CN115643123B - Internet of things multi-network fusion experiment system and method - Google Patents
Internet of things multi-network fusion experiment system and method Download PDFInfo
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Abstract
The invention discloses an Internet of things multi-network fusion experimental system and method, particularly relates to the field of Internet of things, and is used for solving the problems that the accuracy of an acquisition process and the implementation speed of a debugging scheme are not pertinently monitored in the conventional multi-network fusion monitoring system, so that the actual analysis result is uncertain, and the debugging effect is not timely; the system comprises a data acquisition module, an adjusting operation module, a communication gateway module and a data analysis module; according to the invention, the accuracy of the data acquisition process is judged, and feedback type cyclic acquisition is carried out, so that the accuracy of data acquisition is ensured, corresponding adjustment means is formulated according to the acquired data, and corresponding adjustment time is detected, so that the adjustment capability of the whole system is evaluated, and the follow-up personnel can conveniently overhaul.
Description
Technical Field
The invention relates to the technical field of Internet of things, in particular to a multi-network integration experiment system and method of the Internet of things.
Background
The accuracy of the collected data and the communication speed among all devices can not be guaranteed due to the fact that the collected data are numerous and the network compatibility problem after multi-network fusion exists in the existing multi-network fusion laboratory.
In view of the above problems, the present invention proposes a solution.
Disclosure of Invention
In order to overcome the above defects in the prior art, embodiments of the present invention provide an internet-of-things multi-network fusion experimental system and method, which determine the accuracy of a data acquisition process and perform feedback type cyclic acquisition, so as to ensure the accuracy of data acquisition, formulate a corresponding adjustment means according to the acquired data, and detect a corresponding adjustment time to evaluate the adjustment capability of the whole system, thereby facilitating subsequent staff to perform maintenance, and solving the problems proposed in the background art.
In order to achieve the purpose, the invention provides the following technical scheme:
an Internet of things multi-network fusion experiment system comprises a data acquisition module, an adjusting operation module, a communication gateway module and a data analysis module;
the communication gateway module is used for being responsible for data communication among multi-network convergence;
the data acquisition module is used for acquiring environmental information in a laboratory, state information of the cluster nodes 6 and working state information in the adjustment operation module, and sending the information to the data analysis module for analysis through the communication gateway module;
the data analysis module is used for determining whether the acquisition process of the data acquisition module meets the actually required accuracy;
if the accuracy requirement is not met, generating a re-measuring signal and sending the re-measuring signal to the data acquisition module through the communication gateway module, and the data acquisition module re-acquires the environmental information in the laboratory until the accuracy requirement is met;
if the accuracy requirement is met, formulating an adjusting scheme according to the specific deviation of the environment information, and sending an adjusting command to an adjusting operation module through the communication gateway module;
the adjusting operation module carries out corresponding adjustment after receiving an adjusting command of the data analysis module;
and the data analysis module acquires working state information in the adjusting operation module to confirm whether the adjusting speed meets the actual requirement, and if not, early warning output is carried out.
In a preferred embodiment, the data acquisition module comprises a plurality of cluster nodes 6, and each cluster node 6 comprises a plurality of homogeneous sensor nodes 7;
after the corresponding environment information is collected by each sensor node 7, the corresponding environment information is sent to the cluster nodes 6 in a unified mode, and after the data collected by each sensor are added and averaged by the cluster nodes 6, information transmission is carried out.
In a preferred embodiment, the status information of the cluster nodes 6 includes a longest deviation value of data receiving time of each cluster node 6, a damaged value of sensor nodes of each cluster node 6, and an intact occupancy of sensor nodes of each cluster node 6;
after the data analysis module receives the longest deviation value of the data receiving time of each cluster node 6, the damage value of the sensor node of each cluster node 6 and the perfect occupation ratio of the sensor node of each cluster node 6, the accurate evaluation coefficient E of each cluster node 6 is obtained through formula calculation;
the data analysis module sets standard accurate gradient values as R1 and R2, and R1 is less than R2;
comparing the precision evaluation coefficient E with a standard precision gradient value:
if the accurate evaluation coefficient E is smaller than R1, generating a normal cluster node 6 signal;
if the accurate evaluation coefficient E is larger than or equal to R2, generating a risk cluster node and giving an alarm;
and if the accurate evaluation coefficient E is more than or equal to R1 and less than R2, generating an error cluster node and analyzing the whole cluster node 6.
In a preferred embodiment, the data analysis module analyzes the whole cluster node 6 by the following specific method:
if the environmental information received by the data analysis module comprises information sent by the risk cluster node, generating inaccurate information;
if the received environmental information does not include the information sent by the risk cluster node but includes the information sent by the error cluster node, calculating the proportion of the error cluster node in the whole cluster node 6, and comparing the proportion of the error cluster node in the whole cluster node 6 with the proportion of the rated cluster node:
if the proportion of the error cluster nodes to the whole cluster nodes 6 is smaller than the proportion of the rated cluster nodes, generating accurate information, otherwise generating inaccurate information;
and if the received environment information does not include the information sent by the risk cluster node or the information sent by the error cluster node, generating accurate information.
In a preferred embodiment, the operating state information in the regulation operation module includes the completion time of the individual regulation scheme, the internal switching time of the superior regulation scheme, and the overall completion time of the superior regulation scheme;
after the data analysis module obtains the working state information in the adjustment operation module, the specific analysis process is as follows:
calculating an adjustment evaluation coefficient R according to the completion time of the individual adjustment scheme, the internal switching time of the superior adjustment scheme and the overall completion time of the superior adjustment scheme;
comparing the adjustment evaluation coefficient R to a standard adjustment threshold:
and if the adjustment evaluation coefficient R is larger than or equal to the standard adjustment threshold, carrying out early warning prompt on the adjustment evaluation coefficient R, so that subsequent personnel can conveniently carry out targeted inspection and maintenance, otherwise, generating no data signal.
In a preferred embodiment, the system further comprises a data storage module for receiving data generated by the internet of things multi-network fusion experimental system in the whole management process.
An Internet of things multi-network fusion experiment method is based on the Internet of things multi-network fusion experiment system, and comprises the following steps:
s1, collecting environmental information in a laboratory;
s2, analyzing the laboratory environment information with the acquisition accuracy meeting the requirement, and determining a laboratory environment adjusting means;
and S3, collecting and analyzing the adjustment process information, determining whether the adjustment means of the whole system is in accordance with expectation, and performing corresponding feedback.
The multi-network integration experiment system and method of the Internet of things have the technical effects and advantages that:
according to the invention, the accuracy of the data acquisition process is judged, and feedback type cyclic acquisition is carried out, so that the accuracy of data acquisition is ensured, corresponding adjustment means is formulated according to the acquired data, and corresponding adjustment time is detected, so that the adjustment capability of the whole system is evaluated, and the follow-up personnel can conveniently overhaul.
Drawings
FIG. 1 is a flow chart of an experimental method for multi-network fusion of the Internet of things of the invention;
FIG. 2 is a schematic structural diagram of a multi-network fusion experimental system of the Internet of things;
FIG. 3 is an architecture diagram of a multi-network fusion experimental system of the Internet of things;
reference numerals: 4. a data analysis module; 5. a data storage module; 6. a cluster node; 7. and (6) a sensor node.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
According to the system and the method for the multi-network fusion experiment of the Internet of things, accuracy of a data acquisition process is judged, feedback type cyclic acquisition is carried out, accuracy of data acquisition is guaranteed, corresponding adjusting means are formulated according to the acquired data, and corresponding adjusting time is detected so as to evaluate adjusting capacity of the whole system, and follow-up personnel can conveniently overhaul.
Example 1
FIG. 1 is a flow chart of an experimental method for multi-network fusion of the Internet of things; the method comprises the following steps:
and S1, collecting environmental information in a laboratory.
And S2, analyzing the laboratory environment information with the acquisition accuracy meeting the requirement, and determining the laboratory environment adjusting means.
And S3, collecting and analyzing the adjustment process information, determining whether the adjustment means of the whole system is in accordance with expectation, and performing corresponding feedback.
Specifically, in step S1, the environmental information is for reflecting an environmental state of the laboratory, such as temperature and humidity, smoke concentration, radiation signal, etc. in the laboratory, and is collected by the respective corresponding sensors and sent to the data analysis module 4 through the cluster node 6 for analysis.
Wherein, every cluster node 6 includes a plurality of sensor node 7, and each sensor node 7 gathers corresponding environmental information, it needs to explain that all only has a sensor type in each cluster node 6, for example, the sensor in the a cluster node is smoke transducer, and it is responsible for carrying out smog detection to many places in the laboratory to guarantee to gather everywhere smog concentration in the laboratory at the very first time.
After the corresponding environment information is collected by each sensor node 7, the corresponding environment information is sent to the cluster nodes 6 in a unified mode, and the cluster nodes 6 average the data collected by each sensor and then transmit the information.
In step S2, after acquiring each environmental information of the laboratory, analyzing the acquired process first, and determining whether the accuracy meets the experimental requirements, wherein the specific determination process is as follows:
and collecting the longest deviation value of the data receiving time of each cluster of nodes 6, the damage value of the sensor nodes of each cluster of nodes 6 and the perfect occupation ratio of the sensor nodes of each cluster of nodes 6, and respectively marking the values as T, S and Z.
The maximum data receiving time deviation value is a difference value between the time of the sensor data received fastest and the time of the sensor data received slowest in each cluster node 6, and the larger the maximum data receiving time deviation value is, the larger the deviation of the sending arrival time of each sensor is, and because the cluster node 6 sends the laboratory environment information at a certain moment, the more inaccurate the result is when the monitoring effect at the certain moment is comprehensively evaluated; for example, the number of the temperature sensors is 3, the time a +1, and the time a +2 when the temperature sensors transmit the collected temperature to the cluster node 6 are respectively, and then the longest deviation value of the data receiving time of the cluster node 6 is 2.
The sensor node damage value is how many sensor nodes 7 of the cluster node 6 are damaged together, and the more inaccurate the value of the sensor node damage value after the cluster node 6 is averaged, i.e. the worse the accuracy.
The sensor node health occupation ratio refers to the sensor health rate of the cluster node 6, and the higher the health occupation ratio is, the higher the accuracy is.
Therefore, the accurate evaluation coefficient E of each cluster node 6 is obtained through formula calculation, and the specific calculation expression is as follows:
in the formula, b1, b2, b3 are respectively the longest deviation value of data receiving time, the damage value of the sensor node of each cluster node 6 and the preset proportionality coefficient of the damage ratio of the sensor node of each cluster node 6, and b3> b1> b2>0.
And setting the standard accurate gradient values as R1 and R2, wherein R1 is less than R2.
And comparing the precision evaluation coefficient E with the standard precision gradient value to determine whether the precision of each cluster node 6 meets the requirement.
If the accurate evaluation coefficient E is smaller than R1, the accuracy of the cluster node 6 is within the error range, and the actual use requirement is met.
If the accurate evaluation coefficient E is larger than or equal to R2, the cluster node 6 is marked as a risk cluster node and an alarm is given, which indicates that the accuracy deviation actual requirement is too far and the actual requirement can not be met at all.
If the accurate evaluation coefficient E is larger than or equal to R1 and smaller than R2, the accuracy of the cluster node 6 is larger than the actual error requirement, but the cluster node does not reach the early warning interval, at the moment, the cluster node is marked as an error cluster node, and the whole cluster node 6 is analyzed.
And if the received environmental information comprises the information sent by the risk cluster node, marking the received environmental information as inaccurate information.
If the received environmental information does not include the information sent by the risk cluster node but includes the information sent by the error cluster node, calculating the proportion of the error cluster node in the whole cluster node 6, comparing the proportion of the error cluster node in the whole cluster node 6 with the proportion of the rated cluster node, if the proportion of the error cluster node in the whole cluster node 6 is smaller than the proportion of the rated cluster node, indicating that the environmental information is accurate information, otherwise, indicating that the environmental information is inaccurate information.
If the received environment information does not include the information sent by the risk cluster node or the information sent by the error cluster node, the received environment information is indicated to be accurate information, and the accurate information is marked.
In step S2, when the received environmental information is inaccurate information, the environmental information is collected again, and when the received environmental information is accurate information, whether a specific numerical value of the environmental information meets a laboratory requirement is determined, and corresponding adjustment is performed according to the state of each environmental information. The specific adjustment means is set according to actual needs, and the adjustment can be completed by adopting the prior art, which is not described herein. For example, when the received illumination intensity information is too weak, the curtain in the laboratory is controlled to be opened or the light is controlled to be opened so as to meet the illumination requirement in the laboratory.
In step S3, after the adjusting means is determined, it is determined whether each adjusting scheme in the adjusting means has a time chain sequence, that is, whether a certain adjusting scheme has a plurality of sub-adjusting schemes therein, and the next adjusting scheme can be implemented only after the pre-adjusting scheme is completed. And dividing each regulation scheme into an individual regulation scheme and a superior regulation scheme, wherein the individual regulation scheme is a regulation scheme without multiple sub-schemes, acquiring the completion time of the individual regulation scheme, the internal switching time of the superior regulation scheme and the overall completion time of the superior regulation scheme, and respectively marking the completion time as Dt, jt and St. The internal switching time of the upper regulation scheme refers to the switching buffering time of each sub-scheme in the upper regulation scheme, and the regulation evaluation coefficient R is calculated by a formula, wherein the specific calculation expression is as follows:
in the formula, g1, g2 and g3 are respectively preset proportionality coefficients of the completion time of the independent regulation scheme, the internal switching time of the superior regulation scheme and the overall completion time of the superior regulation scheme, and g2> g1> g3>0.
The adjustment evaluation coefficient reflects the execution capacity of the adjustment scheme, and the shorter the completion time of the individual adjustment scheme, the internal switching time of the higher-level adjustment scheme, and the overall completion time of the higher-level adjustment scheme, the stronger the execution capacity of the adjustment scheme, and conversely, the poorer the execution capacity of the adjustment scheme.
And comparing the regulation evaluation coefficient R with a standard regulation threshold value to determine the solution speed of the regulation scheme in the system.
If the adjustment evaluation coefficient R is larger than or equal to the standard adjustment threshold, the integral adjustment speed in the system is lower than an expected value, and at the moment, early warning prompt is carried out on the integral adjustment speed, so that subsequent personnel can conveniently check and maintain pertinently. Otherwise, the integral adjusting speed in the system is consistent with the expectation, and the relevant secondary adjustment is not needed.
The preset proportionality coefficient mentioned above is used for balancing the proportion weight of each item of data in formula calculation, thereby promoting the accuracy of the calculation result; the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and a corresponding weight factor coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relationship between the parameters and the quantized values is not affected.
Example 2
The embodiment 2 of the present invention is different from the embodiment 1 in that the embodiment introduces an internet of things multi-network fusion experimental system.
Fig. 2 and fig. 3 respectively show a schematic structural diagram and a system architecture diagram of the internet of things multi-network fusion experimental system. The system mainly comprises a data acquisition module, an adjustment operation module, a communication gateway module, a data analysis module 4 and a data storage module 5.
The data acquisition unit is used for acquiring environmental information in the laboratory, such as temperature and humidity, smoke concentration, radiation signals and the like. The sensor cluster comprises various sensor structures, wherein the number of each sensor is multiple, and the sensor structures are in signal connection with a communication gateway module through a cluster node 6.
The data acquisition unit is also used for acquiring the state information of each cluster node 6 in the laboratory, and the state information of the cluster nodes 6 comprises the longest deviation value of the data receiving time of each cluster node 6, the damage value of the sensor nodes of each cluster node 6 and the perfect occupation ratio of the sensor nodes of each cluster node 6, and is sent to the data analysis module 4 through the communication gateway module.
Meanwhile, the data acquisition unit is also used for acquiring working state information in the adjusting operation module, the working state information comprises the completion time of the independent adjusting scheme, the internal switching time of the superior adjusting scheme and the overall completion time of the superior adjusting scheme, and the data acquisition unit sends the working state information to the data analysis module 4 through the communication gateway module.
The communication gateway module is responsible for data communication among multi-network convergence, and is mainly used for data communication among the zigbee node, the zigbee coordinator, and the ethernet in this embodiment.
The data analysis module 4 is used for analyzing the environment signal sent by the data acquisition unit, correspondingly generating a corresponding adjusting means signal, sending the adjusting means signal to the communication gateway module, acquiring the working state information in the adjusting operation module, confirming whether the adjusting speed meets the actual requirement, and if not, performing early warning output.
The adjusting operation module is used for adjusting various items in the laboratory according to the adjusting means signal and comprises various operation control modules. Such as a curtain control module, an LED lamp control module, an air conditioning control module, and a ventilation control module, etc.
The data storage module 5 is used for receiving data generated by the internet of things multi-network fusion experiment system in the whole management process and storing the data in the local database, and a remote user can inquire various data through a browser.
When the laboratory environment information acquisition device is used, the data acquisition module acquires the laboratory environment information and sends the laboratory environment information to the data analysis module 4 through the communication gateway module for analysis, the data analysis module 4 firstly determines whether the acquisition process of the data acquisition module meets the accuracy required actually, if the acquisition process does not meet the requirement of generating a re-measurement signal, the data acquisition module re-acquires the laboratory environment information until the accuracy requirement is met, if the acquisition process meets the requirement, an adjustment scheme is formulated according to the specific deviation of the environment information, an adjustment command is sent to the adjustment operation module through the communication gateway module, and the adjustment operation module performs corresponding control and adjustment; meanwhile, the adjusting operation module feeds each adjusting time back to the data analysis module 4 through the communication gateway module for analysis, the data analysis module 4 confirms whether the adjusting speed meets the actual requirement, and if not, early warning is carried out to prompt follow-up workers to overhaul.
The laboratory monitoring system is combined with the technology of the Internet of things, a WSN gateway designed by a wireless sensing network ZigBee and an Ethernet multi-network fusion gateway is utilized, and a bridge for data communication between the WSN gateway and an external network is built by integrating various communication modes of a wireless local area network and the Ethernet; the problems that the traditional internet of things security system devices are different in standard and not universal in interface are solved.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. The procedures or functions according to the embodiments of the present application are wholly or partially generated when the computer instructions or the computer program are loaded or executed on a computer. 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 on a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (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, data center, etc. that contains one or more collections of 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. The semiconductor medium may be a solid state disk.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the module units is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
And finally: the above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that are within the spirit and principle of the present invention are intended to be included in the scope of the present invention.
Claims (5)
1. The utility model provides a thing networking multi-network fuses experimental system which characterized in that: the system comprises a data acquisition module, an adjusting operation module, a communication gateway module and a data analysis module;
the communication gateway module is used for being responsible for data communication among multi-network convergence;
the data acquisition module is used for acquiring environmental information in a laboratory, state information of cluster nodes and working state information in the adjustment operation module, and sending the information to the data analysis module for analysis through the communication gateway module;
the data analysis module is used for determining whether the acquisition process of the data acquisition module meets the actually required accuracy;
if the accuracy requirement is not met, generating a re-measuring signal and sending the re-measuring signal to the data acquisition module through the communication gateway module, and the data acquisition module re-acquires the environmental information in the laboratory until the accuracy requirement is met;
if the accuracy requirement is met, formulating an adjusting scheme according to the specific deviation of the environment information, and sending an adjusting command to an adjusting operation module through the communication gateway module;
the adjusting operation module performs corresponding adjustment after receiving an adjusting command of the data analysis module;
the data analysis module acquires working state information in the adjusting operation module to determine whether the adjusting speed meets the actual requirement, and if not, early warning output is carried out;
the data acquisition module comprises a plurality of cluster nodes, and each cluster node comprises a plurality of similar sensor nodes;
after the corresponding environment information is collected by each sensor node, the sensor nodes uniformly send the corresponding environment information to the cluster nodes, and after the data collected by each sensor are added and averaged by the cluster nodes, the information is transmitted;
the state information of the cluster nodes comprises the longest deviation value of the receiving time of each cluster node, the damage value of each cluster node sensor node and the intact occupation ratio of each cluster node sensor node;
the data analysis module receives the longest deviation value of the data receiving time of each cluster node, the damage value of the sensor node of each cluster node and the perfect occupation ratio of the sensor node of each cluster node, respectively marks the data receiving time longest deviation value, the damage value of the sensor node of each cluster node and the perfect occupation ratio of the sensor node of each cluster node as T, S and Z, and obtains the accurate evaluation coefficient E of each cluster node through formula calculation, wherein the specific calculation expression is as follows:
in the formula, b1, b2 and b3 are respectively a data receiving time longest deviation value, a damage value of each cluster node sensor node and a preset proportionality coefficient of a damage proportion of each cluster node sensor node, and b3> b1> b2>0;
the data analysis module sets standard accurate gradient values as R1 and R2, and R1 is less than R2;
comparing the precision evaluation coefficient E with a standard precision gradient value:
if the accurate evaluation coefficient E is smaller than R1, generating a normal cluster node signal;
if the accurate evaluation coefficient E is larger than or equal to R2, generating a risk cluster node and giving an alarm;
and if the accurate evaluation coefficient E is more than or equal to R1 and less than R2, generating an error cluster node and analyzing the whole cluster node.
2. The internet of things multi-network fusion experimental system according to claim 1, characterized in that: the specific method for analyzing the whole cluster node by the data analysis module is as follows:
if the environmental information received by the data analysis module comprises information sent by the risk cluster node, generating inaccurate information;
if the received environmental information does not include the information sent by the risk cluster node but includes the information sent by the error cluster node, calculating the proportion of the error cluster node to the whole cluster node, and comparing the proportion of the error cluster node to the whole cluster node with the proportion of the rated cluster node:
if the proportion of the error cluster nodes to the whole cluster nodes is smaller than the proportion of the rated cluster nodes, generating accurate information, otherwise, generating inaccurate information;
and if the received environment information does not include the information sent by the risk cluster node or the information sent by the error cluster node, generating accurate information.
3. The internet of things multi-network fusion experimental system according to claim 2, characterized in that: the working state information in the adjusting operation module comprises the completion time of an individual adjusting scheme, the internal switching time of a superior adjusting scheme and the overall completion time of the superior adjusting scheme;
after the data analysis module obtains the working state information in the adjustment operation module, the specific analysis process is as follows:
calculating an adjustment evaluation coefficient R according to the completion time of the individual adjustment scheme, the internal switching time of the superior adjustment scheme and the overall completion time of the superior adjustment scheme;
comparing the adjustment evaluation coefficient R to a standard adjustment threshold:
and if the adjustment evaluation coefficient R is larger than or equal to the standard adjustment threshold, carrying out early warning prompt on the adjustment evaluation coefficient R, so that subsequent personnel can conveniently carry out targeted inspection and maintenance, otherwise, generating no data signal.
4. The Internet of things multi-network fusion experimental system according to claim 3, characterized in that: the system also comprises a data storage module used for receiving data generated in the whole management process of the Internet of things multi-network fusion experiment system.
5. An experiment method for multi-network fusion of the internet of things is based on the experiment system for multi-network fusion of the internet of things as claimed in any one of claims 1 to 4, and is characterized in that: the method comprises the following steps:
s1, collecting environmental information in a laboratory;
s2, analyzing the laboratory environment information with the acquisition accuracy meeting the requirement, and determining a laboratory environment adjusting means;
and S3, collecting and analyzing the adjustment process information, determining whether the adjustment means of the whole system is in accordance with expectation, and performing corresponding feedback.
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