CN117809433B - Internet of things equipment-closing processing method and system supporting accurate fusion early warning - Google Patents
Internet of things equipment-closing processing method and system supporting accurate fusion early warning Download PDFInfo
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Abstract
The disclosure provides an Internet of things gateway equipment processing method and system supporting accurate fusion early warning, and relates to the technical field of Internet of things gateways, wherein the method comprises the following steps: acquiring disaster monitoring data by connecting monitoring sensing equipment through a gateway; receiving disaster monitoring data, and preprocessing the disaster monitoring data by a gateway through a preset preprocessing algorithm; based on disaster monitoring data, respectively establishing a criterion expression of each type of monitoring sensor, a multi-parameter criterion expression combination and macroscopic phenomenon conditions; setting a triggering condition of an early warning model; based on the triggering condition of the early warning model, an operation judgment result of the early warning model is obtained, and early warning information is issued. According to the method and the device, the technical problem that the accuracy of the early warning of the Internet of things is low due to low edge computing efficiency and low monitoring data linkage efficiency in the prior art can be solved, the purposes of improving the edge computing efficiency and the monitoring data linkage efficiency are achieved, and the technical effect of improving the accuracy of the early warning of the Internet of things is achieved.
Description
Technical Field
The disclosure relates to the technical field of internet of things, in particular to an internet of things gateway equipment processing method and system supporting accurate fusion early warning.
Background
The geological disasters come from disastrous results of natural and artificial actions on geological environments, and the geological disasters are various in variety, wide in distribution and large in harm, so that a method for enhancing disaster prevention and reduction construction and reducing casualties and property loss caused by the geological disasters is needed. At present, most of the existing data early warning analysis devices do not have edge capability, data cannot be filtered and screened locally, and once the acquired value obtained by a single monitoring device exceeds a set early warning threshold value, early warning is started, and accurate judgment early warning of comprehensive factors such as multiple conditions and multiple bases cannot be combined.
In summary, in the prior art, the accuracy of the internet of things related early warning is low due to the low edge computing efficiency and the low monitoring data linkage efficiency.
Disclosure of Invention
The disclosure provides an Internet of things gateway equipment processing method and system supporting accurate fusion early warning, which are used for solving the technical problem that in the prior art, the accuracy of the Internet of things gateway early warning is low due to low edge computing efficiency and low monitoring data linkage efficiency.
According to a first aspect of the present disclosure, there is provided an internet of things gateway device processing method supporting accurate fusion early warning, including: acquiring disaster monitoring data through gateway connection monitoring sensing equipment, wherein the disaster monitoring data comprises ground disaster data, hydrological data and meteorological data; receiving the disaster monitoring data, and preprocessing the disaster monitoring data by a gateway through a preset preprocessing algorithm; based on disaster monitoring data, respectively establishing a criterion expression of each type of monitoring sensor, a multi-parameter criterion expression combination and macroscopic phenomenon conditions; setting an early warning model triggering condition according to the criterion expression, the multi-parameter criterion expression combination and the macroscopic phenomenon condition of the various types of monitoring sensors, wherein the early warning model triggering condition comprises a single-parameter early warning criterion, a multi-parameter early warning criterion combination and a macroscopic phenomenon and microscopic data combined early warning criterion; and acquiring an operation judgment result of the early warning model based on the triggering condition of the early warning model, and issuing early warning information to a target platform in the comprehensive monitoring early warning platform through a gateway according to the operation judgment result of the early warning model.
According to a second aspect of the present disclosure, there is provided an internet of things gateway device processing system supporting accurate fusion early warning, including: the disaster monitoring data acquisition module is used for acquiring disaster monitoring data by connecting the gateway with monitoring sensing equipment, wherein the disaster monitoring data comprises ground disaster data, hydrological data and meteorological data; the preprocessing module is used for receiving the disaster monitoring data and preprocessing the disaster monitoring data by a gateway through a preset preprocessing algorithm; the criterion expression obtaining module is used for respectively establishing criterion expressions of various types of monitoring sensors, multi-parameter criterion expression combinations and macroscopic phenomenon conditions based on disaster monitoring data; the early warning model triggering condition acquisition module is used for setting an early warning model triggering condition according to the criterion expression, the multi-parameter criterion expression combination and the macroscopic phenomenon condition of each type of monitoring sensor, wherein the early warning model triggering condition comprises a single-parameter early warning criterion, a multi-parameter early warning criterion combination and a macroscopic phenomenon and microscopic data combined early warning criterion; and the operation judgment result obtaining module is used for obtaining the operation judgment result of the early warning model based on the triggering condition of the early warning model, and issuing early warning information to a target platform in the comprehensive monitoring early warning platform through a gateway according to the operation judgment result of the early warning model.
According to a third aspect of the present disclosure, a computer device comprises a memory storing a computer program and a processor implementing a method capable of performing any one of the first aspects.
According to a fourth aspect of the present disclosure, a computer-readable storage medium has stored thereon a computer program which, when executed by a processor, implements a method capable of performing any of the first aspects.
One or more technical solutions provided in the present disclosure have at least the following technical effects or advantages: according to the disaster monitoring method, disaster monitoring data are obtained through gateway connection monitoring sensing equipment, wherein the disaster monitoring data comprise ground disaster data, hydrological data and meteorological data; receiving the disaster monitoring data, and preprocessing the disaster monitoring data by a gateway through a preset preprocessing algorithm; based on disaster monitoring data, respectively establishing a criterion expression of each type of monitoring sensor, a multi-parameter criterion expression combination and macroscopic phenomenon conditions; setting an early warning model triggering condition according to the criterion expression, the multi-parameter criterion expression combination and the macroscopic phenomenon condition of the various types of monitoring sensors, wherein the early warning model triggering condition comprises a single-parameter early warning criterion, a multi-parameter early warning criterion combination and a macroscopic phenomenon and microscopic data combined early warning criterion; based on the triggering condition of the early warning model, an operation judgment result of the early warning model is obtained, and early warning information is issued to a target platform in the comprehensive monitoring early warning platform through a gateway according to the operation judgment result of the early warning model, so that the technical problem that the accuracy of the early warning of the Internet of things is low due to low edge computing efficiency and low monitoring data linkage efficiency in the prior art is solved, the aims of improving the edge computing efficiency and the monitoring data linkage efficiency are achieved, and the technical effect of improving the early warning accuracy of the Internet of things is achieved.
It should be understood that the description of this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
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For a clearer description of the present disclosure or of the prior art, the drawings used in the description of the embodiments or of the prior art will be briefly described, it being obvious that the drawings in the description below are only exemplary and that other drawings may be obtained, without inventive effort, by a person skilled in the art, from the provided drawings.
Fig. 1 is a schematic flow chart of an internet of things gateway device processing method supporting accurate fusion early warning according to an embodiment of the present disclosure;
Fig. 2 is a schematic structural diagram of an internet of things gateway device processing system supporting accurate fusion early warning according to an embodiment of the present disclosure;
Fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the disclosure.
Reference numerals illustrate: the system comprises a disaster monitoring data obtaining module 11, a preprocessing module 12, a criterion expression obtaining module 13, an early warning model triggering condition obtaining module 14, an operation judgment result obtaining module 15, a computer device 100, a processor 101, a memory 102 and a bus 103.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Example 1
The embodiment of the disclosure provides a method for processing an internet of things gateway device supporting accurate fusion early warning, which is described with reference to fig. 1, and includes:
The method provided by the embodiment of the disclosure comprises the following steps:
Acquiring disaster monitoring data through gateway connection monitoring sensing equipment, wherein the disaster monitoring data comprises ground disaster data, hydrological data and meteorological data;
Specifically, disaster monitoring data is obtained by connecting monitoring sensing equipment through a gateway. The disaster monitoring data includes ground disaster data, hydrological data, and meteorological data. For example, ground disaster data includes collapse, debris flow, ground subsidence, ground fissures, landslide, slope, and the like. Hydrologic data include water level, flow, rainfall, water quality, groundwater, evaporation, silt, etc. The meteorological data comprise temperature, cloud cover, prediction, precipitation, sunlight, humidity, wind speed, water temperature and the like. The gateway connection monitoring sensing equipment is equipment such as an RS485 sensor field deployment Internet of things gateway.
Receiving the disaster monitoring data, and preprocessing the disaster monitoring data by a gateway through a preset preprocessing algorithm;
specifically, the disaster monitoring data are received and acquired, and are preprocessed by the gateway through a standard score algorithm or a principal component analysis algorithm in a preset preprocessing algorithm and a Laida criterion to obtain a preprocessing result, so that fusion early warning of the disaster monitoring data is carried out.
Based on disaster monitoring data, respectively establishing a criterion expression of each type of monitoring sensor, a multi-parameter criterion expression combination and macroscopic phenomenon conditions;
specifically, based on disaster monitoring data, the monitored disaster type and calculated value are obtained, a criterion expression of a monitoring sensor is established, and alarm level parameters of each criterion are set. Further, according to the monitoring disaster type, a calculated value is determined according to the monitoring disaster type, wherein the calculated value comprises a monitoring value, an accumulated value and a deformation rate, and the monitored disaster type is any data type of ground disaster data, hydrological data and meteorological data. Further, setting alarm level parameters of each criterion to the calculated value to obtain an early warning threshold. And according to the monitoring data of the sensor, forming a trigger condition by the calculated value and the early warning level threshold value, and judging whether the obtained calculated value meets the early warning level threshold value. Further, the criterion parameters of the historical model are obtained through the model management library. And searching based on the big data to obtain a discrimination case, obtaining the criterion parameters of the discrimination case based on the discrimination case, and combining the criterion parameters to obtain combined criterion parameters. And fitting by using the combination criterion parameters and the discrimination results to obtain the multi-parameter criterion expression combination of each discrimination case. Further, field data of personnel observing disaster sites are acquired. And carrying out macroscopic trend analysis based on the field data, and determining macroscopic trend data. And calculating the weight of the macroscopic trend data of the channel by the weight calculation, and obtaining the weight score of the macroscopic trend data by calculating the weight of the macroscopic trend data. And screening to obtain macroscopic trend data weight with higher macroscopic trend data weight score, and determining macroscopic phenomenon conditions.
Setting an early warning model triggering condition according to the criterion expression, the multi-parameter criterion expression combination and the macroscopic phenomenon condition of the various types of monitoring sensors, wherein the early warning model triggering condition comprises a single-parameter early warning criterion, a multi-parameter early warning criterion combination and a macroscopic phenomenon and microscopic data combined early warning criterion;
Specifically, the triggering conditions of the early warning model comprise single-parameter early warning criteria, multi-parameter early warning criteria combination and macroscopic phenomenon and microscopic data combined early warning criteria. The early warning is carried out by utilizing early warning criterion parameters of single equipment, namely, when real-time monitoring data such as water content, cracks, earth surface displacement, inclination angle, mud level and the like uploaded by monitoring equipment are subjected to data preprocessing, a criterion expression reaching the early warning level of one sensing equipment in the model is triggered, and the gateway can immediately issue an early warning message. Correspondingly, the final early warning level is determined by comprehensively considering early warning criterion expression parameters of a plurality of sensing devices. The criterion expressions within the monitoring point model are interrelated. In the expression, "and operation" needs to meet the conditions of the related multiple devices, and after the data reach the predetermined early warning level criterion expression, the gateway can immediately issue early warning information. Or calculating, the gateway can immediately issue the early warning message only after the data in any one device reaches the condition of the predetermined early warning level criterion expression. Correspondingly, the macroscopic phenomenon is used as a preliminary early warning mode, the trend of the macroscopic phenomenon is set according to the direction category of disaster extension of the monitoring point, and the preliminary early warning level of the macroscopic phenomenon is divided according to the score by visually judging the disaster on the spot of personnel. And combining the pre-warning criteria to form a multi-parameter multi-dimensional predictive pre-warning model based on macroscopic phenomenon trend and microscopic monitoring data.
And acquiring an operation judgment result of the early warning model based on the triggering condition of the early warning model, and issuing early warning information to a target platform in the comprehensive monitoring early warning platform through a gateway according to the operation judgment result of the early warning model.
Specifically, according to the triggering condition of the early warning model, the operation judging result of the early warning model is obtained, and according to the operation judging result of the early warning model, disaster early warning information can be timely and accurately sent to the target platform, so that technical support is provided for improving early warning precision, and real-time dynamic tracking, monitoring and early warning are implemented on geological disasters.
The disaster early warning information can be timely and accurately sent out by using a reasonable early warning analysis model. The whole monitoring and early warning process can process monitoring data in real time and stably, the general early warning level is effectively scheduled and built by the early warning model, the early warning model is intelligently selected, real-time and timely issuing of error early warning information can be guaranteed, technical support is provided for improving early warning precision, and real-time dynamic tracking, monitoring and early warning are implemented on geological disasters.
The method provided by the embodiment of the disclosure further comprises the following steps:
Carrying out standardized processing on the disaster monitoring data by adopting a standard score algorithm, and scaling to a preset range; and/or the number of the groups of groups,
Performing dimension reduction processing on the disaster monitoring data by adopting a principal component analysis algorithm; and/or the number of the groups of groups,
And (3) checking the disaster monitoring data by adopting a Laida criterion, marking and eliminating abnormal data, and obtaining preprocessed disaster monitoring data.
Specifically, the standard score algorithm is a standard score obtained by converting a certain original score, and the standard score can make the values which are not compared originally become comparable. Further, the disaster monitoring data are subjected to standardized processing by adopting a standard score algorithm, and scaled to be within a preset range. The preset range is a range in which data processing can be performed. The algorithm formula is as follows:
Where μ is the mean of the set of data, σ is the standard deviation of the set of data, and Z is the standard score. For example, the rainfall collection value for a certain period in the area is 22, the average rainfall in the area is 14, the standard error is 2, and at this time, the Z-score in this example is:
the sampling result of this time is lower than the average value by 4 standard deviations. The algorithm can detect abnormal data in the data such as rainfall, water content and the like, and mark elimination is carried out.
Further, the principal component analysis algorithm retains as much information as possible by reducing the number of data indexes to perform dimension reduction of high-dimensional data and extract principal characteristic components of the data. Further, the disaster monitoring data is subjected to dimension reduction processing by adopting a principal component analysis algorithm, so that dimension reduction feature vectors are obtained. And (3) carrying out centering and normalization on all the features, calculating a covariance matrix of the sample, solving the feature values and the feature vectors for the covariance matrix, and selecting the feature vectors corresponding to the K largest feature values as main components. For example, if data of debris flow in himalayan mountain is obtained, the data of debris flow is obtained by the topography condition, the loose material source condition, and the water source condition.
Further, the rada criterion is to assume that a group of detection data only contains random errors, calculate it to obtain standard deviation, determine a section according to a certain probability, consider that the errors exceeding the section are not random errors but coarse errors, and reject the data containing the errors. Further, the disaster monitoring data are checked by adopting the Laida rule, and abnormal data are marked and removed. Calculating the average value and standard deviation, the upper limit and the lower limit of the data, wherein the upper limit is equal to the average value plus 3 times of the standard deviation, the lower limit is equal to the average value minus 3 times of the standard deviation, comparing the relation between the data point and the upper limit and the lower limit, and judging the data point as abnormal data and marking and removing if the data point is larger than the upper limit or smaller than the lower limit. For example, in meteorological data, if the temperature in the data is in the range of 20-30 ℃, if the temperature of the random error is 40 ℃, marking and rejecting are carried out.
Further, the disaster monitoring data are subjected to standardized processing by adopting a standard score algorithm and subjected to dimension reduction processing by adopting a principal component analysis algorithm, and then the disaster monitoring data are inspected by adopting a Laida criterion. Accordingly, disaster monitoring data are standardized by adopting a standard score algorithm, and then the disaster monitoring data are checked by adopting a Leida criterion. Or performing dimension reduction processing on the disaster monitoring data by adopting a principal component analysis algorithm, and then checking the disaster monitoring data by adopting a Laida criterion.
The disaster monitoring data are received, preprocessed by the gateway through a preset preprocessing algorithm, and then fused and pre-warned.
The method provided by the embodiment of the disclosure further comprises the following steps:
Determining a calculated value according to the monitored disaster type, wherein the calculated value comprises a monitored value, an accumulated value and a deformation rate;
setting alarm level parameters of each criterion to obtain an early warning threshold;
and according to the monitoring data of the sensor, forming a trigger condition by the calculated value and the early warning level threshold value, and constructing a criterion expression of each type of monitoring sensor through numerical comparison or mathematical operation.
Specifically, according to the type of the monitored disaster, the calculated value is determined according to the type of the monitored disaster, wherein the calculated value comprises a monitored value, an accumulated value and a deformation rate. For example, if the monitored disaster type is weather data, the calculated values may include temperature monitoring values and the like.
Further, setting alarm level parameters of each criterion to the calculated value to obtain an early warning threshold. The criterion is a decision criterion. For example, the alarm level parameter is set for the temperature monitoring value of the calculated value according to the criterion, and the early warning threshold is 40 ℃ when the temperature monitoring value is below 40 ℃.
Further, according to the monitoring data of the sensor, the calculated value and the early warning level threshold value form a trigger condition, and whether the calculated value meets the early warning level threshold value is judged. The calculated value and the early warning threshold value can be combined into a trigger condition according to the data of various types of sensors, and the early warning criterion expression of the sensors is formed through mathematical operations such as numerical comparison, OR, AND and the like. The early warning threshold is set for a customer or according to meteorological data. And constructing criterion expressions of various types of monitoring sensors through numerical comparison. Or constructing the criterion expression of each type of monitoring sensor through mathematical operation. Or the criterion expression of each type of monitoring sensor is constructed through numerical comparison and mathematical operation. Further, the numerical comparison is to compare the calculated value with the early warning level threshold value, and the comparison result is obtained and judged. And the mathematical operation is to perform a difference operation on the calculated value and the early warning level threshold value, obtain a difference result and judge.
Based on disaster monitoring data, criterion expressions of various types of monitoring sensors are established, and early warning efficiency can be improved.
The method provided by the embodiment of the disclosure further comprises the following steps:
Obtaining criterion parameters of a historical model through a model management library, wherein the criterion parameters comprise a monitoring value, an accumulated value and a deformation rate;
acquiring a discrimination case, and carrying out criterion parameter analysis based on the discrimination case to obtain a combined criterion parameter;
And fitting to obtain multi-parameter criterion expression combinations of each discrimination case by utilizing the combination criterion parameters and discrimination results.
Specifically, the criterion parameters of the historical model are obtained through the model management library, and the criterion parameters of the historical model comprise specific values sensed by sensors such as monitoring values, accumulated values, deformation rates and the like. Further, searching is carried out based on the big data to obtain a discrimination case, criterion parameters of the discrimination case are obtained based on the discrimination case, and the criterion parameters are combined to obtain combined criterion parameters. Further, the combination criterion parameters and the discrimination results are utilized to obtain the multi-parameter criterion expression combination of each discrimination case by fitting. For example, the combination of criterion expressions in the early warning model of a landslide monitoring disaster point mainly comprises the criterion parameters such as rainfall, earth surface displacement, inclination angle and the like.
The disaster early warning information can be timely and accurately sent out by establishing the multi-parameter criterion expression combination.
The method provided by the embodiment of the disclosure further comprises the following steps:
Acquiring site data of personnel observing disaster sites;
performing macroscopic trend analysis based on the field data, and determining macroscopic trend data;
Carrying out weight calculation on the macroscopic trend data through a weight calculation channel to obtain a macroscopic trend data weight score;
And screening according to the macroscopic trend data weight scores to determine the macroscopic phenomenon conditions.
Specifically, the field data of personnel observing the disaster site is acquired. The field data of personnel observing disaster sites comprise the field data of personnel observing swelling, cracks and the like.
Further, macroscopic trend analysis is performed based on the field data to determine macroscopic trend data. Macroscopic trend data is a macroscopic trend description including the occurrence of discontinuous pull cracks, complete penetration of pull cracks, significant bulge swelling, and the like.
Further, the weight of the macroscopic trend data of the channel is calculated through the weight calculation, and the weight score of the macroscopic trend data is obtained through the weight calculation of the macroscopic trend data. The macroscopic trend data weight scores were 10, 15, 20, etc.
Further, screening is carried out according to the macroscopic trend data weight scores, macroscopic trend data weights with higher macroscopic trend data weight scores are obtained through screening, and macroscopic phenomenon conditions are determined. And the system is used for auxiliary early warning, so that decision-making personnel can clearly change the disaster trend.
Wherein, macroscopic phenomenon conditions are established, auxiliary early warning can be carried out, and disaster variation trend is obtained.
The method provided by the embodiment of the disclosure further comprises the following steps:
acquiring disaster monitoring data of a monitoring point according to the macroscopic phenomenon condition as a preliminary early warning triggering condition;
matching the disaster monitoring data of the monitoring points with macroscopic phenomenon conditions, and determining a preliminary early warning level according to the weight scores of macroscopic trend data;
acquiring a multi-parameter criterion expression combination according to the disaster discrimination type of the monitoring point, and performing microscopic discrimination by utilizing the multi-parameter criterion expression combination to determine a microscopic discrimination level;
And carrying out multi-parameter and multi-dimensional fusion by utilizing the preliminary early warning grade and the microcosmic discrimination grade to obtain the operation judgment result of the early warning model.
Specifically, the macroscopic phenomenon is used as a preliminary early warning mode, the trend of the macroscopic phenomenon is set according to the direction category of disaster extension of the monitoring point, and the preliminary early warning level of the macroscopic phenomenon is divided according to the score by visually judging the disaster on the spot of personnel. Further, according to the disaster discrimination type of the monitoring point, a multi-parameter criterion expression combination is obtained, and the multi-parameter criterion expression combination is utilized to conduct microscopic discrimination, so that the microscopic discrimination level is determined. And combining the early warning criteria to form multi-parameter and multi-dimensional predictive early warning of macroscopic phenomenon trend and microscopic monitoring data, and obtaining the operation judgment result of the early warning model.
Example two
Based on the same inventive concept as the method for processing the internet of things gateway device supporting the accurate fusion early warning in the foregoing embodiment, which is described with reference to fig. 2, the present disclosure further provides a system for processing the internet of things gateway device supporting the accurate fusion early warning, where the system includes:
The disaster monitoring data acquisition module 11 is used for acquiring disaster monitoring data through gateway connection monitoring sensing equipment, wherein the disaster monitoring data comprises ground disaster data, hydrological data and meteorological data;
The preprocessing module 12 is used for receiving the disaster monitoring data, and preprocessing the disaster monitoring data by a gateway through a preset preprocessing algorithm;
the criterion expression obtaining module 13 is used for respectively establishing criterion expressions, multi-parameter criterion expression combinations and macroscopic phenomenon conditions of various types of monitoring sensors based on disaster monitoring data;
The early warning model triggering condition obtaining module 14 is used for setting an early warning model triggering condition according to the criterion expression, the multi-parameter criterion expression combination and the macroscopic phenomenon condition of the various types of monitoring sensors, wherein the early warning model triggering condition comprises a single-parameter early warning criterion, a multi-parameter early warning criterion combination and a macroscopic phenomenon+microscopic data combined early warning criterion;
The operation judgment result obtaining module 15 is configured to obtain an operation judgment result of the early warning model based on the triggering condition of the early warning model, and issue early warning information to a target platform in the comprehensive monitoring and early warning platform through a gateway according to the operation judgment result of the early warning model.
Further, the system further comprises:
The standardized processing module is used for carrying out standardized processing on the disaster monitoring data by adopting a standard score algorithm and scaling the disaster monitoring data to a preset range; and/or the number of the groups of groups,
The dimension reduction feature vector obtaining module is used for carrying out dimension reduction processing on the disaster monitoring data by adopting a principal component analysis algorithm; and/or the number of the groups of groups,
And the abnormal data marking module is used for checking the disaster monitoring data by adopting a Laida criterion, marking and eliminating the abnormal data, and obtaining the preprocessed disaster monitoring data.
Further, the system further comprises:
the calculation value obtaining module is used for determining a calculation value according to the type of the monitored disaster, wherein the calculation value comprises a monitored value, an accumulated value and a deformation rate;
The early warning threshold obtaining module is used for setting the warning level parameters of each criterion and obtaining an early warning threshold;
The criterion expression obtaining module is used for forming a trigger condition by the calculated value and the early warning level threshold according to the monitoring data of the sensor, and constructing the criterion expressions of various types of monitoring sensors through numerical comparison or mathematical operation.
Further, the system further comprises:
The system comprises a criterion parameter acquisition module of a historical model, a criterion parameter analysis module and a parameter analysis module, wherein the criterion parameter acquisition module of the historical model is used for acquiring the criterion parameter of the historical model through a model management library, and the criterion parameter comprises a monitoring value, an accumulated value and a deformation rate;
the combined criterion parameter obtaining module is used for obtaining a discrimination case and carrying out criterion parameter analysis based on the discrimination case to obtain combined criterion parameters;
the criterion expression combination obtaining module is used for obtaining the multi-parameter criterion expression combination of each discrimination case by fitting by utilizing the combination criterion parameters and the discrimination results.
Further, the system further comprises:
The system comprises a field data acquisition module, a disaster monitoring module and a disaster monitoring module, wherein the field data acquisition module is used for acquiring field data of personnel observing a disaster site;
The macroscopic trend data acquisition module is used for carrying out macroscopic trend analysis based on the field data and determining macroscopic trend data;
the macroscopic trend data weight score obtaining module is used for carrying out weight calculation on the macroscopic trend data through a weight calculation channel to obtain macroscopic trend data weight scores;
and the macro phenomenon condition obtaining module is used for screening according to the macro trend data weight scores and determining the macro phenomenon conditions.
Further, the system further comprises:
The monitoring point disaster monitoring data acquisition module is used for acquiring monitoring point disaster monitoring data according to the macroscopic phenomenon condition serving as a preliminary early warning triggering condition;
The preliminary early warning grade obtaining module is used for matching the disaster monitoring data of the monitoring points with macroscopic phenomenon conditions and determining the preliminary early warning grade according to the weight scores of macroscopic trend data;
The micro discrimination level acquisition module is used for acquiring a multi-parameter criterion expression combination according to the disaster discrimination type of the monitoring point, performing micro discrimination by utilizing the multi-parameter criterion expression combination, and determining the micro discrimination level;
And the operation judgment result obtaining module is used for carrying out multi-parameter and multi-dimensional fusion by utilizing the preliminary early warning grade and the microcosmic judgment grade to obtain the operation judgment result of the early warning model.
The specific example of the method for processing the internet of things related to equipment supporting the accurate fusion early warning in the first embodiment is also applicable to the system for processing the internet of things related to equipment supporting the accurate fusion early warning in the first embodiment, and by the foregoing detailed description of the method for processing the internet of things related to equipment supporting the accurate fusion early warning, a person skilled in the art can clearly know the system for processing the internet of things related to equipment supporting the accurate fusion early warning in the first embodiment, so that the description is omitted herein for brevity. The device disclosed in the embodiment corresponds to the method disclosed in the embodiment, so that the description is simpler, and the relevant points refer to the description of the method.
Example III
Fig. 3 is a schematic diagram according to a third embodiment of the present disclosure, as shown in fig. 3, a computer device 100 in the present disclosure may include: a processor 101 and a memory 102.
A memory 102 for storing a program; the memory 102 may include a volatile memory (english: volatile memory), such as a random-access memory (RAM), such as a static random-access memory (SRAM), a double data rate synchronous dynamic random-access memory (DDR SDRAM), etc.; the memory may also include a non-volatile memory (English) such as a flash memory (English). The memory 102 is used to store computer programs (e.g., application programs, functional modules, etc. that implement the methods described above), computer instructions, etc., which may be stored in one or more of the memories 102 in partitions. And the above-described computer programs, computer instructions, data, etc. may be invoked by the processor 101.
The computer programs, computer instructions, etc. described above may be stored in one or more of the memories 102 in partitions. And the above-described computer programs, computer instructions, etc. may be invoked by the processor 101.
A processor 101 for executing a computer program stored in a memory 102 to implement the steps of the method according to the above-mentioned embodiment.
Reference may be made in particular to the description of the embodiments of the method described above.
The processor 101 and the memory 102 may be separate structures or may be integrated structures integrated together. When the processor 101 and the memory 102 are separate structures, the memory 102 and the processor 101 may be coupled by a bus 103.
The computer device in this embodiment may execute the technical solution in the above method, and the specific implementation process and the technical principle are the same, which are not described herein again.
According to an embodiment of the present disclosure, the present disclosure further provides a computer readable storage medium having stored thereon a computer program which, when executed, implements the steps provided by any of the above embodiments.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel or sequentially or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (7)
1. The method for processing the Internet of things gateway equipment supporting accurate fusion early warning is characterized by being applied to a comprehensive monitoring early warning platform, and comprises the following steps:
Acquiring disaster monitoring data through gateway connection monitoring sensing equipment, wherein the disaster monitoring data comprises ground disaster data, hydrological data and meteorological data;
receiving the disaster monitoring data, and preprocessing the disaster monitoring data by a gateway through a preset preprocessing algorithm;
based on disaster monitoring data, respectively establishing a criterion expression of each type of monitoring sensor, a multi-parameter criterion expression combination and macroscopic phenomenon conditions;
Setting an early warning model triggering condition according to the criterion expression, the multi-parameter criterion expression combination and the macroscopic phenomenon condition of the various types of monitoring sensors, wherein the early warning model triggering condition comprises a single-parameter early warning criterion, a multi-parameter early warning criterion combination and a macroscopic phenomenon and microscopic data combined early warning criterion;
based on the triggering condition of the early warning model, an operation judgment result of the early warning model is obtained, and early warning information is issued to a target platform in the comprehensive monitoring early warning platform through a gateway according to the operation judgment result of the early warning model;
Wherein establishing macro phenomenon conditions includes:
Acquiring site data of personnel observing disaster sites;
performing macroscopic trend analysis based on the field data, and determining macroscopic trend data;
Carrying out weight calculation on the macroscopic trend data through a weight calculation channel to obtain a macroscopic trend data weight score;
screening according to the macroscopic trend data weight scores, and determining the macroscopic phenomenon conditions;
wherein, when the triggering condition of the early warning model is a combined early warning criterion of macroscopic phenomenon and microscopic data, based on the triggering condition of the early warning model, an operation judgment result of the early warning model is obtained, comprising:
acquiring disaster monitoring data of a monitoring point according to the macroscopic phenomenon condition as a preliminary early warning triggering condition;
matching the disaster monitoring data of the monitoring points with macroscopic phenomenon conditions, and determining a preliminary early warning level according to the weight scores of macroscopic trend data;
acquiring a multi-parameter criterion expression combination according to the disaster discrimination type of the monitoring point, and performing microscopic discrimination by utilizing the multi-parameter criterion expression combination to determine a microscopic discrimination level;
And carrying out multi-parameter and multi-dimensional fusion by utilizing the preliminary early warning grade and the microcosmic discrimination grade to obtain the operation judgment result of the early warning model.
2. The method of claim 1, wherein receiving the disaster monitoring data, preprocessing the disaster monitoring data by a gateway using a preset preprocessing algorithm, comprises:
Carrying out standardized processing on the disaster monitoring data by adopting a standard score algorithm, and scaling to a preset range; and/or the number of the groups of groups,
Performing dimension reduction processing on the disaster monitoring data by adopting a principal component analysis algorithm; and/or the number of the groups of groups,
And (3) checking the disaster monitoring data by adopting a Laida criterion, marking and eliminating abnormal data, and obtaining preprocessed disaster monitoring data.
3. The method of claim 1, wherein establishing a criterion expression for each type of monitoring sensor based on disaster monitoring data comprises:
Determining a calculated value according to the monitored disaster type, wherein the calculated value comprises a monitored value, an accumulated value and a deformation rate;
setting alarm level parameters of each criterion to obtain an early warning threshold;
and according to the monitoring data of the sensor, forming a trigger condition by the calculated value and the early warning level threshold value, and constructing a criterion expression of each type of monitoring sensor through numerical comparison or mathematical operation.
4. The method of claim 1, wherein establishing a combination of multiparameter criteria expressions comprises:
Obtaining criterion parameters of a historical model through a model management library, wherein the criterion parameters comprise a monitoring value, an accumulated value and a deformation rate;
acquiring a discrimination case, and carrying out criterion parameter analysis based on the discrimination case to obtain a combined criterion parameter;
And fitting to obtain multi-parameter criterion expression combinations of each discrimination case by utilizing the combination criterion parameters and discrimination results.
5. An internet of things gateway device processing system supporting accurate fusion early warning, which is characterized in that the system is used for implementing the internet of things gateway device processing method supporting accurate fusion early warning according to any one of claims 1-4, and comprises:
The disaster monitoring data acquisition module is used for acquiring disaster monitoring data by connecting the gateway with monitoring sensing equipment, wherein the disaster monitoring data comprises ground disaster data, hydrological data and meteorological data;
the preprocessing module is used for receiving the disaster monitoring data and preprocessing the disaster monitoring data by a gateway through a preset preprocessing algorithm;
The criterion expression obtaining module is used for respectively establishing criterion expressions of various types of monitoring sensors, multi-parameter criterion expression combinations and macroscopic phenomenon conditions based on disaster monitoring data;
The early warning model triggering condition acquisition module is used for setting an early warning model triggering condition according to the criterion expression, the multi-parameter criterion expression combination and the macroscopic phenomenon condition of each type of monitoring sensor, wherein the early warning model triggering condition comprises a single-parameter early warning criterion, a multi-parameter early warning criterion combination and a macroscopic phenomenon and microscopic data combined early warning criterion;
and the operation judgment result obtaining module is used for obtaining the operation judgment result of the early warning model based on the triggering condition of the early warning model, and issuing early warning information to a target platform in the comprehensive monitoring early warning platform through a gateway according to the operation judgment result of the early warning model.
6. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1-4 when the computer program is executed.
7. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1-4.
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