CN114666370B - SaaS intelligent fire control monitor platform based on internet of things - Google Patents
SaaS intelligent fire control monitor platform based on internet of things Download PDFInfo
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Abstract
The invention provides a SaaS intelligent fire control monitoring platform based on the technology of the Internet of things, which comprises the following components: and a monitoring module: based on a preset internet of things and a multi-sensor fusion technology, acquiring fire control monitoring information in a preset range; the SaaS analysis module: based on a preset SaaS platform, carrying out online information category analysis and data conversion processing on the received fire control monitoring information to generate SaaS analysis data; and (3) a deployment module: and receiving the SaaS analysis data, and deploying the intelligent fire control monitoring platform through the SaaS analysis data.
Description
Technical Field
The invention relates to the technical fields of intelligent monitoring platforms and intelligent fire protection, in particular to a SaaS intelligent fire protection monitoring platform based on the technology of the Internet of things.
Background
The fire-fighting monitoring platform in the prior art generally adopts a wired transmission mode, is easily blown when a fire disaster comes, so that the fire-fighting platform is paralyzed or data are not transmitted in time, the maintenance cost is high, and meanwhile, only the fire disaster which occurs can be found, so that irreparable labor and material costs are caused.
The disclosed patent CN 113379993A discloses a SaaS intelligent fire-fighting monitoring platform based on the technology of the Internet of things, which is used for solving the problem that when a fire disaster occurs, a line for transmitting data is possibly blown out first, so that the data cannot be transmitted to the SaaS platform in time, and therefore, a fire disaster event cannot be found in time. But the utilization of the SaaS platform is not comprehensive enough, the renting cost is high, and the processing facing the fire-fighting condition is not intelligent and flexible enough.
Disclosure of Invention
The invention provides a SaaS intelligent fire-fighting monitoring platform based on the Internet of things technology, which aims to solve the problems.
The invention provides a SaaS intelligent fire control monitoring platform based on the technology of the Internet of things, which comprises the following components:
and a monitoring module: based on a preset internet of things and a multi-sensor fusion technology, acquiring fire control monitoring information in a preset range;
the SaaS analysis module: based on a preset SaaS platform, carrying out online information category analysis and data conversion processing on the received fire control monitoring information to generate SaaS analysis data;
and (3) a deployment module: and receiving the SaaS analysis data, and deploying the intelligent fire control monitoring platform through the SaaS analysis data.
As an embodiment of the present technical solution, the monitoring module includes:
Fusion sensing monitoring unit: the method comprises the steps of acquiring sensing monitoring information in a preset range through a preset multi-sensor device, fusing the sensing monitoring information based on a preset multi-sensor fusion technology, and determining fused sensing monitoring information; wherein,
the multi-sensing device at least comprises a temperature sensor, a photoelectric sensor and a displacement sensor;
image monitoring information unit: the image monitoring device is used for capturing image monitoring information in a preset range through a preset camera device;
fire control monitoring information unit: the system comprises a preset control terminal, a preset sensing monitoring terminal, a fire control monitoring terminal and a fire control monitoring terminal, wherein the preset control terminal is used for acquiring the fusion sensing monitoring information and the image monitoring information of the fire control monitoring terminal;
a wireless transmission unit: and the fire control monitoring information is transmitted to the fire control online analysis module.
As an embodiment of the present technical solution, the SaaS analysis module includes:
on-line analysis unit: the fire control monitoring system is used for analyzing and screening the received fire control monitoring information on line to obtain fire control screening information;
a data conversion unit: the fire control screening information is used for carrying out data conversion on the fire control screening information to generate fire control screening data;
a data statistics unit: and carding and counting fire screening data based on a preset time line, and transmitting the counted fire screening data to a preset SaaS platform to obtain SaaS analysis data.
As an embodiment of the present technical solution, the online analysis unit includes:
information category subunit: matching the fire monitoring information with preset information categories, and determining the information category corresponding to the fire monitoring information; wherein,
the information category includes: fire monitoring sensing category, fire monitoring image category; the fire monitoring image categories include: image category, image category;
feature extraction subunit: extracting class characteristics of fire monitoring information based on information classes corresponding to the fire monitoring information;
the category characteristics include: dangerous characteristic features, dangerous hidden danger features and safety features;
screening subunits: screening the fire monitoring information based on the category characteristics, and determining screening results; wherein,
when the screening result is that the category characteristic of the anti-monitoring information is dangerous characteristic or dangerous hidden danger characteristic, acquiring fire control screening information through screening;
and when the screening result is that the category characteristic of the anti-monitoring information is a safety characteristic, storing the anti-monitoring information into a preset cloud storage.
As an embodiment of the present technical solution, the data conversion unit includes:
cleaning subunit: the fire control screening information processing method comprises the steps of performing cleaning analysis on fire control screening information and determining cleaning target information; wherein,
The cleaning target information includes: repeated information, missing information and abnormal information; wherein,
the missing information is information of content missing or format missing; the abnormal information is abnormal numerical value or abnormal format information;
cleaning mode subunit: the method comprises the steps of performing pattern matching according to cleaning target information and a preset cleaning database, obtaining a cleaning pattern corresponding to the cleaning target, performing cleaning treatment according to the cleaning pattern, and generating fire control cleaning information; wherein,
the cleaning mode at least comprises a redundancy removal mode and a duplication removal mode;
data transformation subunit: and the information format is used for identifying the fire control cleaning information, and based on the information format, the fire control cleaning information is converted and processed to generate fire control screening data.
As an embodiment of the present technical solution, the data conversion subunit is configured to identify an information format of fire protection cleaning information, and perform conversion processing on the fire protection cleaning information based on the information format, to generate fire protection screening data, and includes:
identifying an information format of fire control cleaning information;
performing conversion matching according to the information format and a preset format database, and determining a conversion type corresponding to the fire control cleaning information;
And carrying out conversion treatment on the cleaning information according to the conversion type to generate fire control screening data.
As an embodiment of the present technical solution, the data statistics unit includes:
sequencing subunit: the fire control sorting data are used for carrying out time marking on the fire control screening data according to a time line corresponding to the fire control screening data, sorting the corresponding fire control screening data according to the mark size of the time marking, and generating fire control sorting data;
carding subunit: the method comprises the steps of sequentially carrying out effective data carding on fire control sequencing data according to a label sequence corresponding to the fire control sequencing data, and determining a carding result; wherein,
when the fire control sequencing data are effective data, acquiring the effective data;
when the fire sequencing data are invalid data, deleting the invalid data;
SaaS analysis data subunit: and the method is used for transmitting the counted effective data to a preset SaaS platform to obtain SaaS analysis data.
As an embodiment of the present technical solution, the SaaS analysis data subunit is configured to transmit the counted valid data to a preset SaaS platform, and obtain SaaS analysis data, where the obtaining SaaS analysis data includes:
based on a preset SaaS platform, receiving a SaaS service request of a user side;
Comparing the SaaS service request with a preset service database to determine the request category of the SaaS service request;
the request category includes: the dangerous characteristic request analysis category and the dangerous hidden danger request analysis category;
based on the dangerous characteristic request analysis category, transmitting corresponding dangerous characteristic effective data in the counted effective data to a preset dangerous degree analysis model to obtain dangerous degree analysis data;
based on the risk potential request analysis category, transmitting corresponding risk potential effective data in the counted effective data to a preset risk potential prediction model to obtain risk potential analysis data;
and determining SaaS analysis data according to the risk degree analysis data and the hidden danger risk analysis data.
As an embodiment of the present technical solution, the deployment module includes:
an acquisition unit: for obtaining SaaS analysis data set { x } 1 ′,x 2 ′,…,x n ' data set { x } of SaaS analysis time corresponding to SaaS analysis data 1 ,x 2 ,…,x n Data set of SaaS acquisition time corresponding to SaaS analysis data
Analysis time delay unit: for analyzing data sets { x by means of said SaaS 1 ′,x 2 ′,…,x n ' data set { x } of SaaS analysis time corresponding to SaaS analysis data 1 ,x 2 ,…,x n Data set of SaaS acquisition time corresponding to SaaS analysis dataCalculating the SaaS analysis time delay ∈ ->
Wherein,analysis data x for the p-th SaaS in the SaaS analysis data set p ' corresponding SaaS analysis time x p And all acquisition times y p Is a SaaS analysis delay of->Analysis data x for the p-th SaaS in the SaaS analysis data set p ' corresponding SaaS analysis time x p And the q-th acquisition time->The SaaS analysis time delay, x p Analysis data x for the p-th SaaS in the SaaS analysis data set p ' corresponding SaaS analysis time, y p Analysis data x for the p-th SaaS in the SaaS analysis data set p ' all acquisition times corresponding, +.>Analysis data x for the p-th SaaS in the SaaS analysis data set p ' corresponding q-th acquisition time, wherein p is a variable, and p is more than or equal to 1 and less than or equal to n; />Minimum transmission speed for transmitting data for SaaS platform, < > for>Analysis time x for SaaS p At the time of SaaS analysis data x p ' transmission speed; />Analysis time x for SaaS p With acquisition time y p Data size, θ of transmission data at the time p Analysis of data x for SaaS p ' data size, τ is an initial time delay influence coefficient, and γ is initial time delay of data analyzed by the SaaS platform;
the deployment unit: for analyzing time delay by the SaaSAnd calculating deployment balance rho of the SaaS analysis fire control monitoring platform, and deploying the intelligent fire control monitoring platform based on the deployment balance rho.
As an embodiment of the present technical solution, the deployment unit includes:
an average delay subunit: for analyzing time delay according to the SaaSAnd a dirichlet function D, calculating an average time delay delta of the SaaS analysis fire control monitoring platform:
wherein,to analyze data x for the p-th SaaS in the data set for SaaS p ' corresponding SaaS analysis time x p And the q-th acquisition time->Dirichlet function of>Is->Corresponding standard acquisition time; epsilon is an average time delay influence coefficient;
deploying a balancing subunit: for analyzing time delay according to the SaaSAnd SaaS analyzes the average time delay delta of the fire control monitoring platform, and calculates deployment balance ρ:
wherein σ is the deployment balance influence coefficient;
deployment subunit: the intelligent fire control monitoring platform is used for carrying out balance adjustment on the platform running speed of the intelligent fire control monitoring platform through the deployment balance rho and deploying the intelligent fire control monitoring platform after the balance adjustment;
the platform running speed at least comprises a SaaS acquisition speed, a SaaS analysis data speed and a SaaS analysis time delay.
The beneficial effects of the invention are as follows:
compared with the prior art, the technical scheme improves the supervision efficiency of the fire-fighting platform through online service analysis, simultaneously saves the cost of establishing the platform by oneself based on the SaaS service platform, improves the core products, services and solutions for users, and creates sustainable value and potential growth.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a block diagram of a SaaS intelligent fire control monitoring platform based on the technology of the Internet of things in an embodiment of the invention;
fig. 2 is a block diagram of a monitoring module in a SaaS intelligent fire-fighting monitoring platform based on the internet of things in an embodiment of the invention;
fig. 3 is a block diagram of a SaaS analysis module in a SaaS intelligent fire-fighting monitoring platform based on the internet of things in an embodiment of the invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
It will be understood that when an element is referred to as being "mounted" or "disposed" on another element, it can be directly on the other element or be indirectly on the other element. When an element is referred to as being "connected to" another element, it can be directly or indirectly connected to the other element.
It is to be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like are merely for convenience in describing and simplifying the description based on the orientation or positional relationship shown in the drawings, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus are not to be construed as limiting the invention.
Furthermore, it should be noted that in this document relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Example 1:
according to fig. 1, an embodiment of the present invention provides a SaaS intelligent fire-fighting monitoring platform based on the internet of things technology, including:
and a monitoring module: based on a preset internet of things and a multi-sensor fusion technology, acquiring fire control monitoring information in a preset range;
the SaaS analysis module: based on a preset SaaS platform, carrying out online information category analysis and data conversion processing on the received fire control monitoring information to generate SaaS analysis data;
and (3) a deployment module: and receiving the SaaS analysis data, and deploying the intelligent fire control monitoring platform through the SaaS analysis data.
The working principle of the technical scheme is as follows:
the SaaS (Software-as-a-Service) intelligent fire control monitoring platform comprises a monitoring module, a SaaS analysis module and a deployment module, wherein the monitoring module collects fire control monitoring information by utilizing a multi-sensor device, screens and processes the fire control monitoring information, different monitoring nodes on an Internet of things network collect fire control detection information received by different monitoring services by utilizing a multi-sensor fusion technology through the monitoring module, the SaaS analysis module carries out online information category analysis and data conversion processing on the received fire control monitoring information based on a preset SaaS platform, the SaaS platform is a Software layout model which is specially designed for network delivery and is applied, a user can conveniently carry out online analysis and solution on Service demands of the user, the SaaS platform generates SaaS analysis data, and the deployment module receives the SaaS analysis data and deploys the intelligent fire control monitoring platform through the SaaS analysis data, so that the intelligent fire control monitoring platform is configured and optimized.
The beneficial effects of the technical scheme are as follows:
compared with the prior art, the technical scheme improves the supervision efficiency of the fire-fighting platform through online service analysis, simultaneously saves the cost of establishing the platform by oneself based on the SaaS service platform, improves the core products, services and solutions for users, and creates sustainable value and potential growth.
Example 2:
according to fig. 2, the present technical solution provides an embodiment, where the monitoring module includes:
fusion sensing monitoring unit: the method comprises the steps of acquiring sensing monitoring information in a preset range through a preset multi-sensor device, fusing the sensing monitoring information based on a preset multi-sensor fusion technology, and determining fused sensing monitoring information; wherein,
the multi-sensing device at least comprises a temperature sensor, a photoelectric sensor and a displacement sensor;
image monitoring information unit: the image monitoring device is used for capturing image monitoring information in a preset range through a preset camera device;
fire control monitoring information unit: the system comprises a preset control terminal, a preset sensing monitoring terminal, a fire control monitoring terminal and a fire control monitoring terminal, wherein the preset control terminal is used for acquiring the fusion sensing monitoring information and the image monitoring information of the fire control monitoring terminal;
A wireless transmission unit: and the fire control monitoring information is transmitted to the fire control online analysis module.
The working principle of the technical scheme is as follows:
the technical scheme includes that the intelligent fire control monitoring system comprises a fusion sensing monitoring unit, an image monitoring information unit, a fire control monitoring information unit and a wireless transmission unit, wherein the wireless transmission unit is used for wirelessly transmitting fire control monitoring information to a fire control analysis module through the Internet of things; the fire control monitoring information comprises sensing monitoring information and image monitoring information; the fire control monitoring information unit is used for acquiring sensing monitoring information and image monitoring information, the fusion sensing monitoring unit is used for acquiring sensing monitoring information in a preset range through a preset sensing device (a temperature sensor, a photoelectric sensor and a displacement sensor) and transmitting the sensing monitoring information to the wireless transmission unit, and the image monitoring information unit is used for acquiring image monitoring information in a preset area through a preset panoramic camera and other camera shooting equipment and transmitting the image monitoring information to the wireless transmission unit.
The beneficial effects of the technical scheme are as follows:
compared with the prior art, the technical scheme detects the fire-fighting conditions in the monitoring range, such as whether the fire-fighting conditions are needed or not, through mutual comparison of the camera device and the sensor device, and based on the corresponding fire-fighting conditions, data of the corresponding sensor device are acquired, and original data are provided for later fire-fighting condition analysis.
Example 3:
according to fig. 3, the present technical solution provides an embodiment, where the SaaS analysis module includes:
on-line analysis unit: the fire control monitoring system is used for analyzing and screening the received fire control monitoring information on line to obtain fire control screening information;
a data conversion unit: the fire control screening information is used for carrying out data conversion on the fire control screening information to generate fire control screening data;
a data statistics unit: and carding and counting fire screening data based on a preset time line, and transmitting the counted fire screening data to a preset SaaS platform to obtain SaaS analysis data.
The working principle of the technical scheme is as follows:
the SaaS analysis module comprises an online analysis unit, a data conversion unit and a data statistics unit, wherein the online analysis unit is used for analyzing and screening the received fire control monitoring information online to obtain fire control screening information, screening fire control monitoring information types of the received fire control monitoring information, identifying and analyzing the fire control monitoring information according to the fire control monitoring information types, sensing and identifying the fire control monitoring information into first fire control monitoring information, and determining the sensing and monitoring types of the sensing and monitoring information in the first fire control monitoring information by comparing and classifying the sensing and monitoring information, and grouping the sensing and monitoring information according to the sensing and monitoring types to generate a plurality of sensing data sets; the image monitoring information is identified and analyzed into second fire monitoring information, the fire monitoring information is at least composed of first fire monitoring information and second fire monitoring information, the conversion unit is used for carrying out data conversion on fire screening information to generate fire screening data, data in different data source data formats are converted into standardized data, the data statistics unit is used for combing and counting the fire screening data based on a preset time line, the counted fire screening data are transmitted to a preset SaaS platform to obtain SaaS analysis data, the service demands of users can be matched through the SaaS analysis data, and corresponding services are provided.
The beneficial effects of the technical scheme are as follows: .
Compared with the prior art, the technical scheme receives the fire-fighting data and the user data through the SaaS platform and analyzes the service required by the user data, so that the fire-fighting data can be timely obtained on line, and the timeliness and timeliness are achieved.
Example 4:
the present technical solution provides an embodiment, the online analysis unit includes:
information category subunit: matching the fire monitoring information with preset information categories, and determining the information category corresponding to the fire monitoring information; wherein,
the information category includes: fire monitoring sensing category, fire monitoring image category; the fire monitoring image categories include: image category, image category;
feature extraction subunit: extracting class characteristics of fire monitoring information based on information classes corresponding to the fire monitoring information;
the category characteristics include: dangerous characteristic features, dangerous hidden danger features and safety features;
screening subunits: screening the fire monitoring information based on the category characteristics, and determining screening results; wherein,
when the screening result is that the category characteristic of the anti-monitoring information is dangerous characteristic or dangerous hidden danger characteristic, acquiring fire control screening information through screening;
And when the screening result is that the category characteristic of the anti-monitoring information is a safety characteristic, storing the anti-monitoring information into a preset cloud storage.
The working principle of the technical scheme is as follows:
the on-line analysis unit comprises an information category subunit, a feature extraction subunit and a screening subunit, wherein the information category subunit matches fire monitoring information with preset information categories to determine the information category corresponding to the fire monitoring information; the information category comprises a fire monitoring sensing category and a fire monitoring image category, and the fire monitoring image category can divide the corresponding monitoring images and determine the monitoring areas corresponding to the monitoring images; the fire monitoring image categories comprise image categories and image categories; the feature extraction subunit extracts category features of the fire monitoring information based on information categories corresponding to the fire monitoring information, extracts corresponding features related to fire hazards and fire hazards, can record the number of the fire hazard features, and generates a marked marking sequence number, wherein the category features comprise dangerous feature, dangerous hazard feature and safety feature; the screening subunit screens the fire monitoring information based on the category characteristics and determines screening results; when the screening result is that the category characteristic of the anti-monitoring information is the dangerous characteristic or the dangerous hidden danger characteristic, acquiring fire control screening information through screening; when the screening result is that the category characteristics of the anti-monitoring information are safety characteristics, the anti-monitoring information is stored in a preset cloud storage, and the fire-fighting monitoring platform needs to evaluate and analyze the fire-fighting condition of the monitoring area, so that the technical scheme divides the fire-fighting monitoring platform into three types of dangerous characteristic characteristics, dangerous hidden danger characteristics and safety characteristics, provides different services for users aiming at different characteristics, and improves the user experience.
The beneficial effects of the technical scheme are as follows:
according to the technical scheme, the conditions of the monitoring area are divided, dangers, namely dangerous characteristic features, dangerous hidden danger features and dangerous hidden danger features are generated, the dangerous hidden danger and the dangerous safety features are divided into three grades, different model channels are used for each grade, and the problem solving efficiency is improved.
Example 5:
the present technical solution provides an embodiment, where the data conversion unit includes:
cleaning subunit: the fire control screening information processing method comprises the steps of performing cleaning analysis on fire control screening information and determining cleaning target information; wherein,
the cleaning target information includes: repeated information, missing information and abnormal information; wherein,
the missing information is information of content missing or format missing; the abnormal information is abnormal numerical value or abnormal format information;
cleaning mode subunit: the method comprises the steps of performing pattern matching according to cleaning target information and a preset cleaning database, obtaining a cleaning pattern corresponding to the cleaning target, performing cleaning treatment according to the cleaning pattern, and generating fire control cleaning information; wherein,
the cleaning mode at least comprises a redundancy removal mode and a duplication removal mode;
data transformation subunit: and the information format is used for identifying the fire control cleaning information, and based on the information format, the fire control cleaning information is converted and processed to generate fire control screening data.
The working principle of the technical scheme is as follows:
the data conversion unit comprises a cleaning subunit, a cleaning mode subunit and a data conversion subunit, wherein the cleaning subunit is used for cleaning and analyzing fire screening information and determining cleaning target information; the cleaning target information comprises repeated information, missing information and abnormal information; the missing information is information of content missing or format missing; the abnormal information is information of abnormal numerical value or abnormal format; the cleaning mode subunit is used for carrying out mode matching according to the cleaning target information and a preset cleaning database, acquiring a cleaning mode corresponding to the cleaning target, carrying out filtering judgment through a data error rate corresponding to the cleaning target information and an error rate allowed by the preset cleaning database, judging whether cleaning treatment is needed or not, and carrying out cleaning treatment according to the cleaning mode to generate fire-fighting cleaning information; the cleaning mode at least comprises a redundancy removing mode and a duplication removing mode, and is used for improving the quality of acquired data, the data conversion subunit is used for identifying the information format of fire control cleaning information, converting the fire control cleaning information based on the information format, generating fire control screening data, and standardizing and normalizing the fire control data.
The beneficial effects of the technical scheme are as follows:
according to the technical scheme, the acquired data is cleaned, converted, standardized and formatted, so that the normalization of the data is improved, the data quality is improved, and high-quality and high-efficiency source data is provided for subsequent data processing and reading.
Example 6:
the technical scheme provides an embodiment, the data conversion subunit is used for identifying the information format of fire control cleaning information, and based on the information format, carries out conversion processing on the fire control cleaning information to generate fire control screening data, and includes:
identifying an information format of fire control cleaning information;
performing conversion matching according to the information format and a preset format database, and determining a conversion type corresponding to the fire control cleaning information;
and carrying out conversion treatment on the cleaning information according to the conversion type to generate fire control screening data.
The working principle of the technical scheme is as follows:
the data conversion subunit is used for identifying the information format of the fire control cleaning information, converting the fire control cleaning information based on the information format to generate fire control screening data, classifying the format of the fire control cleaning information, screening fire control variables according to the format types, determining the conversion types corresponding to the fire control cleaning information, wherein the information format comprises information in different formats such as different file formats, continuous or discrete formats, ordered or unordered formats and the like, so that the cleaning information is converted according to the conversion types preset by the SaaS platform to generate the fire control screening data, and normalizing the acquired data.
The beneficial effects of the technical scheme are as follows:
according to the technical scheme, the fire control screening data are acquired, metadata is improved for standardization of the SaaS platform, different data are processed, and the accuracy of requirements provided by the SaaS platform for users is improved.
Example 7:
the present technical solution provides an embodiment, where the data statistics unit includes:
sequencing subunit: the fire control sorting data are used for carrying out time marking on the fire control screening data according to a time line corresponding to the fire control screening data, sorting the corresponding fire control screening data according to the mark size of the time marking, and generating fire control sorting data;
carding subunit: the method comprises the steps of sequentially carrying out effective data carding on fire control sequencing data according to a label sequence corresponding to the fire control sequencing data, and determining a carding result; wherein,
when the fire control sequencing data are effective data, acquiring the effective data;
when the fire sequencing data are invalid data, deleting the invalid data;
SaaS analysis data subunit: and the method is used for transmitting the counted effective data to a preset SaaS platform to obtain SaaS analysis data.
The working principle of the technical scheme is as follows:
the sorting subunit is used for sorting fire-fighting data according to a time line corresponding to the fire-fighting screening data, the fire-fighting data can be further mined out through the data line, so that the workload of workers is reduced, the fire-fighting screening data are subjected to time marking, the corresponding fire-fighting screening data are sorted according to the mark size of the time marking, fire-fighting sorting data are generated, the fire-fighting data are sorted according to the sequence, the time sequence logic of fire fighting can be clarified, the data are easier to comb, the carding subunit is used for sequentially carrying out effective data carding on the fire-fighting sorting data according to the mark sequence corresponding to the fire-fighting sorting data, the carding result is determined, the effective data are useful data for analyzing fire-fighting conditions, and when a large number of data features are extracted, similar images can be pruned, and the working efficiency of a terminal is improved. When the fire control sequencing data is effective data, the effective data is acquired; when the fire-fighting sequencing data are invalid data, deleting the invalid data, and transmitting the counted valid data to a preset SaaS platform by the SaaS analysis data subunit to obtain SaaS analysis data, and carrying out corresponding analysis on the fire-fighting condition by the analysis data so as to be convenient for analyzing and predicting the corresponding dangerous situation.
The beneficial effects of the technical scheme are as follows:
according to the technical scheme, the fire control conditions are analyzed, corresponding SaaS analysis data are generated, the corresponding fire control conditions are deduced and predicted according to the service demands of users, corresponding solutions are generated, the working efficiency of staff is improved, and the work load is reduced.
Example 8:
the technical scheme provides an embodiment, the SaaS analysis data subunit is configured to transmit the counted valid data to a preset SaaS platform, and obtain SaaS analysis data, including:
based on a preset SaaS platform, receiving a SaaS service request of a user side;
comparing the SaaS service request with a preset service database to determine the request category of the SaaS service request;
the request category includes: the dangerous characteristic request analysis category and the dangerous hidden danger request analysis category;
based on the dangerous characteristic request analysis category, transmitting corresponding dangerous characteristic effective data in the counted effective data to a preset dangerous degree analysis model to obtain dangerous degree analysis data;
based on the risk potential request analysis category, transmitting corresponding risk potential effective data in the counted effective data to a preset risk potential prediction model to obtain risk potential analysis data;
And determining SaaS analysis data according to the risk degree analysis data and the hidden danger risk analysis data.
The working principle of the technical scheme is as follows:
according to the technical scheme, the counted effective data are transmitted to a preset SaaS platform, the SaaS service request of a user side is received, the service request of the user can comprise monitoring of a corresponding monitoring platform, analysis of an acquisition scheme, suppression of hidden danger problem treatment or optimization treatment of an intelligent fire-fighting monitoring platform, the SaaS service request is compared with a preset service database, and the request type of the SaaS service request is determined; the request category includes: the dangerous characteristic request analysis category and the dangerous hidden danger request analysis category; aiming at different user service demands, classifying the user service demands so as to enter different solving channels, simultaneously solving problems by multiple processes, improving the working efficiency, requesting analysis types based on dangerous characteristics, transmitting corresponding dangerous characteristic effective data in the counted effective data to a preset dangerous degree analysis model, and acquiring dangerous degree analysis data; the risk degree analysis model is used for analyzing and dividing the degree of the danger, a solution corresponding to a user is quickly given, based on the risk hidden danger request analysis category, the corresponding risk hidden danger effective data in the counted effective data is transmitted to the preset hidden danger risk prediction model, the hidden danger risk analysis data is obtained, and the risk prediction model is used for processing hidden danger of a monitoring area, so that the safety of the monitoring area is improved.
The beneficial effects of the technical scheme are as follows: and processing the service demands through the divided different channels, and classifying the service demands of the users. Therefore, risk degree evaluation is carried out on different fire-fighting conditions, efficiency analysis speed on the different fire-fighting conditions is improved, and reaction time of time occurrence is shortened.
Example 9:
the technical scheme provides an embodiment, the deployment module comprises:
an acquisition unit: for obtaining SaaS analysis data set { x } 1 ′,x 2 ′,…,x n ' data set { x } of SaaS analysis time corresponding to SaaS analysis data 1 ,x 2 ,…,x n Data set of SaaS acquisition time corresponding to SaaS analysis data
Analysis time delay unit: for analyzing data sets { x by means of said SaaS 1 ′,x 2 ′,…,x n ' data set { x } of SaaS analysis time corresponding to SaaS analysis data 1 ,x 2 ,…,x n Data set of SaaS acquisition time corresponding to SaaS analysis dataCalculating the SaaS analysis time delay ∈ ->
Wherein,analysis data x for the p-th SaaS in the SaaS analysis data set p ' corresponding SaaS analysis time x p And all acquisition times y p Is a SaaS analysis delay of->Analysis data x for the p-th SaaS in the SaaS analysis data set p ' corresponding SaaS analysis time x p And the q-th acquisition time->The SaaS analysis time delay, x p Analysis data x for the p-th SaaS in the SaaS analysis data set p ' corresponding SaaS analysis time, y p Analysis data x for the p-th SaaS in the SaaS analysis data set p ' all acquisition times corresponding, +.>Analysis data x for the p-th SaaS in the SaaS analysis data set p ' corresponding qth acquisition time, wherein p is a variable, an1≤p≤n；/>Minimum transmission speed for transmitting data for SaaS platform, < > for>Analysis time x for SaaS p At the time of SaaS analysis data x p ' transmission speed; />Analysis time x for SaaS p With acquisition time y p Data size, θ of transmission data at the time p Analysis of data x for SaaS p ' data size, τ is an initial time delay influence coefficient, and γ is initial time delay of data analyzed by the SaaS platform;
the deployment unit: for analyzing time delay by the SaaSCalculating deployment balance rho of the SaaS analysis fire control monitoring platform, and deploying the intelligent fire control monitoring platform based on the deployment balance rho;
the working principle of the technical scheme is as follows: in the prior art, single management is usually carried out on calculation speed or transmission arrangement and transmission, and although the adjustment is simple and the change of the data processing speed is obvious, the optimal acquisition, transmission and analysis control of a platform are difficult to realize; in the technical scheme, the analysis time of the platform analysis data and the acquisition time corresponding to the analyzed data are used for carrying out the analysis time delay calculation of the platform data, the data acquisition time and the number of data acquisition devices can be regulated and controlled through the time delay calculation, the deployment balance is calculated through the analysis time delay, and the deployment operation is carried out on the platform according to the deployment balance;
The beneficial effects of the technical scheme are as follows: through time delay analysis, the analysis efficiency of platform deployment is greatly improved, and meanwhile, the influence factors of the platform efficiency are visually displayed, and through adjustment of deployment balance, the monitoring force and analysis force of the platform on the fire fighting data are greatly mastered.
Example 10:
the technical scheme provides an embodiment, the deployment unit comprises:
an average delay subunit: for analyzing time delay according to the SaaSAnd a dirichlet function D, calculating an average time delay delta of the SaaS analysis fire control monitoring platform:
wherein,to analyze data x for the p-th SaaS in the data set for SaaS p ' corresponding SaaS analysis time x p And the q-th acquisition time->Dirichlet function of>Is->Corresponding standard acquisition time; epsilon is an average time delay influence coefficient;
deploying a balancing subunit: for analyzing time delay according to the SaaSAnd SaaS analyzes the average time delay delta of the fire control monitoring platform, and calculates deployment balance ρ:
wherein σ is the deployment balance influence coefficient;
deployment subunit: the intelligent fire control monitoring platform is used for carrying out balance adjustment on the platform running speed of the intelligent fire control monitoring platform through the deployment balance rho and deploying the intelligent fire control monitoring platform after the balance adjustment;
The platform running speed at least comprises a SaaS acquisition speed, a SaaS analysis data speed and a SaaS analysis time delay;
the working principle of the technical scheme is as follows: in the calculation of the deployment unit, calculating average time delay through analyzing the time delay and a preset Dirichlet function, calculating deployment balance according to the average time delay, and comparing when the deployment balance is used for adjusting the platform running speed of the intelligent fire control monitoring platform;
the beneficial effects of the technical scheme are as follows: by calculating the average time delay, the pertinence of the deployment balance is improved, when the platform deployment is regulated, the platform deployment can be specifically regulated in a certain direction through the real-time deployment balance, the difficulty of the platform deployment is reduced, and the deployment efficiency is improved.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (6)
1. SaaS intelligent fire control monitor platform based on internet of things, include:
and a monitoring module: based on a preset internet of things and a multi-sensor fusion technology, acquiring fire control monitoring information in a preset range;
the SaaS analysis module: based on a preset SaaS platform, carrying out online information category analysis and data conversion processing on the received fire control monitoring information to generate SaaS analysis data;
And (3) a deployment module: receiving the SaaS analysis data, and deploying the intelligent fire control monitoring platform through the SaaS analysis data;
the SaaS analysis module comprises:
on-line analysis unit: the fire control monitoring system is used for analyzing and screening the received fire control monitoring information on line to obtain fire control screening information;
a data conversion unit: the fire control screening information is used for carrying out data conversion on the fire control screening information to generate fire control screening data;
a data statistics unit: carding and counting fire screening data based on a preset time line, and transmitting the counted fire screening data to a preset SaaS platform to obtain SaaS analysis data;
the data statistics unit includes:
sequencing subunit: the fire control sorting data are used for carrying out time marking on the fire control screening data according to a time line corresponding to the fire control screening data, sorting the corresponding fire control screening data according to the mark size of the time marking, and generating fire control sorting data;
carding subunit: the method comprises the steps of sequentially carrying out effective data carding on fire control sequencing data according to a label sequence corresponding to the fire control sequencing data, and determining a carding result; wherein,
when the fire control sequencing data are effective data, acquiring the effective data;
When the fire sequencing data are invalid data, deleting the invalid data;
SaaS analysis data subunit: the method comprises the steps of transmitting the counted effective data to a preset SaaS platform to obtain SaaS analysis data;
the SaaS analysis data subunit is configured to transmit the counted valid data to a preset SaaS platform, and obtain SaaS analysis data, where the step of obtaining the SaaS analysis data includes:
based on a preset SaaS platform, receiving a SaaS service request of a user side;
comparing the SaaS service request with a preset service database to determine the request category of the SaaS service request;
the request category includes: the dangerous characteristic request analysis category and the dangerous hidden danger request analysis category;
based on the dangerous characteristic request analysis category, transmitting corresponding dangerous characteristic effective data in the counted effective data to a preset dangerous degree analysis model to obtain dangerous degree analysis data;
based on the risk potential request analysis category, transmitting corresponding risk potential effective data in the counted effective data to a preset risk potential prediction model to obtain risk potential analysis data;
determining SaaS analysis data according to the risk degree analysis data and the hidden danger risk analysis data;
The deployment module comprises:
an acquisition unit: the method comprises the steps of acquiring SaaS analysis data, saaS analysis time corresponding to the SaaS analysis data and SaaS acquisition time corresponding to the SaaS analysis data;
analysis time delay unit: the method comprises the steps of calculating SaaS analysis time delay according to the SaaS analysis data, saaS analysis time corresponding to the SaaS analysis data and SaaS acquisition time corresponding to the SaaS analysis data;
the deployment unit: the intelligent fire control monitoring platform deployment method is used for calculating the deployment balance of the SaaS analysis fire control monitoring platform through the SaaS analysis time delay and deploying the intelligent fire control monitoring platform based on the deployment balance.
2. The SaaS intelligent fire-fighting monitoring platform based on the internet of things technology as set forth in claim 1, wherein the monitoring module comprises:
fusion sensing monitoring unit: the method comprises the steps of acquiring sensing monitoring information in a preset range through a preset multi-sensor device, fusing the sensing monitoring information based on a preset multi-sensor fusion technology, and determining fused sensing monitoring information; wherein,
the multi-sensing device at least comprises a temperature sensor, a photoelectric sensor and a displacement sensor;
image monitoring information unit: the image monitoring device is used for capturing image monitoring information in a preset range through a preset camera device;
Fire control monitoring information unit: the system comprises a preset control terminal, a preset sensing monitoring terminal, a fire control monitoring terminal and a fire control monitoring terminal, wherein the preset control terminal is used for acquiring the fusion sensing monitoring information and the image monitoring information of the fire control monitoring terminal;
a wireless transmission unit: and the fire control monitoring information is transmitted to the SaaS analysis module.
3. The SaaS intelligent fire-fighting monitoring platform based on the internet of things technology as set forth in claim 1, wherein the online analysis unit comprises:
information category subunit: matching the fire monitoring information with preset information categories, and determining the information category corresponding to the fire monitoring information; wherein,
the information category includes: fire monitoring sensing category, fire monitoring image category; the fire monitoring image categories include: image category, image category;
feature extraction subunit: extracting class characteristics of fire monitoring information based on information classes corresponding to the fire monitoring information;
the category characteristics include: dangerous characteristic features, dangerous hidden danger features and safety features;
screening subunits: screening the fire monitoring information based on the category characteristics, and determining screening results; wherein,
when the screening result is that the class characteristics of the fire control monitoring information are dangerous characteristic characteristics or dangerous hidden danger characteristics, acquiring fire control screening information through screening;
And when the screening result is that the class characteristics of the fire monitoring information are safety characteristics, storing the fire monitoring information into a preset cloud storage.
4. The SaaS intelligent fire-fighting monitoring platform based on the internet of things technology as set forth in claim 1, wherein the data conversion unit comprises:
cleaning subunit: the fire control screening information processing method comprises the steps of performing cleaning analysis on fire control screening information and determining cleaning target information; wherein,
the cleaning target information includes: repeated information, missing information and abnormal information; wherein,
the missing information is information of content missing or format missing; the abnormal information is abnormal numerical value or abnormal format information;
cleaning mode subunit: the method comprises the steps of performing pattern matching according to cleaning target information and a preset cleaning database, obtaining a cleaning pattern corresponding to the cleaning target, performing cleaning treatment according to the cleaning pattern, and generating fire control cleaning information; wherein,
the cleaning mode at least comprises a redundancy removal mode and a duplication removal mode;
data transformation subunit: and the information format is used for identifying the fire control cleaning information, and based on the information format, the fire control cleaning information is converted and processed to generate fire control screening data.
5. The SaaS intelligent fire control monitor platform based on internet of things as set forth in claim 4, wherein the data conversion subunit is configured to identify an information format of fire control cleaning information, and perform conversion processing on the fire control cleaning information based on the information format, to generate fire control screening data, and the fire control screening data comprises:
identifying an information format of fire control cleaning information;
performing conversion matching according to the information format and a preset format database, and determining a conversion type corresponding to the fire control cleaning information;
and carrying out conversion treatment on the cleaning information according to the conversion type to generate fire control screening data.
6. The SaaS intelligent fire-fighting monitoring platform based on the internet of things technology as set forth in claim 1, wherein the deployment unit comprises:
an average delay subunit: the method is used for calculating the average time delay of the SaaS analysis fire control monitoring platform according to the SaaS analysis time delay and a preset Dirichlet function:
deploying a balancing subunit: the method is used for calculating deployment balance according to the SaaS analysis time delay and the average time delay of the SaaS analysis fire control monitoring platform:
deployment subunit: the intelligent fire control monitoring platform is used for carrying out balance adjustment on the platform running speed of the intelligent fire control monitoring platform through the deployment balance and deploying the intelligent fire control monitoring platform after the balance adjustment;
The platform running speed at least comprises a SaaS acquisition speed, a SaaS analysis data speed and a SaaS analysis time delay.
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