CN116166499A - Data monitoring method and device, electronic equipment and nonvolatile storage medium - Google Patents

Abstract

The application discloses a data monitoring method, a data monitoring device, electronic equipment and a nonvolatile storage medium. Wherein the method comprises the following steps: determining the equipment type of the fire-fighting equipment corresponding to the original data and an analysis rule corresponding to the equipment type, wherein the original data is real-time data uploaded by the fire-fighting equipment; analyzing the original data according to the analysis rule to obtain target data, wherein the target data is used for representing the running state of the fire-fighting equipment; carrying out statistical analysis on the target data according to different time dimensions to generate a target report, and predicting whether the fire fighting equipment has fault hidden danger according to the target report; and sending alarm information to a mobile terminal corresponding to the fire-fighting equipment under the condition that the fire-fighting equipment is predicted to have fault hidden danger or abnormal data in the target data is detected. The method and the device solve the technical problems that fire-fighting hidden danger and police condition discovery efficiency are low because mass data uploaded by intelligent fire-fighting equipment cannot be reasonably stored and efficiently analyzed at present.

Description

Data monitoring method and device, electronic equipment and nonvolatile storage medium

Technical Field

The present disclosure relates to the field of data monitoring technologies, and in particular, to a data monitoring method, a data monitoring device, an electronic device, and a nonvolatile storage medium.

Background

Along with the continuous deep of intelligent fire control concepts, intelligent fire control equipment installed in each key unit and place is more and more, but large-scale complex data storage and calculation are carried out on massive data such as operation, alarm, faults and the like uploaded by a plurality of intelligent networking equipment, so that real-time monitoring is carried out on dynamic information of the fire control equipment, and it is very critical to carry out police situation research and judgment and fire accident investigation on each fire department and carry out daily operation and maintenance on enterprise fire safety and timely response when a fire occurs. However, mass data uploaded by intelligent fire-fighting equipment cannot be reasonably stored and efficiently analyzed at present, so that problems of fire-fighting hidden danger, low alarm condition discovery efficiency and the like are caused.

In view of the above problems, no effective solution has been proposed at present.

Disclosure of Invention

The embodiment of the application provides a data monitoring method, a device, electronic equipment and a nonvolatile storage medium, which are used for at least solving the technical problems of fire-fighting hidden danger and low alarm condition discovery efficiency caused by the fact that mass data uploaded by intelligent fire-fighting equipment cannot be reasonably stored and efficiently analyzed at present.

According to an aspect of the embodiments of the present application, there is provided a data monitoring method, including: determining the equipment type of the fire-fighting equipment corresponding to the original data and an analysis rule corresponding to the equipment type, wherein the original data is real-time data uploaded by the fire-fighting equipment; analyzing the original data according to the analysis rule to obtain target data, wherein the target data is used for representing the running state of the fire-fighting equipment; carrying out statistical analysis on the target data according to different time dimensions to generate a target report, and predicting whether the fire fighting equipment has fault hidden danger according to the target report; and sending alarm information to a mobile terminal corresponding to the fire-fighting equipment under the condition that the fire-fighting equipment is predicted to have fault hidden danger or abnormal data in the target data is detected.

Optionally, the original data includes a plurality of data units; analyzing the original data according to the analysis rule, wherein the obtaining of the target data comprises the following steps: determining a data type identifier of the data unit; determining a data structure template corresponding to the data type identifier according to the analysis rule; and analyzing the data unit according to the data structure template to obtain target data.

Optionally, the target data includes: the system comprises state information and detail data, wherein the state data is state identification data which is uploaded by fire-fighting equipment and used for representing the running state of the equipment, and the state data comprises at least one of the following components: the normal operation information, the abnormal alarm information and the abnormal fault information are analog quantity data which are uploaded by the fire-fighting equipment and used for representing the operation state of the fire-fighting equipment, and the detail data corresponding to the fire-fighting equipment of different equipment types are different; according to the analysis rule, analyzing the original data to obtain target data, and then further comprising: judging that abnormal data exists in the target data under the condition that abnormal alarm information or abnormal fault information exists in the state information; and under the condition that the detail data does not meet the corresponding preset threshold condition, judging that abnormal data exists in the target data.

Optionally, the target data further includes: identification information of the fire fighting equipment and installation position information of the fire fighting equipment; according to the analysis rule, analyzing the original data to obtain target data, and then further comprising: generating virtual equipment corresponding to the fire-fighting equipment in an initial map of a target area according to the identification information and the installation position information of the fire-fighting equipment, and sending the virtual equipment to a front-end interface for displaying, wherein the target area is an area monitored by the fire-fighting equipment; and adding the state information of the fire-fighting equipment to the virtual equipment for display.

Optionally, performing statistical analysis on the target data according to different time dimensions, generating a target report, and predicting whether the fire fighting equipment has a fault hidden trouble according to the target report includes: and counting target data of the fire fighting equipment in different time dimensions to obtain a target report, wherein the time dimensions comprise at least one of the following: day dimension, month dimension, quarter dimension, and year dimension; determining a target association relation of the fire-fighting equipment according to the target report, wherein the target association relation is used for representing the change trend of state parameters of the fire-fighting equipment along with time, and the state parameters comprise at least one of the following: the equipment alarm rate, the equipment fault rate and the equipment false alarm rate; and predicting whether the fire-fighting equipment has hidden trouble according to the target association relation.

Optionally, before determining the device type corresponding to the original data, further includes: the method comprises the steps that original data sent through a target communication protocol is collected through an edge computing gateway, wherein the target communication protocol is a protocol used for data transmission of fire-fighting equipment; and converting the original data into a target format to obtain the original data in the target format, wherein the target format is a preset data format with uniform format.

Optionally, sending the alarm information to the mobile terminal corresponding to the fire protection device includes: determining an alarm level of the alarm information and a pushing rule corresponding to the alarm level; determining a corresponding mobile equipment identification sequence according to the pushing rule and the identification information of the fire fighting equipment corresponding to the alarm information, wherein each mobile equipment identification in the mobile equipment identification sequence is ordered from high to low according to the pushing priority; and sending the alarm information to the corresponding mobile equipment according to the sequence of the mobile equipment identification sequences.

According to another aspect of the embodiments of the present application, there is also provided a data monitoring apparatus, including: the rule determining module is used for determining the equipment type of the fire-fighting equipment corresponding to the original data and the analysis rule corresponding to the equipment type, wherein the original data is real-time data uploaded by the fire-fighting equipment; the data analysis module is used for analyzing the original data according to analysis rules to obtain target data, wherein the target data is used for representing the running state of the fire-fighting equipment; the report generation module is used for carrying out statistical analysis on the target data according to different time dimensions, generating a target report, and predicting whether the fire fighting equipment has hidden trouble or not according to the target report; the alarm prompting module is used for sending alarm information to the mobile terminal corresponding to the fire-fighting equipment under the condition that the fire-fighting equipment is predicted to have fault hidden danger or abnormal data in the target data is detected.

According to still another aspect of the embodiments of the present application, there is further provided an electronic device, including a processor, where the processor is configured to execute a program, and the program executes a data monitoring method.

According to still another aspect of the embodiments of the present application, there is further provided a nonvolatile storage medium, where the nonvolatile storage medium includes a stored computer program, and a device where the nonvolatile storage medium is located executes the data monitoring method by running the computer program.

In the embodiment of the application, the equipment type of the fire-fighting equipment corresponding to the original data and the analysis rule corresponding to the equipment type are determined, wherein the original data are real-time data uploaded by the fire-fighting equipment; analyzing the original data according to the analysis rule to obtain target data, wherein the target data is used for representing the running state of the fire-fighting equipment; carrying out statistical analysis on the target data according to different time dimensions to generate a target report, and predicting whether the fire fighting equipment has fault hidden danger according to the target report; under the condition that the fire-fighting equipment is predicted to have fault hidden trouble or abnormal data in target data is detected, alarm information is sent to a mobile terminal corresponding to the fire-fighting equipment, and millions of throughput is achieved by building an edge calculation acquisition gateway cluster, a Kafka message middleware and a Flink real-time calculation cluster. The method and the system realize real-time and efficient analysis processing of mass data reported by various Internet of things devices in the fire-fighting industry, and timely provide analyzed abnormal information such as alarms, faults and the like for a top-level business system. Meanwhile, by adopting Hive off-line calculation to analyze all the equipment which is stored in the HBase after being calculated and cleaned in real time to report information, the data accuracy is improved, early warning and research and judgment are carried out on hidden danger of the fire protection equipment, and further the technical problems that the existing mass data uploaded by intelligent fire protection equipment cannot be reasonably stored and efficiently analyzed, and the hidden danger and the alarm condition are found out with low efficiency are solved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:

FIG. 1 is a block diagram of the hardware architecture of a computer terminal (or electronic device) for implementing a method of data monitoring according to an embodiment of the present application;

FIG. 2 is a schematic diagram of a method flow of data monitoring provided according to an embodiment of the present application;

fig. 3 is a schematic diagram of a method flow of real-time monitoring of a fire protection internet of things according to an embodiment of the present application;

fig. 4 is a schematic diagram of a fire protection internet of things real-time monitoring system architecture according to an embodiment of the present application;

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

Detailed Description

In order to make the present application solution better understood by those skilled in the art, the following description will be made in detail and with reference to the accompanying drawings in the embodiments of the present application, it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.

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

In the related art, the mass data uploaded by intelligent fire-fighting equipment cannot be effectively analyzed at present, so that the problems of fire-fighting hidden danger and low alarm condition discovery efficiency exist. In order to solve this problem, related solutions are provided in the embodiments of the present application, and are described in detail below.

In accordance with the embodiments of the present application, a method embodiment of data monitoring is provided, it being noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system, such as a set of computer executable instructions, and, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order other than that illustrated herein.

The method embodiments provided by the embodiments of the present application may be performed in a mobile terminal, a computer terminal, or similar computing device. Fig. 1 shows a block diagram of a hardware architecture of a computer terminal (or electronic device) for implementing a data monitoring method. As shown in fig. 1, the computer terminal 10 (or electronic device 10) may include one or more processors 102 (shown as 102a, 102b, … …,102 n) which may include, but are not limited to, a microprocessor MCU or a processing device such as a programmable logic device FPGA, a memory 104 for storing data, and a transmission module 106 for communication functions. In addition, the method may further include: a display, an input/output interface (I/O interface), a Universal Serial BUS (USB) port (which may be included as one of the ports of the BUS), a network interface, a power supply, and/or a camera. It will be appreciated by those of ordinary skill in the art that the configuration shown in fig. 1 is merely illustrative and is not intended to limit the configuration of the electronic device described above. For example, the computer terminal 10 may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.

It should be noted that the one or more processors 102 and/or other data processing circuits described above may be referred to generally herein as "data processing circuits. The data processing circuit may be embodied in whole or in part in software, hardware, firmware, or any other combination. Furthermore, the data processing circuitry may be a single stand-alone processing module, or incorporated, in whole or in part, into any of the other elements in the computer terminal 10 (or electronic device). As referred to in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of the path of the variable resistor termination to interface).

The memory 104 may be used to store software programs and modules of application software, such as program instructions/data storage devices corresponding to the data monitoring method in the embodiments of the present application, and the processor 102 executes the software programs and modules stored in the memory 104, thereby executing various functional applications and data processing, that is, implementing the data monitoring method described above. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The transmission means 106 is arranged to receive or transmit data via a network. The specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module for communicating with the internet wirelessly.

The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the computer terminal 10 (or electronic device).

In the above operating environment, the embodiment of the present application provides a data monitoring method, and fig. 2 is a schematic diagram of a data monitoring method flow provided according to the embodiment of the present application, as shown in fig. 2, where the method includes the following steps:

step S202, determining the equipment type of the fire-fighting equipment corresponding to the original data and an analysis rule corresponding to the equipment type, wherein the original data is real-time data uploaded by the fire-fighting equipment;

In some embodiments of the present application, before determining the device type corresponding to the original data, the method further includes the following steps: the method comprises the steps that original data sent through a target communication protocol is collected through an edge computing gateway, wherein the target communication protocol is a protocol used for data transmission of fire-fighting equipment; and converting the original data into a target format to obtain the original data in the target format, wherein the target format is a preset data format with uniform format.

Specifically, the intelligent smoke sensing, intelligent electricity consumption, intelligent fire hydrant and other fire-fighting internet of things equipment upload equipment original operation data into a platform in a communication mode such as a base station and the like, an edge computing gateway is built by adopting Netty and other technologies, original data uploaded by intelligent equipment which collects TCP, UDP, MQTT, HTTP and other communication protocols (namely the target communication protocols) are received in real time, preliminary analysis is carried out to obtain a custom JSON data format, confirmation information is replied to the equipment, and then the custom JSON format data (namely the target format) is put into a Kafka message middleware cluster.

Step S204, analyzing the original data according to the analysis rule to obtain target data, wherein the target data is used for representing the running state of the fire-fighting equipment;

In some embodiments of the present application, the original data includes a plurality of data units; according to the analysis rule, analyzing the original data to obtain target data, wherein the method comprises the following steps: determining a data type identifier of the data unit; determining a data structure template corresponding to the data type identifier according to the analysis rule; and analyzing the data unit according to the data structure template to obtain target data.

In some embodiments of the present application, the target data includes: the system comprises state information and detail data, wherein the state data is state identification data which is uploaded by fire-fighting equipment and used for representing the running state of the equipment, and the state data comprises at least one of the following components: the normal operation information, the abnormal alarm information and the abnormal fault information are analog quantity data which are uploaded by the fire-fighting equipment and used for representing the operation state of the fire-fighting equipment, and the detail data corresponding to the fire-fighting equipment of different equipment types are different; according to the analysis rule, analyzing the original data to obtain target data, and then further comprising the following steps: judging that abnormal data exists in the target data under the condition that abnormal alarm information or abnormal fault information exists in the state information; and under the condition that the detail data does not meet the corresponding preset threshold condition, judging that abnormal data exists in the target data.

It should be noted that, the content of the data included in the detail data of each type of device is different, for example, the smoke sensing device needs to include smoke concentration; the intelligent electric equipment needs to contain voltage values, current values and the like; the liquid level device needs to contain water level information and the like.

Specifically, the data is split into three types of original data, detail data and analysis data. Based on the built Flink real-time computing cluster, real-time subscribing the mass data uploaded by the equipment in the Kafka cluster, and writing the mass data into a data stream processing platform and an original data table of Hbase; meanwhile, the original data stream is subjected to analysis processing by combining with a fire-fighting national standard protocol, the data stream is subjected to preliminary cleaning, useless heartbeat data packets are removed, the data stream information of fire-fighting Internet of things equipment such as smoke feeling, gas feeling, user information transmission devices, intelligent fire hydrants and the like is split into independent real-time calculation subtasks of each type of equipment, the data stream is further analyzed to be three types of state information such as normal operation information, abnormal alarm information and abnormal fault information through the real-time calculation subtasks of each type of equipment to serve as analysis data, and the analysis data are written into an analysis data table of HBase; and meanwhile, taking out analog value information such as current, voltage, temperature, liquid level, hydraulic pressure, smoke concentration, combustible gas concentration and the like which are monitored and reported by the equipment and writing the analog value information into an HBase database and a MYSQL database as detailed data for offline calculation tasks and top-level business systems.

As an alternative implementation manner, redis can be adopted to record the latest reporting data time of each device, and the offline and online of the devices can be judged according to the expiration failure principle of the Redis data.

In some embodiments of the present application, the target data further includes: identification information of the fire fighting equipment and installation position information of the fire fighting equipment; according to the analysis rule, analyzing the original data to obtain target data, and then further comprising the following steps: generating virtual equipment corresponding to the fire-fighting equipment in an initial map of a target area according to the identification information and the installation position information of the fire-fighting equipment, and sending the virtual equipment to a front-end interface for displaying, wherein the target area is an area monitored by the fire-fighting equipment; and adding the state information of the fire-fighting equipment to the virtual equipment for display.

Specifically, static information of the equipment is input through a visual web interface (i.e. the front-end interface), equipment models, types, installation positions and unique equipment identifiers which are analyzed in real-time calculation are combined, an equipment shadow (i.e. the virtual equipment) is built, and historical alarm numbers, fault numbers, historical monitoring information, running information numbers, last online time, total uploading data size and the like of each piece of equipment are stored in a MYSQL relational database and an elastic search.

Step S206, carrying out statistical analysis on the target data according to different time dimensions, generating a target report, and predicting whether the fire-fighting equipment has fault hidden danger according to the target report;

in some embodiments of the present application, performing statistical analysis on target data according to different time dimensions, generating a target report, and predicting whether a fire fighting device has a fault hidden danger according to the target report includes the following steps: and counting target data of the fire fighting equipment in different time dimensions to obtain a target report, wherein the time dimensions comprise at least one of the following: day dimension, month dimension, quarter dimension, and year dimension; determining a target association relation of the fire-fighting equipment according to the target report, wherein the target association relation is used for representing the change trend of state parameters of the fire-fighting equipment along with time, and the state parameters comprise at least one of the following: the equipment alarm rate, the equipment fault rate and the equipment false alarm rate; and predicting whether the fire-fighting equipment has hidden trouble according to the target association relation.

Specifically, the device report information stored in the HBase and the device state information stored in the MySQL database in the third step are combined for statistical analysis, reports with various dimensions (namely, the target report) are generated, the health degree of the device is judged according to the monitoring data reported by the device such as the values of signal intensity, signal to noise ratio, signal receiving power, red light pollution index and the like, and meanwhile, the potential fire hidden danger is prejudged by combining the device alarm rate, the fault rate and the false alarm rate.

Step S208, when the fire-fighting equipment is predicted to have a fault hidden trouble or abnormal data in the target data is detected, alarm information is sent to a mobile terminal corresponding to the fire-fighting equipment.

In some embodiments of the present application, sending alert information to a mobile terminal corresponding to a fire protection device includes the steps of: determining an alarm level of the alarm information and a pushing rule corresponding to the alarm level; determining a corresponding mobile equipment identification sequence according to the pushing rule and the identification information of the fire fighting equipment corresponding to the alarm information, wherein each mobile equipment identification in the mobile equipment identification sequence is ordered from high to low according to the pushing priority; and sending the alarm information to the corresponding mobile equipment according to the sequence of the mobile equipment identification sequences.

Specifically, the pushing rules of each type of responsible person of each area or each building of each unit are configured according to alarm types and fault types, after abnormal information such as alarms and faults uploaded by equipment is judged in real time calculation, the equipment is reported to related responsible persons by adopting a hierarchical early warning mechanism in a nearby pushing mode such as app, short messages and telephones, and installation position information and the like in equipment information management are combined to push the related responsible persons to help the related responsible persons to quickly locate the problem, and respond to the problem.

The data monitoring method in steps S202 to S208 in the embodiment of the present application is further described below.

Fig. 4 is a schematic diagram of a fire-fighting internet of things real-time monitoring system architecture provided according to an embodiment of the present application, and as shown in fig. 4, the system is composed of five modules, namely, a device information acquisition gateway module, a data real-time computing and processing analysis module, a device information management module, a big data offline computing module and a message classification pushing module. The method comprises the steps of constructing an equipment edge computing gateway layer based on Netty, collecting original data uploaded by intelligent equipment of protocols such as TCP, UDP, MQTT, HTTP in real time, putting the original data into a Kafka message middleware, carrying out real-time computing subscription consumption Kafka by using Flink, and analyzing and cleaning the original data of equipment in the Kafka. And filtering out state data such as alarm, fault and the like. And further analyzing and processing the abnormal data to obtain a result, and providing the result to a top-level business system, particularly informing a responsible person of the equipment to process the abnormality. And storing the original data and analysis result data uploaded by all the devices into big data HBase for Hive offline calculation to generate statistical reports of time dimensions of day, week, month, season and year, smoke sensation, gas sensation, intelligent electricity consumption and other various device type dimensions.

Fig. 3 is a schematic diagram of a method flow of real-time monitoring of a fire-fighting internet of things according to an embodiment of the present application, where the method in fig. 3 may be applied to the system architecture in fig. 4, as shown in fig. 3, and the method includes the following steps:

step S302, collecting equipment information;

specifically, the information acquisition gateway is built according to each type of communication protocol, specifically, the gateway can be built according to the transverse expansion server of the receiving equipment quantity, and the gateway of each type of communication mode equipment is independently built so as not to interfere with each other. The Nginx or cloud manufacturers, such as the space wing cloud, are adopted for load balancing, and the data throughput of each type of acquisition gateway is increased. And when the intelligent equipment alarms, fails or normally reports heartbeat operation information. The platform gateway intercepts data packets transmitted to the acquisition gateway by the equipment according to the data format specified by the related protocol and the decimal 6464 packet header 3535 packet tail, wherein the data packets are transmitted to the platform by the base station or the Ethernet through the preset platform receiving information IP address and port in the equipment. And finally resolving the character string into a Hex hexadecimal character string. For example:

4040FA53010207380E0C08162100000000002500010001003000020201010167207EF60504001B4A011B391B401B31051B361B3932322F30382F31322031353A31303A313407380E0C0816192323。

wherein 4040 is the hexadecimal of 6464 and 2323 is the hexadecimal of 3535. The data is taken as original data, added with the time of receiving the data, is combined into JSON format data, and is put into original data topic of Kafka message middleware, if equipment needs to be responded, equipment data is received while long connection of the equipment is kept, and the equipment is immediately replied according to the original data to confirm the reception.

Step S304, data real-time calculation processing analysis;

specifically, one or more real-time computing tasks are used to subscribe to original data topic in the Kafka message middleware at the same time, and the original data and the data receiving time are resolved. The raw data is further parsed according to the relevant protocol. The source address of 13-18 bytes and the destination address of 19-24 bytes of the original data are combined into a unique identification (equipment number) of the equipment. Further acquiring the lengths of the application data units of 25 and 26 bytes, and intercepting the application data units in the original data according to the lengths. Then the command byte of the 27 th byte is acquired,

for example, according to the relevant protocol (i.e. the above analysis rule), the analysis is: 1 control command, 2 send data, 3 acknowledge, 4 request, 5 reply, 6 deny, etc. And combining the equipment number, the application data unit and the command type into Json data, and shunting each command byte into different data streams for further application data processing.

Each different subtask receives the data stream and analyzes the application data in the data stream into service data in detail. If the data unit identifier is '1', the data is the state of the system of the building fire-fighting facility uploaded by the equipment, and the states of normal operation, fire alarm, fault and the like are carried out according to the corresponding data structure. And taking out the equipment number, the state type, the state occurrence time, the data receiving time, the alarm type, the system to which the alarm belongs, the equipment type and the state code to be combined into json service data, and shunting to the next service data processing task. If the data unit identifier is '2', the data is the operation state of the device uploading fire-fighting equipment component, and the related information of the device is also taken out, and the component address and the component type are added to be combined into json service data. If the data unit identifier is '3', the data is the analog value of the building fire-fighting equipment component uploaded by the equipment, and specific monitoring values such as current, voltage, gas concentration, temperature, flow, water pressure, water level and the like uploaded by the equipment are analyzed according to the data structure. And analyzing alarm, heartbeat, operation, fault and analog quantity information by taking the data stream as source data, and storing the information into HBase and MySQL databases.

As an alternative implementation manner, the time interval of uploading data by each device in nearly five times can be recorded, the average value is taken as the heartbeat time of normal operation of each device and recorded in Redis, and the heartbeat time is set as key expiration time of the Redis. And updating the expiration time of the key corresponding to the device in Redis each time the device uploads data is received. If the corresponding key of the device in Redis is expired, the device does not interact with the platform when the normal heartbeat time is exceeded, and the device is judged to be offline by using independent Flink computing task monitoring.

Step S306, managing equipment information;

specifically, static information such as equipment numbers, equipment names, alarm thresholds, equipment installation addresses, buildings, geographical longitude and latitude of equipment installation addresses, units to which equipment belongs, contacts of units to which the equipment belongs, contact phones of units to which the equipment belongs, monitoring centers to which the equipment belongs, operators on duty of monitoring centers to which the equipment belongs, contact phones of operators on duty of monitoring centers to which the equipment belongs, maintenance companies to which the equipment belongs, maintenance company to which the equipment belongs, contact phones of maintenance company to which the equipment belongs, fire departments to which the equipment belongs, contact phones of fire departments to which the equipment belongs and the like is recorded in a MYSQL relational database and an elastic search. And reporting original data and service data by combining equipment stored in Hbase, establishing equipment shadow, and displaying dynamic and static information of the equipment through a visual page according to the historical alarm number, the fault number, the historical monitoring information, the running information number, the last online time and the total uploading data size of each equipment.

Step S308, big data off-line calculation;

specifically, select, where, etc. of Hive SQL are used to query daily device alarm information, fault information, operation information within HBase. Converting Hive SQL into a MapReduce program, and generating a statistical report of the daily dimension, zhou Weidu, the monthly dimension, the quarternary dimension and the annual dimension of each unit, each building and each type of equipment in an accumulated mode according to the occurrence time of alarm, fault and running information and equipment type fields. The calculation result is imported into a MySQL database for use by the business system. Meanwhile, the health degree of each device is judged according to the analog quantity information which is reported by each device and exists in the HBase. If the reported battery power or voltage is too low and is lower than thirty percent of the normal power, the health degree of the equipment is judged to be abnormal. And adopting wireless communication equipment, wherein the reported signal strength is lower than a threshold value, and judging the health degree of the equipment as abnormal. The alarm threshold value of each type of equipment or each equipment can be set according to actual requirements.

Step S310, message classification pushing.

Specifically, the application system provides a visual web interface, and the pushing rules are configured according to the alarm level, the fault level, the pushing sequence, the step-by-step pushing time (taking seconds as a unit), the pushing department (networking unit, operation center, fire department, maintenance company), the pushing job (networking unit attendant, fire safety responsible person, operation center attendant, maintenance company maintenance engineer, fire department fire attendant), whether to push a short message, whether to push a phone, whether to push an App, whether to push a web page, and the supervision class (general unit, key unit, small place, home user) of the unit where the equipment is located. And (3) compiling alarm and fault abnormal information which are analyzed in the timing task and put into the Kafka message queue in real time by using java. According to the information of the unit responsible person contact telephone, the operation mechanism contact telephone, the fire-fighting mechanism, the maintenance company and the like of the equipment belonging unit maintained in the equipment management module, the unique identification (equipment number) of each piece of abnormal information is related to the three-party short message voice cloud platform, so that the short message, the telephone, the app and the web page are pushed to the person in a grading manner according to the configured pushing rule, and the pushing content is as follows: the unit name of alarm and fault equipment, the building, the floor, the equipment name, the equipment installation address, the alarm time and the alarm category (fire alarm and fault) are taken. Real-time hierarchical pushing is accomplished based on the high availability and high performance of Redis. Each untreated warning condition and fault is uniformly placed in the Redis, the level of the untreated warning condition/fault pushed is recorded, the warning condition/fault which is not treated in the Redis is checked per second Zhong Shishi, if the warning condition/fault is pushed to the last level, the warning condition/fault is deleted in the Redis, and the push is proved to be finished.

Through the steps, millions of throughput is achieved by building an edge computing acquisition gateway cluster, a Kafka message middleware and a Flink real-time computing cluster. The method and the system realize real-time and efficient analysis processing of mass data reported by various Internet of things devices in the fire-fighting industry, and timely provide analyzed abnormal information such as alarms, faults and the like for a top-level business system. Meanwhile, by adopting Hive off-line calculation to analyze all the equipment which is stored in the HBase after being calculated and cleaned in real time to report information, the data accuracy is improved, early warning and research and judgment are carried out on hidden danger of the fire protection equipment, and further the technical problems that the existing mass data uploaded by intelligent fire protection equipment cannot be reasonably stored and efficiently analyzed, and the hidden danger and the alarm condition are found out with low efficiency are solved.

According to an embodiment of the present application, an embodiment of a data monitoring device is also provided. Fig. 5 is a schematic structural diagram of a data monitoring device according to an embodiment of the present application. As shown in fig. 5, the apparatus includes:

the rule determining module 50 is configured to determine an equipment type of the fire fighting equipment corresponding to the original data, and an parsing rule corresponding to the equipment type, where the original data is real-time data uploaded by the fire fighting equipment;

In some embodiments of the present application, before the rule determining module 50 determines the device type corresponding to the original data, the method further includes: the method comprises the steps that original data sent through a target communication protocol is collected through an edge computing gateway, wherein the target communication protocol is a protocol used for data transmission of fire-fighting equipment; and converting the original data into a target format to obtain the original data in the target format, wherein the target format is a preset data format with uniform format.

The data analysis module 52 is configured to analyze the original data according to an analysis rule to obtain target data, where the target data is used to characterize an operation state of the fire fighting equipment;

in some embodiments of the present application, the original data includes a plurality of data units; the data parsing module 52 parses the original data according to parsing rules, and the obtaining the target data includes: determining a data type identifier of the data unit; determining a data structure template corresponding to the data type identifier according to the analysis rule; and analyzing the data unit according to the data structure template to obtain target data.

In some embodiments of the present application, the target data includes: the system comprises state information and detail data, wherein the state data is state identification data which is uploaded by fire-fighting equipment and used for representing the running state of the equipment, and the state data comprises at least one of the following components: the normal operation information, the abnormal alarm information and the abnormal fault information are analog quantity data which are uploaded by the fire-fighting equipment and used for representing the operation state of the fire-fighting equipment, and the detail data corresponding to the fire-fighting equipment of different equipment types are different; the data parsing module 52 parses the original data according to parsing rules, and further includes: judging that abnormal data exists in the target data under the condition that abnormal alarm information or abnormal fault information exists in the state information; and under the condition that the detail data does not meet the corresponding preset threshold condition, judging that abnormal data exists in the target data.

In some embodiments of the present application, the target data further includes: identification information of the fire fighting equipment and installation position information of the fire fighting equipment; the data parsing module 52 parses the original data according to parsing rules, and further includes: generating virtual equipment corresponding to the fire-fighting equipment in an initial map of a target area according to the identification information and the installation position information of the fire-fighting equipment, and sending the virtual equipment to a front-end interface for displaying, wherein the target area is an area monitored by the fire-fighting equipment; and adding the state information of the fire-fighting equipment to the virtual equipment for display.

The report generation module 54 is configured to perform statistical analysis on the target data according to different time dimensions, generate a target report, and predict whether the fire fighting equipment has a fault hidden danger according to the target report;

in some embodiments of the present application, the report generation module 54 performs statistical analysis on the target data according to different time dimensions, and generating a target report and predicting whether the fire fighting equipment has a fault hidden danger according to the target report includes: and counting target data of the fire fighting equipment in different time dimensions to obtain a target report, wherein the time dimensions comprise at least one of the following: day dimension, month dimension, quarter dimension, and year dimension; determining a target association relation of the fire-fighting equipment according to the target report, wherein the target association relation is used for representing the change trend of state parameters of the fire-fighting equipment along with time, and the state parameters comprise at least one of the following: the equipment alarm rate, the equipment fault rate and the equipment false alarm rate; and predicting whether the fire-fighting equipment has hidden trouble according to the target association relation.

The alarm prompting module 56 is configured to send alarm information to a mobile terminal corresponding to the fire-fighting equipment when it is predicted that the fire-fighting equipment has a fault hidden trouble or abnormal data is detected to be present in the target data.

In some embodiments of the present application, the alert prompt module 56 transmitting alert information to a mobile terminal corresponding to a fire protection device includes: determining an alarm level of the alarm information and a pushing rule corresponding to the alarm level; determining a corresponding mobile equipment identification sequence according to the pushing rule and the identification information of the fire fighting equipment corresponding to the alarm information, wherein each mobile equipment identification in the mobile equipment identification sequence is ordered from high to low according to the pushing priority; and sending the alarm information to the corresponding mobile equipment according to the sequence of the mobile equipment identification sequences.

According to the method, the gateway cluster is used for collecting data uploaded by the fire-fighting Internet of things equipment, and the data are processed, analyzed and stored in real time in the computing processing service. The method has the advantages that abnormal information such as smoke feeling, gas feeling, temperature feeling, user information transmission devices, intelligent fire hydrants, intelligent electricity consumption and the like, which are uploaded by fire-fighting things-connected equipment, is subjected to real-time processing such as alarm upper and lower limit judgment and sensor fault judgment, and judgment results of the abnormal information of the equipment are sent to classified push service in real time, classified push information is sent to networking units, fire-fighting supervision departments and maintenance service institutions to which the equipment belongs in a classified manner, the passive is changed into active, early discovery and early warning are achieved, the fire-fighting hidden danger processing efficiency is improved, and life safety and economic loss caused by fire-fighting hidden danger are greatly reduced.

Note that each module in the data monitoring apparatus may be a program module (for example, a set of program instructions for implementing a specific function), or may be a hardware module, and for the latter, it may be represented by the following form, but is not limited thereto: the expression forms of the modules are all a processor, or the functions of the modules are realized by one processor.

It should be noted that, the data monitoring device provided in the present embodiment may be used to execute the data monitoring method shown in fig. 2, so that the explanation of the data monitoring method is also applicable to the embodiments of the present application, and is not repeated herein.

The embodiment of the application also provides a nonvolatile storage medium, which comprises a stored computer program, wherein the equipment where the nonvolatile storage medium is located executes the following data monitoring method by running the computer program: determining the equipment type of the fire-fighting equipment corresponding to the original data and an analysis rule corresponding to the equipment type, wherein the original data is real-time data uploaded by the fire-fighting equipment; analyzing the original data according to the analysis rule to obtain target data, wherein the target data is used for representing the running state of the fire-fighting equipment; carrying out statistical analysis on the target data according to different time dimensions to generate a target report, and predicting whether the fire fighting equipment has fault hidden danger according to the target report; and sending alarm information to a mobile terminal corresponding to the fire-fighting equipment under the condition that the fire-fighting equipment is predicted to have fault hidden danger or abnormal data in the target data is detected.

The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments.

In the foregoing embodiments of the present application, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.

In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.

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

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

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.

The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application and are intended to be comprehended within the scope of the present application.

Claims (10)

1. A method of data monitoring, comprising:

determining the equipment type of the fire-fighting equipment corresponding to the original data and an analysis rule corresponding to the equipment type, wherein the original data is real-time data uploaded by the fire-fighting equipment;

analyzing the original data according to the analysis rule to obtain target data, wherein the target data is used for representing the running state of the fire-fighting equipment;

carrying out statistical analysis on the target data according to different time dimensions to generate a target report, and predicting whether the fire fighting equipment has fault hidden danger according to the target report;

and sending alarm information to a mobile terminal corresponding to the fire-fighting equipment under the condition that the fire-fighting equipment is predicted to have fault hidden danger or abnormal data in the target data is detected.

2. The method according to claim 1, wherein the raw data includes a plurality of data units; according to the analysis rule, analyzing the original data to obtain target data comprises the following steps:

Determining a data type identifier of the data unit;

determining a data structure template corresponding to the data type identifier according to the analysis rule;

and analyzing the data unit according to the data structure template to obtain the target data.

3. The data monitoring method of claim 2, wherein the target data comprises: status information and detail data, wherein the status data is status identification data which is uploaded by the fire fighting equipment and is used for representing the running status of the equipment, and the status data comprises at least one of the following: the system comprises normal operation information, abnormal alarm information and abnormal fault information, wherein detail data are analog quantity data which are uploaded by the fire-fighting equipment and used for representing the operation state of the fire-fighting equipment, and the detail data corresponding to the fire-fighting equipment of different equipment types are different; according to the analysis rule, analyzing the original data to obtain target data, and then further comprising:

judging that abnormal data exists in the target data under the condition that the abnormal alarm information or the abnormal fault information exists in the state information;

and under the condition that the detail data does not meet the corresponding preset threshold condition, judging that abnormal data exists in the target data.

4. The data monitoring method according to claim 3, wherein the target data further comprises: identification information of the fire fighting equipment and installation position information of the fire fighting equipment; according to the analysis rule, analyzing the original data to obtain target data, and then further comprising:

generating virtual equipment corresponding to the fire-fighting equipment in an initial map of a target area according to the identification information and the installation position information of the fire-fighting equipment, and sending the virtual equipment to a front-end interface for displaying, wherein the target area is an area monitored by the fire-fighting equipment;

and adding the state information of the fire-fighting equipment to the virtual equipment for display.

5. The data monitoring method of claim 1, wherein statistically analyzing the target data according to different time dimensions, generating a target report and predicting whether the fire fighting equipment has a fault hidden trouble according to the target report comprises:

and counting the target data of the fire fighting equipment in different time dimensions to obtain the target report, wherein the time dimensions comprise at least one of the following: day dimension, month dimension, quarter dimension, and year dimension;

Determining a target association relation of the fire-fighting equipment according to the target report, wherein the target association relation is used for representing the change trend of state parameters of the fire-fighting equipment along with time, and the state parameters comprise at least one of the following: the equipment alarm rate, the equipment fault rate and the equipment false alarm rate;

and predicting whether the fire-fighting equipment has hidden trouble or not according to the target association relation.

6. The data monitoring method according to claim 1, further comprising, before determining the device type to which the original data corresponds:

collecting the original data sent by a target communication protocol through an edge computing gateway, wherein the target communication protocol is a protocol used for data transmission by the fire fighting equipment;

and converting the original data into a target format to obtain the original data in the target format, wherein the target format is a preset data format with unified format.

7. The data monitoring method of claim 1, wherein transmitting the alert information to the mobile terminal corresponding to the fire protection device comprises:

determining an alarm level of the alarm information and a pushing rule corresponding to the alarm level;

Determining a corresponding mobile equipment identification sequence according to the pushing rule and the identification information of the fire fighting equipment corresponding to the alarm information, wherein each mobile equipment identification in the mobile equipment identification sequence is ordered from high to low according to the pushing priority;

and sending the alarm information to the corresponding mobile equipment according to the sequence of the mobile equipment identification sequence.

8. A data monitoring device, comprising:

the rule determining module is used for determining the equipment type of the fire-fighting equipment corresponding to the original data and the analysis rule corresponding to the equipment type, wherein the original data is real-time data uploaded by the fire-fighting equipment;

the data analysis module is used for analyzing the original data according to the analysis rule to obtain target data, wherein the target data is used for representing the running state of the fire-fighting equipment;

the report generation module is used for carrying out statistical analysis on the target data according to different time dimensions, generating a target report, and predicting whether the fire fighting equipment has fault hidden danger or not according to the target report;

and the alarm prompting module is used for sending alarm information to the mobile terminal corresponding to the fire-fighting equipment under the condition that the fire-fighting equipment is predicted to have fault hidden danger or abnormal data in the target data is detected.

9. An electronic device comprising a processor, characterized in that the processor is configured to run a program, wherein the program is configured to perform the data monitoring method according to any of claims 1 to 7 when run.

10. A non-volatile storage medium, characterized in that the non-volatile storage medium comprises a stored computer program, wherein the device in which the non-volatile storage medium is located performs the data monitoring method according to any one of claims 1 to 7 by running the computer program.

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