CN111104291A

CN111104291A - Environment monitoring method, device and system based on Internet of things and monitoring server

Info

Publication number: CN111104291A
Application number: CN201911380813.7A
Authority: CN
Inventors: 孟小峰
Original assignee: 孟小峰
Current assignee: Shanghai electric digital Ecological Technology Co.,Ltd.
Priority date: 2019-12-27
Filing date: 2019-12-27
Publication date: 2020-05-05
Anticipated expiration: 2039-12-27
Also published as: CN111986062A; CN111104291B; CN111970356A

Abstract

The embodiment of the application provides an environment monitoring method, an environment monitoring device, an environment monitoring system and an environment monitoring server based on the Internet of things. Therefore, the reliability and accuracy of monitoring can be considered from the whole monitoring area, and the reliability of the monitoring data in the monitoring area is improved and the waste of monitoring resources is reduced by combining the processing procedure link of background monitoring data.

Description

Environment monitoring method, device and system based on Internet of things and monitoring server

Technical Field

The application relates to the technical field of Internet of things, in particular to an environment monitoring method, device and system based on the Internet of things and a monitoring server.

Background

With the rapid development of the current world industrialization process, the environmental problem becomes more serious, and how to perform effective environmental monitoring is very important for the feedback of environmental improvement.

Currently, in a traditional environment monitoring scheme, a specific environment monitoring device is usually installed at a fixed environment monitoring point, so as to realize online monitoring of the corresponding environment monitoring point. However, in the monitoring scheme, reliability and accuracy of monitoring are not considered from the whole monitoring area, and a processing procedure link of background monitoring data is not considered, so that the situation that the monitoring data of the whole monitoring area is not credible due to problems existing in the monitoring process of a certain environmental monitoring point is likely to cause waste of a large amount of resources.

Disclosure of Invention

In order to overcome at least the above disadvantages in the prior art, an object of the present application is to provide an environment monitoring method, device, system and monitoring server based on the internet of things, which can consider the reliability and accuracy of monitoring from the whole monitoring area, and combine the processing procedure link of background monitoring data, thereby improving the reliability of the monitoring data in the monitoring area and reducing the waste of monitoring resources.

In a first aspect, the application provides an environmental monitoring method based on the internet of things, which is applied to an environmental monitoring system based on the internet of things, the environmental monitoring system based on the internet of things comprises a monitoring server and environmental monitoring devices which are in communication connection with the monitoring server and are respectively arranged at a plurality of environmental monitoring points, and the method comprises the following steps:

the method comprises the steps that a monitoring server obtains task grades of monitoring tasks of a current monitoring queue in a region to be monitored, and obtains a first task process node sequence generated by each monitoring task from the current monitoring queue according to the task grades of the monitoring tasks of the current monitoring queue in the region to be monitored;

when the monitoring server monitors that an environment monitoring result corresponding to the first task process node sequence exists in environment monitoring background data, determining an updated second task process node sequence according to the environment monitoring result, and determining a time-space domain feature difference between the second task process node sequence and the first task process node sequence;

the monitoring server generates linkage Internet of things control information of each monitoring task according to the time-space domain feature difference, and sends corresponding linkage Internet of things control instructions to at least part of corresponding environment monitoring devices according to the linkage Internet of things control information;

and the environment monitoring device executes linkage environment monitoring operation according to the linkage Internet of things control command and the associated environment monitoring device.

In a possible design of the first aspect, the step of obtaining, from the current monitoring queue, a first task process node sequence generated by each monitoring task according to a task class of the monitoring task of the current monitoring queue in the area to be monitored includes:

respectively establishing a task time sequence node sequence and a task empty sequence node sequence of each monitoring task according to the task grade of the monitoring task of the current monitoring queue in the area to be monitored;

calculating a time sequence node characteristic graph and an empty sequence node characteristic graph of each monitoring task and an idle conversion graph in time according to the task time sequence node sequence and the task empty sequence node sequence of each monitoring task;

performing graph feature extraction operation of each monitoring task according to the time sequence node feature graph and the empty sequence node feature graph of each monitoring task in time and idle graph conversion, and obtaining a graph feature extraction result of each monitoring task;

determining at least one graphic feature vector position identified from the graphic feature extraction result of each monitoring task as a current graphic feature vector position;

matching the graphic feature vector position information of the current graphic feature vector position with the graphic feature vector position information of each graphic feature vector position to be determined;

when the graphic feature vector position information of the current graphic feature vector position is matched with the graphic feature vector position information of any one to-be-determined graphic feature vector position, determining the current graphic feature vector position as the to-be-determined graphic feature vector position, and updating a position graphic flag bit of the to-be-determined graphic feature vector position, otherwise, determining the current graphic feature vector position as a new to-be-determined graphic feature vector position, and establishing the position graphic flag bit of the new to-be-determined graphic feature vector position, wherein the position graphic flag bit comprises: vector direction information, vector position point location information, and a direction angle and a relative direction angle of each vector direction of the to-be-determined graphic feature vector position in the graphic feature vector position data;

according to the position graphic mark bit of each graphic feature vector position to be determined, acquiring position graphic feature information in the position graphic mark bit through a first task process model, wherein the position graphic feature information is obtained by calculation according to a task function corresponding to the monitoring task through the first task process model through the vector direction information, the vector position point location information and the direction angle and the relative direction angle of each vector direction;

extracting a plurality of target graphic feature segments from the position graphic feature information as designated graphic feature segments, and after the designated graphic feature segments are determined, respectively processing the designated graphic feature segments according to the feature segment nodes and the vector directions of the designated graphic feature segments to calculate the feature confidence information of each designated graphic feature segment in the position graphic feature information;

determining first feature segmentation feature information and first feature segmentation direction angle information of a position graphic zone bit in a second task process model according to feature confidence information of the designated graphic feature segmentation in the position graphic feature information, and calculating first feature segmentation feature information and first feature segmentation direction angle information corresponding to the feature confidence information of each designated graphic feature segmentation, wherein the first feature segmentation feature information and the first feature segmentation direction angle information are updated in real time along with dynamic change of the designated graphic feature segmentation in the position graphic feature information;

calculating the specified graphic feature segment according to the first feature segment feature information and the first feature segment direction angle information which are currently corresponding to the specified graphic feature segment through the second task process model to obtain the semantic information of the feature segment machine, so that the second task process model always calculates the specified graphic feature segment and continuously processes the specified graphic feature segment in real time;

processing the semantic information of the feature segmentation machine of the specified graphic feature segmentation in the position graphic feature information according to the feature segmentation nodes and the vector direction information of the specified graphic feature segmentation to obtain the feature segmentation nodes of the next segment, calculating the corresponding first feature segmentation feature information and the first feature segmentation direction angle information of the next segment, transmitting the first feature segmentation feature information and the first feature segmentation direction angle information to the second task process model, and controlling the second task process model to process the specified graphic feature segmentation;

when the designated graphic feature segment is interrupted in the position graphic zone bit, processing the designated graphic feature segment according to the feature segment node and vector direction information of the designated graphic feature segment and the interruption feature vector during interruption;

calculating a plurality of identification feature segmentation nodes which can be associated with the specified graphic feature segmentation and corresponding segmentation confidence coefficients thereof through processing, processing according to the identification feature segmentation node with the maximum segmentation confidence coefficient by the second task process model, processing an interruption feature vector during interruption in position graphic feature information according to the plurality of identification feature segmentation nodes by the first task process model, finishing the processing by the second task process model when the specified graphic feature segmentation is detected by the first task process model, processing the specified graphic feature segmentation according to the vector direction information thereof, and acquiring the semantic information of a feature segmentation machine of the specified graphic feature segmentation;

after the first task process model determines the appointed graphic feature segment, calculating a segment graphic average value of the appointed graphic feature segment, calibrating the appointed graphic feature segment by the second task process model according to first feature segment feature information and first feature segment direction angle information converted by the segment graphic average value, re-calibrating the appointed graphic feature segment in the semantic information of the feature segment machine, and calculating a segment node calculation value of the appointed graphic feature segment in the semantic information of the feature segment machine;

and according to the size sequence of the segmentation node calculation values of each appointed graphic feature segment in the semantic information of the feature segmentation machine, acquiring a first task process node sequence generated by the monitoring task from the current monitoring queue.

In a possible design of the first aspect, the step of determining an updated second task process node sequence according to the environmental monitoring result includes:

acquiring a change parameter process sequence corresponding to the first task process node sequence according to the environment monitoring result, wherein the change parameter process sequence comprises a calibration process and a controllable process, and the controllable process comprises a strong correlation process and a weak correlation process;

determining process control information corresponding to a first task process sequence according to the process characteristic information corresponding to the calibration process in the variable parameter process sequence, the strong correlation process and the weak correlation process;

determining process control characteristic parameters of a first task process sequence and a parameter configuration source of each process control characteristic parameter according to process control information corresponding to the determined first task process sequence;

acquiring the number of task processes required by the first task process sequence for updating according to the process control characteristic parameters and the parameter configuration source of each process control characteristic parameter;

if the number of the task processes is larger than the set number, detecting whether the number of the current idle environment monitoring nodes is larger than the set number;

when the number of the current idle environment monitoring nodes is larger than the set number, updating the first task process sequence according to process control information corresponding to the first task process sequence to obtain a reference task process sequence and an inheritance task process sequence, and after the reference task process sequence and the inheritance task process sequence are subjected to inheritance fusion, obtaining a corresponding fusion task process sequence to generate a corresponding fusion task process sequence graph;

and determining an updated second task process node sequence according to the fusion task process sequence graph.

In a possible design of the first aspect, the step of determining an updated second task process node sequence according to the fused task process sequence graph includes:

determining node position information of the fusion task process nodes and space point characteristic information of node space points in the fusion task process sequence graph according to the segment length and segment characteristics of the graph segment nodes in the fusion task process sequence graph;

determining a corresponding fusion task process node according to the node position information, and determining a corresponding node space point according to the space point characteristic information;

fitting the fusion task process nodes and the node space points to determine a fitting representation graph corresponding to the fusion task process sequence graph;

segmenting the fitted representation graph into a plurality of task process segments and a plurality of process space segments;

aiming at any task process segment, segmenting the task process segment in a region of the fitting representation graph representing a fusion task process node to obtain a plurality of subtask process segments, and determining segment characteristics of the plurality of subtask process segments;

for any process space segment, segmenting the direction of the process space segment representing the fusion task process node in the fitting representation graph to obtain a plurality of sub-process space segments, and determining the space segment characteristics of the plurality of sub-process space segments;

and sequentially carrying out feature fusion on the segmentation features of the plurality of sub-task process segments and the space segmentation features of the plurality of sub-process space segments to obtain an updated second task process node sequence.

In one possible design of the first aspect, the determining a difference in time-space domain characteristics between the second sequence of task process nodes and the first sequence of task process nodes includes:

calculating sequence feature vectors and sequence feature dynamic amplitudes corresponding to the second task process node sequence and the first task process node sequence in a preset feature space, and obtaining a target feature quantization value corresponding to the minimum sequence feature dynamic amplitude of each feature in the sequence feature vectors and the sequence feature dynamic amplitudes; wherein, the target characteristic quantization value is a set characteristic quantization value in the preset characteristic space;

processing among a plurality of set characteristic quantization values related to the target characteristic quantization value according to the sequence characteristic vector, calculating a sequence characteristic dynamic amplitude corresponding to each set characteristic quantization value, and taking the set characteristic quantization value corresponding to the minimum sequence characteristic dynamic amplitude in the sequence characteristic dynamic amplitudes corresponding to all the set characteristic quantization values as a characteristic quantization difference value of each characteristic;

taking the difference features formed by the feature quantization difference values of all the features as the difference features of the sequence feature vector and the sequence feature dynamic amplitude to obtain a first difference feature corresponding to the second task process node sequence and a second difference feature corresponding to the first task process node sequence;

calculating a feature difference condition between the first difference feature and the second difference feature;

fusing the first difference characteristic and the second difference characteristic according to the characteristic difference condition to obtain a fused difference characteristic;

populating the fused difference features onto a settings update component, the settings update component configured to perform a feature update on the fused difference features;

the fusion difference feature is divided by the set updating component to form a plurality of fusion difference features to be processed, and an updating flag bit association relationship is established according to the corresponding relationship between the difference elements in the fusion difference features to be processed and the updating flag bits contained in the feature characterization information of the fusion difference features to be processed in the process of forming the fusion difference features to be processed, wherein the established updating flag bit association relationship contains the updating flag bits corresponding to all the difference elements in the fusion difference features to be processed;

for the difference element in the fusion difference feature to be processed, determining the update grade corresponding to the feature segment where the difference element is located when the fusion difference feature to be processed is processed currently, recording the determined update grade into the update flag bit association relation, and establishing the corresponding relation between the recorded update grade and the update flag bit corresponding to the difference element in the update flag bit association relation;

monitoring the updating grade corresponding to each feature segment in the fusion difference feature to be processed in the process of updating the fusion difference feature to be processed, and determining the updating grade required for updating the fusion difference feature to be processed when the updating grade corresponding to at least one updating marker bit in the association relation of the updating marker bits is monitored to be changed and the updating grade adopted for updating the fusion difference feature to be processed is determined to be required to be changed;

updating the fusion difference feature to be processed by using the determined updating grade required for updating the fusion difference feature to be processed currently so as to determine the time-space domain feature difference between the second task process node sequence and the first task process node sequence.

In a possible design of the first aspect, the step of generating linkage internet of things control information of each monitoring task according to the time-space domain feature difference, and sending a corresponding linkage internet of things control instruction to at least part of corresponding environment monitoring devices according to the linkage internet of things control information includes:

determining subtask parameters of each subtask in each monitoring task and duration of a channel occupied by the subtask according to the time-space domain characteristic difference;

determining the parameters of the internet of things linkage communication process of the linkage communication process required by the assignment of the subtasks in each monitoring task according to the subtask parameters of the subtasks in each monitoring task and the duration of the channels occupied by the subtasks;

determining linkage Internet of things control information of each subtask according to the Internet of things linkage communication process parameters of the linkage communication process required by each subtask;

establishing a corresponding relation between the control information of the linkage internet of things and other subtasks in the monitoring task according to the identification sequence of the linkage internet of things, and taking the subtasks with the corresponding relation as linkable subtasks;

and combining each linkable subtask according to the corresponding relation to generate linked Internet of things control information of each monitoring task, and sending a corresponding linked Internet of things control instruction to at least part of corresponding environment monitoring devices according to the linked Internet of things control information.

In a second aspect, an embodiment of the present application further provides an environment monitoring method based on the internet of things, which is applied to a monitoring server, where the monitoring server is in communication connection with environment monitoring devices respectively disposed at a plurality of environment monitoring points, and the method includes:

acquiring the task grade of the monitoring task of the current monitoring queue in the area to be monitored, and acquiring a first task process node sequence generated by each monitoring task from the current monitoring queue according to the task grade of the monitoring task of the current monitoring queue in the area to be monitored;

when it is monitored that an environment monitoring result corresponding to the first task process node sequence exists in environment monitoring background data, determining an updated second task process node sequence according to the environment monitoring result, and determining a time-space domain characteristic difference between the second task process node sequence and the first task process node sequence;

and generating linkage Internet of things control information of each monitoring task according to the time-space domain characteristic difference, and sending corresponding linkage Internet of things control instructions to at least part of corresponding environment monitoring devices according to the linkage Internet of things control information, so that the environment monitoring devices execute linkage environment monitoring operation according to the linkage Internet of things control instructions and the associated environment monitoring devices.

In a third aspect, an embodiment of the present application further provides an environment monitoring device based on the internet of things, which is applied to a monitoring server, where the monitoring server is in communication connection with environment monitoring devices respectively disposed at a plurality of environment monitoring points, and the device includes:

the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring the task grade of the monitoring task of the current monitoring queue in a region to be monitored, and acquiring a first task process node sequence generated by each monitoring task from the current monitoring queue according to the task grade of the monitoring task of the current monitoring queue in the region to be monitored;

the determining module is used for determining an updated second task process node sequence according to an environment monitoring result when the environment monitoring result corresponding to the first task process node sequence exists in the environment monitoring background data, and determining the time-space domain characteristic difference between the second task process node sequence and the first task process node sequence;

and the generation and sending module is used for generating linkage Internet of things control information of each monitoring task according to the time-space domain characteristic difference and sending a corresponding linkage Internet of things control instruction to at least part of corresponding environment monitoring devices according to the linkage Internet of things control information so that the environment monitoring devices execute linkage environment monitoring operation according to the linkage Internet of things control instruction and the associated environment monitoring devices.

In a fourth aspect, an embodiment of the present application further provides an environment monitoring system based on the internet of things, where the environment monitoring system based on the internet of things includes a monitoring server and environment monitoring devices, which are in communication connection with the monitoring server and are respectively arranged at a plurality of environment monitoring points;

the monitoring server is used for acquiring the task grade of the monitoring task of the current monitoring queue in the area to be monitored, and acquiring a first task process node sequence generated by each monitoring task from the current monitoring queue according to the task grade of the monitoring task of the current monitoring queue in the area to be monitored;

when the monitoring server monitors that an environment monitoring result corresponding to the first task process node sequence exists in the environment monitoring background data, the monitoring server is further used for determining an updated second task process node sequence according to the environment monitoring result and determining a time-space domain characteristic difference between the second task process node sequence and the first task process node sequence;

the monitoring server is further used for generating linkage Internet of things control information of each monitoring task according to the time-space domain feature difference, and sending corresponding linkage Internet of things control instructions to at least part of corresponding environment monitoring devices according to the linkage Internet of things control information;

and the environment monitoring device is used for executing linkage environment monitoring operation according to the linkage Internet of things control command and the associated environment monitoring device.

In a fifth aspect, the present embodiments also provide a monitoring server, where the monitoring server includes a processor, a machine-readable storage medium, and a network interface, where the machine-readable storage medium, the network interface, and the processor are connected through a bus system, the network interface is configured to be communicatively connected to at least one environment monitoring device, the machine-readable storage medium is configured to store a program, instructions, or codes, and the processor is configured to execute the program, instructions, or codes in the machine-readable storage medium to perform the method for monitoring an environment based on an internet of things in any one of the possible designs of the first aspect or the first aspect.

In a sixth aspect, an embodiment of the present application provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are detected on a computer, the instructions cause the computer to perform the method for monitoring an environment based on an internet of things in the first aspect or any one of the possible designs of the first aspect.

Based on any one aspect, the method obtains the task grade of the monitoring task of the current monitoring queue in the area to be monitored through the server, to obtain the first task process node sequence generated by each monitoring task from the current monitoring queue, when the environment monitoring background data is monitored to have the environment monitoring result corresponding to the first task process node sequence, determining the updated second task process node sequence according to the environment monitoring result, and determining the time-space domain characteristic difference between the second task process node sequence and the first task process node sequence, and generating linkage Internet of things control information of each monitoring task according to the time-space domain characteristic difference, and sending corresponding linkage Internet of things control instructions to at least part of corresponding environment monitoring devices to enable the environment monitoring devices and the associated environment monitoring devices to execute linkage environment monitoring operation. Therefore, the reliability and accuracy of monitoring can be considered from the whole monitoring area, and the reliability of the monitoring data in the monitoring area is improved and the waste of monitoring resources is reduced by combining the processing procedure link of background monitoring data.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.

Fig. 1 is a schematic view of an application scenario of an environment monitoring system based on the internet of things according to an embodiment of the present application;

fig. 2 is a schematic flowchart of an environment monitoring method based on the internet of things according to an embodiment of the present application; one of them;

fig. 3 is a schematic flowchart of an environment monitoring method based on the internet of things according to an embodiment of the present application; a second step;

fig. 4 is a functional module schematic diagram of an environment monitoring device based on the internet of things according to an embodiment of the present application;

fig. 5 is a block diagram schematically illustrating a structure of a monitoring server for implementing the foregoing environment monitoring method based on the internet of things according to the embodiment of the present application.

Detailed Description

The present application will now be described in detail with reference to the drawings, and the specific operations in the method embodiments may also be applied to the apparatus embodiments or the system embodiments. In the description of the present application, "at least one" includes one or more unless otherwise specified. "plurality" means two or more. For example, at least one of A, B and C, comprising: a alone, B alone, a and B in combination, a and C in combination, B and C in combination, and A, B and C in combination. In this application, "/" means "or, for example, A/B may mean A or B; "and/or" herein is merely an association describing an association of devices, meaning that there may be three relationships, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone.

Fig. 1 is an interaction diagram of an internet of things-based environment monitoring system 10 according to an embodiment of the present application. The internet of things based environment monitoring system 10 may include a monitoring server 100 and an environment monitoring device 200 communicatively connected to the monitoring server 100, and the monitoring server 100 may include a processor executing instructions. The internet of things based environmental monitoring system 10 shown in fig. 1 is merely one possible example, and in other possible embodiments, the internet of things based environmental monitoring system 10 may include only a portion of the components shown in fig. 1 or may include other components.

In some embodiments, the monitoring server 100 may be a single server or a group of servers. The server group may be centralized or distributed (for example, the monitoring server 100 may be a distributed system). In some embodiments, the monitoring server 100 may be local or remote to the environmental monitoring device 200. For example, the monitoring server 100 may access information stored in the environmental monitoring device 200 and a database, or any combination thereof, via a network. As another example, the monitoring server 100 may be directly connected to at least one of the environmental monitoring device 200 and a database to access information and/or data stored therein. In some embodiments, the monitoring server 100 may be implemented on a cloud platform; by way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud (communicuted), a distributed cloud, an inter-cloud, a multi-cloud, and the like, or any combination thereof.

In some embodiments, the monitoring server 100 may include a processor. The processor may process information and/or data related to the service request to perform one or more of the functions described herein. A processor may include one or more processing cores (e.g., a single-core processor (S) or a multi-core processor (S)). Merely by way of example, a Processor may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction Set Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller Unit, a reduced Instruction Set computer (reduced Instruction Set computer), a microprocessor, or the like, or any combination thereof.

The network may be used for the exchange of information and/or data. In some embodiments, one or more components (e.g., monitoring server 100, environmental monitoring devices 200, and a database) in the internet of things based environmental monitoring system 10 may send information and/or data to other components. In some embodiments, the network may be any type of wired or wireless network, or combination thereof. Merely by way of example, the Network 130 may include a wired Network, a Wireless Network, a fiber optic Network, a telecommunications Network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a Public Switched Telephone Network (PSTN), a bluetooth Network, a ZigBee Network, a Near Field Communication (NFC) Network, or the like, or any combination thereof. In some embodiments, the network may include one or more network access points. For example, the network may include wired or wireless network access points, such as base stations and/or network switching nodes, through which one or more components of the internet of things-based environment monitoring system 10 may connect to the network to exchange data and/or information.

The aforementioned database may store data and/or instructions. In some embodiments, the database may store data assigned to the environmental monitoring device 200. In some embodiments, the database may store data and/or instructions for the exemplary methods described herein. In some embodiments, the database may include mass storage, removable storage, volatile Read-write Memory, or Read-Only Memory (ROM), among others, or any combination thereof. By way of example, mass storage may include magnetic disks, optical disks, solid state drives, and the like; removable memory may include flash drives, floppy disks, optical disks, memory cards, zip disks, tapes, and the like; volatile read-write memory may include Random Access Memory (RAM); the RAM may include Dynamic RAM (DRAM), Double data Rate Synchronous Dynamic RAM (DDR SDRAM); static RAM (SRAM), Thyristor-Based Random Access Memory (T-RAM), Zero-capacitor RAM (Zero-RAM), and the like. By way of example, ROMs may include Mask Read-Only memories (MROMs), Programmable ROMs (PROMs), Erasable Programmable ROMs (PERROMs), Electrically Erasable Programmable ROMs (EEPROMs), compact disk ROMs (CD-ROMs), digital versatile disks (ROMs), and the like. In some embodiments, the database may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, across clouds, multiple clouds, or the like, or any combination thereof.

In some embodiments, the database may be connected to a network to communicate with one or more components of the internet of things based environmental monitoring system 10 (e.g., monitoring server 100, environmental monitoring device 200, etc.). One or more components of the internet of things based environmental monitoring system 10 may access data or instructions stored in a database via a network. In some embodiments, the database may be directly connected to one or more components of the Internet of things based environmental monitoring system 10 (e.g., the monitoring server 100, the environmental monitoring device 200, etc.; or, in some embodiments, the database may be part of the monitoring server 100.

In order to solve the technical problem in the foregoing background, fig. 2 is a schematic flow chart of the environment monitoring method based on the internet of things provided in the embodiment of the present application, and the environment monitoring method based on the internet of things provided in the embodiment may be executed by the environment monitoring system 10 based on the internet of things shown in fig. 1, which is described in detail below.

Step S110, the monitoring server 100 obtains the task level of the monitoring task of the current monitoring queue in the area to be monitored, and obtains the first task process node sequence generated by each monitoring task from the current monitoring queue according to the task level of the monitoring task of the current monitoring queue in the area to be monitored.

Step S120, when the monitoring server 100 monitors that the environment monitoring background data has the environment monitoring result corresponding to the first task process node sequence, determining the updated second task process node sequence according to the environment monitoring result, and determining the time-space domain feature difference between the second task process node sequence and the first task process node sequence.

Step S130, the monitoring server 100 generates linkage internet of things control information of each monitoring task according to the time-space domain feature difference, and sends a corresponding linkage internet of things control instruction to at least part of the corresponding environment monitoring devices 200 according to the linkage internet of things control information.

In step S140, the environmental monitoring device 200 executes the linkage environmental monitoring operation with the associated environmental monitoring device 200 according to the linkage internet of things control instruction.

Based on the design, the embodiment obtains the task grade of the monitoring task of the current monitoring queue in the area to be monitored through the server, to obtain the first task process node sequence generated by each monitoring task from the current monitoring queue, when the environment monitoring background data is monitored to have the environment monitoring result corresponding to the first task process node sequence, determining the updated second task process node sequence according to the environment monitoring result, and determining the time-space domain characteristic difference between the second task process node sequence and the first task process node sequence, and therefore, linkage internet of things control information of each monitoring task is generated according to the time-space domain characteristic difference, and corresponding linkage internet of things control instructions are sent to at least part of the corresponding environment monitoring devices 200, so that the environment monitoring devices 200 and the associated environment monitoring devices 200 execute linkage environment monitoring operation. Therefore, the reliability and accuracy of monitoring can be considered from the whole monitoring area, and the reliability of the monitoring data in the monitoring area is improved and the waste of monitoring resources is reduced by combining the processing procedure link of background monitoring data.

In a possible design, for step S110, according to the task level of the monitoring task in the current monitoring queue in the area to be monitored, the present embodiment may respectively establish a task time sequence node sequence and a task empty sequence node sequence of each monitoring task.

And then, calculating a time sequence node characteristic graph and an empty sequence node characteristic graph of each monitoring task and an idle conversion graph in time according to the task time sequence node sequence and the task empty sequence node sequence of each monitoring task.

And then, performing graph feature extraction operation of each monitoring task according to the time sequence node feature graph and the empty sequence node feature graph of each monitoring task in time for changing graphs, and obtaining a graph feature extraction result of each monitoring task.

Then, at least one graphics feature vector position identified from the graphics feature extraction result of each monitoring task can be determined as the current graphics feature vector position.

Then, the position information of the current graphic feature vector position may be respectively matched with the position information of the graphic feature vector at each of the graphic feature vector positions to be determined.

Then, when the graphic feature vector position information of the current graphic feature vector position matches with the graphic feature vector position information of any one graphic feature vector position to be determined, determining that the current graphic feature vector position is the graphic feature vector position to be determined, and updating the position graphic flag bit of the graphic feature vector position to be determined, otherwise, determining that the current graphic feature vector position is the new graphic feature vector position to be determined, and establishing the position graphic flag bit of the new graphic feature vector position to be determined, wherein the position graphic flag bit comprises: vector direction information, vector position point location information, and a direction angle and a relative direction angle of each vector direction of the image feature vector position to be determined in the image feature vector position data.

And then, according to the position graphic mark position of each graphic feature vector position to be determined, acquiring position graphic feature information in the position graphic mark position through a first task process model, wherein the position graphic feature information is obtained through vector direction information, vector position point position information, and direction angles and relative direction angles of each vector direction through the first task process model and calculated according to a task function corresponding to the monitoring task.

Then, a plurality of target graphic feature segments can be extracted from the position graphic feature information to be used as designated graphic feature segments, and after the designated graphic feature segments are determined, the designated graphic feature segments are respectively processed according to the feature segment nodes and the vector directions of the designated graphic feature segments, and the feature confidence information of each designated graphic feature segment in the position graphic feature information is calculated.

Then, according to the feature confidence information of the designated graphic feature segment in the location graphic feature information, determining first feature segment feature information and first feature segment direction angle information of a location graphic flag bit in a second task process model, and calculating first feature segment feature information and first feature segment direction angle information corresponding to the feature confidence information of each designated graphic feature segment, wherein the first feature segment feature information and the first feature segment direction angle information are updated in real time along with the dynamic change of the designated graphic feature segment in the location graphic feature information.

Then, the second task process model can calculate the specified graphic feature segment according to the first feature segment feature information and the first feature segment direction angle information which are currently corresponding to the specified graphic feature segment, and obtains the semantic information of the feature segment machine, so that the second task process model always calculates the specified graphic feature segment and continuously processes the specified graphic feature segment in real time.

Then, the semantic information of the feature segmentation machine of the specified graphic feature segment can be processed in the position graphic feature information according to the feature segmentation nodes and the vector direction information of the specified graphic feature segment to obtain the feature segmentation nodes of the next segment, and the first feature segmentation feature information and the first feature segmentation direction angle information corresponding to the next segment are calculated and transmitted to the second task process model, so that the second task process model is controlled to process the specified graphic feature segment.

Then, when the designated graphic feature segment is interrupted in the position graphic flag bit, the designated graphic feature segment can be processed according to the feature segment node and vector direction information of the designated graphic feature segment and the interruption feature vector during interruption.

Then, a plurality of identification feature segmentation nodes which can be associated with the specified graphic feature segmentation and corresponding segmentation confidence coefficients thereof can be calculated through processing, the identification feature segmentation nodes with the maximum segmentation confidence coefficients are processed through a second task process model, the interruption feature vector during interruption is processed in the position graphic feature information according to the identification feature segmentation nodes through a first task process model, when the specified graphic feature segmentation is detected by the first task process model, the second task process model finishes the processing, the specified graphic feature segmentation is processed according to the vector direction information thereof, and the semantic information of the feature segmentation machine is obtained.

Then, after the first task process model determines the designated graphic feature segment, the segment graphic mean value of the designated graphic feature segment is calculated, the second task process model calibrates the designated graphic feature segment according to the first feature segment feature information and the first feature segment direction angle information converted by the segment graphic mean value, recalibrates the designated graphic feature segment in the feature segment machine semantic information, and calculates the segment node calculation value of the designated graphic feature segment in the feature segment machine semantic information.

Then, the first task process node sequence generated by the monitoring task can be obtained from the current monitoring queue according to the size sequence of the segmentation node calculation value of each designated graphic feature segment in the semantic information of the feature segmentation machine.

In a possible design, for step S120, the embodiment may specifically obtain a variation parameter process sequence corresponding to the first task process node sequence according to the environment monitoring result, where the variation parameter process sequence includes a calibration process and a controllable process, and the controllable process includes a strong association process and a weak association process.

Then, the process control information corresponding to the first task process sequence may be determined according to the process characteristic information, the strong association process, and the weak association process corresponding to the calibration process in the variable parameter process sequence.

Then, according to the determined process control information corresponding to the first task process sequence, the process control characteristic parameters of the first task process sequence and the parameter configuration source of each process control characteristic parameter may be determined.

Then, the number of task processes required for updating the first task process sequence may be obtained according to the process control characteristic parameters and the parameter configuration source of each process control characteristic parameter.

If the number of the task processes is larger than the set number, detecting whether the number of the current idle environment monitoring nodes is larger than the set number, updating the first task process sequence according to process control information corresponding to the first task process sequence when the number of the current idle environment monitoring nodes is larger than the set number, obtaining a reference task process sequence and an inheritance task process sequence, and obtaining a corresponding fusion task process sequence after the reference task process sequence and the inheritance task process sequence are subjected to inheritance fusion so as to generate a corresponding fusion task process sequence graph. Therefore, the updated second task process node sequence can be determined according to the fusion task process sequence graph.

For example, in one possible design, the node position information of the fusion task process node and the spatial point feature information of the node spatial point in the fusion task process sequence graph may be determined according to the segment length and the segment feature of the graph segment node in the fusion task process sequence graph.

Then, the corresponding fusion task process node can be determined according to the node position information, and the corresponding node space point can be determined according to the space point characteristic information.

Then, the fusion task process nodes and the node space points can be fitted to determine a fitting representation graph corresponding to the fusion task process sequence graph.

The fitted representation graph is segmented into a plurality of task process segments and a plurality of process space segments.

Then, for any one task process segment, the task process segment is segmented in a region of the fitting representation graph representing the fusion task process node to obtain a plurality of subtask process segments, and the segment characteristics of the plurality of subtask process segments are determined.

Then, for any process space segment, the direction of the process space segment representing the fusion task process node in the fitting representation graph is segmented to obtain a plurality of sub-process space segments, and the space segment characteristics of the plurality of sub-process space segments are determined.

And then, feature fusion can be sequentially carried out on the segmentation features of the plurality of sub-task process segments and the spatial segmentation features of the plurality of sub-process spatial segments, so that an updated second task process node sequence is obtained.

In a possible design, for step S120, in this embodiment, a sequence feature vector and a sequence feature dynamic amplitude corresponding to the second task process node sequence and the first task process node sequence may be calculated in a preset feature space, so as to obtain a target feature quantization value corresponding to a minimum sequence feature dynamic amplitude of each feature in the sequence feature vector and the sequence feature dynamic amplitude. The target characteristic quantization value is a set characteristic quantization value in a preset characteristic space.

Then, processing may be performed between multiple set characteristic quantization values associated with the target characteristic quantization value according to a sequence characteristic vector, and a sequence characteristic dynamic amplitude corresponding to each set characteristic quantization value is calculated, and a set characteristic quantization value corresponding to a minimum sequence characteristic dynamic amplitude among the sequence characteristic dynamic amplitudes corresponding to all set characteristic quantization values is used as a characteristic quantization difference value of each characteristic.

Then, the difference features formed by the feature quantized difference values of all the features can be used as the difference features of the sequence feature vector and the sequence feature dynamic amplitude to obtain a first difference feature corresponding to the second task process node sequence and a second difference feature corresponding to the first task process node sequence.

Next, a feature difference between the first difference feature and the second difference feature may be calculated.

Then, the first difference feature and the second difference feature may be fused according to the feature difference condition to obtain a fused difference feature.

The fused difference features can then be populated onto a settings update component that is used to perform feature updates on the fused difference features.

Then, the fusion difference feature may be segmented by using the setting updating component to form a plurality of fusion difference features to be processed, and in the process of forming the plurality of fusion difference features to be processed, an updating flag bit association relationship is established according to a correspondence relationship between the difference elements in the fusion difference features to be processed and the updating flag bits included in the feature characterization information of the fusion difference features to be processed, wherein the established updating flag bit association relationship includes the updating flag bits corresponding to all the difference elements in the fusion difference features to be processed.

Then, for the difference element in the fusion difference feature to be processed, the update grade corresponding to the feature segment where the difference element is located when the fusion difference feature to be processed is currently processed is determined, the determined update grade is recorded in the update flag bit association relationship, and the corresponding relationship between the recorded update grade and the update flag bit corresponding to the difference element in the update flag bit association relationship is established.

Then, in the process of updating the fusion difference feature to be processed, the update level currently corresponding to each feature segment included in the fusion difference feature to be processed is monitored, and when the update level corresponding to at least one update flag bit in the association relationship of the update flag bits is monitored to be changed and the update level adopted by the fusion difference feature to be processed which needs to be updated currently is determined, the update level needed by the fusion difference feature to be processed which needs to be updated currently is determined.

The determined update level currently required to update the fusion difference feature to be processed may then be used to update the fusion difference feature to be processed to determine a time-space domain feature difference between the second sequence of task process nodes and the first sequence of task process nodes.

In one possible design, for step S130, the embodiment may further determine a subtask parameter of each subtask in each monitoring task and a duration of a channel occupied by the subtask according to the time-space domain feature difference.

And then, determining the parameters of the internet of things linkage communication process of the linkage communication process required by the assignment of the subtasks in each monitoring task according to the subtask parameters of the subtasks in each monitoring task and the duration of the channels occupied by the subtasks.

And then, determining linkage internet of things control information of each subtask according to the internet of things linkage communication process parameters of the linkage communication process required by each subtask.

Then, a corresponding relation can be established between the control information of the linkage internet of things and other subtasks in the monitoring task according to the identification sequence of the linkage internet of things, and the subtasks with the corresponding relation can be used as linkage subtasks.

Then, each linkable subtask can be combined according to the corresponding relationship to generate linked internet of things control information of each monitoring task, and a corresponding linked internet of things control instruction is sent to at least part of the corresponding environment monitoring devices 200 according to the linked internet of things control information.

Further, fig. 3 shows a flow chart of another internet-of-things-based environment monitoring method provided in this application, and unlike the above embodiment, the present internet-of-things-based environment monitoring method is executed by the monitoring server 100, it can be understood that steps involved in the internet-of-things-based environment monitoring method to be described next are already described in the above embodiment of the method executed by the internet-of-things-based environment monitoring system 10, and specific details of the respective steps can be described with reference to the above embodiment, and are not described in detail here, and only the steps executed by the monitoring server 100 are briefly described below.

Step S210, acquiring the task grade of the monitoring task of the current monitoring queue in the area to be monitored, and acquiring a first task process node sequence generated by each monitoring task from the current monitoring queue according to the task grade of the monitoring task of the current monitoring queue in the area to be monitored.

Step S220, when it is monitored that the environment monitoring result corresponding to the first task process node sequence exists in the environment monitoring background data, determining the updated second task process node sequence according to the environment monitoring result, and determining the time-space domain characteristic difference between the second task process node sequence and the first task process node sequence.

Step S230, generating linkage internet of things control information of each monitoring task according to the time-space domain feature difference, and sending a corresponding linkage internet of things control instruction to at least part of the corresponding environment monitoring devices 200 according to the linkage internet of things control information, so that the environment monitoring devices 200 execute linkage environment monitoring operation with the associated environment monitoring devices 200 according to the linkage internet of things control instruction.

Fig. 4 is a schematic functional module diagram of the environment monitoring device 300 based on the internet of things according to the embodiment of the present application, and in this embodiment, functional modules of the environment monitoring device 300 based on the internet of things may be divided according to the embodiment of the method executed by the monitoring server 100. For example, the functional blocks may be divided for the respective functions, or two or more functions may be integrated into one processing block. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. It should be noted that, the division of the modules in the present application is schematic, and is only a logical function division, and there may be another division manner in actual implementation. For example, in the case of dividing each function module according to each function, the environment monitoring apparatus 300 based on the internet of things shown in fig. 4 is only a schematic diagram of the apparatus. The internet of things-based environment monitoring apparatus 300 may include an obtaining module 310, a determining module 320, and a generating and sending module 330, and the functions of the functional modules of the internet of things-based environment monitoring apparatus 300 are described in detail below.

The obtaining module 310 is configured to obtain a task level of a monitoring task of a current monitoring queue in a region to be monitored, and obtain a first task process node sequence generated by each monitoring task from the current monitoring queue according to the task level of the monitoring task of the current monitoring queue in the region to be monitored.

The determining module 320 is configured to determine, when it is monitored that an environment monitoring result corresponding to the first task process node sequence exists in the environment monitoring background data, an updated second task process node sequence according to the environment monitoring result, and determine a time-space domain feature difference between the second task process node sequence and the first task process node sequence.

And the generation and sending module 330 is configured to generate linkage internet of things control information of each monitoring task according to the time-space domain feature difference, and send a corresponding linkage internet of things control instruction to at least part of corresponding environment monitoring devices according to the linkage internet of things control information, so that the environment monitoring devices execute linkage environment monitoring operation according to the linkage internet of things control instruction and the associated environment monitoring devices.

Further, fig. 5 is a schematic structural diagram of a monitoring server 100 for executing the foregoing environment monitoring method based on the internet of things according to the embodiment of the present application. As shown in FIG. 5, the monitoring server 100 may include a network interface 110, a machine-readable storage medium 120, a processor 130, and a bus 140. The processor 130 may be one or more, and one processor 130 is illustrated in fig. 5 as an example. The network interface 110, the machine-readable storage medium 120, and the processor 130 may be connected by a bus 140 or otherwise, as exemplified by the connection by the bus 140 in fig. 5.

The machine-readable storage medium 120 is a computer-readable storage medium, and can be used to store software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the method for monitoring an environment based on the internet of things in the embodiment of the present application (for example, the obtaining module 310, the determining module 320, and the generating and sending module 330 of the environment monitoring apparatus 300 based on the internet of things shown in fig. 4). The processor 130 executes various functional applications and data processing of the terminal device by detecting software programs, instructions and modules stored in the machine-readable storage medium 120, that is, the above-mentioned environment monitoring method based on the internet of things is implemented, and details are not repeated herein.

The machine-readable storage medium 120 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the machine-readable storage medium 120 may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile memory may be a Read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash memory. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of example, but not limitation, many forms of RAM are available, such as Static random access memory (Static RAM, SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic random access memory (Synchronous DRAM, SDRAM), Double data rate Synchronous Dynamic random access memory (DDR SDRAM), Enhanced Synchronous SDRAM (ESDRAM), Synchronous link SDRAM (SLDRAM), and direct memory bus RAM (DR RAM). It should be noted that the memories of the systems and methods described herein are intended to comprise, without being limited to, these and any other suitable memory of a publishing node. In some examples, machine-readable storage medium 120 may further include memory located remotely from processor 130, which may be connected to monitoring server 100 over 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 processor 130 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method embodiments may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 130. The processor 130 may be a general-purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor.

The monitoring server 100 may exchange information with other devices (e.g., the environmental monitoring apparatus 200) through the network interface 110. Network interface 110 may be a circuit, bus, transceiver, or any other device that may be used to exchange information. Processor 130 may send and receive information using network interface 110.

In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.

Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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 changes and modifications may be made in the embodiments of the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the embodiments of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to encompass such modifications and variations.

Claims

1. The environment monitoring method based on the Internet of things is applied to an environment monitoring system based on the Internet of things, and comprises a monitoring server and environment monitoring devices which are in communication connection with the monitoring server and are respectively arranged at a plurality of environment monitoring points, wherein the method comprises the following steps:

2. The Internet of things-based environment monitoring method according to claim 1, wherein the step of obtaining a first task process node sequence generated by each monitoring task from the current monitoring queue according to the task level of the monitoring task of the current monitoring queue in the area to be monitored comprises:

3. The internet of things-based environment monitoring method according to claim 1, wherein the step of determining the updated second task process node sequence according to the environment monitoring result comprises:

4. The internet of things-based environment monitoring method according to claim 3, wherein the step of determining an updated second task process node sequence according to the fused task process sequence graph comprises:

5. The internet of things based environmental monitoring method of claim 1, wherein the step of determining the time-space domain signature differences between the second sequence of task process nodes and the first sequence of task process nodes comprises:

6. The Internet of things-based environment monitoring method according to claim 1, wherein the step of generating linkage Internet of things control information of each monitoring task according to the time-space domain feature difference and sending corresponding linkage Internet of things control instructions to at least part of corresponding environment monitoring devices according to the linkage Internet of things control information comprises the steps of:

7. The environment monitoring method based on the Internet of things is applied to a monitoring server, the monitoring server is in communication connection with environment monitoring devices respectively arranged at a plurality of environment monitoring points, and the method comprises the following steps:

8. The utility model provides an environment monitoring device based on thing networking which characterized in that is applied to monitoring server, monitoring server and the environment monitoring device communication connection who sets up respectively at a plurality of environment monitoring point, the device includes:

9. A monitoring server, comprising a processor, a machine-readable storage medium, and a network interface, the machine-readable storage medium, the network interface, and the processor being connected by a bus system, the network interface being configured to communicatively connect with at least one environmental monitoring device, the machine-readable storage medium being configured to store a program, instructions, or code, and the processor being configured to execute the program, instructions, or code in the machine-readable storage medium to perform the internet of things based environmental monitoring method of claim 7.

10. The environment monitoring system based on the Internet of things is characterized by comprising a monitoring server and environment monitoring devices which are in communication connection with the monitoring server and are respectively arranged at a plurality of environment monitoring points;