warning method based on data of the Internet of things. The detection data of the target sensor is further processed through the preset risk early warning model, the processed detection data is judged, the final judgment result is more accurate, and the situation that the target sensor generates false detection data due to the reason of the target sensor can be avoided, so that the target sensor is in false alarm can be avoided.

The risk early warning method based on the internet of things data provided by the embodiment of the application is explained in detail by a specific example with reference to the accompanying drawings. The risk early warning method provided by the embodiment of the application is applied to a server of an internet of things platform, and can be implemented by pre-installing: the computer equipment for presetting the risk early warning algorithm is realized by running the algorithm or software. Fig. 1 is a schematic flow chart of a risk early warning method based on internet of things data according to an embodiment of the present application. As shown in fig. 1, the method includes:

s101, acquiring detection data of at least one sensor in Internet of things data of a preset industrial place.

Wherein, at least one sensor is set up at least one check point of predetermineeing industrial site respectively. In this embodiment, in order to perform real-time security detection on a plurality of industrial devices, that is, major hazard sources, in a preset industrial location, a plurality of detection points are set around each industrial device, and each detection point is provided with at least one sensor for detecting whether leakage or abnormality occurs in the industrial device, where the preset industrial location may include: chemical sites, smelting sites, steel sites, etc., types of sensors may include: temperature sensor, pressure sensor, liquid level sensor, vibration sensor, gas concentration sensor, etc.

Each sensor is connected into an internet of things platform of a preset industrial place, so that detection data of each sensor can be obtained in real time through the internet of things platform.

S102, determining whether each sensor meets the corresponding trigger early warning condition according to the detection data of each sensor.

Specifically, each sensor has a corresponding pre-warning threshold, for example, the gas concentration sensor has a gas concentration pre-warning threshold range, the pressure sensor has a pressure pre-warning threshold, whether each sensor meets a corresponding trigger pre-warning condition is determined according to the detection data of each sensor and the corresponding pre-warning threshold, and if the detection data of the gas concentration sensor is not in the gas concentration pre-warning threshold range, the gas concentration sensor is determined to meet the corresponding trigger pre-warning condition.

And S103, if the target sensor meets the corresponding triggering early warning condition, determining whether the target sensor is a preconfigured false alarm preventing sensor.

Specifically, when each sensor is connected to the internet of things platform, a corresponding identifier is configured according to the type of each sensor, namely whether the sensor is an anti-false alarm sensor or not, for example, when a fire alarm sensor is connected to the internet of things platform, the fire alarm sensor is configured to be a non-anti-false alarm sensor, when the fire alarm sensor meets the corresponding triggering early warning condition, corresponding alarm information is directly generated to trigger an alarm, and when the gas concentration sensor is connected to the internet of things platform, the gas concentration sensor is configured to be the anti-false alarm sensor, further processing is needed to be conducted on detection data of the gas concentration sensor, so that the internet of things platform also needs to determine whether the target sensor is a pre-configured anti-false alarm sensor according to the identifier of the target sensor after judging that the target sensor meets the corresponding triggering early warning condition.

And S104, if the target sensor is a pre-configured false alarm prevention sensor, processing detection data of the target sensor by adopting a pre-set risk early warning model to obtain a risk early warning detection result.

The risk early warning detection result comprises: the detection data of the target sensor is further processed through a preset risk early-warning model to obtain processed detection data, and a risk early-warning detection result corresponding to the target sensor is generated according to the processed detection data to obtain an early-warning feature set corresponding to the target sensor.

S105, determining whether the target sensor has false alarm risks according to the feature quantity in the early warning feature set.

And S106, if the false alarm risk does not exist, generating corresponding alarm information based on the detection data of the target sensor.

Specifically, whether the target sensor has false alarm risk is determined according to feature data in the early warning feature set, if the false alarm risk does not exist, corresponding alarm information is generated according to detection data of the target sensor, so that the internet of things platform reports the alarm information, the target sensor alarms, if the false alarm risk exists, it is determined that the detection data of the target sensor is abnormal, and the alarm information of the target sensor is stopped reporting according to the alarm information generated by the detection data of the target sensor as false alarm.

In summary, the embodiment of the application provides a risk early warning method based on internet of things data, which is applied to a server of an internet of things platform and includes: acquiring detection data of at least one sensor in internet of things data of a preset industrial place, wherein the at least one sensor is respectively arranged at least one detection point of the preset industrial place, determining whether each sensor meets corresponding triggering early warning conditions according to the detection data of each sensor, determining whether the target sensor is a preconfigured false alarm preventing sensor if the target sensor meets the corresponding triggering early warning conditions, and processing the detection data of the target sensor by adopting a preset risk early warning model if the target sensor is the preconfigured false alarm preventing sensor to obtain a risk early warning detection result, wherein the risk early warning detection result comprises: and finally, determining whether the target sensor has false alarm risks according to the feature quantity in the early-warning feature set, and generating corresponding alarm information based on the detection data of the target sensor if the target sensor does not have false alarm risks.

The embodiment of the application also provides a possible implementation manner of the risk early warning method, and the preset risk early warning model comprises the following steps: a plurality of data processing rules and risk judgment rules; fig. 2 is a second flow chart of a risk early warning method based on internet of things data according to an embodiment of the present application. As shown in fig. 2, processing detection data of the target sensor by using a preset risk early-warning model to obtain a risk early-warning detection result includes:

s201, determining whether the target sensor meets processing trigger conditions corresponding to a plurality of data processing rules.

In this embodiment, when the processing trigger conditions of the plurality of data processing rules are related to the configuration identifier of the target sensor when the target sensor is connected to the internet of things platform, it is determined whether the target sensor meets the processing trigger conditions corresponding to the plurality of data processing rules according to the identifier of the target sensor.

S202, if the target sensor meets the processing trigger conditions of the target data processing rule in the plurality of data processing rules, the target data processing rule is adopted to process the detection data of the target sensor.

And S203, determining whether the processed detection data is in an early warning state by adopting a risk judging rule, and adding the processed detection data into an early warning feature set if the processed detection data is in the early warning state.

Optionally, if the target data processing rule is the first data processing rule, a correction template of the first data processing rule is adopted to perform correction processing on the detection data of the target sensor, so as to obtain corrected data, where the processed detection data includes: corrected data.

Specifically, if the target data processing rule is a first data processing rule, the target sensor meets a processing trigger condition of the first data processing rule, wherein the first data processing rule is a detection data correction rule, the processing trigger condition of the first data processing rule is that an identifier of the target sensor needs to be corrected, and a correction template of the first data processing rule is adopted to correct the detection data of the target sensor to obtain corrected data.

For example, if the target sensor is an electrochemical sensor, the chemical material in the electrochemical sensor is depleted with time, and thus the sensitivity of the electrochemical sensor is also reduced, so that the result of detecting the data is biased, and the correction template includes: the error threshold range of the detection data corresponding to different time periods is, for example, 10, the error threshold range of the detection data can be up and down floated by 0.1, namely 9.9-10.1, within one month, and is also a preset error threshold range, the error threshold range of the detection data can be up and down floated by 0.5, namely 9.5-10.5, within three months, if the detection data of the electrochemical sensor in the third month is 10.4, and when the detection data is not corrected, the detection data of the electrochemical sensor is not within the preset error threshold range, and after the detection data is corrected, the error threshold range of the detection data of the electrochemical sensor in three months is obtained.

And determining whether the processed detection data, namely the corrected data, is in an early warning state or not by adopting a risk judging rule, if the corrected data is not in an early warning threshold range of the target sensor, determining that the corrected detection data is in the early warning state, and adding the corrected detection data into an early warning feature set, so that the feature quantity in the early warning feature set corresponding to the current target sensor is 1.

Optionally, if the target data processing rule is a second data processing rule, adopting a continuity verification algorithm of the second data processing rule to perform continuity verification on the detection data of the target sensor to obtain continuous verification data; the processed detection data further comprises: the data is continuously validated.

Specifically, if the target data processing rule is a second data processing rule, the target sensor meets a processing triggering condition of the second data processing rule, wherein the second data processing rule is a detection data continuity verification rule, the processing triggering condition of the second data processing rule is that an identifier of the target sensor needs to be subjected to detection data continuity verification is included in an identifier of the target sensor, and a continuity verification algorithm of the second data processing rule is adopted to carry out continuity verification on detection data of the target sensor, so that continuous verification data is obtained.

For example, if the target sensor is a gas concentration sensor, detecting the gas concentration around the industrial equipment in real time, and if 180 pieces of detection data are generated within 1 minute, performing continuity verification on the 180 pieces of detection data by adopting a continuity verification algorithm, and judging that the times of continuously exceeding an early warning threshold value exist to obtain continuous verification data.

And adopting a risk judging rule to determine whether the processed detection data, namely the continuous verification data (the times exceeding the early warning threshold value) is larger than the preset times, if the continuous verification data is larger than the preset times, determining that the continuous verification data is in an early warning state, and adding the continuous verification data into an early warning feature set, so that the feature quantity in the early warning feature set corresponding to the current target sensor is 1.

Optionally, if the target data processing rule is a third data processing rule, acquiring association data of the target sensor by adopting a relativity verification algorithm of the third data processing rule, and carrying out relativity verification on the detection data of the target sensor and the association data to obtain association verification data; the processed detection data further comprises: and associating the verification data.

Specifically, if the target data processing rule is a third data processing rule, the target sensor meets a processing triggering condition of the third data processing rule, wherein the third data processing rule is a detection data relevance verification rule, the processing triggering condition of the third data processing rule is that an identifier of the target sensor needs to be subjected to relevance verification of the detection data is included in the identifier of the target sensor, and relevance verification is performed on detection data of the target sensor and relevance data of the target sensor by adopting a relevance verification algorithm of the third data processing rule, so that relevance verification data is obtained.

In one example, if the target sensor is a gas concentration sensor and is disposed around the target industrial device, and a plurality of other gas concentration sensors are disposed around the target industrial device, all the gas concentrations around the industrial device are detected, association data of the target sensor, that is, the other plurality of gas concentration sensors around the industrial device, are obtained, and association verification is performed on the detection data of the target sensor and the detection data of the other plurality of gas concentration sensors through a relationship verification algorithm, so as to obtain association verification data, where if the detection data of the other plurality of gas concentration sensors all exceeds an early warning threshold range, and the detection data of the target sensor exceeds the early warning threshold range, it may be determined that there is a risk of false alarm of the target sensor.

In another example, if the target sensor is a substance leakage sensor, the correlation data of the target sensor, that is, the pressure sensor is obtained, and correlation verification is performed on the detection data of the target sensor and the detection data of the pressure sensor through a correlation verification algorithm to obtain correlation verification data, where if the detection data of the substance leakage sensor is linearly decreased and the detection data of the pressure sensor is unchanged, it is determined that there is a risk of false alarm of the target sensor.

And adopting a risk judging rule to determine whether the processed detection data, namely the associated verification data, meets a preset condition, if the associated verification data meets the preset condition, determining that the associated verification data is in an early warning state, and adding the early warning state to an early warning feature set, so that the feature quantity in the early warning feature set corresponding to the current target sensor is 1.

It should be noted that, the target sensor may satisfy both the triggering condition of the first data processing rule and the triggering condition of the second data processing rule, and then process the detection data of the target sensor according to different data processing rules, so as to obtain the early warning feature set finally.

In the method provided by the embodiment of the application, whether the target sensor meets the processing triggering conditions corresponding to the data processing rules is determined; if the target sensor meets the processing trigger conditions of the target data processing rules in the data processing rules, the target data processing rules are adopted to process the detection data of the target sensor, the risk judging rules are adopted to determine whether the processed detection data are in an early warning state, and if the processed detection data are in the early warning state, the processed detection data are added into the early warning feature set. The detection data of the target sensor is processed through the target data processing rule, and whether the target sensor has false alarm risk can be accurately determined according to the processed detection data.

The embodiment of the application also provides a possible implementation manner of the risk early warning method, and the risk early warning detection result further comprises: a non-early warning feature set; processing the detection data of the target sensor by adopting a preset risk early-warning model to obtain a risk early-warning detection result, and further comprising:

and if the processed detection data is not in the early warning state, adopting a risk judgment rule and adding the risk judgment rule into the non-early warning feature set.

Specifically, according to a risk judging rule, if the processed detection data is not in an early warning state, the detection data is added into a non-early warning feature set, so that the number of features in the non-early warning feature set corresponding to the current target sensor is 1.

The embodiment of the application also provides a possible implementation manner of the risk early warning method, and according to the feature quantity in the early warning feature set, determining whether the target sensor has a false alarm risk comprises the following steps:

if the feature quantity in the early warning feature set is not 0, determining that the target sensor does not have false alarm risk.

If the feature quantity in the early warning feature set is 0, determining that the target sensor has false alarm risk.

If the feature quantity in the early warning feature set is not 0, the detection data of the target sensor is indicated to be still in an early warning state after being processed according to the target data processing rule, and it is determined that the target sensor is not in a false alarm risk, and an abnormal condition occurs in industrial equipment. If the feature quantity in the early warning feature set is 0, indicating that the detected data of the target sensor is processed according to the target data processing rule, and the processed data is not in an early warning state, determining that the target sensor has false alarm risk, and no abnormal condition exists in industrial equipment.

The embodiment of the application also provides a possible implementation manner of the complete risk early warning method, and fig. 3 is a third flow chart of the risk early warning method based on the internet of things data. As shown in fig. 3, the method further includes:

s301, acquiring detection data of at least one sensor in Internet of things data of a preset industrial place.

S302, determining whether each sensor meets the corresponding trigger early warning condition according to the detection data of each sensor.

S403, if the target sensor meets the corresponding triggering early warning condition, determining whether the target sensor is a preconfigured false alarm preventing sensor.

S304, if the target sensor is a pre-configured false alarm prevention sensor, determining whether the target sensor meets the processing trigger conditions corresponding to the data processing rules.

S305, if the target sensor meets the processing trigger condition of the first data processing rule in the plurality of data processing rules, correcting the detection data of the target sensor by adopting a correction template of the first data processing rule to obtain corrected data.

S306, if the target sensor meets the processing trigger condition of the second data processing rule in the plurality of data processing rules, adopting a continuity verification algorithm of the second data processing rule to perform continuity verification on the detection data of the target sensor, and obtaining continuous verification data.

S307, if the target sensor meets the processing trigger condition of a third data processing rule in the plurality of data processing rules, acquiring the associated data of the target sensor by adopting a relativity verification algorithm of the third data processing rule, and carrying out relativity verification on the detection data and the associated data of the target sensor to obtain the associated verification data.

And S308, determining whether the processed detection data is in an early warning state by adopting a risk judging rule.

S309, if the processed detection data is in an early warning state, adding the detection data into an early warning feature set.

And S310, if the processed detection data is not in the early warning state, adding the detection data into the non-early warning feature set.

And S311, if the feature quantity in the early warning feature set is not 0, determining that the target sensor does not have false alarm risk.

S312, if the feature quantity in the early warning feature set is 0, determining that the target sensor has false alarm risk.

Specifically, steps S301 to S312 in this embodiment are all explained in detail in the above steps S101 to S203, and are not described in detail herein.

The following further explains the risk early warning device and the computer device based on the data of the internet of things provided in any of the embodiments of the present application, and specific implementation processes and technical effects thereof are the same as those of the corresponding method embodiments, and for brevity, no part is mentioned in this embodiment, and reference may be made to corresponding contents in the method embodiments.

Fig. 4 is a schematic functional module diagram of a risk early warning device based on internet of things data according to an embodiment of the present application. As shown in fig. 4, the risk early warning device 100 includes:

an acquiring module 110, configured to acquire detection data of at least one sensor in internet of things data of a preset industrial location, where the at least one sensor is respectively disposed at least one detection point of the preset industrial location;

a determining module 120, configured to determine, according to the detection data of each sensor, whether each sensor meets a corresponding trigger pre-warning condition;

the determining module 120 is further configured to determine whether the target sensor is a pre-configured false alarm preventing sensor if the target sensor meets a corresponding trigger pre-warning condition;

the processing module 130 is configured to process the detection data of the target sensor by using a preset risk early-warning model if the target sensor is a preconfigured false alarm prevention sensor, so as to obtain a risk early-warning detection result, where the risk early-warning detection result includes: early warning feature sets;

the determining module 120 is further configured to determine whether a false alarm risk exists in the target sensor according to the feature number in the early warning feature set;

and the generating module 140 is configured to generate corresponding alarm information based on the detection data of the target sensor if there is no false alarm risk.

Optionally, the preset risk early warning model includes: a plurality of data processing rules and risk judgment rules; the processing module 130 is further configured to determine whether the target sensor meets a processing trigger condition corresponding to the plurality of data processing rules; if the target sensor meets the processing trigger conditions of the target data processing rules in the plurality of data processing rules, processing the detection data of the target sensor by adopting the target data processing rules; and determining whether the processed detection data is in an early warning state or not by adopting a risk judging rule, and adding the processed detection data into an early warning feature set if the processed detection data is in the early warning state.

Optionally, the risk early warning detection result further includes: a non-early warning feature set; the processing module 130 is further configured to add the risk judgment rule to the non-early warning feature set if the processed detection data is not in the early warning state.

Optionally, the processing module 130 is further configured to, if the target data processing rule is the first data processing rule, perform correction processing on the detection data of the target sensor by using a correction template of the first data processing rule, so as to obtain corrected data; the processed detection data comprises: corrected data.

Optionally, the processing module 130 is further configured to, if the target data processing rule is a second data processing rule, perform continuity verification on the detection data of the target sensor by using a continuity verification algorithm of the second data processing rule, so as to obtain continuous verification data; the processed detection data further comprises: the data is continuously validated.

Optionally, the processing module 130 is further configured to, if the target data processing rule is a third data processing rule, acquire association data of the target sensor by adopting a relational verification algorithm of the third data processing rule, and perform relevance verification on the detection data and the association data of the target sensor to obtain association verification data; the processed detection data further comprises: and associating the verification data.

Optionally, the determining module 120 is further configured to determine that the target sensor does not have a false alarm risk if the feature number in the early warning feature set is not 0; if the feature quantity in the early warning feature set is 0, determining that the target sensor has false alarm risk.

The foregoing apparatus is used for executing the method provided in the foregoing embodiment, and its implementation principle and technical effects are similar, and are not described herein again.

The above modules may be one or more integrated circuits configured to implement the above methods, for example: one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASICs), or one or more microprocessors, or one or more field programmable gate arrays (Field Programmable Gate Array, abbreviated as FPGAs), etc. For another example, when a module above is implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a central processing unit (Central Processing Unit, CPU) or other processor that may invoke the program code. For another example, the modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

Fig. 5 is a schematic diagram of a computer device provided in an embodiment of the present application, where the computer device may be used for risk early warning based on internet of things data. As shown in fig. 5, the computer device 200 includes: a processor 210, a storage medium 220, and a bus 230.

The storage medium 220 stores machine-readable instructions executable by the processor 210. When the computer device is running, the processor 210 communicates with the storage medium 220 via the bus 230, and the processor 210 executes the machine-readable instructions to perform the steps of the method embodiments described above. The specific implementation manner and the technical effect are similar, and are not repeated here.

Optionally, the present application further provides a storage medium 220, where the storage medium 220 stores a computer program, which when executed by a processor performs the steps of the above-mentioned method embodiments. The specific implementation manner and the technical effect are similar, and are not repeated here.

In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in hardware plus software functional units.

The integrated units implemented in the form of software functional units described above may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (english: processor) to perform some of the steps of the methods according to the embodiments of the invention. And the aforementioned storage medium includes: u disk, mobile hard disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.

The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily appreciate variations or alternatives within the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.

Claims

1. The risk early warning method based on the Internet of things data is characterized by being applied to a server of an Internet of things platform; the method comprises the following steps:

2. The method of claim 1, wherein the pre-set risk early warning model comprises: a plurality of data processing rules and risk judgment rules; the processing the detection data of the target sensor by adopting a preset risk early-warning model to obtain a risk early-warning detection result comprises the following steps:

3. The method of claim 2, wherein the risk early warning detection result further comprises: a non-early warning feature set; the method for processing the detection data of the target sensor by adopting the preset risk early-warning model to obtain a risk early-warning detection result further comprises the following steps:

4. The method of claim 2, wherein processing the detection data of the target sensor using the target data processing rule comprises:

5. The method of claim 2, wherein processing the detection data of the target sensor using the target data processing rule comprises:

6. The method of claim 2, wherein processing the detection data of the target sensor using the target data processing rule comprises:

7. The method of claim 1, wherein determining whether the target sensor is at risk of false alarms based on the number of features in the set of early warning features comprises:

8. The utility model provides a risk early warning device based on thing networking data which characterized in that is applied to the server of thing networking platform, the device includes:

9. A computer device, comprising: a processor, a storage medium, and a bus, the storage medium storing program instructions executable by the processor, the processor and the storage medium communicating over the bus when the computer device is running, the processor executing the program instructions to perform the steps of the risk early warning method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when executed by a processor, performs the steps of the risk early warning method according to any one of claims 1 to 7.